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How to write an APA methods section

How to Write an APA Methods Section | With Examples

Published on February 5, 2021 by Pritha Bhandari . Revised on June 22, 2023.

The methods section of an APA style paper is where you report in detail how you performed your study. Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods .

In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample , measures, and procedures used.

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Structuring an apa methods section.

Participants

Example of an APA methods section

Other interesting articles, frequently asked questions about writing an apa methods section.

The main heading of “Methods” should be centered, boldfaced, and capitalized. Subheadings within this section are left-aligned, boldfaced, and in title case. You can also add lower level headings within these subsections, as long as they follow APA heading styles .

To structure your methods section, you can use the subheadings of “Participants,” “Materials,” and “Procedures.” These headings are not mandatory—aim to organize your methods section using subheadings that make sense for your specific study.

Note that not all of these topics will necessarily be relevant for your study. For example, if you didn’t need to consider outlier removal or ways of assigning participants to different conditions, you don’t have to report these steps.

The APA also provides specific reporting guidelines for different types of research design. These tell you exactly what you need to report for longitudinal designs , replication studies, experimental designs , and so on. If your study uses a combination design, consult APA guidelines for mixed methods studies.

Detailed descriptions of procedures that don’t fit into your main text can be placed in supplemental materials (for example, the exact instructions and tasks given to participants, the full analytical strategy including software code, or additional figures and tables).

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Begin the methods section by reporting sample characteristics, sampling procedures, and the sample size.

Participant or subject characteristics

When discussing people who participate in research, descriptive terms like “participants,” “subjects” and “respondents” can be used. For non-human animal research, “subjects” is more appropriate.

Specify all relevant demographic characteristics of your participants. This may include their age, sex, ethnic or racial group, gender identity, education level, and socioeconomic status. Depending on your study topic, other characteristics like educational or immigration status or language preference may also be relevant.

Be sure to report these characteristics as precisely as possible. This helps the reader understand how far your results may be generalized to other people.

The APA guidelines emphasize writing about participants using bias-free language , so it’s necessary to use inclusive and appropriate terms.

Sampling procedures

Outline how the participants were selected and all inclusion and exclusion criteria applied. Appropriately identify the sampling procedure used. For example, you should only label a sample as random if you had access to every member of the relevant population.

Of all the people invited to participate in your study, note the percentage that actually did (if you have this data). Additionally, report whether participants were self-selected, either by themselves or by their institutions (e.g., schools may submit student data for research purposes).

Identify any compensation (e.g., course credits or money) that was provided to participants, and mention any institutional review board approvals and ethical standards followed.

Sample size and power

Detail the sample size (per condition) and statistical power that you hoped to achieve, as well as any analyses you performed to determine these numbers.

It’s important to show that your study had enough statistical power to find effects if there were any to be found.

Additionally, state whether your final sample differed from the intended sample. Your interpretations of the study outcomes should be based only on your final sample rather than your intended sample.

Write up the tools and techniques that you used to measure relevant variables. Be as thorough as possible for a complete picture of your techniques.

Primary and secondary measures

Define the primary and secondary outcome measures that will help you answer your primary and secondary research questions.

Specify all instruments used in gathering these measurements and the construct that they measure. These instruments may include hardware, software, or tests, scales, and inventories.

To cite hardware, indicate the model number and manufacturer.
To cite common software (e.g., Qualtrics), state the full name along with the version number or the website URL .
To cite tests, scales or inventories, reference its manual or the article it was published in. It’s also helpful to state the number of items and provide one or two example items.

Make sure to report the settings of (e.g., screen resolution) any specialized apparatus used.

For each instrument used, report measures of the following:

Reliability : how consistently the method measures something, in terms of internal consistency or test-retest reliability.
Validity : how precisely the method measures something, in terms of construct validity or criterion validity .

Giving an example item or two for tests, questionnaires , and interviews is also helpful.

Describe any covariates—these are any additional variables that may explain or predict the outcomes.

Quality of measurements

Review all methods you used to assure the quality of your measurements.

These may include:

training researchers to collect data reliably,
using multiple people to assess (e.g., observe or code) the data,
translation and back-translation of research materials,
using pilot studies to test your materials on unrelated samples.

For data that’s subjectively coded (for example, classifying open-ended responses), report interrater reliability scores. This tells the reader how similarly each response was rated by multiple raters.

Report all of the procedures applied for administering the study, processing the data, and for planned data analyses.

Data collection methods and research design

Data collection methods refers to the general mode of the instruments: surveys, interviews, observations, focus groups, neuroimaging, cognitive tests, and so on. Summarize exactly how you collected the necessary data.

Describe all procedures you applied in administering surveys, tests, physical recordings, or imaging devices, with enough detail so that someone else can replicate your techniques. If your procedures are very complicated and require long descriptions (e.g., in neuroimaging studies), place these details in supplementary materials.

To report research design, note your overall framework for data collection and analysis. State whether you used an experimental, quasi-experimental, descriptive (observational), correlational, and/or longitudinal design. Also note whether a between-subjects or a within-subjects design was used.

For multi-group studies, report the following design and procedural details as well:

how participants were assigned to different conditions (e.g., randomization),
instructions given to the participants in each group,
interventions for each group,
the setting and length of each session(s).

Describe whether any masking was used to hide the condition assignment (e.g., placebo or medication condition) from participants or research administrators. Using masking in a multi-group study ensures internal validity by reducing research bias . Explain how this masking was applied and whether its effectiveness was assessed.

Participants were randomly assigned to a control or experimental condition. The survey was administered using Qualtrics (https://www.qualtrics.com). To begin, all participants were given the AAI and a demographics questionnaire to complete, followed by an unrelated filler task. In the control condition , participants completed a short general knowledge test immediately after the filler task. In the experimental condition, participants were asked to visualize themselves taking the test for 3 minutes before they actually did. For more details on the exact instructions and tasks given, see supplementary materials.

Data diagnostics

Outline all steps taken to scrutinize or process the data after collection.

This includes the following:

Procedures for identifying and removing outliers
Data transformations to normalize distributions
Compensation strategies for overcoming missing values

To ensure high validity, you should provide enough detail for your reader to understand how and why you processed or transformed your raw data in these specific ways.

Analytic strategies

The methods section is also where you describe your statistical analysis procedures, but not their outcomes. Their outcomes are reported in the results section.

These procedures should be stated for all primary, secondary, and exploratory hypotheses. While primary and secondary hypotheses are based on a theoretical framework or past studies, exploratory hypotheses are guided by the data you’ve just collected.

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This annotated example reports methods for a descriptive correlational survey on the relationship between religiosity and trust in science in the US. Hover over each part for explanation of what is included.

The sample included 879 adults aged between 18 and 28. More than half of the participants were women (56%), and all participants had completed at least 12 years of education. Ethics approval was obtained from the university board before recruitment began. Participants were recruited online through Amazon Mechanical Turk (MTurk; www.mturk.com). We selected for a geographically diverse sample within the Midwest of the US through an initial screening survey. Participants were paid USD $5 upon completion of the study.

A sample size of at least 783 was deemed necessary for detecting a correlation coefficient of ±.1, with a power level of 80% and a significance level of .05, using a sample size calculator (www.sample-size.net/correlation-sample-size/).

The primary outcome measures were the levels of religiosity and trust in science. Religiosity refers to involvement and belief in religious traditions, while trust in science represents confidence in scientists and scientific research outcomes. The secondary outcome measures were gender and parental education levels of participants and whether these characteristics predicted religiosity levels.

Religiosity

Religiosity was measured using the Centrality of Religiosity scale (Huber, 2003). The Likert scale is made up of 15 questions with five subscales of ideology, experience, intellect, public practice, and private practice. An example item is “How often do you experience situations in which you have the feeling that God or something divine intervenes in your life?” Participants were asked to indicate frequency of occurrence by selecting a response ranging from 1 (very often) to 5 (never). The internal consistency of the instrument is .83 (Huber & Huber, 2012).

Trust in Science

Trust in science was assessed using the General Trust in Science index (McCright, Dentzman, Charters & Dietz, 2013). Four Likert scale items were assessed on a scale from 1 (completely distrust) to 5 (completely trust). An example question asks “How much do you distrust or trust scientists to create knowledge that is unbiased and accurate?” Internal consistency was .8.

Potential participants were invited to participate in the survey online using Qualtrics (www.qualtrics.com). The survey consisted of multiple choice questions regarding demographic characteristics, the Centrality of Religiosity scale, an unrelated filler anagram task, and finally the General Trust in Science index. The filler task was included to avoid priming or demand characteristics, and an attention check was embedded within the religiosity scale. For full instructions and details of tasks, see supplementary materials.

For this correlational study , we assessed our primary hypothesis of a relationship between religiosity and trust in science using Pearson moment correlation coefficient. The statistical significance of the correlation coefficient was assessed using a t test. To test our secondary hypothesis of parental education levels and gender as predictors of religiosity, multiple linear regression analysis was used.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Measures of central tendency
Chi square tests
Confidence interval
Quartiles & Quantiles

Methodology

Cluster sampling
Stratified sampling
Thematic analysis
Cohort study
Peer review
Ethnography

Research bias

Implicit bias
Cognitive bias
Conformity bias
Hawthorne effect
Availability heuristic
Attrition bias
Social desirability bias

In your APA methods section , you should report detailed information on the participants, materials, and procedures used.

Describe all relevant participant or subject characteristics, the sampling procedures used and the sample size and power .
Define all primary and secondary measures and discuss the quality of measurements.
Specify the data collection methods, the research design and data analysis strategy, including any steps taken to transform the data and statistical analyses.

You should report methods using the past tense , even if you haven’t completed your study at the time of writing. That’s because the methods section is intended to describe completed actions or research.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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How to Write a Methods Section for a Psychology Paper

Tips and Examples of an APA Methods Section

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Brianna Gilmartin

The methods section of an APA format psychology paper provides the methods and procedures used in a research study or experiment . This part of an APA paper is critical because it allows other researchers to see exactly how you conducted your research.

Method refers to the procedure that was used in a research study. It included a precise description of how the experiments were performed and why particular procedures were selected. While the APA technically refers to this section as the 'method section,' it is also often known as a 'methods section.'

The methods section ensures the experiment's reproducibility and the assessment of alternative methods that might produce different results. It also allows researchers to replicate the experiment and judge the study's validity.

This article discusses how to write a methods section for a psychology paper, including important elements to include and tips that can help.

What to Include in a Method Section

So what exactly do you need to include when writing your method section? You should provide detailed information on the following:

Research design
Participants
Participant behavior

The method section should provide enough information to allow other researchers to replicate your experiment or study.

Components of a Method Section

The method section should utilize subheadings to divide up different subsections. These subsections typically include participants, materials, design, and procedure.

Participants

In this part of the method section, you should describe the participants in your experiment, including who they were (and any unique features that set them apart from the general population), how many there were, and how they were selected. If you utilized random selection to choose your participants, it should be noted here.

For example: "We randomly selected 100 children from elementary schools near the University of Arizona."

At the very minimum, this part of your method section must convey:

Basic demographic characteristics of your participants (such as sex, age, ethnicity, or religion)
The population from which your participants were drawn
Any restrictions on your pool of participants
How many participants were assigned to each condition and how they were assigned to each group (i.e., randomly assignment , another selection method, etc.)
Why participants took part in your research (i.e., the study was advertised at a college or hospital, they received some type of incentive, etc.)

Information about participants helps other researchers understand how your study was performed, how generalizable the result might be, and allows other researchers to replicate the experiment with other populations to see if they might obtain the same results.

In this part of the method section, you should describe the materials, measures, equipment, or stimuli used in the experiment. This may include:

Testing instruments
Technical equipment
Any psychological assessments that were used
Any special equipment that was used

For example: "Two stories from Sullivan et al.'s (1994) second-order false belief attribution tasks were used to assess children's understanding of second-order beliefs."

For standard equipment such as computers, televisions, and videos, you can simply name the device and not provide further explanation.

Specialized equipment should be given greater detail, especially if it is complex or created for a niche purpose. In some instances, such as if you created a special material or apparatus for your study, you might need to include an illustration of the item in the appendix of your paper.

In this part of your method section, describe the type of design used in the experiment. Specify the variables as well as the levels of these variables. Identify:

The independent variables
Dependent variables
Control variables
Any extraneous variables that might influence your results.

Also, explain whether your experiment uses a within-groups or between-groups design.

For example: "The experiment used a 3x2 between-subjects design. The independent variables were age and understanding of second-order beliefs."

The next part of your method section should detail the procedures used in your experiment. Your procedures should explain:

What the participants did
How data was collected
The order in which steps occurred

For example: "An examiner interviewed children individually at their school in one session that lasted 20 minutes on average. The examiner explained to each child that he or she would be told two short stories and that some questions would be asked after each story. All sessions were videotaped so the data could later be coded."

Keep this subsection concise yet detailed. Explain what you did and how you did it, but do not overwhelm your readers with too much information.

Tips for How to Write a Methods Section

In addition to following the basic structure of an APA method section, there are also certain things you should remember when writing this section of your paper. Consider the following tips when writing this section:

Use the past tense : Always write the method section in the past tense.
Be descriptive : Provide enough detail that another researcher could replicate your experiment, but focus on brevity. Avoid unnecessary detail that is not relevant to the outcome of the experiment.
Use an academic tone : Use formal language and avoid slang or colloquial expressions. Word choice is also important. Refer to the people in your experiment or study as "participants" rather than "subjects."
Use APA format : Keep a style guide on hand as you write your method section. The Publication Manual of the American Psychological Association is the official source for APA style.
Make connections : Read through each section of your paper for agreement with other sections. If you mention procedures in the method section, these elements should be discussed in the results and discussion sections.
Proofread : Check your paper for grammar, spelling, and punctuation errors.. typos, grammar problems, and spelling errors. Although a spell checker is a handy tool, there are some errors only you can catch.

After writing a draft of your method section, be sure to get a second opinion. You can often become too close to your work to see errors or lack of clarity. Take a rough draft of your method section to your university's writing lab for additional assistance.

A Word From Verywell

The method section is one of the most important components of your APA format paper. The goal of your paper should be to clearly detail what you did in your experiment. Provide enough detail that another researcher could replicate your study if they wanted.

Finally, if you are writing your paper for a class or for a specific publication, be sure to keep in mind any specific instructions provided by your instructor or by the journal editor. Your instructor may have certain requirements that you need to follow while writing your method section.

Frequently Asked Questions

While the subsections can vary, the three components that should be included are sections on the participants, the materials, and the procedures.

Describe who the participants were in the study and how they were selected.
Define and describe the materials that were used including any equipment, tests, or assessments
Describe how the data was collected

To write your methods section in APA format, describe your participants, materials, study design, and procedures. Keep this section succinct, and always write in the past tense. The main heading of this section should be labeled "Method" and it should be centered, bolded, and capitalized. Each subheading within this section should be bolded, left-aligned and in title case.

The purpose of the methods section is to describe what you did in your experiment. It should be brief, but include enough detail that someone could replicate your experiment based on this information. Your methods section should detail what you did to answer your research question. Describe how the study was conducted, the study design that was used and why it was chosen, and how you collected the data and analyzed the results.

Erdemir F. How to write a materials and methods section of a scientific article ? Turk J Urol . 2013;39(Suppl 1):10-5. doi:10.5152/tud.2013.047

Kallet RH. How to write the methods section of a research paper . Respir Care . 2004;49(10):1229-32. PMID: 15447808.

American Psychological Association. Publication Manual of the American Psychological Association (7th ed.). Washington DC: The American Psychological Association; 2019.

American Psychological Association. APA Style Journal Article Reporting Standards . Published 2020.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Research Methods in Psychology - 4th American Edition

(40 reviews)

Carrie Cuttler, Washington State University

Rajiv S. Jhangiani, Kwantlen Polytechnic University

Dana C. Leighton, Texas A&M University, Texarkana

ISBN 13: 9781999198107

Publisher: Kwantlen Polytechnic University

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Beth Mechlin, Associate Professor of Psychology & Neuroscience, Earlham College on 3/19/24

This is an extremely comprehensive text for an undergraduate psychology course about research methods. It does an excellent job covering the basics of a variety of types of research design. It also includes important topics related to research... read more

Comprehensiveness rating: 5 see less

Content Accuracy rating: 5

I did not notice any errors in this text.

Relevance/Longevity rating: 5

The content is very relevant. It will likely need to be updated over time in order to keep research examples relevant. Additionally, APA formatting guidelines may need to be updated when a new publication manual is released. However, these should be easy updates for the authors to make when the time comes.

Clarity rating: 5

This text is very clear and easy to follow. The explanations are easy for college students to understand. The authors use a lot of examples to help illustrate specific concepts. They also incorporate a variety of relevant outside sources (such as videos) to provide additional examples.

Consistency rating: 5

The text is consistent and flows well from one section to the next. At the end of each large section (similar to a chapter) the authors provide key takeaways and exercises.

Modularity rating: 5

This text is very modular. It is easy to pick and choose which sections you want to use in your course when. Each section can stand alone fairly easily.

Organization/Structure/Flow rating: 5

The text is very well organized. Information flows smoothly from one topic to the next.

Interface rating: 5

The interface is great. The text is easy to navigate and the images display well (I only noticed 1 image in which the formatting was a tad off).

Grammatical Errors rating: 5

I did not notice any grammatical errors.

Cultural Relevance rating: 5

The text is culturally relevant.

This is an excellent text for an undergraduate research methods course in the field of Psychology. I have been using the text for my Research Methods and Statistics course for a few years now. This text focuses on research methods, so I do use another text to cover statistical information. I do highly recommend this text for research methods. It is comprehensive, clear, and easy for students to use.

Reviewed by William Johnson, Lecturer, Old Dominion University on 1/12/24

This textbook covers every topic that I teach in my Research Methods course aside from psychology careers (which I would not really expect it to cover). read more

This textbook covers every topic that I teach in my Research Methods course aside from psychology careers (which I would not really expect it to cover).

I have not noticed any inaccurate information (other than directed students to read Malcolm Gladwell). I appreciate that the textbook includes information on research errors that have not been supported by replication efforts, such as embodied cognition.

Many of the basic concepts of research methods are rather timeless, but I appreciate that the text includes newer research as examples while also including "classic" studies that exemplify different methods.

The writing is clear and simple. The keywords are bolded and reveal a definition when clicked, which students often find very helpful. Many of the figures are very helpful in helping students understand various methods (I really like the ones in the single-subject design subchapter).

The book is very consistent in its terminology and writing style, which I see as a positive compared to other open psychology textbooks where each chapter is written by subject matter experts (such as the NOBA intro textbook).

Modularity rating: 4

I teach this textbook almost entirely in order (except for moving chapters 12 & 13 earlier in the semester to aid students in writing Results sections in their final papers). I think that the organization and consistency of the book reduces its modularity, in that earlier chapters are genuinely helpful for later chapters.

Organization/Structure/Flow rating: 4

I preferred the organization of previous editions, which had "Theory in Research" as its own chapter. If I were organizing the textbook, I am not sure that I would have out descriptive or inferential statistics as the final two chapters (I would have likely put Chapter 11: Presenting Your Research as the final chapter). I also would not have put information about replicability and open science in the inferential statistics section.

The text is easy to read and the formatting is attractive. My only minor complaint is that some of the longer subchapters can be a pretty long scroll, but I understand the desire for their only to be one page per subchapter/topic.

I have not noticed any grammatical errors.

Cultural Relevance rating: 3

I do not think the textbook is insensitive, but there is not much thought given to adapting research instruments across cultures. For instance, talking about how different constructs might have different underlying distributions in different cultures would be useful for students. In the survey methods section, a discussion of back translation or emic personality trait measurement/development for example might be a nice addition.

I choose to use this textbook in my methods classes, but I do miss the organization of the previous American editions. Overall, I recommend this textbook to my colleagues.

Reviewed by Brianna Ewert, Psychology Instructor, Salish Kootenai College on 12/30/22

This text includes the majority of content included in our undergraduate Research Methods in Psychology course. The glossary provides concise definitions of key terms. This text includes most of the background knowledge we expect our students to... read more

Comprehensiveness rating: 4 see less

The content I have read is accurate and error-free.

The content is relevant and up-to-date.

The text is clear and concise. I find it pleasantly readable and anticipate undergraduate students will find it readable and understandable as well.

The terminology appears to be consistent throughout the text.

The modular sections stand alone and lend themselves to alignment with the syllabus of a particular course. I anticipate readily selecting relevant modules to assign in my course.

The book is logically organized with clear and section headings and subheadings. Content on a particular topic is easy to locate.

The text is easy to navigate and the format/design are clean and clear. There are not interface issues, distortions or distracting format in the pdf or online versions.

The text is grammatically correct.

Cultural Relevance rating: 4

I have not found culturally insensitive and offensive language or content in the text. For my courses, I would add examples and supplemental materials that are relevant for students at a Tribal College.

This textbook includes supplemental instructor materials, included slides and worksheets. I plan to adopt this text this year in our Research Methods in Psychology course. I expect it to be a benefit to the course and students.

Reviewed by Sara Peters, Associate Professor of Psychology, Newberry College on 11/3/22

This text serves as an excellent resource for introducing survey research methods topics to undergraduate students. It begins with a background of the science of psychology, the scientific method, and research ethics, before moving into the main types of research. This text covers experimental, non-experimental, survey, and quasi-experimental approaches, among others. It extends to factorial and single subject research, and within each topic is a subset (such as observational research, field studies, etc.) depending on the section.

I could find no accuracy issues with the text, and appreciated the discussions of research and cited studies.

There are revised editions of this textbook (this being the 4th), and the examples are up to date and clear. The inclusion of exercises at the end of each chapter offer potential for students to continue working with material in meaningful ways as they move through the book and (and course).

The prose for this text is well aimed at the undergraduate population. This book can easily be utilized for freshman/sophomore level students. It introduces the scientific terminology surrounding research methods and experimental design in a clear way, and the authors provide extensive examples of different studies and applications.

Terminology is consistent throughout the text. Aligns well with other research methods and statistics sources, so the vocabulary is transferrable beyond the text itself.

Navigating this book is a breeze. There are 13 chapters, and each have subsections that can be assigned. Within each chapter subsection, there is a set of learning objectives, and paragraphs are mixed in with tables and figures for students to have different visuals. Different application assignments within each chapter are highlighted with boxes, so students can think more deeply given a set of constructs as they consider different information. The last subsection in each chapter has key summaries and exercises.

The sections and topics in this text are very straightforward. The authors begin with an introduction of psychology as a science, and move into the scientific method, research ethics, and psychological measurement. They then present multiple different research methodologies that are well known and heavily utilized within the social sciences, before concluding with information on how to present your research, and also analyze your data. The text even provides links throughout to other free resources for a reader.

This book can be navigated either online (using a drop-down menu), or as a pdf download, so students can have an electronic copy if needed. All pictures and text display properly on screen, with no distortions. Very easy to use.

There were no grammatical errors, and nothing distracting within the text.

This book includes inclusive material in the discussion of research ethics, as well as when giving examples of the different types of research approaches. While there is always room for improvement in terms of examples, I was satisfied with the breadth of research the authors presented.

This text provides an overview of both research methods, and a nice introduction to statistics for a social science student. It would be a good choice for a survey research methods class, and if looking to change a statistics class into an open resource class, could also serve as a great resource.

Reviewed by Sharlene Fedorowicz, Adjunct Professor, Bridgewater State University on 6/23/21

The comprehensiveness of this book was appropriate for an introductory undergraduate psychology course. Critical topics are covered that are necessary for psychology students to obtain foundational learning concepts for research. Sections within the text and each chapter provide areas for class discussion with students to dive deeper into key concepts for better learning comprehension. The text covered APA format along with examples of research studies to supplement the learning. The text segues appropriately by introducing the science of psychology, followed by scientific method and ethics before getting into the core of scientific research in the field of psychology. Details are provided in quantitative and qualitative research, correlations, surveys, and research design. Overall, the text is fully comprehensive and necessary introductory research concepts.

The text appears to be accurate with no issues related to content.

Relevance/Longevity rating: 4

The text provided relevant research information to support the learning. The content was up-to-date with a variety of different examples related to the different fields of psychology. However, some topics such as in the pseudoscience section were not very relevant and bordered the line of beliefs. Here, more current or relevant solid examples would provide more relevancy in this part of the text. Bringing in more solid or concrete examples that are more current for students may have been more appropriate such as lack of connection between information found on social media versus real science.

The language and flow of the chapters accompanied by the terms, concepts, and examples of applied research allows for clarity of learning content. Terms were introduced at the appropriate time with the support of concepts and current or classic research. The writing style flows nicely and segues easily from concept to concept. The text is easy for students to understand and grasp the details related to psychological research and science.

The text provides consistency in the outline of each chapter. The beginning section chapter starts objectives as an overview to help students unpack the learning content. Key terms are consistently bolded followed by concept or definition and relevant examples. Research examples are pertinent and provide students with an opportunity to understand application of the contents. Practice exercises are provided with in the chapter and at the and in order for students to integrate learning concepts from within the text.

Sections and subsections are clearly organized and divided appropriately for ease-of-use. The topics are easily discernible and follow the flow of ideal learning routines for students. The sections and subsections are consistently outlined for each concept module. The modularity provides consistency allowing for students to focus on content rather than trying to discern how to pull out the information differently from each chapter or section. In addition, each section and subsection allow for flexibility in learning or expanding concepts within the content area.

The organization of the textbook was easy to follow and each major topic was outlined clearly. However, the chapter on presenting research may be more appropriately placed toward the end of the book rather than in the middle of the chapters related to research and research design. In addition, more information could have been provided upfront around APA format so that students could identify the format of citations within the text as practice for students throughout the book.

The interface of the book lends itself to a nice layout with appropriate examples and links to break up the different sections in the chapters. Examples where appropriate and provided engagement opportunities for the students for each learning module. Images and QR codes or easily viewed and used. Key terms are highlighted in relevant figures, graphs, and tables were appropriately placed. Overall, the interface of the text assisted with the organization and flow of learning material.

No grammatical errors were detected in this book.

The text appears to be culturally sensitive and not offensive. A variety of current and classic research examples are relevant. However, more examples of research from women, minorities, and ethnicities would strengthen the culture of this textbook. Instructors may need to supplement some research in this area to provide additional inclusivity.

Overall, I was impressed by the layout of the textbook and the ease of use. The layout provides a set of expectations for students related to the routine of how the book is laid out and how students will be able to unpack the information. Research examples were relevant, although I see areas where I will supplement information. The book provides opportunities for students to dive deeper into the learning and have rich conversations in the classroom. I plan to start using the psychology textbook for my students starting next year.

Reviewed by Anna Behler, Assistant Professo, North Carolina State University on 6/1/21

The text is very thorough and covers all of the necessary topics for an undergraduate psychology research methods course. There is even coverage of qualitative research, case studies, and the replication crisis which I have not seen in some other... read more

There were no issues with the accuracy of the text.

The content is very up to date and relevant for a research methods course. The only updates that will likely be necessary in the coming years are updates to examples and modifications to the section on APA formatting.

The clarity of the writing was good, and the chapters were written in a way that was accessible and easy to follow.

I did not note any issues with consistency.

Each chapter is divided into multiple subsections. This makes the chapters even easier to read, as they are broken down into short and easy to navigate sections. These sections make it easy to assign readings as needed depending on which topics are being covered in class.

Organization/Structure/Flow rating: 3

The organization was one of the few areas of weakness, and I felt that the chapters were ordered somewhat oddly. However, this is something that is easily fixed, as chapters (and even subsections) can be assigned in whatever order is needed.

There were no issues of note with the interface, and the PDF of the text was easy to navigate.

The text was well written and there were no grammatical/writing errors of note.

Overall, the book did not contain any notable instances of bias. However, it would probably be appropriate to offer a more thorough discussion of the WEIRD problem in psychology research.

Reviewed by Seth Surgan, Professor, Worcester State University on 5/24/21

Pitched very well for a 200-level Research Methods course. This text provided students with solid basis for class discussion and the further development of their understanding of fundamental concepts. read more

No issues with accuracy.

Coverage was on target, relevant, and applicable, with good examples from a variety of subfields within Psychology.

Clearly written -- students often struggle with the dry, technical nature of concepts in Research Methods. Part of the reason I chose this text in the first place was how favorably it compared to other options in terms of clarity.

No problems with inconsistent of shifting language. This is extremely important in Research Methods, where there are many closely related terms. Language was consistent and compatible with other textbook options that were available to my students.

Chapters are broken down into sections that are reasonably sized and conceptually appropriate.

The organization of this textbook fit perfectly with the syllabus I've been using (in one form or another) for 15+ years.

This textbook was easy to navigate and available in a variety of formats.

No problems at all.

Examples show an eye toward inclusivity. I did not detect any insensitive or offensive examples or undertones.

I have used this textbook for a 200-level Research Methods course run over a single summer session. This was my first experience using an OER textbook and I don't plan on going back.

Reviewed by Laura Getz, Assistant Professor, University of San Diego on 4/29/21

The topics covered seemed to be at an appropriate level for beginner undergraduate psychology students; the learning objectives for each subsection and the key takeaways and exercises for each chapter are also very helpful in guiding students’ attention to what is most relevant. The glossary is also thorough and a good resource for clear definitions. I would like to see a final chapter on a “big picture” or integrating key ideas of replication, meta-analysis, and open science.

Content Accuracy rating: 4

For the most part, I like the way information is presented. I had a few specific issues with definitions for ordinal variables being quantitative (1st, 2nd, 3rd aren’t really numbers as much as ranked categories), the lack of specificity about different forms of validity (face, content, criterion, and discriminant all just labeled “validity” whereas internal and external validity appear in different sections), and the lack of clear distinction between correlational and quasi-experimental variables (e.g., in some places, country of origin is listed as making a design quasi-experimental, but in other chapters it is defined as correlational).

Some of the specific studies/experiments mentioned do not seem like the best or most relevant for students to learn about the topics, but for the most part, content is up-to-date and can definitely be updated with new studies to illustrate concepts with relative ease.

Besides the few concepts I listed above in “accuracy”, I feel the text was very accessible, provides clear definitions, and many examples to illustrate any potential technical/jargon terms.

I did not notice any issues with inconsistent terms except for terms that do have more than one way of describing the same concept (e.g., 2-sample vs. independent samples t-test)

I assigned the chapters out of order with relative ease, and students did not comment about it being burdensome to navigate.

The order of chapters sometimes did not make sense to me (e.g., Experimental before Non-experimental designs, Quasi-experimental designs separate from other non-experimental designs, waiting until Chapter 11 to talk about writing), but for the most part, the chapter subsections were logical and clear.

Interface rating: 4

I had no issues navigating the online version of the textbook other than taking a while to figure out how to move forward and back within the text itself rather than going back to the table of contents (this might just be a browser issue, but is still worth considering).

No grammatical errors of note.

There was nothing explicitly insensitive or offensive about the text, but there were many places where I felt like more focus on diversity and individual differences could be helpful. For example, ethics and history of psychological testing would definitely be a place to bring in issues of systemic racism and/or sexism and a focus on WEIRD samples (which is mentioned briefly at another point).

I was very satisfied with this free resource overall, and I recommend it for beginning level undergraduate psychology research methods courses.

Reviewed by Laura Stull, Associate Professor, Anderson University on 4/23/21

This book covers essential topics and areas related to conducting introductory psychological research. It covers all critical topics, including the scientific method, research ethics, research designs, and basic descriptive and inferential statistics. It even goes beyond other texts in terms of offering specific guidance in areas like how to conduct research literature searches and psychological measurement development. The only area that appears slightly lacking is detailed guidance in the mechanics of writing in APA style (though excellent basic information is provided in chapter 11).

All content appears accurate. For example, experimental designs discussed, descriptive and inferential statistical guidance, and critical ethical issues are all accurately addressed, See comment on relevance below regarding some outdated information.

Relevance/Longevity rating: 3

Chapter 11 on APA style does not appear to cover the most current version of the APA style guide (7th edition). While much of the information in Chapter 11 is still current, there are specifics that did change from 6th to 7th edition of the APA manual and so, in order to be current, this information would have to be supplemented with external sources.

The book is extremely well organized, written in language and terms that should be easily understood by undergraduate freshmen, and explains all necessary technical jargon.

The text is consistent throughout in terms of terminology and the organizational framework (which aids in the readability of the text).

The text is divided into intuitive and common units based on basic psychological research methodology. It is clear and easy to follow and is divided in a way that would allow omission of some information if necessary (such as "single subject research") or reorganization of information (such as presenting survey research before experimental research) without disruption to the course as a whole.

As stated previously, the book is organized in a clear and logical fashion. Not only are the chapters presented in a logical order (starting with basic and critical information like overviews of the scientific method and research ethics and progressing to more complex topics like statistical analyses).

No issues with interface were noted. Helpful images/charts/web resources (e.g., Youtube videos) are embedded throughout and are even easy to follow in a print version of the text.

No grammatical issues were noted.

No issues with cultural bias are noted. Examples are included that address topics that are culturally sensitive in nature.

I ordered a print version of the text so that I could also view it as students would who prefer a print version. I am extremely impressed with what is offered. It covers all of the key content that I am currently covering with a (non-open source) textbook in an introduction to research methods course. The only concern I have is that APA style is not completely current and would need to be supplemented with a style guide. However, I consider this a minimal issue given all of the many strengths of the book.

Reviewed by Anika Gearhart, Instructor (TT), Leeward Community College on 4/22/21

Includes the majority of elements you expect from a textbook covering research methods. Some topics that could have been covered in a bit more depth were factorial research designs (no coverage of 3 or more independent variables) and external... read more

Nothing found that was inaccurate.

Looks like a few updates could be made to chapter 11 to bring it up to date with APA 7. Otherwise, most examples are current.

Very clear, a great fit for those very new to the topic.

The framework is clear and logical, and the learning objectives are very helpful for orienting the reader immediately to the main goals of each section.

Subsections are well-organized and clear. Titles for sections and subsections are clear.

Though I think the flow of this textbook for the most part is excellent, I would make two changes: move chapter 5 down with the other chapters on experimental research and move chapter 11 to the very end. I feel that this would allow for a more logical presentation of content.

The webpage navigation is easy to use and intuitive, the ebook download works as designed, and the page can be embedded directly into a variety of LMS sites or used with a variety of devices.

I found no grammatical errors in this book.

While there were some examples of studies that included participants from several cultures, the book does not touch on ecological validity, an important external validity issue tied to cultural psychology, and there is no mention of the WEIRD culture issue in psychology, which seems somewhat necessary when orienting new psychology students to research methods today.

I currently use and enjoy this textbook in my research methods class. Overall, it has been a great addition to the course, and I am easily able to supplement any areas that I feel aren't covered with enough breadth.

Reviewed by Amy Foley, Instructor/Field & Clinical Placement Coordinator, University of Indianapolis on 3/11/21

This text provides a comprehensive overview of the research process from ideation to proposal. It covers research designs common to psychology and related fields. read more

This text provides a comprehensive overview of the research process from ideation to proposal. It covers research designs common to psychology and related fields.

Accurate information!

This book is current and lines up well with the music therapy research course I teach as a supplemental text for students to understand research designs.

Clear language for psychology and related fields.

The format of the text is consistent. I appreciate the examples, different colored boxes, questions, and links to external sources such as video clips.

It is easy to navigate this text by chapters and smaller units within each chapter. The only confusion that has come from using this text includes the fact that the larger units have roman numerals and the individual chapters have numbers. I have told students to "read unit six" and they only read the small chapter 6, not the entire unit for example.

Flows well!

I have not experienced any interface issues.

I have not found any grammar errors.

Book appears culturally relevant.

This is a great resource for research methods courses in psychology or related fields. I am glad to have used several chapters of this text within the music therapy research course I teach where students learn about research design and then create their own research proposal.

Reviewed by Veronica Howard, Associate Professor, University of Alaska Anchorage on 1/11/21, updated 1/11/21

VERY impressed by the coverage of single subject designs. I would recommend this content to colleagues. read more

VERY impressed by the coverage of single subject designs. I would recommend this content to colleagues.

Content appears accurate.

By expanding to include more contemporary research perspectives, the authors have created a wonderful dynamic that permits the text to be the foundation for many courses as well as revision and remixing for other authors.

Book easy to read, follow.

Consistency rating: 4

Content overall consistent. Only mild inconsistency in writing style between chapters.

Exceptionally modular. All content neatly divided into units with smaller portions. This would be a great book to use in a course that meets bi-weekly, or adapted into other formats.

Content organized in a clear and logical fashion, and would guide students through a semester-long course on research methods, starting with review content, broad overview of procedures (including limitations), then highlighting less common (though relevant) procedures.

Rich variety of formats for use.

No errors found.

I would appreciate more cultural examples.

Reviewed by Greg Mullin, Associate Professor, Bunker Hill Community College on 12/30/20, updated 1/6/21

I was VERY pleased with the comprehensiveness of the text. I believe it actually has an edge over the publisher-based text that I've been using for years. Each major topic was thoroughly covered with more than enough detail on individual concepts. read more

I did not find any errors within the text. The authors provided an unbiased representation of research methods in psychology.

The content connects to classic, timeless examples in the field, but also mixes in a fair amount of more current, relatable examples. I feel like I'll be able to use this version of the text for many years without its age showing.

The authors present a clear and efficient writing style throughout that is rich with relatable examples. The only area that may be a bit much for undergraduate-level student understanding is the topic of statistics. I personally scale back my discussion of statistics in my Intro to Research Methods course, but for those that prefer a deeper dive, the higher-level elements are there.

I did not notice any shifts with the use of terminology or with the structural framework of the text. The text is very consistent and organized in an easily digestible way.

The authors do a fantastic job breaking complex topics down into manageable chunks both as a whole and within chapters. As I was reading, I could easily see how I could align my current approach of teaching Intro to Research Methods with their modulated presentation of the material.

I effortlessly moved through the text given the structural organization. All topics are presented in a logical fashion that allowed each message to be delivered to the reader with ease.

I read the text through the PDF version and found no issue with the interface. All image and text-based material was presented clearly.

I cannot recall coming across any grammatical errors. The text is very well written.

I did not find the text to be culturally insensitive in any way. The authors use inclusive language and even encourage that style of writing in the chapter on Presenting Your Research. I would have liked to see more cross-cultural research examples and more of an extended effort to include the theme of diversity throughout, but at no point did I find the text to be offensive.

This is a fantastic text and I look forward to adopting it for my Intro to Research Methods course in the Spring. :)

Reviewed by Maureen O'Connell, Adjunct Professor, Bunker Hill Community College on 12/15/20, updated 12/18/20

This text edition has covered all ideas and areas of research methods in psychology. It has provided a glossary of terms, sample APA format, and sample research papers. read more

This text edition has covered all ideas and areas of research methods in psychology. It has provided a glossary of terms, sample APA format, and sample research papers.

The content is unbiased, accurate, and I did not find any errors in the text.

The content is current and up-to-date. I found that the text can be added to should material change, the arrangement of the text/content makes it easily accessible to add material, if necessary.

The text is clear, easy to understand, simplistic writing at times, but I find this text easy for students to comprehend. All text is relevant to the content of behavioral research.

The text and terminology is consistent.

The text is organized well and sectioned appropriately. The information is presented in an easy-to-read format, with sections that can be assigned at various points during the semester and the reader can easily locate this.

The topics in the text are organized in a logical and clear manner. It flows really well.

The text is presented well, including charts, diagrams, and images. There did not appear to be any confusion with this text.

The text contains no grammatical errors.

The text was culturally appropriate and not offensive. Clear examples of potential biases were outlined in this text which I found quite helpful for the reader.

Overall, I found this to be a great edition. Much of the time I spend researching outside material for students has been included in this text. I enjoyed the format, easier to navigate, helpful to students by providing an updated version of discussions and practice assignments, and visually more appealing.

Reviewed by Brittany Jeye, Assistant Professor of Psychology, Worcester State University on 6/26/20

All of the main topics in a Research Methods course are covered in this textbook (e.g., scientific method, ethics, measurement, experimental design, hypothesis testing, APA style, etc.). Some of these topics are not covered as in-depth as in other Research Method textbooks I have used previously, but this actually may be a positive depending on the students and course level (that is, students may only need a solid overview of certain topics without getting overwhelmed with too many details). It also gives the instructor the ability to add content as needed, which helps with flexibility in course design.

I did not note any errors or inaccurate/biasing statements in the text.

For the most part, everything was up to date. There was a good mix of classic research and newer studies presented and/or used as examples, which kept the chapters interesting, topical and relevant. I only noted the section on APA Style in the chapter “Presenting Your Research” which may need some updating to be in line with the new APA 7th edition. However, there should be only minor edits needed (the chapter itself was great overview and introduction to the main points of APA style) and it looks like they should be relatively easy to implement.

The text was very well-written and was presented at an accessible level for undergraduates new to Research Methods. Terms were well-defined with a helpful glossary at the end of the textbook.

The consistent structure of the textbook is huge positive. Each chapter begins with learning objectives and ends with bulleted key takeaways. There are also good exercises and learning activities for students at the end of each chapter. Instructors may need to add their own activities for chapters that do not go into a lot of depth (there are also instructor resources online, which may have more options available).

This is one of the biggest strengths of this textbook, in my opinion. I appreciate how each chapter is broken down into clearly defined subsections. The chapters and the subsections, in particular, are not lengthy, which is great for students’ learning. These subsections could be reorganized and used in a variety of ways to suit the needs of a particular course (or even as standalone subsections).

The topics were presented in a logical manner. As mentioned above, since the textbook is very modular, I feel that you could easily rearrange the chapters to fit your needs (for example, presenting survey design before experimental research or making the presenting your research section a standalone unit).

I downloaded the textbook as an ebook, which was very easy to use/navigate. There were no problems reading any of the text or figures/tables. I also appreciated that you could open the ebook using a variety of apps (Kindle, iBook, etc.) depending on your preference (and this is good for students who have a variety of technical needs).

There were no grammatical errors noted.

The examples were inclusive of races, ethnicity and background and there were not any examples that were culturally insensitive or offensive in any way. In future iterations of the replicability section, it may be beneficial to touch upon the “weird” phenomena in psychology research (that many studies use participants who are western, educated and from industrialized, rich and democratic countries) as a point to engage students in improving psychological practices.

I will definitely consider switching to this textbook in the future for Research Methods.

Reviewed by Alice Frye, Associate Teaching Professor, University of Massachusetts Lowell on 6/22/20

Hits all the necessary marks from ways of knowing to measurement, research designs, and presentation. Comparable in detail and content to other Research Methods texts I have used for teaching. read more

Hits all the necessary marks from ways of knowing to measurement, research designs, and presentation. Comparable in detail and content to other Research Methods texts I have used for teaching.

Correct and to the point. Complex ideas such as internal consistency reliability and discriminant validity are well handled--correct descriptions that are also succinct and articulated simply and with clear examples that are easy for a student reader to grasp.

Seems likely to have good staying power. One area that has changed quickly in the past is the usefulness of various research data bases. So it is possible that portion could become more quickly outdated, but there is no predicting that. The current descriptions are useful.

Very clearly written without being condescending, overly casual or clunky.

Excellent consistency throughout in terms of organization, language usage, level of detail and tone.

Imho this is one of the particular strengths of the text. Chapters are well divided into discrete parts, which seems likely to be a benefit in cohorts of students who are increasingly accustomed to digesting small amounts of information.

Well organized, straightforward structure that is maintained throughout.

No problems with the interface.

The grammar level is another notable strength. Ideas are articulated clearly, and with sophistication, but in a syntactically very straightforward manner.

The text isn't biased or offensive. I wish that to illustrate various points and research designs it had drawn more frequently on research studies that incorporate a specific focus on race and ethnicity.

This is a very good text. As good as any for profit text I have used to teach a research methods course, if not better.

Reviewed by Lauren Mathieu-Frasier, Adjunct Instructor, University of Indianapolis on 1/13/20

As other reviews have mentioned, this textbook provides a comprehensive look at multiple concepts for an introductory course in research methods in psychology. Some of the concepts (i.e., variables, external validity) are briefly described and glossed over that it will take additional information, examples, and reinforcement from instructors in the classroom. Other sections and concepts, like ethics or reporting of research were well-described and thorough.

It appeared that the information was accurate, error-free, and unbiased.

The information is up-to-date. In the section on APA presentation, it looks like the minor adjustments to the APA Publication Manual 7th Edition would need to be included. However, this section gives a good foundation and the instructor can easily implement the changes.

Clarity rating: 4

The text is clearly written written and provides an appropriate context when terminology is used.

There aren't any issues with consistency in the textbook.

The division of smaller sections can be beneficial when reading it and assigning it to classes. The sections are clearly organized based on learning objectives.

The textbook is organized in a logical, clear manner. There may be topics that instructors choose to present in a different manner (non-experimental and survey research prior to experimental). However, this doesn't generally impact the organization and flow of the book.

While reading and utilizing the book, there weren't any navigation issues that could impact the readability of the book. Students could find this textbook easy to use.

Grammatical errors were not noted.

There weren't any issues with cultural sensitivity in the examples of studies used in the textbook.

Reviewed by Tiffany Kindratt, Assistant Professor, University of Texas at Arlington on 1/1/20

The text is comprehensive with an effective glossary of terms at the end. It would be beneficial to include additional examples and exercises for students to better understand concepts covered in Chapter II, Overview of the Scientific Method,... read more

The text is accurate and there are minimal type/grammatical errors throughout. The verbiage is written in an unbiased manner consistently throughout the textbook.

The content is up-to-date, and examples can be easily updated for future versions. As a public health instructor, I would be interested in seeing examples of community-based examples in future versions. The current examples provided are relevant for undergraduate public health students as well as psychology students.

The text is written in a clear manner. The studies used can be easily understood by undergraduate students in other social science fields, such as public health. More examples and exercises using inferential statistics would be helpful for students to better grasp the concepts.

The framework for each chapter and terminology used are consistent. It is helpful that each section within each chapter begins with learning objectives and the chapter ends with key takeaways and exercises.

The text is clearly divided into sections within each chapter. When I first started reviewing this textbook, I thought each section was actually a very short chapter. I would recommend including a listing of all of the objectives covered in each chapter at the beginning to improve the modularity of the text.

Some of the topics do not follow a logical order. For example, it would be more appropriate to discuss ethics before providing the overview of the scientific method. It would be better to discuss statistics used to determine results before describing how to write manuscripts. However, the text is written in a way that that the chapters could be assigned to students in a different order without impacting the students’ comprehension of the concepts.

I did not encounter any interface issues when reviewing this text. All links worked and there were no distortions of the images or charts that may confuse the reader. There are several data tables throughout the text which are left-aligned and there is a large amount of empty white space next it. I would rearrange the text in future versions to make better use of this space.

The text contains minimal grammatical errors.

The examples are culturally relevant. I did not see any examples that may be considered culturally insensitive or offensive in any way.

As an instructor for an undergraduate public health sciences and methods course, I will consider using some of the content in this text to supplement the current textbook in the future.

Reviewed by Mickey White, Assistant Professor, East Tennessee State University on 10/23/19

The table of contents is well-formatted and comprehensive. Easy to navigate and find exactly what is needed, students would be able to quickly find needed subjects. read more

The table of contents is well-formatted and comprehensive. Easy to navigate and find exactly what is needed, students would be able to quickly find needed subjects.

Content appears to be accurate and up-to-date.

This text is useful and relevant, particularly with regard to expressing and reporting descriptive statistics and results. As APA updates, the text will be easy to edit, as the sections are separated.

Easy to read and engaging.

Chapters were laid out in a consistent manner, which allows readers to know what is coming. The subsections contained a brief overview and terminology was consistent throughout. The glossary added additional information.

Sections and subsections are delineated in a usable format.

The key takeaways were useful, including the exercises at the end of each chapter.

Reading the book online is a little difficult to navigate page-by-page, but e-pub and PDF formats are easy to navigate.

No errors noted.

Would be helpful to have a clearer exploration of cultural factors impacting research, including historical bias in assessment and research outside of research ethics.

Reviewed by Robert Michael, Assistant Professor, University of Louisiana at Lafayette on 10/14/19

Successfully spans the gamut of topics expected in a Research Methods textbook. Some topics are covered in-depth, while others are addressed only at a surface level. Instructors may therefore need to carefully arrange class material for topics in... read more

The factual content was error-free, according to my reading. I did spot a few grammatical and typographical errors, but they were infrequent and minor.

Great to see nuanced—although limited—discussion of issues with Null Hypothesis Significance Testing, Reproducibility in Psychological Science, and so forth. I expect that these areas are likely to grow in future editions, perhaps supplementing or even replacing more traditional material.

Extremely easy to read with multiple examples throughout to illustrate the principles being covered. Many of these examples are "classics" that students can easily relate to. Plus, who doesn't like XKCD comics?

The textbook is structured sensibly. At times, certain authors' "voices" seemed apparent in the writing, but I suspect this variability is unlikely to be noticed by or even bothersome to the vast majority of readers.

The topics are easily divisible and seem to follow routine expectations. Instructors might find it beneficial and/or necessary to incorporate some of the statistical thinking and learning into various earlier chapters to facilitate student understanding in-the-moment, rather than trying to leave all the statistics to the end.

Sensible and easy-to-follow structure. As per "Modularity", the Statistical sections may benefit from instructors folding in such learning throughout, rather than only at the end.

Beautifully presented, crisp, easy-to-read and navigate. Caveat: I read this online, in a web-browser, on only one device. I haven't tested across multiple platforms.

High quality writing throughout. Only a few minor slip-ups that could be easily fixed.

Includes limited culturally relevant material where appropriate.

Reviewed by Matthew DeCarlo, Assistant Professor, Radford University on 6/26/19

The authors do a great job of simplifying the concepts of research methods and presenting them in a way that is understandable. There is a tradeoff between brevity and depth here. Faculty who adopt this textbook may need to spend more time in class going in depth into concepts, rather than relying on the textbook for all of the information related to key concepts. The text does not cover qualitative methods in detail.

The textbook provides an accurate picture of research methods. The tone is objective and without bias.

The textbook is highly relevant and up to date. Examples are drawn from modern theories and articles.

The writing is a fantastic mix of objective and authoritative while also being approachable.

The book coheres well together. Each chapter and section are uniform.

This book fits very well within a traditional 16 week semester, covering roughly a chapter per week. One could take out specific chapters and assign them individually if research methods is taught in a different way from a standard research textbook.

Content is very well organized. The table of contents is easy to navigate and each chapter is presented in a clear and consistent manner. The use of a two-tier table of contents is particularly helpful.

Standard pressbooks interface, which is great. Uses all of the standard components of Pressbooks well, though the lack of H5P and interactive content is a drawback.

I did not notice any grammar errors.

Cultural Relevance rating: 2

The book does not deal with cultural competence and humility in the research process. Integration of action research and decolonization perspectives would be helpful.

Reviewed by Christopher Garris, Associate Professor, Metropolitan State University of Denver on 5/24/19

Most content areas in this textbook were covered appropriately extensively. Notably, this textbook included some content that is commonly missing in other textbooks (e.g. presenting your research). There were some areas where more elaboration and more examples were needed. For example, the section covering measurement validities included all the important concepts, but needed more guidance for student comprehension. Also, the beginning chapters on 'common sense' reasoning and pseudoscience seemed a little too brief.

Overall, this textbook appeared to be free from glaring errors. There were a couple of instances of concern, but were not errors, per se. For example, the cut-off for Cronbach's alpha was stated definitively at .80, while this value likely would be debated among researchers.

This textbook was presented in such a way that seemed protect it from becoming obsolete within the next few years. This is important for continued, consistent use of the book. The authors have revised this book, and those revisions are clearly summarized in the text. Importantly, the APA section of the textbook appears to be up-to-date. Also, the use of QR codes throughout the text is a nice touch that students may appreciate.

Connected to comprehensiveness, there are some important content areas that I felt were lacking in elaboration and examples (e.g. testing the validity of measurement; introduction of experimental design), which inhibits clarity. Overall, however, the topics seemed to be presented in a straightforward, accessible manner. The textbook includes links to informative videos and walk-throughs where appropriate, which seem to be potentially beneficial for student comprehension. The textbook includes tools designed to aid learning, namely "Key Takeaways" and "Exercises" sections at the end of most modules, but not all. "Key Takeaways" seemed valuable, as they were a nice bookend to the learning objectives stated at the beginning of each module. "Exercises" did not appear to be as valuable, especially for the less-motivated student. On their face, these seemed to be more designed for instructors to use as class activities/active learning. Lastly, many modules of the textbook were text-heavy and visually unappealing. While this is superficial, the inclusion of additional graphics, example boxes, or figures in these text-heavy modules might be beneficial.

The textbook appeared to be internally consistent with its approach and use of terminology.

The textbook had a tendency to 'throw out' big concepts very briefly in earlier modules (e.g. sampling, experimental/non-experimental design), and then cover them in more detail in later modules. This would have been less problematic if the text would explicitly inform the student that these concepts would be elaborated upon later. Beyond this issue, the textbook seems to lend itself to being divided up and used on module-by-module basis.

The organization of the chapters did not make intuitive sense to me. The fact that correlation followed experimental research, and that descriptive research was the second-to-last module in the sequence was confusing. That said, textbook is written in such a way that an instructor easily assign the modules in the order that works best for their class.

Overall, the interface worked smoothly and there were few technical issues. Where there were issues (e.g. inconsistent spacing between lines and words), they were negligible.

The text seemed to be free from glaring grammatical problems.

Because this is a methodology textbook, it does not lend itself to too much cultural criticism. That said, the book did not rely on overly controversial examples, but also didn't shy away from important cultural topics (e.g. gender stereotypes, vaccines).

Reviewed by Michel Heijnen, Assistant Professor, University of North Carolina Wilmington on 3/27/18

The book covers all areas related to research methods, not only for the field of psychology, but also to other related fields like exercise science. Topics include ethics, developing a research questions, experimental designs, non-experimental... read more

Reviewed content is accurate and seems free of any personal bias.

The topic of research methods in general is not expected to change quickly. It is not expected that this text will become obsolete in the near future. Furthermore, for both the field of psychology as well as other related fields, the examples will continue to have an application to explain certain concepts and will not be outdated soon, even with new research emerging every day.

The text is written so an undergraduate student should be able to understand the concepts. The examples provided in the text greatly contribute to the understanding of the topics and the proposed exercises at the end of each chapter will further apply the knowledge.

The layout and writing style are consistent throughout the text.

Layout of the text is clear, with multiple subsection within each chapter. Each chapter can easily be split into multiple subsection to assign to students. No evidence of self-refers was observed, and individual chapters could be assigned to students without needed to read all preceding chapters. For example, Chapter 4 may not be particularly useful to students outside of psychology, but an instructor can easily reorganize the text and skip this chapter while students can still understand following chapters.

Topics are addressed in a logical manner. Overall, an introduction to research is provided first (including ethics to research), which is followed by different types of research, and concludes with types of analysis.

No images or tables are distorted, making the text easy to read.

No grammatical errors observed in text.

Text is not offensive and does not appear to be culturally insensitive.

I believe that this book is a great resource and, as mentioned previously, can be used for a wider audience than just psychology as the basics of research methods can be applied to various fields, including exercise science.

Reviewed by Chris Koch, Professor of Psychology, George Fox University on 3/27/18

All appropriate areas and topics are covered in the text. In that sense, this book is equivalent to other top texts dealing with research methods in psychology. The appeal of this book is the brevity and clarity. Therefore, some may find that, although the topics are covered, topics may not be covered as thoroughly they might like. Overall, the coverage is solid for an introductory course in research methods.

In terms of presentation, this book could be more comprehensive. Each chapter does start with a set of learning objectives and ends with "takeaways" and a short set of exercises. However, it lacks detailed chapter outlines, summaries, and glossaries. Furthermore, an index does not accompany the text.

I found the book to be accurate with content being fairly presented. There was no underlying bias throughout the book.

This is an introductory text for research methods. The basics of research methods have been consistent for some time. The examples used in the text fit the concepts well. Therefore, it should not be quickly dated. It is organized in such a way that sections could be easily modified with more current examples as needed.

The text is easy to read. It is succinct yet engaging. Examples are clear and terminology is adequately defined.

New terms and concepts are dealt with chapter by chapter. However, those things which go across chapters are consistently presented.

The material for each chapter is presented in subsections with each subsection being tied to a particular learning objective. It is possible to use the book by subsection instead of by chapter. In fact, I did that during class by discussing the majority of one chapter, discussing another chapter, and then covering what I previously skipped,

In general, the book follows a "traditional" organization, matching the organization of many competing books. As mentioned in regard to modularity, I did not follow the organization of the book exactly as it was laid out. This may not necessarily reflect poorly on the book, however, since I have never followed the order of any research methods book. My three exams covered chapter 1 through 4, chapters 5, 6, part of 8, and chapters 7, the remainder of 8, 9, and 10. Once we collected data I covered chapters 11 through 13.

Interface rating: 3

The text and images are clear and distortion free. The text is available in several formats including epub, pdf, mobi, odt, and wxr. Unfortunately, the electronic format is not taken full advantage of. The text could be more interactive. As it is, it is just text and images. Therefore, the interface could be improved.

The book appeared to be well written and edited.

I did not find anything in the book that was culturally insensitive or offensive. However, more examples of cross-cultural research could be included.

I was, honestly, surprised by how much I liked the text. The material was presented in easy to follow format that is consistent with how I think about research methods. That made the text extremely easy to use. Students also thought the book was highly accessible Each chapter was relatively short but informative and easy to read.

Reviewed by Kevin White, Assistant Professor, East Carolina University on 2/1/18

This book covers all relevant topics for an introduction to research methods course in the social sciences, including measurement, sampling, basic research design, and ethics. The chapters were long enough to be somewhat comprehensive, but short enough to be digestible for students in an introductory-level class. Student reviews of the book have so far been very positive. The only section of the text for which more detail may be helpful is 2.3 (Reviewing the Research Literature), in which more specific instructions related to literature searches may be helpful to students.

I did not notice any issues related to accuracy. Content appeared to be accurate, error-free, and unbiased.

One advantage of this book is that it is relevant to other applied fields outside of psychology (e.g., social work, counseling, etc.). Also, the exercises at the end of chapter sections are helpful.

The clarity of the text provides students with succinct definitions for research-related concepts, without unnecessary discipline-specific jargon. One suggestion for future editions would be to make the distinctions between different types of non-experimental research a bit more clear for students in introductory classes (e.g., "Correlational Research" in Section 7.2).

Formatting and terminology was consistent throughout this text.

A nice feature of this book is that instructors can select individual sections within chapters, or even jump between sections within chapters. For example, Section 1.4 may not fit for a class that is less clinically-oriented in nature.

The flow of the text was appropriate, with ethics close to the beginning of the book (and an entire chapter devoted to it), and descriptive/inferential statistics at the end.

I did not notice any problems related to interface. I had no trouble accessing or reading the text, and the images were clear.

The text contained no discernible grammatical errors.

The book does not appear to be culturally insensitive in any discernible way, and explicitly addresses prejudice in research (e.g., Section 5.2). However, I think that continuing to add more examples that relate to specific marginalized groups would help improve the text (and especially exercises).

Overall, this book is very useful for an introductory research methods course in psychology or social work, and I highly recommend.

Reviewed by Elizabeth Do, Instructor, Virginia Commonwealth University on 2/1/18

Although this textbook does provide good information regarding introductory concepts necessary for the understanding of correlational designs, and is presented in a logical order. It does not, however, cover qualitative methodologies, or research... read more

There does not seem to be any errors within the text.

Since this textbook covers a topic that is unlikely to change over the years and it's content is up-to-date, it remains relevant to the field.

The textbook is written at an appropriate level for undergraduate students and is useful in that it does explain important terminology.

There does not seem to be any major inconsistencies within the text.

Overall, the text is very well organized - it is separated into chapters that are divided up into modules and within each module, there are clear learning objectives. It is also helpful that the textbook includes useful exercises for students to practice what they've read about from the text.

The topics covered by this textbook are presented in an order that is logical. The writing is clear and the examples are very useful. However, more information could be provided in some of the chapters and it would be useful to include a table of contents that links to the different chapters within the PDF copy, for reader's ease in navigation when looking for specific terms and/or topics.

Overall, the PDF copy of the textbook made it easy to read; however, there did seem to be a few links that were missing. Additionally, it would be helpful to have some of the graphs printed in color to help with ease of following explanations provided by the text. The inclusion of a table of contents would also be useful for greater ease with navigation.

There does not seem to be any grammatical errors in the textbook. Also, the textbook is written in a clear way, and the information flows nicely.

This textbook focuses primarily on examples from the United States. It does not seem to be culturally insensitive or offensive in anyway and I liked that it included content regarding the avoidance of biased language (chapter 11).

This textbook makes the material very accessible, and it is easy to read/follow examples.

Reviewed by Eric Lindsey, Professor, Penn State University Berks Campus on 2/1/18

The content of the Research Methods in Psychology textbook was very thorough and covered what I would consider to be the important concepts and issues pertaining to research methods. I would judge that the textbook has a comparable coverage of information to other textbooks I have reviewed, including the current textbook I am using. The range of scholarly sources included in the textbook was good, with an appropriate balance between older and classic research examples and newer more cutting edge research information. Overall, the textbook provides substantive coverage of the science of conducting research in the field of psychology, supported by good examples, and thoughtful questions.

The textbook adopts a coherent and student-friendly format, and offers a precise introduction to psychological research methodology that includes consideration of a broad range of qualitative and quantitative methods to help students identify and evaluate the best approach for their research needs. The textbook offers a detailed review of the way that psychological researchers approach their craft. The author guides the reader through all aspects of the research process including formulating objectives, choosing research methods, securing research participants, as well as advice on how to effectively collect, analyze and interpret data and disseminate those findings to others through a variety of presentation and publication venues. The textbook offers relevant supplemental information in textboxes that is highly relevant to the material in the accompanying text and should prove helpful to learners. Likewise the graphics and figures that are included are highly relevant and clearly linked to the material presented in the text. The information covered by the textbook reflects an accurate summary of current techniques and methods used in research in the field of psychology. The presentation of information addresses the pros and cons of different research strategies in an objective and evenhanded way.

The range of scholarly sources included in the textbook was good, with an appropriate balance between older, classic research evidence and newer, cutting edge research. Overall, the textbook provides substantive coverage of the science on most topics in research methods of psychology, supported by good case studies, and thoughtful questions. The book is generally up to date, with adequate coverage of basic data collection methods and statistical techniques. Likewise the review of APA style guidelines is reflects the current manual and I like the way the author summarizes changes from the older version of the APA manual. The organization of the textbook does appear to lend itself to editing and adding new information with updates in the future.

I found the textbook chapters to be well written, in a straightforward yet conversational manner. It gives the reader an impression of being taught by a knowledgeable yet approachable expert. The writing style gives the learner a feeling of being guided through the lessons and supported in a very conversational approach. The experience of reading the textbook is less like being taught and more like a colleague sharing information. Furthermore, the style keeps the reader engaged but doesn't detract from its educational purpose. I also appreciate that the writing is appropriately concise. No explanations are so wordy as to overwhelm or lull the reader to sleep, but at the same time the information is not so vague that the reader can't understand the point at all.

The book’s main aim is to enable students to develop their own skills as researchers, so they can generate and advance common knowledge on a variety of psychological topics. The book achieves this objective by introducing its readers, step-by-step, to psychological research design, while maintaining an excellent balance between substance and attention grabbing examples that is uncommon in other research methods textbooks. Its accessible language and easy-to-follow structure and examples lend themselves to encouraging readers to move away from the mere memorization of facts, formulas and techniques towards a more critical evaluation of their own ideas and work – both inside and outside the classroom. The content of the chapters have a very good flow that help the reader to connect information in a progressive manner as they proceed through the textbook.

Each chapter goes into adequate depth in reviewing both past and current research related to the topic that it covers for an undergraduate textbook on research methods in psychology. The information within each chapter flows well from point-to-point, so that the reader comes away feeling like there is a progression in the information presented. The only limitation that I see is that I felt the author could do a little more to let the reader know how information is connected from chapter to chapter. Rather than just drawing the reader’s attention to things that were mentioned in previous chapters, it would be nice to have brief comments about how issues in one chapter relate to topics covered in previous chapters.

In my opinion the chapters are arranged in easily digestible units that are manageable in 30-40 minute reading sessions. In fact, the author designed the chapters of the textbook in a way to make it easy to chunk information, and start and stop to easily pick up where one leaves off from one reading session to another. I also found the flow of information to be appropriate, with chapters containing just the right amount of detail for use in my introductory course in research methods in psychology.

The book is organized into thirteen chapters. The order of the chapters offers a logical progression from a broad overview of information about the principles and theory behind research in psychology, to more specific issues concerning the techniques and mechanics of conducting research. Each chapter ends with a summary of key takeaways from the chapter and exercises that do more than ask for content regurgitation. I find the organization of the textbook to be effective, and matches my approach to the course very well. I would not make any changes to the overall format with the exception of moving chapter 11 on presenting research to the end of the textbook, after the chapters on statistical analysis and interpretation.

I found the quality of the appearance of the textbook to be very good. The textbook features appropriate text and section/header font sizes that allow for an adequate zooming level to read large or smalls sections of text, that will give readers flexibility to match their personal preference. There are learning objectives at the start of each chapter to help students know what to expect. Key terms are highlighted in a separate color that are easily distinguishable in the body of the page. There are very useful visuals in every chapter, including tables, figures, and graphs. Relevant supplemental information is also highlighted in well formatted text boxes that are color coded to indicate what type of information is included. My only criticism is that the photographs included in the text are of low quality, and there are so few in the textbook that I feel it would have been better to just leave them out.

I found no grammatical errors in my review of the textbook. The textbook is generally well written, and the style of writing is at a level that is appropriate for an undergraduate class.

Although the textbook contains no instances of presenting information that is cultural insensitive or offensive, it does not offer an culturally inclusive review of information pertaining to research methods in psychology. I found no inclusion of examples of research conducting with non European American samples included in the summary of studies. Likewise the authors do place much attention on the issue of cultural sensitivity when conducing research. If there is one major weakness of the textbook I would say it is in this area, but based on my experience it is not an uncommon characteristic of textbooks on research methods in psychology.

Reviewed by zehra peynircioglu, Professor, American University on 2/1/18

Comprehensiveness rating: 3 see less

Short and sweet in most areas. Covers the basic concepts, not very comprehensively but definitely adequately so for a general beginning-level research methods course. For instance, I would liked to have seen a "separate" chapter on correlational research (there is one on single subject research and one on survey research), a discussion of the importance of providing a theoretical rationale for "getting an idea" (most students are fine with finding interesting and feasible project ideas but cannot give a theoretical rationale) before or after Chapter 4 on Theory, or a chapter on neuroscientific methods, which are becoming more and more popular. Nevertheless, it touches on most traditional areas that are in other books.

I did not find any errors or biases

This is one area where there is not much danger of going obsolete any time soon. The examples might need to be updated periodically (my students tend to not like dated materials, however relevant), but that should be easy.

Very clear and accessible prose. Despite the brevity, the concepts are put forth quite clearly. I like the "not much fluff" mentality. There is also adequate explanations of jargon and technical terminology.

I could not find any inconsistencies. The style and exposition frameworks are also quite consistent.

Yes, the modularity is fine. The chapters follow a logical pattern, so there should not be too much of a need for jumping around. And even if jumping around is needed depending on teaching style, the sections are solid in terms of being able to stand alone (or as an accompaniment to lectures).

Yes, the contents is ordered logically and the high modularity helps with any reorganization that an instructor may favor. In my case, for instance, Ch. 1 is fine, but I would skip it because it's mostly a repetition of what most introductory psychology books also say. I would also discuss non-experimental methods before going into experimental design. But such changes are easy to do, and if someone followed the book's own organization, there would also be a logical flow.

As far as I could see, the text is free of significant interface issues, at least in the pdf version

I could not find any errors.

As far as I could see, the book was culturally relevant.

I loved the short and sweet learning objectives, key takeaway sections, and the exercises. They are not overwhelming and can be used in class discussions, too.

Reviewed by George Woodbury, Graduate Student, Miami University, Ohio on 6/20/17

This text covers the typical areas for an undergraduate psychology course in research design. There is no table of contents included with the downloadable version, although there is a table of contents on the website (which excludes sub-sections of chapters). The sections on statistics are not extensive enough to be useful in and of themselves, but they are useful for transitions to a follow-up statistics course. There does not seem to be a glossary of terms, which made it difficult at times for my read through and I assume later for students who decide to print the text. The text is comprehensive without being wordy or tedious.

Relatively minor errors; There does not seem to be explicit cultural or methodological bias in the text.

The content is up-to-date, and examples from the psychology literature are generally within the last 25 years. Barring extensive restructuring in the fundamentals of methodology and design in psychology, any updates will be very easy to implement.

Text will be very clear and easy to read for students fluent in English. There is little jargon/technical terminology used, and the vocabulary that is provided in the text is contemporary

There do not seem to be obvious shifts in the terminology or the framework. The text is internally consistent in that regard.

The text is well divided into chapter and subsections. Each chapter is relatively self-contained, so there are little issues with referring to past material that may have been skipped. The learning objectives at the beginning of the chapter are very useful. Blocks of text are well divided with headings.

As mentioned above, the topics of the text follow the well-established trajectory of undergraduate psychology courses. This makes it very logical and clear.

The lack of a good table of contents made it difficult to navigate the text for my read through. There were links to an outside photo-hosting website (flickr) for some of the stock photos, which contained the photos of the original creators of the photos. This may be distracting or confusing to readers. However, the hyperlinks in general helped with navigation with the PDF.

No more grammatical errors than a standard, edited textbook.

Very few examples explicitly include other races, ethnicities, or backgrounds, however the examples seem to intentionally avoid cultural bias. Overall, the writing seems to be appropriately focused on avoiding culturally insensitive or offensive content.

After having examined several textbooks on research design and methodology related to psychology, this book stands out as superior.

Reviewed by Angela Curl, Assistant Professor, Miami University (Ohio) on 6/20/17

"Research Methods in Psychology" covers most research method topics comprehensively. The author does an excellent job explaining main concepts. The chapter on causation is very detailed and well-written as well as the chapter on research ethics. However, the explanations of data analysis seem to address upper level students rather than beginners. For example, in the “Describing Statistical Relationships” chapter, the author does not give detailed enough explanations for key terms. A reader who is not versed in research terminology, in my opinion, would struggle to understand the process. While most topics are covered, there are some large gaps. For example, this textbook has very little content related to qualitative research methods (five pages).

The content appears to be accurate and unbias.

The majority of the content will not become obsolete within a short time period-- many of the information can be used for the coming years, as the information provided is, overall, general in nature. The notably exceptions are the content on APA Code of Ethics and the APA Publication Manual, which both rely heavily on outdated versions, which limits the usefulness of these sections. In addition, it would be helpful to incorporate research studies that have been published after 2011.

The majority of the text is clear, with content that is easy for undergraduate students to read and understand. The key points included in the chapters are helpful, but some chapters seem to be missing key points (i.e., the key points do not accurately represent the overall chapter).

The text seems to be internally consistent in its terminology and organization.

Each chapter is broken into subsections that can be used alone. For example, section 5.2 covers reliability and validity of measurement. This could be extremely helpful for educators to select specific content for assigned readings.

The topics are presented in a logical matter for the most part. However, the PDF version of the book does not include a table of contents, and none of the formats has a glossary or index. This can make it difficult to quickly navigate to specific topics or terms, especially when explanations do not appear where expected. For example, the definitions of independent and dependent variables is provided under the heading “Correlation Does Not Imply Causation” (p. 22).

The text is consistent but needs more visual representations throughout the book, rather than heavily in some chapters and none at all in other chapters. Similarly, the text within the chapters is not easily readable due to the large sections of text with little to no graphics or breaks.

The interface of the text is adequate. However, the formatting of the PDF is sometimes weak. For example, the textbook has a number of pages with large blank spaces and other pages are taken up with large photos or graphics. The number of pages (and cost of printing) could have been reduced, or more graphics added to maximize utility.

I found no grammatical errors.

Text appears to be culturally sensitive. I appreciated the inclusion of the content about avoiding biased language (chapter 11).

Instructors who adopt this book would likely benefit from either selecting certain chapters/modules and/or integrating multiple texts together to address the shortcomings of this text. Further, the sole focus on psychology limits the use of this textbook for introductory research methods for other disciplines (e.g., social work, sociology).

Reviewed by Pramit Nadpara, Assistant Professor, Virginia Commonwealth University on 4/11/17

The text book provides good information in certain areas, while not comprehensive information in other areas. The text provides practical information, especially the section on survey development was good. Additional information on sampling... read more

There are no errors.

Research method is a common topic and the fundamentals of it will not change over the years. Therefore, the book is relevant and will not become obsolete.

Clarity rating: 3

The text in the book is clear. Certain aspects of the text could have been presented more clearly. For example, the section on main effects and interactions are some concepts that students may have difficulty understanding. Those areas could be explained more clearly with an example.

Consistency rating: 3

Graphs in the book lacks titles and variable names. Also, the format of chapter title page needs to be consistent.

At times there were related topics spread across several chapters. This could be corrected for a better read by the audience..

The book text is very clear, and the flow from one topic to the next was adequate. However, having a outline would help the reader.

The PDF copy of the book was a easy read. There were few links that were missing though.

There were no grammatical errors.

The text is not offensive and examples in it are mostly based on historical US based experiments.

I would start of by saying that I am a supporter of the Open Textbook concept. In this day and age, there are a variety of Research Methods book/text available on the market. While this book covers research methods basics, it cannot be recommended in its current form as an acceptable alternative to the standard text. Modifications to the text as recommended by myself and other reviewers might improve the quality of this book in the future.

Reviewed by Meghan Babcock, Instructor, University of Texas at Arlington on 4/11/17

This text includes all important areas that are featured in other Research Methods textbooks and are presented in a logical order. The text includes great examples and provides the references which can be assigned as supplemental readings. In addition, the chapters end with exercises that can be completed in class or as part of a laboratory assignment. This text would be a great addition to a Research Methods course or an Introductory Statistics course for Psychology majors.

The content is accurate. I did not find any errors and the material is unbiased.

Yes - the content is up to date and would be easy to update if/when necessary.

The text is written at an appropriate level for undergraduate students and explains important terminology. The research studies that the author references are ones that undergraduate psychology majors should be familiar with. The only section that was questionable to me was that on multiple regression in section 8.3 (Complex Correlational Designs). I am unaware of other introductory Research Methods textbooks that cover this analysis, especially without describing simple regression first.

The text is consistent in terms of terminology. The framework is also consistent - the chapters begin with Learning Objectives and ends with Key Takeaways and Exercises.

The text is divisible into smaller reading sections - possibly too many. The sections are brief, and in some instances too brief (e.g., the section on qualitative research). I think that the section headers are helpful for instructors who plan on using this text in conjunction with another text in their course.

The topics were presented in a logical fashion and are similar to other published Research Methods texts. The writing is very clear and great examples are provided. I think that some of the sections are rather brief and more information and examples could be provided.

I did not see any interface issues. All of the links worked properly and the tables and figures were accurate and free of errors. I particularly liked the figures in section 5.2 on reliability of measurement.

There are three comments that I have about the interface, however. First, I was expecting the keywords in blue font to be linked to a glossary, but they were not. I would have appreciated this feature. Second, I read this text as a PDF on an iPad and this version lacking was the Table of Contents (TOC) feature. Although I was able to view the TOC in different versions, I would have appreciated it in the PDF version. Also, it would be nice if the TOC was clickable (i.e., you could click on a section and it automatically directed you to that section). Third, I think the reader of this text would benefit from a glossary at the end of each chapter and/or an index at the end of the text. The "Key Takeaways" sections at the end of each chapter were helpful, but I think that a glossary would be a nice addition as well.

I did not notice any grammatical errors of any kind. The text was easy to read and I think that undergraduate students would agree.

The text was not insensitive or offensive to any races, ethnicities, or backgrounds. I appreciated the section on avoiding biased language when writing manuscripts (e.g., using 'children with learning disabilities' instead of 'special children' or using 'African American' instead of 'minority').

I think that this text would be a nice addition to a Research Methods & Statistics course in psychology. There are some sections that I found particularly helpful: (1) 2.2 and 2.3 - the author gives detailed information about generating research questions and reviewing the literature; (2) 9.2 - this section focuses on constructing survey questionnaires; (3) 11.2 and 11.3 - the author talks about writing a research report and about presenting at conferences. These sections will be great additions to an undergraduate Research Methods course. The brief introduction to APA style was also helpful, but should be supplemented with the most recent APA style manual.

Reviewed by Shannon Layman, Lecturer, University of Texas at Arlington on 4/11/17

The sections in this textbook are overall more brief than in previous Methods texts that I have used. Sometimes this brevity is helpful in terms of getting to the point of the text and moving on. In other cases, some topics could use a bit more... read more

I did not find any incorrect information or gross language issues.

Basic statistical and/or methodological texts tend to stay current and up-to-date because the topics in this field have not changed over the decades. Any updated methodologies would be found in a more advanced methods text.

The text is very clear and the ideas are easy to follow/ presented in a logical manner. The most helpful thing about this textbook is that the author arrives at the point of the topic very quickly. Another helpful point about this textbook is the relevancy of the examples used. The examples appear to be accessible to a wide audience and do not require specialization or previous knowledge of other fields of psychology.

I feel this text is very consistent throughout. The ideas build on each other and no terms are discussed in later chapters without being established in previous chapters.

Each chapter had multiple subsections which would allow for smaller reading sections throughout the course. The amount of content in each section and chapter appeared to be less than what I have encountered in other Methods texts.

The organization of the topics in this textbook follows the same or similar organization that I see in other textbooks. As I mentioned previously, the ideas build very well throughout the text.

I did not find any issues with navigation or distortion of the figures in the text.

There were not any obvious and/or egregious grammatical errors that I encountered in this text.

This topic is not really an issue with a Methods textbook as the topics are more so conceptual as opposed to topical. That being said, I did not see an issue with any examples used.

I have no other comments than what I addressed previously.

Reviewed by Sarah Allred, Associate Professor, Rutgers University, Camden on 2/8/17

Mixed. For some topics, there is more (and more practical) information than in most textbooks. I appreciated the very practical advice to students about how to plot data (in statistics chapters). Similarly, there is practical advice about how... read more

On the other hand, there is far too little information about some subjects. For example, independent and dependent variables are introduced in passing in an early chapter and then referred to only much later in the text. In my experience, students have a surprisingly difficult time grasping this concept. Another important example is sampling; I would have preferred much more information on types of samples and sampling techniques, and the problems that arise from poor sampling. A third example is the introduction to basic experimental design. Variables, measurement, validity, and reliability are all introduced in one chapter.

I did not see an index or glossary.

I found no errors.

The fundamentals of research methods do not change much. Given the current replication crisis in psychology, it might be helpful to have something about replicability.

Mixed. The text itself is spare and clear. The style of the book is to explain a concept in very few words. There are some excellent aspects of this, but on the other hand, there are some concepts that students have a very difficult time undersatnding if they are not embedded in concrete examples. For example, the section on main effects and interactions shows bar graphs of interactions, but this is presented without variable names or axis titles, and separate from any specific experiment.

Sometimes the chapter stucture is laid out on the title page, and other times it is not. Some graphs lack titles and variable names.

The chapters can be stand alone, but sometimes I found conceptually similar pieces spread across several chapters, and conceptually different pieces in the same chapters.

The individual sentences and paragraphs are always very clear. However, I felt that more tables/outlines of major concepts would have been helpful. For example, perhaps a flow chart of different kinds of experimental designs would be useful. (See section on comprehensiveness for more about organization).

The flow from one topic to the next was adequate.

I read the pdf. Perhaps the interface is more pleasant on other devices, but I found the different formats and fonts in image/captions/main text/figure labels distracting. Many if the instances of apparently hyperlinked (blue) text to do not link to anything.

I found no grammatical errors, and prose is standard academic English.

Like most psychology textbooks available in the US, examples are focused on important experiments in US history.

I really wanted to be happy with this text. I am a supporter of the Open Textbook concept, and I wanted to find this book an acceptable alternative to the variety of Research Methods texts I’ve used. Unfortunately, I cannot recommend this book as superior in quality.

Reviewed by Joel Malin, Assistant Professor, Miami University on 8/21/16

This textbook covers all or nearly all of what I believe are important topics to provide an introduction to research methods in psychology. One minor issue is that the pdf version, which I reviewed, does not include an index or a glossary. As such, it may be difficult for readers to zero in on material that they need, and/or to get a full sense of what will be covered and in what order.

I did not notice errors.

The book provides a solid overview of key issues related to introductory research methods, many of which are nearly timeless.

The writing is clear and accessible. It was easy and pleasing to read.

Terms are clearly defined and build upon each other as the book progresses.

I believe the text is organized in such a way that it could be easily divided into smaller sections.

The order in which material is presented seems to be well thought out and sensible.

I did not notice any issues with the interface. I reviewed the pdf version and thought the images were very helpful.

The book is written in a culturally relevant manner.

Reviewed by Abbey Dvorak, Assistant Professor, University of Kansas on 8/21/16

The text includes basic, essential information needed for students in an introductory research methods course. In addition, the text includes three chapters (i.e., research ethics, theory, and APA style) that are typically absent from or inadequately covered in similar texts. However, I did have some areas of concern regarding the coverage of qualitative and mixed methods approaches, and nonparametric tests. Although the author advocates for the research question to guide the choice of approach and design, minimal attention is given to the various qualitative designs (e.g., phenomenology, narrative, participatory action, etc.) beyond grounded theory and case studies, with no mention of the different types of mixed methods designs (e.g., concurrent, explanatory, exploratory) that are prevalent today. In addition, common nonparametric tests (e.g., Wilcoxon, Mann-Whitney, etc.) and parametric tests for categorical data (e.g., chi-square, Fisher’s exact, etc.) are not mentioned.

The text overall is accurate and free of errors. I noticed in the qualitative research sub-section, the author describes qualitative research in general, but does not mention common practices associated with qualitative research, such as transcribing interviews, coding data (e.g., different approaches to coding, different types of codes), and data analysis procedures. The information that is included appears accurate.

The text appears up-to-date and includes basic research information and classic examples that rarely change, which may allow the text to be used for many years. However, the author may want to add information about mixed methods research, a growing research approach, in order for the text to stay relevant across time.

The text includes clear, accessible, straightforward language with minimal jargon. When the author introduces a new term, the term is immediately defined and described. The author also provides interesting examples to clarify and expand understanding of terms and concepts throughout the text.

The text is internally consistent and uses similar language and vocabulary throughout. The author uses real-life examples across chapters in order to provide depth and insight into the information. In addition, the vocabulary, concepts, and organization are consistent with other research methods textbooks.

The modules are short, concise, and manageable for students; the material within each module is logically focused and related to each other. I may move the modules and the sub-topics within them into a slightly different order for my class, and add the information mentioned above, but overall, this is very good.

The author presents topics and structures chapters in a logical and organized manner. The epub and online version do not include page numbers in the text, but the pdf does; this may be confusing when referencing the text or answering student questions. The book ends somewhat abruptly after the chapter on inferential statistics; the text may benefit from a concluding chapter to bring everything together, perhaps with a culminating example that walks the reader through creating the research question, choosing a research approach/design, etc., all the way to writing the research report.

I used and compared the pdf, epub, and online versions of the text. The epub and online versions include a clickable table of contents, but the pdf does not. The table format is inconsistent across the three versions; in the epub version (viewed through ibooks), the table data does not always line up correctly, making it difficult to interpret quickly. In the pdf and online versions, the table format looks different, but the data are lined up. No index made it difficult to quickly find areas of interest in the text; however, I could use the Find/Search functions in all three versions to search and find needed items.

As I read through this text, I did not detect any glaring grammatical errors. Overall, I think the text is written quite well in a style that is accessible to students.

The author uses inclusive, person-first language, and the text does not seem to be offensive or insensitive. As I read, I did notice that topics such as diversity and cultural competency are absent.

I enjoyed reading this text and am very excited to have a free research methods text for my students that I may supplement as needed. I wish there was a test question bank and/or flashcards for my students to help them study, but perhaps that could be added in the future. Overall, this is a great resource!

Reviewed by Karen Pikula, Psychology Instructor PhD, Central Lakes College on 1/7/16

The text covers all the areas and ideas of the subject of research methods in psychology for the learner that is just entering the field. The authors cover all of the content of an introductory research methods textbook and use exemplary examples that make those concepts relevent to a beginning researcher. As the authors state, the material is presented in such a manner as to encourage learners to not only be effective consumers of current research but also engage as critical thinkers in the many diverse situations one encounters in everyday life.

The content is accurate, error free, and unbiased. It explains both quantiative and qualitative methods in an unbiased manner. It is a bit slim on qualitative. It would be nice to have a bit more information on, for example, creating interview questions, coding, and qualitative data anaylisis.

The text is up to date, having just been revised. This revision was authored by Rajiv Jhangiani (Capilano University, North Vancouver) and includes the addition of a table of contents and cover page that the original text did not have, changes to Chapter 3 (Research Ethics) to include a contemporary example of an ethical breach and to reflect Canadian ethical guidelines and privacy laws, additional information regarding online data collection in Chapter 9 (Survey Research). Jhangiani has correcte of errors in the text and formulae, as well as changing spelling from US to Canadian conventions. The text is also now available in a inexpensive hard copy which students can purchase online or college bookstores can stock. This makes the text current and updates should be minimal.

The text is very easy to read and also very interesting as the authors supplement content with amazing real life examples.

The text is internally consistent in terms of terminology and framework.

This text is easily and readily divisible into smaller reading sections that can be assigned at different points within a course. I am going to use this text in conjunction with the OER OpenStax Psychology text for my Honors Psychology course. I currently use the OER Openstax Psychology textbook for my Positive Psychology course as well as my General Psychology course,

The topics in the text are presented in logical and clear fashion. The way they are presented allows the text to be used in conjuction with other textbooks as a secondary resource.

The text is free of significant interface issues. It is written in a manner that follows the natural process of doing research.

The text contained no noted grammatical errors.

The text is not culturally insensitive or offensive and actually has been revised to accomodate Canadian ethical guidelines as well as those of the APA.

I have to say that I am excited to have found this revised edition. My students will be so happy that there is also a reasonable priced hard coopy for them to purchase. They love the OpenStax Psychology text with the hard copy available from our bookstore. I do wish there were PowerPoints available for the text as well as a test bank. That is always a bonus!

Reviewed by Alyssa Gibbons, Instructor, Colorado State University on 1/7/16

This text covers everything I would consider essential for a first course in research methods, including some areas that are not consistently found in introductory texts (e.g., qualitative research, criticisms of null hypothesis significance testing). The chapters on ethics (Ch. 3) and theory (Ch. 4) are more comprehensive than most I have seen at this level, but not to the extent of information overload; rather, they anticipate and address many questions that undergraduates often have about these issues.

There is no index or table of contents provided in the PDF, and the table of contents on the website is very broad, but the material is well organized and it would not be hard for an instructor to create such a table. Chapter 2.1 is intended to be an introduction to several key terms and ideas (e.g., variable, correlation) that could serve as a sort of glossary.

I found the text to be highly accurate throughout; terms are defined precisely and correctly.

Where there are controversies or differences of opinion in the field, the author presents both sides of the argument in a respectful and unbiased manner. He explicitly discourages students from dismissing any one approach as inherently flawed, discussing not only the advantages and disadvantages of all methods (including nonexperimental ones) but also ways researchers address the disadvantages.

In several places, the textbook explicitly addresses the history and development of various methods (e.g., qualitative research, null hypothesis significance testing) and the ways in which researchers' views have changed. This allows the author to present current thinking and debate in these areas yet still expose students to older ideas they are likely to encounter as they read the research literature. I think this approach sets students up well to encounter future methodological advances; as a field, we refine our methods over time. I think the author could easily integrate new developments in future editions, or instructors could introduce such developments as supplementary material without creating confusion by contradicting the test.

The examples are generally drawn from classic psychological studies that have held up well over time; I think they will appeal to students for some time to come and not appear dated.

The only area in which I did not feel the content was entirely up to date was in the area of psychological measurement; Chapter 5.2 is based on the traditional view and not the more comprehensive modern or holistic view as presented in the 1999 AERA/APA Standards for Educational and Psychological Measurement. However, a comprehensive treatment of measurement validity is probably not necessary for most undergraduates at this stage, and they will certainly encounter the older framework in the research literature.

The textbook does an excellent job of presenting concepts in simple, accessible language without introducing error by oversimplification. The author consistently anticipates common points of confusion, clarifies terms, and even suggests ways for students to remember key distinctions. Terms are clearly and concretely defined when they are introduced. In contrast to many texts I have used, the terms that are highlighted in the text are actually the terms I would want my students to remember and study; the author refrains from using psychological jargon that is not central to the concepts he is discussing.

I noticed no major inconsistencies or gaps.

The division of sections within each chapter is useful; although I liked the overall organization of the text, there were points at which I would likely assign sections in a slightly different order and I felt I could do this easily without loss of continuity. The one place I would have liked more modularity was in the discussion of inferential statistics: t-tests, ANOVA, and Pearson's r are all covered within Chapter 13.2. On the one hand, this enables students to see the relationships and similarities among these tests, but on the other, this is a lot for students to take in at once.

I found the overall organization of the book to be quite logical, mirroring the sequence of steps a researcher would use to develop a research question, design a study, etc. As discussed above, the modularity of the book makes it easy to reorder sections to suit the structure of a particular class (for example, I might have students read the section on APA writing earlier in the semester as they begin drafting their own research proposals). I like the inclusion of ethics very early on in the text, establishing the importance of this topic for all research design choices.

One organizational feature I particularly appreciated was the consistent integration of conceptual and practical ideas; for example, in the discussion of psychological measurement, reliability and validity are discussed alongside the importance of giving clear instructions and making sure participants cannot be identified by their writing implements. This gives students an accurate and honest picture of the research process - some of the choices we make are driven by scientific ideals and some are driven by practical lessons learned. Students often have questions related to these mundane aspects of conducting research and it is helpful to have them so clearly addressed.

Although I didn't encounter any problems per se with the interface, I do think it could be made more user-friendly. For example, references to figures and tables are highlighted in blue, appearing to be hyperlinks, but they were not. Having such links, as well as a linked, easily-navigable and detailed table of contents, would also be helpful (and useful to students who use assistive technology).

I noticed no grammatical errors.

Where necessary, the author uses inclusive language and there is nothing that seems clearly offensive. The examples generally reflect American psychology research, but the focus is on the methods used and not the participants or cultural context. The text could be more intentionally or proactively inclusive, but it is not insensitive or exclusive.

I am generally hard to please when it comes to textbooks, but I found very little to quibble with in this one. It is a very well-written and accessible introduction to research methods that meets students where they are, addressing their common questions, misconceptions, and concerns. Although it's not flashy, the figures, graphics, and extra resources provided are clear, helpful, and relevant.

Reviewed by Moin Syed, Assistant Professor, University of Minnesota on 6/10/15

The text is thorough in terms of covering introductory concepts that are central to experimental and correlational/association designs. I find the general exclusion of qualitative and mixed methods designs hard to defend (despite some researchers’ distaste for the methods). While these approaches were less commonly used in the recent past, they are prevalent in the early years of psychology and are ascending once again. It strikes me as odd to just ignore two whole families of methods that are used within the practice of psychology—definitely not a sustainable approach.

I do very much appreciate the emphasis on those who will both practice and consume psychology, given the wide variety of undergraduate career paths.

One glaring omission is a Table of Contents within the PDF. It would be nice to make this a linked PDF, so that clicking on the entry in a TOC (or cross-references) would jump the reader to the relevant section.

I did not see an errors. The chapter on theory is not as clear as it could be. The section “what is theory” is not very clear, and these are difficulte concepts (difference between theory, hypothesis, etc.). A bit more time spent here could have been good. Also, the discussion of functional, mechanistic, and typological theories leaves out the fourth of Pepper’s metaphors: contextualism. I’m not sure that was intentional and accidental, but it is noticeable!

This is a research methods text focused on experimental and association designs. The basics of these designs do not change a whole lot over time, so there is little likelihood that the main content will become obsolete anytime soon. Some of the examples used are a bit dated, but then again most of them are considered “classics” in the field, which I think are important to retain (and there is at least one “new classic” included in the ethics section, namely the fraudulent research linking autism to the MMR vaccine).

The text is extremely clear and accessible. In fact, it may even be *too* simple for undergraduate use. Then again, students often struggle with methods, so simplicity is good, and the simplicity can also make the book marketable to high school courses (although I doubt many high schools have methods courses).

Yes, quite consistent throughout. Carrying through the same examples into different chapters is a major strength of the text.

I don’ anticipate any problems here.

The book flows well, with brief sections. I do wonder if maybe the sections are too brief? Perhaps too many check-ins? The “key take-aways” usually come after only a few pages. As mentioned above, the book is written at a very basic level, so this brevity is consistent with that approach. It is not a problem, per se, but those considering adopting the text should be aware of this aspect.

No problems here.

I did not detect any grammatical errors. The text flows very well.

The book is fairly typical of American research methods books in that it only focuses on the U.S. context and draws its examples from “mainstream” psychology (e.g., little inclusion of ethnic minority or cross-cultural psychology). However, the text is certainly not insensitive or offensive in any way.

Nice book, thanks for writing it!

Reviewed by Rajiv Jhangiani, Instructor, Capilano University on 10/9/13

The text is well organized and written, integrates excellent pedagogical features, and covers all of the traditional areas of the topic admirably. The final two chapters provide a good bridge between the research methods course and the follow-up course on behavioural statistics. The text integrates real psychological measures, harnesses students' existing knowledge from introductory psychology, includes well-chosen examples from real life and research, and even includes a very practical chapter on the use of APA style for writing and referencing. On the other hand, it does not include a table of contents or an index, both of which are highly desirable. The one chapter that requires significant revision is Chapter 3 (Research Ethics), which is based on the US codes of ethics (e.g., Federal policy & APA code) and does not include any mention of the Canadian Tri-Council Policy Statement.

The very few errors I found include the following: 1. The text should read "The fact that his F score…" instead of "The fact that his t score…" on page 364 2. Some formulae are missing the line that separates the numerator from the denominator. See pages 306, 311, 315, and 361 3. Table 12.3 on page 310 lists the variance as 288 when it is 28.8

The text is up-to-date and will not soon lose relevance. The only things I would add are a brief discussion of the contemporary case of Diederik Stapel's research fraud in the chapter on Research Ethics, as well as some research concerning the external validity of web-based studies (e.g., Gosling et al.'s 2004 article in American Psychologist).

Overall, the style of writing makes this text highly accessible. The writing flows well, is well organized, and includes excellent, detailed, and clear examples and explanations for concepts. The examples often build on concepts or theories students would have covered in their introductory psychology course. Some constructive criticism: 1. When discussing z scores on page 311 it might have been helpful to point out that the mean and SD for a set of calculated z scores are 0 and 1 respectively. Good students will come to this realization themselves, but it is not a bad thing to point it out nonetheless. 2. The introduction of the concept of multiple regression might be difficult for some students to grasp. 3. The only place where I felt short of an explanation was in the use of a research example to demonstrate the use of a line graph on page 318. In this case the explanation in question does not pertain to the line graph itself but the result of the study used, which is so fascinating that students will wish for the researchers' explanation for it.

The text is internally consistent.

The text is organized very well into chapters, modules within each chapter, and learning objectives within each module. Each module also includes useful exercises that help consolidate learning.

As mentioned earlier, the style of writing makes this text highly accessible. The writing flows well, is well organized, and includes excellent, detailed, and clear examples and explanations for concepts. The examples often build on concepts or theories students would have covered in their introductory psychology course. Only rarely did I feel that the author could have assisted the student by demonstrating the set-by-step calculation of a statistic (e.g., on page 322 for the calculation of Pearson's r)

The images, graphs, and charts are clear. The only serious issues that hamper navigation are the lack of a table of contents and an index. Many of the graphs will need to be printed in colour (or otherwise modified) for the students to follow the explanations provided in the text.

The text is written rather well and is free from grammatical errors. Of course, spellings are in the US convention.

The text is not culturally insensitive or offensive. Of course, it is not a Canadian edition and so many of the examples (all of which are easy to comprehend) come from a US context.

I have covered most of these issues in my earlier comments. The only things left to mention are that the author should have clearly distinguished between mundane and psychological realism, and that, in my opinion, the threats to internal validity could have been grouped together and might have been closer to an exhaustive list. This review originated in the BC Open Textbook Collection and is licensed under CC BY-ND.

Chapter 1: The Science of Psychology
Chapter 2: Overview of the Scientific Method
Chapter 3: Research Ethics
Chapter 4: Psychological Measurement
Chapter 5: Experimental Research
Chapter 6: Non-experimental Research
Chapter 7: Survey Research
Chapter 8: Quasi-Experimental Research
Chapter 9: Factorial Designs
Chapter 10: Single-Subject Research
Chapter 11: Presenting Your Research
Chapter 12: Descriptive Statistics
Chapter 13: Inferential Statistics

Ancillary Material

Kwantlen Polytechnic University

About the Book

This fourth edition (published in 2019) was co-authored by Rajiv S. Jhangiani (Kwantlen Polytechnic University), Carrie Cuttler (Washington State University), and Dana C. Leighton (Texas A&M University—Texarkana) and is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Revisions throughout the current edition include changing the chapter and section numbering system to better accommodate adaptions that remove or reorder chapters; continued reversion from the Canadian edition; general grammatical edits; replacement of “he/she” to “they” and “his/her” to “their”; removal or update of dead links; embedded videos that were not embedded; moved key takeaways and exercises from the end of each chapter section to the end of each chapter; a new cover design.

About the Contributors

Dr. Carrie Cuttler received her Ph.D. in Psychology from the University of British Columbia. She has been teaching research methods and statistics for over a decade. She is currently an Assistant Professor in the Department of Psychology at Washington State University, where she primarily studies the acute and chronic effects of cannabis on cognition, mental health, and physical health. Dr. Cuttler was also an OER Research Fellow with the Center for Open Education and she conducts research on open educational resources. She has over 50 publications including the following two published books: A Student Guide for SPSS (1st and 2nd edition) and Research Methods in Psychology: Student Lab Guide. Finally, she edited another OER entitled Essentials of Abnormal Psychology. In her spare time, she likes to travel, hike, bike, run, and watch movies with her husband and son. You can find her online at @carriecuttler or carriecuttler.com.

Dr. Rajiv Jhangiani is the Associate Vice Provost, Open Education at Kwantlen Polytechnic University in British Columbia. He is an internationally known advocate for open education whose research and practice focuses on open educational resources, student-centered pedagogies, and the scholarship of teaching and learning. Rajiv is a co-founder of the Open Pedagogy Notebook, an Ambassador for the Center for Open Science, and serves on the BC Open Education Advisory Committee. He formerly served as an Open Education Advisor and Senior Open Education Research & Advocacy Fellow with BCcampus, an OER Research Fellow with the Open Education Group, a Faculty Workshop Facilitator with the Open Textbook Network, and a Faculty Fellow with the BC Open Textbook Project. A co-author of three open textbooks in Psychology, his most recent book is Open: The Philosophy and Practices that are Revolutionizing Education and Science (2017). You can find him online at @thatpsychprof or thatpsychprof.com.

Dr. Dana C. Leighton is Assistant Professor of Psychology in the College of Arts, Science, and Education at Texas A&M University—Texarkana. He earned his Ph.D. from the University of Arkansas, and has 15 years experience teaching across the psychology curriculum at community colleges, liberal arts colleges, and research universities. Dr. Leighton’s social psychology research lab studies intergroup relations, and routinely includes undergraduate students as researchers. He is also Chair of the university’s Institutional Review Board. Recently he has been researching and writing about the use of open science research practices by undergraduate researchers to increase diversity, justice, and sustainability in psychological science. He has published on his teaching methods in eBooks from the Society for the Teaching of Psychology, presented his methods at regional and national conferences, and received grants to develop new teaching methods. His teaching interests are in undergraduate research, writing skills, and online student engagement. For more about Dr. Leighton see http://www.danaleighton.net and http://danaleighton.edublogs.org

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APA Handbook of Research Methods in Psychology

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Table of Contents

Volume 1 — Foundations, Planning, Measures, and Psychometrics

Part I. Philosophical, Ethical, and Societal Underpinnings of Psychological Research (Chapters 1 – 6) Part II. Planning Research (Chapters 7 – 12) Part III. Measurement Methods (Chapters 13 – 32) Part IV. Psychometrics (Chapters 33 – 38)

Volume 2 — Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological

Part I. Qualitative Research Methods (Chapters 1 – 11) Part II. Working Across Epistemologies, Methodologies, and Methods (Chapters 12 – 15) Part III. Sampling Across People and Time (Chapters 16 – 19) Part IV. Building and Testing Methods (Chapters 20 – 26) Part V. Designs Involving Experimental Manipulations (Chapters 27 – 32) Part VI. Quantitative Research Designs Involving Single Participants or Units (Chapters 33 – 34) Part VII. Designs in Neuropsychology and Biological Psychology (Chapters 35 – 38)

Volume 3 — Data Analysis and Research Publication

Part I. Quantitative Data Analysis (Chapters 1 – 24) Part II. Publishing and the Publication Process (Chapters 25 – 27)

This resource serves as an ideal reference for many different courses, including:

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With significant new and updated content across dozens of chapters, the second edition of the APA Handbook of Research Methods in Psychology presents the most exhaustive treatment available of the techniques psychologists and others have developed to help them pursue a shared understanding of why humans think, feel, and behave the way they do. Across three volumes, the chapters in this indispensable handbook address broad, crosscutting issues faced by researchers: the philosophical, ethical, and societal underpinnings of psychological research. Newly written chapters cover topics such as:

Literature searching
Workflow and reproducibility
Research funding
Neuroimaging
Data analysis methods
Navigating the publishing process
Ethics in scholarly authorship
Research data management and sharing
Applied Psychology
Clinical Psychology
Cognitive Psychology
Developmental Psychology
Education Psychology
Human Development
Neuroscience
Public health

Harris Cooper,

Duke University

Marc N. Coutanche,

University of Pittsburgh

Linda M. McMullen,

University of Saskatchewan (Canada) A.T. Panter,

University of North Carolina

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Ch 2: Psychological Research Methods

Children sit in front of a bank of television screens. A sign on the wall says, “Some content may not be suitable for children.”

Have you ever wondered whether the violence you see on television affects your behavior? Are you more likely to behave aggressively in real life after watching people behave violently in dramatic situations on the screen? Or, could seeing fictional violence actually get aggression out of your system, causing you to be more peaceful? How are children influenced by the media they are exposed to? A psychologist interested in the relationship between behavior and exposure to violent images might ask these very questions.

The topic of violence in the media today is contentious. Since ancient times, humans have been concerned about the effects of new technologies on our behaviors and thinking processes. The Greek philosopher Socrates, for example, worried that writing—a new technology at that time—would diminish people’s ability to remember because they could rely on written records rather than committing information to memory. In our world of quickly changing technologies, questions about the effects of media continue to emerge. Is it okay to talk on a cell phone while driving? Are headphones good to use in a car? What impact does text messaging have on reaction time while driving? These are types of questions that psychologist David Strayer asks in his lab.

Watch this short video to see how Strayer utilizes the scientific method to reach important conclusions regarding technology and driving safety.

You can view the transcript for “Understanding driver distraction” here (opens in new window) .

How can we go about finding answers that are supported not by mere opinion, but by evidence that we can all agree on? The findings of psychological research can help us navigate issues like this.

Introduction to the Scientific Method

Learning objectives.

Explain the steps of the scientific method
Describe why the scientific method is important to psychology
Summarize the processes of informed consent and debriefing
Explain how research involving humans or animals is regulated

photograph of the word "research" from a dictionary with a pen pointing at the word.

Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives. In this section, you’ll see how psychologists use the scientific method to study and understand behavior.

The Scientific Process

A skull has a large hole bored through the forehead.

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see the behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

Process of Scientific Research

Flowchart of the scientific method. It begins with make an observation, then ask a question, form a hypothesis that answers the question, make a prediction based on the hypothesis, do an experiment to test the prediction, analyze the results, prove the hypothesis correct or incorrect, then report the results.

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

Observe a natural phenomenon and define a question about it
Make a hypothesis, or potential solution to the question
Test the hypothesis
If the hypothesis is true, find more evidence or find counter-evidence
If the hypothesis is false, create a new hypothesis or try again
Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests.

A diagram has four boxes: the top is labeled “theory,” the right is labeled “hypothesis,” the bottom is labeled “research,” and the left is labeled “observation.” Arrows flow in the direction from top to right to bottom to left and back to the top, clockwise. The top right arrow is labeled “use the hypothesis to form a theory,” the bottom right arrow is labeled “design a study to test the hypothesis,” the bottom left arrow is labeled “perform the research,” and the top left arrow is labeled “create or modify the theory.”

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 5). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

(a)A photograph shows Freud holding a cigar. (b) The mind’s conscious and unconscious states are illustrated as an iceberg floating in water. Beneath the water’s surface in the “unconscious” area are the id, ego, and superego. The area just below the water’s surface is labeled “preconscious.” The area above the water’s surface is labeled “conscious.”

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Link to Learning

Why the scientific method is important for psychology.

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, as you will read in the Tuskegee Syphilis Study, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound. This section presents how ethical considerations affect the design and implementation of research conducted today.

Research Involving Human Participants

Any experiment involving the participation of human subjects is governed by extensive, strict guidelines designed to ensure that the experiment does not result in harm. Any research institution that receives federal support for research involving human participants must have access to an institutional review board (IRB) . The IRB is a committee of individuals often made up of members of the institution’s administration, scientists, and community members (Figure 6). The purpose of the IRB is to review proposals for research that involves human participants. The IRB reviews these proposals with the principles mentioned above in mind, and generally, approval from the IRB is required in order for the experiment to proceed.

A photograph shows a group of people seated around tables in a meeting room.

An institution’s IRB requires several components in any experiment it approves. For one, each participant must sign an informed consent form before they can participate in the experiment. An informed consent form provides a written description of what participants can expect during the experiment, including potential risks and implications of the research. It also lets participants know that their involvement is completely voluntary and can be discontinued without penalty at any time. Furthermore, the informed consent guarantees that any data collected in the experiment will remain completely confidential. In cases where research participants are under the age of 18, the parents or legal guardians are required to sign the informed consent form.

While the informed consent form should be as honest as possible in describing exactly what participants will be doing, sometimes deception is necessary to prevent participants’ knowledge of the exact research question from affecting the results of the study. Deception involves purposely misleading experiment participants in order to maintain the integrity of the experiment, but not to the point where the deception could be considered harmful. For example, if we are interested in how our opinion of someone is affected by their attire, we might use deception in describing the experiment to prevent that knowledge from affecting participants’ responses. In cases where deception is involved, participants must receive a full debriefing upon conclusion of the study—complete, honest information about the purpose of the experiment, how the data collected will be used, the reasons why deception was necessary, and information about how to obtain additional information about the study.

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Unfortunately, the ethical guidelines that exist for research today were not always applied in the past. In 1932, poor, rural, black, male sharecroppers from Tuskegee, Alabama, were recruited to participate in an experiment conducted by the U.S. Public Health Service, with the aim of studying syphilis in black men (Figure 7). In exchange for free medical care, meals, and burial insurance, 600 men agreed to participate in the study. A little more than half of the men tested positive for syphilis, and they served as the experimental group (given that the researchers could not randomly assign participants to groups, this represents a quasi-experiment). The remaining syphilis-free individuals served as the control group. However, those individuals that tested positive for syphilis were never informed that they had the disease.

While there was no treatment for syphilis when the study began, by 1947 penicillin was recognized as an effective treatment for the disease. Despite this, no penicillin was administered to the participants in this study, and the participants were not allowed to seek treatment at any other facilities if they continued in the study. Over the course of 40 years, many of the participants unknowingly spread syphilis to their wives (and subsequently their children born from their wives) and eventually died because they never received treatment for the disease. This study was discontinued in 1972 when the experiment was discovered by the national press (Tuskegee University, n.d.). The resulting outrage over the experiment led directly to the National Research Act of 1974 and the strict ethical guidelines for research on humans described in this chapter. Why is this study unethical? How were the men who participated and their families harmed as a function of this research?

A photograph shows a person administering an injection.

Learn more about the Tuskegee Syphilis Study on the CDC website .

Research Involving Animal Subjects

This does not mean that animal researchers are immune to ethical concerns. Indeed, the humane and ethical treatment of animal research subjects is a critical aspect of this type of research. Researchers must design their experiments to minimize any pain or distress experienced by animals serving as research subjects.

Whereas IRBs review research proposals that involve human participants, animal experimental proposals are reviewed by an Institutional Animal Care and Use Committee (IACUC) . An IACUC consists of institutional administrators, scientists, veterinarians, and community members. This committee is charged with ensuring that all experimental proposals require the humane treatment of animal research subjects. It also conducts semi-annual inspections of all animal facilities to ensure that the research protocols are being followed. No animal research project can proceed without the committee’s approval.

Introduction to Approaches to Research

Differentiate between descriptive, correlational, and experimental research
Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
Describe the strength and weaknesses of archival research
Compare longitudinal and cross-sectional approaches to research
Explain what a correlation coefficient tells us about the relationship between variables
Describe why correlation does not mean causation
Describe the experimental process, including ways to control for bias
Identify and differentiate between independent and dependent variables

Three researchers review data while talking around a microscope.

Psychologists use descriptive, experimental, and correlational methods to conduct research. Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research.

Experiments are conducted in order to determine cause-and-effect relationships. In ideal experimental design, the only difference between the experimental and control groups is whether participants are exposed to the experimental manipulation. Each group goes through all phases of the experiment, but each group will experience a different level of the independent variable: the experimental group is exposed to the experimental manipulation, and the control group is not exposed to the experimental manipulation. The researcher then measures the changes that are produced in the dependent variable in each group. Once data is collected from both groups, it is analyzed statistically to determine if there are meaningful differences between the groups.

When scientists passively observe and measure phenomena it is called correlational research. Here, psychologists do not intervene and change behavior, as they do in experiments. In correlational research, they identify patterns of relationships, but usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

Watch It: More on Research

If you enjoy learning through lectures and want an interesting and comprehensive summary of this section, then click on the Youtube link to watch a lecture given by MIT Professor John Gabrieli . Start at the 30:45 minute mark and watch through the end to hear examples of actual psychological studies and how they were analyzed. Listen for references to independent and dependent variables, experimenter bias, and double-blind studies. In the lecture, you’ll learn about breaking social norms, “WEIRD” research, why expectations matter, how a warm cup of coffee might make you nicer, why you should change your answer on a multiple choice test, and why praise for intelligence won’t make you any smarter.

You can view the transcript for “Lec 2 | MIT 9.00SC Introduction to Psychology, Spring 2011” here (opens in new window) .

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are, naturalistic observation, case studies, and surveys.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

A photograph shows two police cars driving, one with its lights flashing.

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 9).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 10). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

(a) A photograph shows Jane Goodall speaking from a lectern. (b) A photograph shows a chimpanzee’s face.

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 11). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

A sample online survey reads, “Dear visitor, your opinion is important to us. We would like to invite you to participate in a short survey to gather your opinions and feedback on your news consumption habits. The survey will take approximately 10-15 minutes. Simply click the “Yes” button below to launch the survey. Would you like to participate?” Two buttons are labeled “yes” and “no.”

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

Archival research.

(a) A photograph shows stacks of paper files on shelves. (b) A photograph shows a computer.

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research . In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of observing a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) (Figure 13).

A photograph shows pack of cigarettes and cigarettes in an ashtray. The pack of cigarettes reads, “Surgeon general’s warning: smoking causes lung cancer, heart disease, emphysema, and may complicate pregnancy.”

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

Correlational Research

Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.

It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?

Three scatterplots are shown. Scatterplot (a) is labeled “positive correlation” and shows scattered dots forming a rough line from the bottom left to the top right; the x-axis is labeled “weight” and the y-axis is labeled “height.” Scatterplot (b) is labeled “negative correlation” and shows scattered dots forming a rough line from the top left to the bottom right; the x-axis is labeled “tiredness” and the y-axis is labeled “hours of sleep.” Scatterplot (c) is labeled “no correlation” and shows scattered dots having no pattern; the x-axis is labeled “shoe size” and the y-axis is labeled “hours of sleep.”

Correlation Does Not Indicate Causation

Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, recent research found that people who eat cereal on a regular basis achieve healthier weights than those who rarely eat cereal (Frantzen, Treviño, Echon, Garcia-Dominic, & DiMarco, 2013; Barton et al., 2005). Guess how the cereal companies report this finding. Does eating cereal really cause an individual to maintain a healthy weight, or are there other possible explanations, such as, someone at a healthy weight is more likely to regularly eat a healthy breakfast than someone who is obese or someone who avoids meals in an attempt to diet (Figure 15)? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.

Watch this clip from Freakonomics for an example of how correlation does not indicate causation.

You can view the transcript for “Correlation vs. Causality: Freakonomics Movie” here (opens in new window) .

Illusory Correlations

The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full (Figure 16).

There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.

Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).

We all have a tendency to make illusory correlations from time to time. Try to think of an illusory correlation that is held by you, a family member, or a close friend. How do you think this illusory correlation came about and what can be done in the future to combat them?

Experiments

Causality: conducting experiments and using the data, experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 17).

A photograph shows a child pointing a toy gun.

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

A photograph shows three glass bottles of pills labeled as placebos.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 18).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 19). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

A box labeled “independent variable: type of television programming viewed” contains a photograph of a person shooting an automatic weapon. An arrow labeled “influences change in the…” leads to a second box. The second box is labeled “dependent variable: violent behavior displayed” and has a photograph of a child pointing a toy gun.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 20). If possible, we should use a random sample (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

(a) A photograph shows an aerial view of crowds on a street. (b) A photograph shows s small group of children.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Introduction to Statistical Thinking

Psychologists use statistics to assist them in analyzing data, and also to give more precise measurements to describe whether something is statistically significant. Analyzing data using statistics enables researchers to find patterns, make claims, and share their results with others. In this section, you’ll learn about some of the tools that psychologists use in statistical analysis.

Define reliability and validity
Describe the importance of distributional thinking and the role of p-values in statistical inference
Describe the role of random sampling and random assignment in drawing cause-and-effect conclusions
Describe the basic structure of a psychological research article

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve a healthy weight without changing their diet. But if other scientists could not replicate the results, the original study’s claims would be questioned.

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has suggested that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated (Figure 21). For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

A photograph shows a child being given an oral vaccine.

Reliability and Validity

Dig deeper: everyday connection: how valid is the sat.

Standardized tests like the SAT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT scores in college admissions has generated some controversy on a number of fronts. For one, some researchers assert that the SAT is a biased test that places minority students at a disadvantage and unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of the SAT is grossly exaggerated in how well it is able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

In 2014, College Board president David Coleman expressed his awareness of these problems, recognizing that college success is more accurately predicted by high school grades than by SAT scores. To address these concerns, he has called for significant changes to the SAT exam (Lewin, 2014).

Statistical Significance

Coffee cup with heart shaped cream inside.

Does drinking coffee actually increase your life expectancy? A recent study (Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012) found that men who drank at least six cups of coffee a day also had a 10% lower chance of dying (women’s chances were 15% lower) than those who drank none. Does this mean you should pick up or increase your own coffee habit? We will explore these results in more depth in the next section about drawing conclusions from statistics. Modern society has become awash in studies such as this; you can read about several such studies in the news every day.

Conducting such a study well, and interpreting the results of such studies requires understanding basic ideas of statistics , the science of gaining insight from data. Key components to a statistical investigation are:

Planning the study: Start by asking a testable research question and deciding how to collect data. For example, how long was the study period of the coffee study? How many people were recruited for the study, how were they recruited, and from where? How old were they? What other variables were recorded about the individuals? Were changes made to the participants’ coffee habits during the course of the study?
Examining the data: What are appropriate ways to examine the data? What graphs are relevant, and what do they reveal? What descriptive statistics can be calculated to summarize relevant aspects of the data, and what do they reveal? What patterns do you see in the data? Are there any individual observations that deviate from the overall pattern, and what do they reveal? For example, in the coffee study, did the proportions differ when we compared the smokers to the non-smokers?
Inferring from the data: What are valid statistical methods for drawing inferences “beyond” the data you collected? In the coffee study, is the 10%–15% reduction in risk of death something that could have happened just by chance?
Drawing conclusions: Based on what you learned from your data, what conclusions can you draw? Who do you think these conclusions apply to? (Were the people in the coffee study older? Healthy? Living in cities?) Can you draw a cause-and-effect conclusion about your treatments? (Are scientists now saying that the coffee drinking is the cause of the decreased risk of death?)

Notice that the numerical analysis (“crunching numbers” on the computer) comprises only a small part of overall statistical investigation. In this section, you will see how we can answer some of these questions and what questions you should be asking about any statistical investigation you read about.

Distributional Thinking

When data are collected to address a particular question, an important first step is to think of meaningful ways to organize and examine the data. Let’s take a look at an example.

Example 1 : Researchers investigated whether cancer pamphlets are written at an appropriate level to be read and understood by cancer patients (Short, Moriarty, & Cooley, 1995). Tests of reading ability were given to 63 patients. In addition, readability level was determined for a sample of 30 pamphlets, based on characteristics such as the lengths of words and sentences in the pamphlet. The results, reported in terms of grade levels, are displayed in Figure 23.

Table showing patients' reading levels and pahmphlet's reading levels.

Data vary . More specifically, values of a variable (such as reading level of a cancer patient or readability level of a cancer pamphlet) vary.
Analyzing the pattern of variation, called the distribution of the variable, often reveals insights.

Addressing the research question of whether the cancer pamphlets are written at appropriate levels for the cancer patients requires comparing the two distributions. A naïve comparison might focus only on the centers of the distributions. Both medians turn out to be ninth grade, but considering only medians ignores the variability and the overall distributions of these data. A more illuminating approach is to compare the entire distributions, for example with a graph, as in Figure 24.

Bar graph showing that the reading level of pamphlets is typically higher than the reading level of the patients.

Figure 24 makes clear that the two distributions are not well aligned at all. The most glaring discrepancy is that many patients (17/63, or 27%, to be precise) have a reading level below that of the most readable pamphlet. These patients will need help to understand the information provided in the cancer pamphlets. Notice that this conclusion follows from considering the distributions as a whole, not simply measures of center or variability, and that the graph contrasts those distributions more immediately than the frequency tables.

Finding Significance in Data

Even when we find patterns in data, often there is still uncertainty in various aspects of the data. For example, there may be potential for measurement errors (even your own body temperature can fluctuate by almost 1°F over the course of the day). Or we may only have a “snapshot” of observations from a more long-term process or only a small subset of individuals from the population of interest. In such cases, how can we determine whether patterns we see in our small set of data is convincing evidence of a systematic phenomenon in the larger process or population? Let’s take a look at another example.

Example 2 : In a study reported in the November 2007 issue of Nature , researchers investigated whether pre-verbal infants take into account an individual’s actions toward others in evaluating that individual as appealing or aversive (Hamlin, Wynn, & Bloom, 2007). In one component of the study, 10-month-old infants were shown a “climber” character (a piece of wood with “googly” eyes glued onto it) that could not make it up a hill in two tries. Then the infants were shown two scenarios for the climber’s next try, one where the climber was pushed to the top of the hill by another character (“helper”), and one where the climber was pushed back down the hill by another character (“hinderer”). The infant was alternately shown these two scenarios several times. Then the infant was presented with two pieces of wood (representing the helper and the hinderer characters) and asked to pick one to play with.

The researchers found that of the 16 infants who made a clear choice, 14 chose to play with the helper toy. One possible explanation for this clear majority result is that the helping behavior of the one toy increases the infants’ likelihood of choosing that toy. But are there other possible explanations? What about the color of the toy? Well, prior to collecting the data, the researchers arranged so that each color and shape (red square and blue circle) would be seen by the same number of infants. Or maybe the infants had right-handed tendencies and so picked whichever toy was closer to their right hand?

Well, prior to collecting the data, the researchers arranged it so half the infants saw the helper toy on the right and half on the left. Or, maybe the shapes of these wooden characters (square, triangle, circle) had an effect? Perhaps, but again, the researchers controlled for this by rotating which shape was the helper toy, the hinderer toy, and the climber. When designing experiments, it is important to control for as many variables as might affect the responses as possible. It is beginning to appear that the researchers accounted for all the other plausible explanations. But there is one more important consideration that cannot be controlled—if we did the study again with these 16 infants, they might not make the same choices. In other words, there is some randomness inherent in their selection process.

Maybe each infant had no genuine preference at all, and it was simply “random luck” that led to 14 infants picking the helper toy. Although this random component cannot be controlled, we can apply a probability model to investigate the pattern of results that would occur in the long run if random chance were the only factor.

If the infants were equally likely to pick between the two toys, then each infant had a 50% chance of picking the helper toy. It’s like each infant tossed a coin, and if it landed heads, the infant picked the helper toy. So if we tossed a coin 16 times, could it land heads 14 times? Sure, it’s possible, but it turns out to be very unlikely. Getting 14 (or more) heads in 16 tosses is about as likely as tossing a coin and getting 9 heads in a row. This probability is referred to as a p-value . The p-value represents the likelihood that experimental results happened by chance. Within psychology, the most common standard for p-values is “p < .05”. What this means is that there is less than a 5% probability that the results happened just by random chance, and therefore a 95% probability that the results reflect a meaningful pattern in human psychology. We call this statistical significance .

So, in the study above, if we assume that each infant was choosing equally, then the probability that 14 or more out of 16 infants would choose the helper toy is found to be 0.0021. We have only two logical possibilities: either the infants have a genuine preference for the helper toy, or the infants have no preference (50/50) and an outcome that would occur only 2 times in 1,000 iterations happened in this study. Because this p-value of 0.0021 is quite small, we conclude that the study provides very strong evidence that these infants have a genuine preference for the helper toy.

If we compare the p-value to some cut-off value, like 0.05, we see that the p=value is smaller. Because the p-value is smaller than that cut-off value, then we reject the hypothesis that only random chance was at play here. In this case, these researchers would conclude that significantly more than half of the infants in the study chose the helper toy, giving strong evidence of a genuine preference for the toy with the helping behavior.

Drawing Conclusions from Statistics

Generalizability.

Photo of a diverse group of college-aged students.

One limitation to the study mentioned previously about the babies choosing the “helper” toy is that the conclusion only applies to the 16 infants in the study. We don’t know much about how those 16 infants were selected. Suppose we want to select a subset of individuals (a sample ) from a much larger group of individuals (the population ) in such a way that conclusions from the sample can be generalized to the larger population. This is the question faced by pollsters every day.

Example 3 : The General Social Survey (GSS) is a survey on societal trends conducted every other year in the United States. Based on a sample of about 2,000 adult Americans, researchers make claims about what percentage of the U.S. population consider themselves to be “liberal,” what percentage consider themselves “happy,” what percentage feel “rushed” in their daily lives, and many other issues. The key to making these claims about the larger population of all American adults lies in how the sample is selected. The goal is to select a sample that is representative of the population, and a common way to achieve this goal is to select a r andom sample that gives every member of the population an equal chance of being selected for the sample. In its simplest form, random sampling involves numbering every member of the population and then using a computer to randomly select the subset to be surveyed. Most polls don’t operate exactly like this, but they do use probability-based sampling methods to select individuals from nationally representative panels.

In 2004, the GSS reported that 817 of 977 respondents (or 83.6%) indicated that they always or sometimes feel rushed. This is a clear majority, but we again need to consider variation due to random sampling . Fortunately, we can use the same probability model we did in the previous example to investigate the probable size of this error. (Note, we can use the coin-tossing model when the actual population size is much, much larger than the sample size, as then we can still consider the probability to be the same for every individual in the sample.) This probability model predicts that the sample result will be within 3 percentage points of the population value (roughly 1 over the square root of the sample size, the margin of error. A statistician would conclude, with 95% confidence, that between 80.6% and 86.6% of all adult Americans in 2004 would have responded that they sometimes or always feel rushed.

The key to the margin of error is that when we use a probability sampling method, we can make claims about how often (in the long run, with repeated random sampling) the sample result would fall within a certain distance from the unknown population value by chance (meaning by random sampling variation) alone. Conversely, non-random samples are often suspect to bias, meaning the sampling method systematically over-represents some segments of the population and under-represents others. We also still need to consider other sources of bias, such as individuals not responding honestly. These sources of error are not measured by the margin of error.

Cause and Effect

In many research studies, the primary question of interest concerns differences between groups. Then the question becomes how were the groups formed (e.g., selecting people who already drink coffee vs. those who don’t). In some studies, the researchers actively form the groups themselves. But then we have a similar question—could any differences we observe in the groups be an artifact of that group-formation process? Or maybe the difference we observe in the groups is so large that we can discount a “fluke” in the group-formation process as a reasonable explanation for what we find?

Example 4 : A psychology study investigated whether people tend to display more creativity when they are thinking about intrinsic (internal) or extrinsic (external) motivations (Ramsey & Schafer, 2002, based on a study by Amabile, 1985). The subjects were 47 people with extensive experience with creative writing. Subjects began by answering survey questions about either intrinsic motivations for writing (such as the pleasure of self-expression) or extrinsic motivations (such as public recognition). Then all subjects were instructed to write a haiku, and those poems were evaluated for creativity by a panel of judges. The researchers conjectured beforehand that subjects who were thinking about intrinsic motivations would display more creativity than subjects who were thinking about extrinsic motivations. The creativity scores from the 47 subjects in this study are displayed in Figure 26, where higher scores indicate more creativity.

Image showing a dot for creativity scores, which vary between 5 and 27, and the types of motivation each person was given as a motivator, either extrinsic or intrinsic.

In this example, the key question is whether the type of motivation affects creativity scores. In particular, do subjects who were asked about intrinsic motivations tend to have higher creativity scores than subjects who were asked about extrinsic motivations?

Figure 26 reveals that both motivation groups saw considerable variability in creativity scores, and these scores have considerable overlap between the groups. In other words, it’s certainly not always the case that those with extrinsic motivations have higher creativity than those with intrinsic motivations, but there may still be a statistical tendency in this direction. (Psychologist Keith Stanovich (2013) refers to people’s difficulties with thinking about such probabilistic tendencies as “the Achilles heel of human cognition.”)

The mean creativity score is 19.88 for the intrinsic group, compared to 15.74 for the extrinsic group, which supports the researchers’ conjecture. Yet comparing only the means of the two groups fails to consider the variability of creativity scores in the groups. We can measure variability with statistics using, for instance, the standard deviation: 5.25 for the extrinsic group and 4.40 for the intrinsic group. The standard deviations tell us that most of the creativity scores are within about 5 points of the mean score in each group. We see that the mean score for the intrinsic group lies within one standard deviation of the mean score for extrinsic group. So, although there is a tendency for the creativity scores to be higher in the intrinsic group, on average, the difference is not extremely large.

We again want to consider possible explanations for this difference. The study only involved individuals with extensive creative writing experience. Although this limits the population to which we can generalize, it does not explain why the mean creativity score was a bit larger for the intrinsic group than for the extrinsic group. Maybe women tend to receive higher creativity scores? Here is where we need to focus on how the individuals were assigned to the motivation groups. If only women were in the intrinsic motivation group and only men in the extrinsic group, then this would present a problem because we wouldn’t know if the intrinsic group did better because of the different type of motivation or because they were women. However, the researchers guarded against such a problem by randomly assigning the individuals to the motivation groups. Like flipping a coin, each individual was just as likely to be assigned to either type of motivation. Why is this helpful? Because this random assignment tends to balance out all the variables related to creativity we can think of, and even those we don’t think of in advance, between the two groups. So we should have a similar male/female split between the two groups; we should have a similar age distribution between the two groups; we should have a similar distribution of educational background between the two groups; and so on. Random assignment should produce groups that are as similar as possible except for the type of motivation, which presumably eliminates all those other variables as possible explanations for the observed tendency for higher scores in the intrinsic group.

But does this always work? No, so by “luck of the draw” the groups may be a little different prior to answering the motivation survey. So then the question is, is it possible that an unlucky random assignment is responsible for the observed difference in creativity scores between the groups? In other words, suppose each individual’s poem was going to get the same creativity score no matter which group they were assigned to, that the type of motivation in no way impacted their score. Then how often would the random-assignment process alone lead to a difference in mean creativity scores as large (or larger) than 19.88 – 15.74 = 4.14 points?

We again want to apply to a probability model to approximate a p-value , but this time the model will be a bit different. Think of writing everyone’s creativity scores on an index card, shuffling up the index cards, and then dealing out 23 to the extrinsic motivation group and 24 to the intrinsic motivation group, and finding the difference in the group means. We (better yet, the computer) can repeat this process over and over to see how often, when the scores don’t change, random assignment leads to a difference in means at least as large as 4.41. Figure 27 shows the results from 1,000 such hypothetical random assignments for these scores.

Standard distribution in a typical bell curve.

Only 2 of the 1,000 simulated random assignments produced a difference in group means of 4.41 or larger. In other words, the approximate p-value is 2/1000 = 0.002. This small p-value indicates that it would be very surprising for the random assignment process alone to produce such a large difference in group means. Therefore, as with Example 2, we have strong evidence that focusing on intrinsic motivations tends to increase creativity scores, as compared to thinking about extrinsic motivations.

Notice that the previous statement implies a cause-and-effect relationship between motivation and creativity score; is such a strong conclusion justified? Yes, because of the random assignment used in the study. That should have balanced out any other variables between the two groups, so now that the small p-value convinces us that the higher mean in the intrinsic group wasn’t just a coincidence, the only reasonable explanation left is the difference in the type of motivation. Can we generalize this conclusion to everyone? Not necessarily—we could cautiously generalize this conclusion to individuals with extensive experience in creative writing similar the individuals in this study, but we would still want to know more about how these individuals were selected to participate.

Close-up photo of mathematical equations.

Statistical thinking involves the careful design of a study to collect meaningful data to answer a focused research question, detailed analysis of patterns in the data, and drawing conclusions that go beyond the observed data. Random sampling is paramount to generalizing results from our sample to a larger population, and random assignment is key to drawing cause-and-effect conclusions. With both kinds of randomness, probability models help us assess how much random variation we can expect in our results, in order to determine whether our results could happen by chance alone and to estimate a margin of error.

So where does this leave us with regard to the coffee study mentioned previously (the Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012 found that men who drank at least six cups of coffee a day had a 10% lower chance of dying (women 15% lower) than those who drank none)? We can answer many of the questions:

This was a 14-year study conducted by researchers at the National Cancer Institute.
The results were published in the June issue of the New England Journal of Medicine , a respected, peer-reviewed journal.
The study reviewed coffee habits of more than 402,000 people ages 50 to 71 from six states and two metropolitan areas. Those with cancer, heart disease, and stroke were excluded at the start of the study. Coffee consumption was assessed once at the start of the study.
About 52,000 people died during the course of the study.
People who drank between two and five cups of coffee daily showed a lower risk as well, but the amount of reduction increased for those drinking six or more cups.
The sample sizes were fairly large and so the p-values are quite small, even though percent reduction in risk was not extremely large (dropping from a 12% chance to about 10%–11%).
Whether coffee was caffeinated or decaffeinated did not appear to affect the results.
This was an observational study, so no cause-and-effect conclusions can be drawn between coffee drinking and increased longevity, contrary to the impression conveyed by many news headlines about this study. In particular, it’s possible that those with chronic diseases don’t tend to drink coffee.

This study needs to be reviewed in the larger context of similar studies and consistency of results across studies, with the constant caution that this was not a randomized experiment. Whereas a statistical analysis can still “adjust” for other potential confounding variables, we are not yet convinced that researchers have identified them all or completely isolated why this decrease in death risk is evident. Researchers can now take the findings of this study and develop more focused studies that address new questions.

Explore these outside resources to learn more about applied statistics:

Video about p-values: P-Value Extravaganza
Interactive web applets for teaching and learning statistics
Inter-university Consortium for Political and Social Research where you can find and analyze data.
The Consortium for the Advancement of Undergraduate Statistics
Find a recent research article in your field and answer the following: What was the primary research question? How were individuals selected to participate in the study? Were summary results provided? How strong is the evidence presented in favor or against the research question? Was random assignment used? Summarize the main conclusions from the study, addressing the issues of statistical significance, statistical confidence, generalizability, and cause and effect. Do you agree with the conclusions drawn from this study, based on the study design and the results presented?
Is it reasonable to use a random sample of 1,000 individuals to draw conclusions about all U.S. adults? Explain why or why not.

How to Read Research

In this course and throughout your academic career, you’ll be reading journal articles (meaning they were published by experts in a peer-reviewed journal) and reports that explain psychological research. It’s important to understand the format of these articles so that you can read them strategically and understand the information presented. Scientific articles vary in content or structure, depending on the type of journal to which they will be submitted. Psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract, introduction, methods, results, discussion, and references.

Abstract : the abstract is the concise summary of the article. It summarizes the most important features of the manuscript, providing the reader with a global first impression on the article. It is generally just one paragraph that explains the experiment as well as a short synopsis of the results.
Introduction : this section provides background information about the origin and purpose of performing the experiment or study. It reviews previous research and presents existing theories on the topic.
Method : this section covers the methodologies used to investigate the research question, including the identification of participants , procedures , and materials as well as a description of the actual procedure . It should be sufficiently detailed to allow for replication.
Results : the results section presents key findings of the research, including reference to indicators of statistical significance.
Discussion : this section provides an interpretation of the findings, states their significance for current research, and derives implications for theory and practice. Alternative interpretations for findings are also provided, particularly when it is not possible to conclude for the directionality of the effects. In the discussion, authors also acknowledge the strengths and limitations/weaknesses of the study and offer concrete directions about for future research.

Watch this 3-minute video for an explanation on how to read scholarly articles. Look closely at the example article shared just before the two minute mark.

https://digitalcommons.coastal.edu/kimbel-library-instructional-videos/9/

Practice identifying these key components in the following experiment: Food-Induced Emotional Resonance Improves Emotion Recognition.

In this chapter, you learned to

define and apply the scientific method to psychology
describe the strengths and weaknesses of descriptive, experimental, and correlational research
define the basic elements of a statistical investigation

Putting It Together: Psychological Research

Psychologists use the scientific method to examine human behavior and mental processes. Some of the methods you learned about include descriptive, experimental, and correlational research designs.

Watch the CrashCourse video to review the material you learned, then read through the following examples and see if you can come up with your own design for each type of study.

You can view the transcript for “Psychological Research: Crash Course Psychology #2” here (opens in new window).

Case Study: a detailed analysis of a particular person, group, business, event, etc. This approach is commonly used to to learn more about rare examples with the goal of describing that particular thing.

Ted Bundy was one of America’s most notorious serial killers who murdered at least 30 women and was executed in 1989. Dr. Al Carlisle evaluated Bundy when he was first arrested and conducted a psychological analysis of Bundy’s development of his sexual fantasies merging into reality (Ramsland, 2012). Carlisle believes that there was a gradual evolution of three processes that guided his actions: fantasy, dissociation, and compartmentalization (Ramsland, 2012). Read Imagining Ted Bundy (http://goo.gl/rGqcUv) for more information on this case study.

Naturalistic Observation : a researcher unobtrusively collects information without the participant’s awareness.

Drain and Engelhardt (2013) observed six nonverbal children with autism’s evoked and spontaneous communicative acts. Each of the children attended a school for children with autism and were in different classes. They were observed for 30 minutes of each school day. By observing these children without them knowing, they were able to see true communicative acts without any external influences.

Survey : participants are asked to provide information or responses to questions on a survey or structure assessment.

Educational psychologists can ask students to report their grade point average and what, if anything, they eat for breakfast on an average day. A healthy breakfast has been associated with better academic performance (Digangi’s 1999).
Anderson (1987) tried to find the relationship between uncomfortably hot temperatures and aggressive behavior, which was then looked at with two studies done on violent and nonviolent crime. Based on previous research that had been done by Anderson and Anderson (1984), it was predicted that violent crimes would be more prevalent during the hotter time of year and the years in which it was hotter weather in general. The study confirmed this prediction.

Longitudinal Study: researchers recruit a sample of participants and track them for an extended period of time.

In a study of a representative sample of 856 children Eron and his colleagues (1972) found that a boy’s exposure to media violence at age eight was significantly related to his aggressive behavior ten years later, after he graduated from high school.

Cross-Sectional Study: researchers gather participants from different groups (commonly different ages) and look for differences between the groups.

In 1996, Russell surveyed people of varying age groups and found that people in their 20s tend to report being more lonely than people in their 70s.

Correlational Design: two different variables are measured to determine whether there is a relationship between them.

Thornhill et al. (2003) had people rate how physically attractive they found other people to be. They then had them separately smell t-shirts those people had worn (without knowing which clothes belonged to whom) and rate how good or bad their body oder was. They found that the more attractive someone was the more pleasant their body order was rated to be.
Clinical psychologists can test a new pharmaceutical treatment for depression by giving some patients the new pill and others an already-tested one to see which is the more effective treatment.

American Cancer Society. (n.d.). History of the cancer prevention studies. Retrieved from http://www.cancer.org/research/researchtopreventcancer/history-cancer-prevention-study

American Psychological Association. (2009). Publication Manual of the American Psychological Association (6th ed.). Washington, DC: Author.

American Psychological Association. (n.d.). Research with animals in psychology. Retrieved from https://www.apa.org/research/responsible/research-animals.pdf

Arnett, J. (2008). The neglected 95%: Why American psychology needs to become less American. American Psychologist, 63(7), 602–614.

Barton, B. A., Eldridge, A. L., Thompson, D., Affenito, S. G., Striegel-Moore, R. H., Franko, D. L., . . . Crockett, S. J. (2005). The relationship of breakfast and cereal consumption to nutrient intake and body mass index: The national heart, lung, and blood institute growth and health study. Journal of the American Dietetic Association, 105(9), 1383–1389. Retrieved from http://dx.doi.org/10.1016/j.jada.2005.06.003

Chwalisz, K., Diener, E., & Gallagher, D. (1988). Autonomic arousal feedback and emotional experience: Evidence from the spinal cord injured. Journal of Personality and Social Psychology, 54, 820–828.

Dominus, S. (2011, May 25). Could conjoined twins share a mind? New York Times Sunday Magazine. Retrieved from http://www.nytimes.com/2011/05/29/magazine/could-conjoined-twins-share-a-mind.html?_r=5&hp&

Fanger, S. M., Frankel, L. A., & Hazen, N. (2012). Peer exclusion in preschool children’s play: Naturalistic observations in a playground setting. Merrill-Palmer Quarterly, 58, 224–254.

Fiedler, K. (2004). Illusory correlation. In R. F. Pohl (Ed.), Cognitive illusions: A handbook on fallacies and biases in thinking, judgment and memory (pp. 97–114). New York, NY: Psychology Press.

Frantzen, L. B., Treviño, R. P., Echon, R. M., Garcia-Dominic, O., & DiMarco, N. (2013). Association between frequency of ready-to-eat cereal consumption, nutrient intakes, and body mass index in fourth- to sixth-grade low-income minority children. Journal of the Academy of Nutrition and Dietetics, 113(4), 511–519.

Harper, J. (2013, July 5). Ice cream and crime: Where cold cuisine and hot disputes intersect. The Times-Picaune. Retrieved from http://www.nola.com/crime/index.ssf/2013/07/ice_cream_and_crime_where_hot.html

Jenkins, W. J., Ruppel, S. E., Kizer, J. B., Yehl, J. L., & Griffin, J. L. (2012). An examination of post 9-11 attitudes towards Arab Americans. North American Journal of Psychology, 14, 77–84.

Jones, J. M. (2013, May 13). Same-sex marriage support solidifies above 50% in U.S. Gallup Politics. Retrieved from http://www.gallup.com/poll/162398/sex-marriage-support-solidifies-above.aspx

Kobrin, J. L., Patterson, B. F., Shaw, E. J., Mattern, K. D., & Barbuti, S. M. (2008). Validity of the SAT for predicting first-year college grade point average (Research Report No. 2008-5). Retrieved from https://research.collegeboard.org/sites/default/files/publications/2012/7/researchreport-2008-5-validity-sat-predicting-first-year-college-grade-point-average.pdf

Lewin, T. (2014, March 5). A new SAT aims to realign with schoolwork. New York Times. Retreived from http://www.nytimes.com/2014/03/06/education/major-changes-in-sat-announced-by-college-board.html.

Lowry, M., Dean, K., & Manders, K. (2010). The link between sleep quantity and academic performance for the college student. Sentience: The University of Minnesota Undergraduate Journal of Psychology, 3(Spring), 16–19. Retrieved from http://www.psych.umn.edu/sentience/files/SENTIENCE_Vol3.pdf

McKie, R. (2010, June 26). Chimps with everything: Jane Goodall’s 50 years in the jungle. The Guardian. Retrieved from http://www.theguardian.com/science/2010/jun/27/jane-goodall-chimps-africa-interview

Offit, P. (2008). Autism’s false prophets: Bad science, risky medicine, and the search for a cure. New York: Columbia University Press.

Perkins, H. W., Haines, M. P., & Rice, R. (2005). Misperceiving the college drinking norm and related problems: A nationwide study of exposure to prevention information, perceived norms and student alcohol misuse. J. Stud. Alcohol, 66(4), 470–478.

Rimer, S. (2008, September 21). College panel calls for less focus on SATs. The New York Times. Retrieved from http://www.nytimes.com/2008/09/22/education/22admissions.html?_r=0

Rothstein, J. M. (2004). College performance predictions and the SAT. Journal of Econometrics, 121, 297–317.

Rotton, J., & Kelly, I. W. (1985). Much ado about the full moon: A meta-analysis of lunar-lunacy research. Psychological Bulletin, 97(2), 286–306. doi:10.1037/0033-2909.97.2.286

Santelices, M. V., & Wilson, M. (2010). Unfair treatment? The case of Freedle, the SAT, and the standardization approach to differential item functioning. Harvard Education Review, 80, 106–134.

Sears, D. O. (1986). College sophomores in the laboratory: Influences of a narrow data base on social psychology’s view of human nature. Journal of Personality and Social Psychology, 51, 515–530.

Tuskegee University. (n.d.). About the USPHS Syphilis Study. Retrieved from http://www.tuskegee.edu/about_us/centers_of_excellence/bioethics_center/about_the_usphs_syphilis_study.aspx.

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grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing

well-developed set of ideas that propose an explanation for observed phenomena

(plural: hypotheses) tentative and testable statement about the relationship between two or more variables

an experiment must be replicable by another researcher

implies that a theory should enable us to make predictions about future events

able to be disproven by experimental results

implies that all data must be considered when evaluating a hypothesis

committee of administrators, scientists, and community members that reviews proposals for research involving human participants

process of informing a research participant about what to expect during an experiment, any risks involved, and the implications of the research, and then obtaining the person’s consent to participate

purposely misleading experiment participants in order to maintain the integrity of the experiment

when an experiment involved deception, participants are told complete and truthful information about the experiment at its conclusion

committee of administrators, scientists, veterinarians, and community members that reviews proposals for research involving non-human animals

research studies that do not test specific relationships between variables

research investigating the relationship between two or more variables

research method that uses hypothesis testing to make inferences about how one variable impacts and causes another

observation of behavior in its natural setting

inferring that the results for a sample apply to the larger population

when observations may be skewed to align with observer expectations

measure of agreement among observers on how they record and classify a particular event

observational research study focusing on one or a few people

list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

subset of individuals selected from the larger population

overall group of individuals that the researchers are interested in

method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships

studies in which the same group of individuals is surveyed or measured repeatedly over an extended period of time

compares multiple segments of a population at a single time

reduction in number of research participants as some drop out of the study over time

relationship between two or more variables; when two variables are correlated, one variable changes as the other does

number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r

two variables change in the same direction, both becoming either larger or smaller

two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation

changes in one variable cause the changes in the other variable; can be determined only through an experimental research design

unanticipated outside factor that affects both variables of interest, often giving the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables

seeing relationships between two things when in reality no such relationship exists

tendency to ignore evidence that disproves ideas or beliefs

group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance

serves as a basis for comparison and controls for chance factors that might influence the results of the study—by holding such factors constant across groups so that the experimental manipulation is the only difference between groups

description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables

researcher expectations skew the results of the study

experiment in which the researcher knows which participants are in the experimental group and which are in the control group

experiment in which both the researchers and the participants are blind to group assignments

people's expectations or beliefs influencing or determining their experience in a given situation

variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group

variable that the researcher measures to see how much effect the independent variable had

subjects of psychological research

subset of a larger population in which every member of the population has an equal chance of being selected

method of experimental group assignment in which all participants have an equal chance of being assigned to either group

consistency and reproducibility of a given result

accuracy of a given result in measuring what it is designed to measure

determines how likely any difference between experimental groups is due to chance

statistical probability that represents the likelihood that experimental results happened by chance

Psychological Science is the scientific study of mind, brain, and behavior. We will explore what it means to be human in this class. It has never been more important for us to understand what makes people tick, how to evaluate information critically, and the importance of history. Psychology can also help you in your future career; indeed, there are very little jobs out there with no human interaction!

Because psychology is a science, we analyze human behavior through the scientific method. There are several ways to investigate human phenomena, such as observation, experiments, and more. We will discuss the basics, pros and cons of each! We will also dig deeper into the important ethical guidelines that psychologists must follow in order to do research. Lastly, we will briefly introduce ourselves to statistics, the language of scientific research. While reading the content in these chapters, try to find examples of material that can fit with the themes of the course.

To get us started:

The study of the mind moved away Introspection to reaction time studies as we learned more about empiricism
Psychologists work in careers outside of the typical "clinician" role. We advise in human factors, education, policy, and more!
While completing an observation study, psychologists will work to aggregate common themes to explain the behavior of the group (sample) as a whole. In doing so, we still allow for normal variation from the group!
The IRB and IACUC are important in ensuring ethics are maintained for both human and animal subjects

Psychological Science: Understanding Human Behavior Copyright © by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Writing Research Papers

Research Paper Structure

Whether you are writing a B.S. Degree Research Paper or completing a research report for a Psychology course, it is highly likely that you will need to organize your research paper in accordance with American Psychological Association (APA) guidelines. Here we discuss the structure of research papers according to APA style.

Major Sections of a Research Paper in APA Style

A complete research paper in APA style that is reporting on experimental research will typically contain a Title page, Abstract, Introduction, Methods, Results, Discussion, and References sections. 1 Many will also contain Figures and Tables and some will have an Appendix or Appendices. These sections are detailed as follows (for a more in-depth guide, please refer to " How to Write a Research Paper in APA Style ”, a comprehensive guide developed by Prof. Emma Geller). 2

What is this paper called and who wrote it? – the first page of the paper; this includes the name of the paper, a “running head”, authors, and institutional affiliation of the authors. The institutional affiliation is usually listed in an Author Note that is placed towards the bottom of the title page. In some cases, the Author Note also contains an acknowledgment of any funding support and of any individuals that assisted with the research project.

One-paragraph summary of the entire study – typically no more than 250 words in length (and in many cases it is well shorter than that), the Abstract provides an overview of the study.

Introduction

What is the topic and why is it worth studying? – the first major section of text in the paper, the Introduction commonly describes the topic under investigation, summarizes or discusses relevant prior research (for related details, please see the Writing Literature Reviews section of this website), identifies unresolved issues that the current research will address, and provides an overview of the research that is to be described in greater detail in the sections to follow.

What did you do? – a section which details how the research was performed. It typically features a description of the participants/subjects that were involved, the study design, the materials that were used, and the study procedure. If there were multiple experiments, then each experiment may require a separate Methods section. A rule of thumb is that the Methods section should be sufficiently detailed for another researcher to duplicate your research.

What did you find? – a section which describes the data that was collected and the results of any statistical tests that were performed. It may also be prefaced by a description of the analysis procedure that was used. If there were multiple experiments, then each experiment may require a separate Results section.

What is the significance of your results? – the final major section of text in the paper. The Discussion commonly features a summary of the results that were obtained in the study, describes how those results address the topic under investigation and/or the issues that the research was designed to address, and may expand upon the implications of those findings. Limitations and directions for future research are also commonly addressed.

List of articles and any books cited – an alphabetized list of the sources that are cited in the paper (by last name of the first author of each source). Each reference should follow specific APA guidelines regarding author names, dates, article titles, journal titles, journal volume numbers, page numbers, book publishers, publisher locations, websites, and so on (for more information, please see the Citing References in APA Style page of this website).

Tables and Figures

Graphs and data (optional in some cases) – depending on the type of research being performed, there may be Tables and/or Figures (however, in some cases, there may be neither). In APA style, each Table and each Figure is placed on a separate page and all Tables and Figures are included after the References. Tables are included first, followed by Figures. However, for some journals and undergraduate research papers (such as the B.S. Research Paper or Honors Thesis), Tables and Figures may be embedded in the text (depending on the instructor’s or editor’s policies; for more details, see "Deviations from APA Style" below).

Supplementary information (optional) – in some cases, additional information that is not critical to understanding the research paper, such as a list of experiment stimuli, details of a secondary analysis, or programming code, is provided. This is often placed in an Appendix.

Variations of Research Papers in APA Style

Although the major sections described above are common to most research papers written in APA style, there are variations on that pattern. These variations include:

Literature reviews – when a paper is reviewing prior published research and not presenting new empirical research itself (such as in a review article, and particularly a qualitative review), then the authors may forgo any Methods and Results sections. Instead, there is a different structure such as an Introduction section followed by sections for each of the different aspects of the body of research being reviewed, and then perhaps a Discussion section.
Multi-experiment papers – when there are multiple experiments, it is common to follow the Introduction with an Experiment 1 section, itself containing Methods, Results, and Discussion subsections. Then there is an Experiment 2 section with a similar structure, an Experiment 3 section with a similar structure, and so on until all experiments are covered. Towards the end of the paper there is a General Discussion section followed by References. Additionally, in multi-experiment papers, it is common for the Results and Discussion subsections for individual experiments to be combined into single “Results and Discussion” sections.

Departures from APA Style

In some cases, official APA style might not be followed (however, be sure to check with your editor, instructor, or other sources before deviating from standards of the Publication Manual of the American Psychological Association). Such deviations may include:

Placement of Tables and Figures – in some cases, to make reading through the paper easier, Tables and/or Figures are embedded in the text (for example, having a bar graph placed in the relevant Results section). The embedding of Tables and/or Figures in the text is one of the most common deviations from APA style (and is commonly allowed in B.S. Degree Research Papers and Honors Theses; however you should check with your instructor, supervisor, or editor first).
Incomplete research – sometimes a B.S. Degree Research Paper in this department is written about research that is currently being planned or is in progress. In those circumstances, sometimes only an Introduction and Methods section, followed by References, is included (that is, in cases where the research itself has not formally begun). In other cases, preliminary results are presented and noted as such in the Results section (such as in cases where the study is underway but not complete), and the Discussion section includes caveats about the in-progress nature of the research. Again, you should check with your instructor, supervisor, or editor first.
Class assignments – in some classes in this department, an assignment must be written in APA style but is not exactly a traditional research paper (for instance, a student asked to write about an article that they read, and to write that report in APA style). In that case, the structure of the paper might approximate the typical sections of a research paper in APA style, but not entirely. You should check with your instructor for further guidelines.

Workshops and Downloadable Resources

For in-person discussion of the process of writing research papers, please consider attending this department’s “Writing Research Papers” workshop (for dates and times, please check the undergraduate workshops calendar).

Downloadable Resources

How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
Example APA Style Research Paper (for B.S. Degree – empirical research) [ PDF ]
Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos

Writing Research Paper Videos

APA Journal Article Reporting Guidelines

Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 3.
Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 26.

External Resources

Formatting APA Style Papers in Microsoft Word
How to Write an APA Style Research Paper from Hamilton University
WikiHow Guide to Writing APA Research Papers
Sample APA Formatted Paper with Comments
Sample APA Formatted Paper
Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 41-60). Washington, DC: American Psychological Association.

2 geller, e. (2018). how to write an apa-style research report . [instructional materials]. , prepared by s. c. pan for ucsd psychology.

Formatting Research Papers
Using Databases and Finding References
What Types of References Are Appropriate?
Evaluating References and Taking Notes
Citing References
Writing a Literature Review
Writing Process and Revising
Improving Scientific Writing
Academic Integrity and Avoiding Plagiarism
Writing Research Papers Videos

AQA A-Level Psychology Past Papers With Answers

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

AQA A-Level Psychology (7182) and AS-Level Psychology (7181) past exam papers and marking schemes. The past papers are free to download for you to use as practice for your exams.

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PSYC 212: Research Methods in Psychology: Writing Your Papers

Background Information
Finding Articles
Writing Your Papers
New York Times & Digital News

APA Style Website includes guidance for writing and citing

Sample Research Paper

A look at the structure of a research paper.

Journal Article Reporting Standards

Where should I state my thesis? Where should I include relevant research? Where do I describe my participants? Where do I tell the reader what the results mean? JARS helps you.

ONLY LOOK AT WHAT YOU NEED. I suggest you NOT read this through from beginning to end. But if you have a question about where to put what ... you can find it here.

St Thomas's Gude to APA 7th

APA Citation Style 7th ed. by Merrie Davidson Last Updated Dec 20, 2023 2951 views this year
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PAST PAPERS: RESEARCH METHODS: AQA A-LEVEL PSYCHOLOGY RESOURCES

Psychology aqa a-level (7182) unit 2: 7182/2.

Full model answers for all of these questions are available here

Sign up to the PsychLogic newsletter at the bottom of this page to download printable AQA A-level Psychology revision notes + AQA A-level Psychology revision guide + A-level Psychology revision tips + more...

The best way to revise Psychology A-level

THE SYLLABUS

METHODS, TECHNIQUES & DESIGN

Primary and secondary data, and meta-analysis. Quantitative and qualitative data
Aims, operationalising variables, IV’s and DV’s
Hypotheses - directional and non-directional
Experimental design - independent groups, repeated measures, matched pairs
Validity – internal and external; extraneous and confounding variables; types of validity and improving validity
Control – random allocation, randomisation, standardisation
Demand characteristics and investigator effects
Reliability; types of reliability and improving reliability
Pilot studies
Correlation analysis – covariables and hypotheses, positive/negative correlations
Observational techniques – use of behavioural categories
Self-report techniques – design of questionnaires and interviews
Case studies
Content analysis & thematic analysis

PARTICIPANTS; ETHICS; FEATURES OF SCIENCE & SCIENTIFIC METHOD; THE ECONOMY

Selecting participants and sampling techniques
The British Psychological Society (BPS) code of ethics and ways of dealing with ethical issues
Forms and instructions
Peer review
Features of science: objectivity, empirical method, replicability and falsifiability, paradigms and paradigm shifts
Reporting psychological investigations
The implications of psychological research for the economy

DESCRIPTIVE STATISTICS

Analysis and interpretation of quantitative data. Measures of central tendency - median, mean, mode. Calculating %’s. Measures of dispersion – range and standard deviation (SD)
Presentation and interpretation of quantitative data – graphs, histograms, bar charts, scattergrams and tables
Analysis and interpretation of correlational data; positive and negative correlations and the interpretation of correlation coefficients
Distributions: normal and skewed

INFERENTIAL STATISTICS

Introduction
Factors affecting choice of statistics test: Spearman’s rho, Pearson’s r, Wilcoxon, Mann-Whitney, related t-test, unrelated t-test, Chi-Squared test
Levels of measurement – nominal, ordinal, interval
Procedures for statistics tests
Probability and significance: use of statistical tables and critical values in interpretation of significance; Type I and Type II errors
Introduction to statistical testing: the sign test

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SPECIMEN PAPER 1 ( Psychology A-level revision)

Read the item and then answer the questions that follow.

A psychologist wanted to see if verbal fluency is affected by whether people think they are presenting information to a small group of people or to a large group of people.

The psychologist needed a stratified sample of 20 people. She obtained the sample from a company employing 60 men and 40 women.

The participants were told that they would be placed in a booth where they would read out an article about the life of a famous author to an audience. Participants were also told that the audience would not be present, but would only be able to hear them and would not be able to interact with them.

There were two conditions in the study, Condition A and Condition B.

Condition A: 10 participants were told the audience consisted of 5 listeners.

Condition B: the other 10 participants were told the audience consisted of 100 listeners.

Each participant completed the study individually. The psychologist recorded each presentation and then counted the number of verbal errors made by each participant.

(a) Identify the dependent variable in this study. (2 marks)

(b) Write a suitable hypothesis for this study. (3 marks)

(c) Identify one extraneous variable that the psychologist should have controlled in the study and explain why it should have been controlled. (3 marks)

(d) Explain one advantage of using a stratified sample of participants in this study. (2 marks)

(e) Explain how the psychologist would have obtained the male participants for her stratified sample. Show your calculations. (3 marks)

(f) The psychologist wanted to randomly allocate the 20 people in her stratified sample to the two conditions. She needed an equal number of males in each condition and an equal number of females in each condition. Explain how she would have done this. (4 marks)

Mean number of verbal errors and standard deviations for both conditions

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 14

(g) What conclusions might the psychologist draw from the data in the table? Refer to the means and standard deviations in your answer. (6 marks)

(h) Read the item and then answer the question that follows.

The psychologist had initially intended to use the range as a measure of dispersion in this study but found that one person in Condition A had made an exceptionally low number of verbal errors.

Explain how using the standard deviation rather than the range in this situation, would improve the study. [3 marks]

(i) Name an appropriate statistical test that could be used to analyse the number of verbal errors in Table 1. Explain why the test you have chosen would be a suitable test in this case. [4 marks]

(j) The psychologist found the results were significant at p<0.05. What is meant by ‘the results were significant at p<0.05’? [2 marks]

(k) Briefly explain one method the psychologist could use to check the validity of the data she collected in this study. [2 marks]

(l) Briefly explain one reason why it is important for research to undergo a peer review process. [2 marks]

(m) Read the item and then answer the question that follows.

The psychologist focused on fluency in spoken communication in her study. Other research has investigated sex differences in non-verbal behaviours such as body language and gestures

Design an observation study to investigate sex differences in non-verbal behaviour of males and females when they are giving a presentation to an audience.

In your answer you should provide details of:

The task for the participants
The behavioural categories to be used and how the data will be recorded
How reliability of the data collection might be established
Ethical issues to be considered.

(Total 12 marks)

SPECIMEN PAPER 2 ( A-level Psychology revision)

Researchers were interested in the spatial awareness skills of motorists. They decided to investigate a possible relationship between different aspects of spatial awareness. Motorists who had between ten and twelve years of driving experience and held a clean driving licence with no penalty points were asked to complete two sets of tasks.

Set 1: To follow a series of instructions and using a map, to identify various locations correctly. This provided a map reading score for each motorist with a maximum score of 20.

Set 2: To complete a series of practical driving tasks accurately. This involved tasks such as driving between cones, driving within lines and parking inside designated spaces. Each motorist was observed completing the Set 2 tasks by a single trained observer who rated each performance by giving the driver a rating out of 10.

The following results were obtained.

Table 1: The map reading scores and driver ratings of motorists

(a) Should the hypothesis be directional? Explain your answer. (2 marks)

(b) Write a suitable hypothesis for this investigation. (3 marks)

(c) Identify a suitable graphical display for the data in Table 1 and briefly explain why this display would be appropriate. (2 marks)

(d) Using the data in Table 1, comment on the relationship between the map reading scores and the driver rating scores of the participants. [3 marks]

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 1

(e) Briefly outline one problem of using a single trained observer to rate the participants’ driving skills in the practical task. Briefly discuss how this data collection method could be modified to improve the reliability of the data collected. (6 marks)

(f) The researchers decided to analyse the data using a Spearman’s rho test. Explain why this is a suitable choice of test for this investigation. (3 marks)

(g) After analysis of the data the researchers obtained a calculated value of r s = 0.808. Using the information in Table 2 above, what conclusion can the researchers draw about the relationship between the map reading and driving skills of the motorists? Explain your answer. [4 marks]

(h) Distinguish between a Type I error and a Type II error. (4 marks)

When the researchers looked at the data collected more closely they noticed possible gender differences in the results.

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 2

(i) What do the mean and standard deviation values suggest about the male and female performances in the investigation? (4 marks)

In a replication of the part of the study in which map reading skills were investigated, 20 men and 20 women completed the original map reading task and the researchers obtained the following data:

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 3

(j) The mean map reading score for both groups together was 12.23. What percentage of the male group scored above the mean score and what percentage of the female group scored above the mean score? Show your calculations. (4 marks)

(k) Using your answers to both 2 previous questions, comment on the performances of the male and the female participants in this study. (2 marks)

(l) Briefly explain one reason why it is important for research to be replicated. (2 marks)

(m) Imagine you have been asked to design a study to investigate possible gender differences in card sorting behaviours. You decide you will ask participants to sort a shuffled pack of playing cards into their suits of hearts, clubs, diamonds and spades. You decide you will time the participants as they do this using a stop watch.

Discuss the following aspects of this investigation:

with reference to the card sorting task, explain how you would ensure that this is made the same task for all participants
one methodological issue you should take into account when obtaining suitable participants for this study and explain how you would deal with this issue
how you would ensure that the experience of your participants is ethical.

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SPECIMEN PAPER 3 ( AQA A-level Psychology revision)

Following previous research indicating the social benefits of green space in urban areas, two psychology students decided to observe social behaviour in public spaces. They focused on two neighbouring towns, Greensville where most public spaces were planted with flowers and vegetables, and Brownton where most public spaces were paved with concrete.

The students compared the instances of considerate behaviours in the two towns.

Considerate behaviour categories included putting litter in the bin, having a dog on a lead and riding a bike with care.

The observations were carried out in four different areas of a similar size in each town on weekdays between the hours of 4.30pm and 6.00pm. The students worked together to ensure inter-observer reliability, recording each target behaviour whenever it occurred.

(a) Should the hypothesis for this research be directional or non-directional? Explain your answer. (2 marks)

(b) Before the observation could begin, the students needed to operationalise the behaviour category ‘riding a bike with care’.

Explain what is meant by operationalisation and suggest two ways in which ‘riding a bike with care’ could have been operationalised. (4 marks)

(c) The students thought that having a dog on a lead was a useful measure of considerate behaviour because it had face validity. Explain what is meant by face validity in this context. (3 marks)

(d) Identify and briefly outline two other types of validity in psychological research. (4 mark

(e) Identify the behaviour sampling method used by the students. Shade one box only.

Time sampling
Pair sampling
Event sampling
Target sampling

(f) Explain how inter-observer reliability could be ensured by working as a pair. (3 marks)

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 4

The students noted that overall more considerate behaviours occurred in Greensville than in Brownton.

(g) Calculate the ratio of considerate behaviours observed in Greensville to considerate behaviours observed in Brownton. Show your workings and present your answer in the simplest form. (3 marks)

(h) The students carried out a Chi-square test on their data. Explain why the Chi-square test was an appropriate test to use in this case. (3 marks)

(i) In order to interpret the results of the Chi-square test the students first needed to work out the degrees of freedom. They used the following formula.

Degrees of freedom (df) = (r–1) x (c–1)

r = number of rows and c = number of columns

Calculate the degrees of freedom for the data in Table 1. Show your workings.

(j) The calculated value of Chi-square was 6.20. Referring to Table 2 below, state whether or not the result of the Chi-square test is significant at the 0.05 level of significance. Justify your answer. (3 marks)

To be significant at the level shown the calculated value of Chi Square must be equal to or greater than the critical/table value

(k) In the discussion section of their report of the investigation the students wanted to further discuss their results in relation to levels of significance.

Write a short paragraph the students could use to do this. (4 marks)

(l) As a follow-up to their observation the students decided to interview some of their peers about inconsiderate behaviours in their 6th Form Centre. The interviews were recorded.

Explain how the students could develop their interview findings by carrying out a content analysis and why content analysis would be appropriate in this case. (3 marks

(m) Suggest one inconsiderate behaviour that the students might focus on in their content analysis. (1 mark)

(n) Design an experiment to investigate the effect of indoor plants on mood in office workers. For your measure of mood, you should devise a measure that would give data suitable for testing at the ordinal level of measurement.

Design – include reference to the experimental design, variables and controls
Materials/Apparatus – describe any special materials required
Data analysis that could be used – include reference to descriptive and inferential analysis.

Justify your choices. (12 marks)

2017 ( AQA A-level Psychology revision guide)

A psychologist wanted to test whether listening to music improves running performance.

The psychologist conducted a study using 10 volunteers from a local gym. The psychologist used a repeated measures design. Half of the participants were assigned to condition A (without music) and half to condition B (with music).

All participants were asked to run 400 metres as fast as they could on a treadmill in the psychology department. All participants were given standardised instruction. All participants wore headphones in both conditions. The psychologist recorded their running time in seconds. The participants returned to the psychology department the following week and repeated the test in the other condition.

(a) Identify the type of experiment used in this study.

(b) Identify the operationalised dependent variable in the study. (2 marks)

The results of the study are given in Table 1 below.

Table 1. Mean number of second taken to complete the 400m run and the standard deviation for both conditions.

(d) Name a more appropriate graph to display the means shown in Table 1. Suggest appropriate X (Horizontal) and Y (vertical) axis labels for your graph choice. (3 marks)

Name of graph

X axis label

Y axis label

(e) What do the mean and standard deviation values in Table 1 suggest about the participants’ performances with and without music? Justify your answer. (4 marks)

(f) Calculate the percentage decrease in the mean time it took participants to run 400 metres when listening to music. Show your workings. Give your answer to three significant figures. (4 marks)

The researcher used a directional hypothesis and analysed the data using a related t-test. The calculated value of t where degrees of freedom (df) = 9 was 1.4377. He decided to use the 5% level of significance.

Table 2. Table of critical values of t

Calculated value of t must be equal to or greater than the critical value in this table for significance to be shown.

(g) Give three reasons why the researcher used a related t-test in this study and, using Table 2, explain whether or not the results are significant (5 marks)

(h) What is meant by a Type II error? Explain why psychologists normally use the 5% level of significance in their research. (3 marks)

(i) Identify one extraneous variable that could have affected the results of this study. Suggest why it would have been important to control this extraneous variable and how it could have been controlled in this study. (3 marks)

(j) The report was submitted for peer review and a number of recommendations were advised.

Describe the process and purposes of peer review. (6 marks)

People’s perception of how they spent their time at the gym is often not very accurate. Some spent more time chatting than on the treadmill. A psychologist decides to observe the actual behaviour of an opportunity sample of gym users at a local gym.

(k) Explain why it is more appropriate for the psychologist to use an observation than a questionnaire in this case. (3 marks)

(l) Design an observational study to investigate how people spent their time at the gym.

In your answer you will be awarded credit for providing appropriate details of

Type of observation with justification
Operationalised behaviour categories
Use of time and/or event sampling with justification
How reliability of data collection could be assessed.

2018 ( A-level Psychology resources)

A psychologist was reading an article about typical dream themes in adults.

Figure 2 shows the main dream themes identified in the article.

Figure 2 Main dream themes

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 5

(a) Using Figure 2, estimate the percentage of dreams that were reported to be about being chased. Shade one box only. [1 mark]

A 4%

B 12%

C 27%

D 42%

The psychologist was interested in finding out whether dream themes differed between box males and females, particularly in terms of social interaction. She decided to conduct a pilot study. Twenty undergraduate students (8 male and 12 female) volunteered for the study. For a six-week period the students were interviewed at 9 am each morning when they arrived at university. Interviewers, who did not know the purpose of the study, carried out and recorded the dream interviews.

(a) What is meant by a pilot study? Explain one possible reason why the psychologist decided to conduct a pilot study for this investigation. [3 marks]

(b) The interviews produced qualitative data. What is meant by qualitative data? Give one strength of collecting qualitative data in this study. [2 marks]

(c) What are investigator effects? Suggest one way in which they could have been minimised during the dream interviews. [3 marks]

Another researcher, who did not know the purpose of the study, carried out a content analysis of the interview data.

(d) Explain how this content analysis could have been conducted. [4 marks]

The psychologist wanted to assess the reliability of the content analysis.

(e) Explain how the reliability of the content analysis could be assessed. [4 marks]

When comparing the data for males and females, the psychologist found that there was a difference in the proportion of friendly and aggressive social interactions. This is shown in Table 2.

Table 2: Percentage of friendly and aggressive social interactions in dreams reported by males and females

A total of 375 dreams reported by males included social interaction.

(f) Use the data in Table 2 to calculate how many of these dreams reported by males were classified as aggressive. Show your workings. [2 marks]

(g) Draw a suitable graphical display to represent the data in Table 2 box. Label your graph appropriately. [4 marks]

The psychologist decided to conduct an experiment to investigate the effect of watching box horror films before going to bed.

A volunteer sample of 50 university students consented to take part in the experiment.

The 50 students were randomly split into two groups. Group 1 watched a horror film before going to bed each night for the first week then a romantic comedy before going to bed each night for the second week. Group 2 watched the romantic comedy in the first week and the horror film in the second week.

When the students woke up each morning, each student received a text message that asked if they had had a nightmare during the night. They could respond ‘yes’ or ‘no’.

(h) Write a brief consent form that would have been suitable for use in this experiment. The consent form should:

include some detail of what participants might expect to happen
refer to ethical issues
be in an appropriate format/style

(i) The psychologist proposed a directional hypothesis.

Write a directional hypothesis for this experiment. [3 marks]

The psychologist used a repeated measures design in this experiment.

(j) Explain why it was important to use a repeated measures design in this case. [2 marks]

The psychologist used counterbalancing in this experiment.

(k) Explain why it was appropriate to use counterbalancing in this experiment. [2 marks]

(l) Explain how the box psychologist could have randomly split the sample of 50 students into the two groups. [3 marks]

The psychologist collated the responses of all the participants over the two-week period and calculated the mean and standard deviation for each condition.

Table 3: Mean number of nightmares reported and the standard deviation for each condition

(m) What do the mean and standard deviation values in Table 3 suggest about the effect of the type of film watched on the occurrence of nightmares? Justify your answer. [4 marks]

The psychologist found that the difference in the number of nightmares reported in the two conditions was significant at p<0.05.

(n) Explain what is meant by ‘significant at p<0.05’ in the context of this experiment. [2 marks]

(o) The psychologist was concerned about the validity of the experiment.

Suggest one possible modification to the design of the experiment and explain how this might improve validity. [3 marks]

2019 ( AQA A-level Psychology resources)

(a) Which of the following does reliability refer to?

Shade one circle only. [1 mark]

A The accuracy of the data

B The consistency of the data

C The levels of the data

D The validity of the data

(b) Which of the following is not a role of peer review in the scientific process?

A To determine whether to award research funding

B To ensure only significant results are published

C To make sure research has high validity and reliability

D To retain the integrity of psychological research

It was recently reported in a newspaper that time spent playing team sports increases happiness levels. A researcher was keen to find out whether this was due to participating in a team activity or due to participating in physical activity, as he could not find any published research on this.

The researcher used a matched-pairs design. He went into the student café and selected the first 20 students he met. Each student was assigned to one of two groups.

Participants in Group A were requested to carry out 3 hours of team sports per week. Participants in Group B were requested to carry out 3 hours of exercise independently in a gym each week. All participants were told not to take part in any other type of exercise for the 4-week duration of the study.

All participants completed a happiness questionnaire at the start and end of the study. The researcher then calculated the improvement in happiness score for each participant.

(d) Which of the following is correct?

A Groups A and B are conditions of the dependent variable and happiness is the independent variable.

B Groups A and B are conditions of the independent variable and happiness is the dependent variable.

C Groups A and B are the controls and happiness is the experimental condition.

D Groups A and B are the experimental conditions and happiness is the control.

(e) Would a directional or non-directional hypothesis be more suitable for the researcher to use? Explain your answer. 2 marks]

(f) Write a suitable hypothesis for this experiment. [3 marks]

(g) Identify the type of sampling method used in this experiment. Explain one limitation of using this sampling method in this study. [3 marks]

(h) Identify one variable on which participants should be matched in this matched-pairs design. Explain how the researcher could assign matched participants to either Group A or Group B. [4 marks]

(i) Explain one strength of using a matched-pairs design rather than a repeated-measures design. [2 marks]

The results of the study are given in Table 2 below.

Table 2 Improvement in happiness scores

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 6

The researcher decided to use the Sign Test to see whether there was a significant difference in the improvement in the scores between the two groups at the 5% level of significance.

(j) Calculate the value of S in this study. Show your workings. [2 marks]

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 7

Significance is shown if the calculated value of S is equal to or less than the critical value.

(k) Explain whether or not there was a significant difference in the improvement in the scores between the two groups. Use your answer to Question 22 and Table 3. [2 marks]

The validity of the data was questioned when the researcher presented his results. The researcher explained that he chose to use the happiness questionnaire because it had high concurrent validity.

(l) Explain what it means for a test to have high concurrent validity. [2 marks]

The questionnaire had high concurrent validity.

Validity was still a concern because the researcher knew which participants were in each experimental group.

(m) Explain how this could have affected the validity of the study. [4 marks]

(n) Using your answer to Question m, suggest one way in which the researcher could modify the study to improve the internal validity of the study? Justify your answer. [4 marks]

A psychology teacher read the researcher’s study on sport and happiness. She considered whether setting group tasks could improve her students’ level of happiness. She decided to conduct an independent groups experiment with 30 students taking A-level Psychology using the same happiness questionnaire.

(o) Suggest an appropriate statistical test the psychology teacher could use to analyse the data. Justify your choice of test. [4 marks]

(p) Design an independent groups experiment that the psychology teacher could conduct.

the aim of the experiment
identification and manipulation of variables including details of the task
controls to minimise the effects of extraneous variables
data handling and analysis – use of descriptive statistics and/or data presentation.

Justify your design choices. [12 marks]

2020 ( A-level Psychology notes)

A study into the relationship between recreational screen time and academic achievement was conducted. Students were asked to self-report the number of hours spent watching TV, playing on their mobile phones or video games (daily recreational screen time) and their end-of-year test performances (academic performance).

The results of the study are shown in Figure 2.

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 8

(a) In which section(s) of a scientific report would you expect to find reference to the results/findings of the investigation?

A The abstract and the results sections only

B The abstract, the discussion and the results sections only

C The results and the discussion sections only

D The results section only

(b) Which of the following correlation co-efficients best describes the data represented in Figure 2?

A –0.80

B –0.25

C +0.25

D +0.80

(c) Identify the type of graph shown in Figure 2 and explain why this is an appropriate graph to use for the data collected. [3 marks]

(d) Explain why it would not be appropriate for the researchers to conclude that increased recreational screen time reduces academic performance. [2 marks]

A psychologist reads a review of a meta-analysis confirming the relationship between recreational screen time and academic performance.

(e) What is meant by the term meta-analysis? [2 marks]

The psychologist decided to design an experiment to test the effects of recreational screen time on children’s academic performance.

The psychologist randomly selected four schools from all the primary schools in her county to take part in the experiment involving Year 5 pupils. Three of the four schools agreed to take part. In total, there were 58 pupils whose parents consented for them to participate. The 58 pupils were then randomly allocated to Group A or Group B.

For the two-week period of the experiment, pupils in Group A had no recreational screen time. Pupils in Group B were allowed unrestricted recreational screen time. At the end of the experiment all pupils completed a 45-minute class test, to achieve a test score.

(f) Complete Table 1 by ticking the statement that best describes the population and the sample in the psychologist’s experiment.

Place one tick in each column. [2 marks]

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 9

(g) Briefly explain why a directional hypothesis would be most suitable for this experiment. [1 mark]

(h) Write an appropriate hypothesis for this experiment. [3 marks]

The results obtained from the experiment are summarised in Table 2.

Table 2 Descriptive statistics for the test performance scores for Group A and Group B

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 10

(i) Using the data in Table 2, explain how the distribution of scores in Group A differs from the distribution of scores in Group B. [4 marks]

(j) What do the mean and standard deviation values in Table 2 suggest about the effect of the recreational screen time on test performance? Justify your answer. [4 marks]

(k) The psychologist wanted to test the statistical significance of the data.

Identify the most appropriate choice of statistical test for analysing the data collected and explain three reasons for your choice in the context of this study. [7 marks]

One criticism of the study is that the pupils were not matched on their typical recreational screen time.

(l) Explain how the psychologist could have matched pupils on their typical recreational screen time across the experimental conditions. [4 marks]

(m) Identify one other variable for which the psychologist could have matched the pupils. Explain how this might have affected the test performance if it was not controlled. [2 marks]

The feedback from one of the schools was that recreational screen time affected pupils’ social interactions. The psychologist decided to investigate this further by using an observation of social interaction during playtime at the school.

(n) Design the observation to investigate pupils’ social interaction in the playground.

In your answer you will be awarded credit for providing appropriate details of:

type of observation, with justification
choice of time sampling or event sampling, with justification
dealing with one relevant ethical issue
assessing reliability of the data through inter-observer reliability.

2021 ( AQA A-level Psychology notes)

A researcher placed an advert in a university psychology department asking for third year students to participate in a sleep experiment.

Each student had a sleep tracker watch to wear at home for the two-week study. Each morning they were asked to open the sleep tracker app to view their sleep quality data on their mobile phones. The students were unaware that the sleep data they could see on their phones had been manipulated by the researcher. Over the two weeks of the study, each student saw that he or she had had poor sleep quality for seven random nights of the experiment and good sleep quality for the remaining nights.

Every morning, after viewing the sleep data, each student completed a questionnaire about the previous night’s sleep. One of the questions asked the students to rate how well rested they felt, on a scale from 1–10, after the previous night’s sleep. Apart from this, students were asked to continue their normal everyday activities.

(a) Which of the following best describes the experimental method used in this study?

Shade one box only. [1 mark]

A Field experiment

B Laboratory experiment

C Natural experiment

D Quasi-experiment

(b) Write a directional hypothesis the researcher might use for this study. [3 marks]

A Opportunity sampling

B Stratified sampling

C Systematic sampling

D Volunteer sampling

(d) Explain one strength and one limitation of using this sampling method in this study. [4 marks]

The researcher collected quantitative data about how well rested the students felt.

(e) Explain one strength of collecting quantitative data in this study. [2 marks]

One ethical issue in this study is deception, as the students were unaware that the sleep data they could see on their phones had been manipulated by the researcher.

(f) Explain one way in which the researcher might deal with the deception in this study. [2 marks]

Apart from the question about how well rested the students felt, the researcher’s questionnaire contained nine other questions. The responses to these questions were not analysed.

(g) Explain one reason why the researcher decided to include these additional questions on the questionnaire. [2 marks]

(h) Explain one limitation of assessing sleep quality using a rating scale of 1–10. [2 marks]

The researcher believed that the actual number of hours slept by the students could have affected the results of the study.

(i) Suggest one other extraneous variable that could have affected the results of this study. Explain why it would have been important to control this extraneous variable and how it could have been controlled in this study. [4 marks]

In a follow-up study, the researcher investigated whether there was a correlation between the number of hours slept and how well rested the students felt.

The researcher randomly selected 18 participants from first-year students at the university.

On the day of the study, each student participant was asked, ‘How many hours did you sleep last night?’ They then had to rate on a scale of 1 to 5 how well rested they felt.

The researcher hypothesised that there would be a positive correlation between the two co-variables.

(j) Outline one reason why it was appropriate to conduct a correlation rather than an experiment in this case. [2 marks]

(k) Describe how the researcher could have used random sampling to obtain the students for this study. [3 marks]

The researcher used Spearman’s rho statistical test to analyse the data from this study.

(l) Explain why Spearman’s rho was a suitable test for this study. Refer to the description of the study in your answer. [4 marks]

The researcher chose to use the 5% level of significance and the calculated correlation coefficient for the Spearman’s rho test was 0.395

AQA PSYCHOLOGY A LEVEL RESEARCH METHODS 11

(m) Identify the appropriate critical value from Table 1. Explain your choice. [4 marks]

(n) Explain whether the researcher’s hypothesis should be accepted. Refer to the critical value identified in Question 21 in your answer. [2 marks]

(o) Explain why the researcher decided to use the 5% level of significance rather than the 1% level in this study. [2 marks]

When the researcher compared the calculated and critical values of rho, he began to wonder if he might have made a Type II error.

(p) Explain what is meant by a Type II error in the context of this study. [2 marks]

(q) Discuss features of science. Refer to one or more examples of psychological research in your answer. [8 marks]

2022 ( A-level Psychology revision notes)

A controlled observation was designed to compare the social behaviours of pre-school children of working parents and pre-school children of stay-at-home parents. The sample consisted of 100 children aged three, who were observed separately. Half of the children had working parents and the other half had stay-at-home parents.

The observation took place in a room which looked like a nursery, with a variety of toys available. In the room, there were four children and one supervising adult. Their behaviour was not recorded.

Each child participant was brought into the room and settled by their parent. The parent then left to sit outside. Each child participant’s behaviour was observed covertly for five minutes while they played in the room.

The observation was conducted in a controlled environment and a standardised script was used when the children and their parents arrived.

(a) Explain why the researcher used a controlled observation and a standardised script in this study. [4 marks]

(b) Identify one limitation of controlled observations. [1 mark]

The researcher used two trained observers to record the social behaviours of each child during the observation.

(c) Give two behavioural categories that the observers could have used in the observation to assess the pre-school children’s social behaviour. Explain why your chosen categories are appropriate. [4 marks]

(d) Describe how the observers could use time sampling to record the social behaviour of each child during the five-minute period. [4 marks]

(e) Explain one strength and one limitation of using time sampling for this observation. [4 marks]

(f) Explain how the reliability of the controlled observation could be assessed through inter-observer reliability. [4 marks]

The data from the observation was summarised by converting the number of agreed observations into a total social behaviour score for each child.

The researcher then conducted a statistical test to identify whether there was a significant difference between the social behaviour scores for the children of stay-at-home parents and those of working parents.

(g) Identify an appropriate statistical test that the researcher could use to analyse the social behaviour scores in this study. Explain three reasons for your choice in the context of this study. [7 marks]

(h) Explain one reason why collecting quantitative data could reduce the validity of this study. [2 marks]

The findings of this study might have implications for the economy.

(i) Explain one or more possible implications of this study for the economy. [3 marks]

This study was written up as a scientific report.

(j) Describe features of the abstract section in a scientific report. [3 marks]

A new TV programme has been developed to increase positive social behaviours in pre-school children.

There is a proposal to carry out an experiment to compare the effects of the new TV programme and an existing TV programme, on positive social behaviours in pre-school children.

A sample of 500 pre-school children and their parents is available for the experiment. The parents have given consent for their children to take part in this experiment.

The experiment will take place over an 8-week period. Data on the children’s social behaviours will be gathered from the parents using a self-report method.

(j) Design the experiment to investigate whether watching the new TV programme leads to an increase in positive social behaviours in the children, compared with watching the existing TV programme.

In your answer you will gain credit for providing appropriate details of the following:

the type of experimental design, with justification
a self-report method of data collection, with justification
how to control one extraneous variable, with justification as to why this would need to be controlled.

Overview – Research Methods

Research methods are how psychologists and scientists come up with and test their theories. The A level psychology syllabus covers several different types of studies and experiments used in psychology as well as how these studies are conducted and reported:

Types of psychological studies (including experiments , observations , self-reporting , and case studies )
Scientific processes (including the features of a study , how findings are reported , and the features of science in general )
Data handling and analysis (including descriptive statistics and different ways of presenting data ) and inferential testing

Note: Unlike all other sections across the 3 exam papers, research methods is worth 48 marks instead of 24. Not only that, the other sections often include a few research methods questions, so this topic is the most important on the syllabus!

Example question: Design a matched pairs experiment the researchers could conduct to investigate differences in toy preferences between boys and girls. [12 marks]

Types of study

There are several different ways a psychologist can research the mind, including:

Experiments
Observation
Self-reporting

Case studies

Each of these methods has its strengths and weaknesses. Different methods may be better suited to different research studies.

Experimental method

The experimental method looks at how variables affect outcomes. A variable is anything that changes between two situations ( see below for the different types of variables ). For example, Bandura’s Bobo the doll experiment looked at how changing the variable of the role model’s behaviour affected how the child played.

Experimental designs

Experiments can be designed in different ways, such as:

Independent groups: Participants are divided into two groups. One group does the experiment with variable 1, the other group does the experiment with variable 2. Results are compared.
Repeated measures: Participants are not divided into groups. Instead, all participants do the experiment with variable 1, then afterwards the same participants do the experiment with variable 2. Results are compared.

A matched pairs design is another form of independent groups design. Participants are selected. Then, the researchers recruit another group of participants one-by-one to match the characteristics of each member of the original group. This provides two groups that are relevantly similar and controls for differences between groups that might skew results. The experiment is then conducted as a normal independent groups design.

Types of experiment

Laboratory vs. field experiment.

Experiments are carried out in two different types of settings:

E.g. Bandura’s Bobo the doll experiment or Asch’s conformity experiments
E.g. Bickman’s study of the effects of uniforms on obedience

Strengths of laboratory experiment over field experiment:

The controlled environment of a laboratory experiment minimises the risk of other variables outside the researchers’ control skewing the results of the trial, making it more clear what (if any) the causal effects of a variable are. Because the environment is tightly controlled, any changes in outcome must be a result of a change in the variable.

Weaknesses of laboratory experiment over field experiment:

However, the controlled nature of a laboratory experiment might reduce its ecological validity . Results obtained in an artificial environment might not translate to real-life. Further, participants may be influenced by demand characteristics : They know they are taking part in a test, and so behave how they think they’re expected to behave rather than how they would naturally behave.

Natural and quasi experiment

Natural experiments are where variables vary naturally. In other words, the researcher can’t or doesn’t manipulate the variables . There are two types of natural experiment:

E.g. studying the effect a change in drug laws (variable) has on addiction
E.g. studying differences between men (variable) and women (variable)

Observational method

The observational method looks at and examines behaviour. For example, Zimbardo’s prison study observed how participants behaved when given certain social roles.

Observational design

Behavioural categories.

An observational study will use behavioural categories to prioritise which behaviours are recorded and ensure the different observers are consistent in what they are looking for.

For example, a study of the effects of age and sex on stranger anxiety in infants might use the following behavioural categories to organise observational data:

Rather than writing complete descriptions of behaviours, the behaviours can be coded into categories. For example, IS = interacted with stranger, and AS = avoided stranger. Researchers can also create numerical ratings to categorise behaviour, like the anxiety rating example above.

Inter-observer reliability : In order for observations to produce reliable findings, it is important that observers all code behaviour in the same way. For example, researchers would have to make it very clear to the observers what the difference between a ‘3’ on the anxiety scale above would be compared to a ‘7’. This inter-observer reliability avoids subjective interpretations of the different observers skewing the findings.

Event and time sampling

Because behaviour is constant and varied, it may not be possible to record every single behaviour during the observation period. So, in addition to categorising behaviour , study designers will also decide when to record a behaviour:

Event sampling: Counting how many times the participant behaves in a certain way.
Time sampling: Recording participant behaviour at regular time intervals. For example, making notes of the participant’s behaviour after every 1 minute has passed.

Note: Don’t get event and time sampling confused with participant sampling , which is how researchers select participants to study from a population.

Types of observation

Naturalistic vs. controlled.

Observations can be made in either a naturalistic or a controlled setting:

E.g. setting up cameras in an office or school to observe how people interact in those environments
E.g. Ainsworth’s strange situation or Zimbardo’s prison study

Covert vs. overt

Observations can be either covert or overt :

E.g. setting up hidden cameras in an office
E.g. Zimbardo’s prison study

Participant vs. non-participant

In observational studies, the researcher/observer may or may not participate in the situation being observed:

E.g. in Zimbardo’s prison study , Zimbardo played the role of prison superintendent himself
E.g. in Bandura’s Bobo the doll experiment and Ainsworth’s strange situation , the observers did not interact with the children being observed

Self-report method

Self-report methods get participants to provide information about themselves. Information can be obtained via questionnaires or interviews .

Types of self-report

Questionnaires.

A questionnaire is a standardised list of questions that all participants in a study answer. For example, Hazan and Shaver used questionnaires to collate self-reported data from participants in order to identify correlations between attachment as infants and romantic attachment as adults.

Questions in a questionnaire can be either open or closed :

>8 hours
E.g. “How did you feel when you thought you were administering a lethal shock?” or “What do you look for in a romantic partner and why?”

Strengths of questionnaires:

Quantifiable: Closed questions provide quantifiable data in a consistent format, which enables to statistically analyse information in an objective way.
Replicability: Because questionnaires are standardised (i.e. pre-set, all participants answer the same questions), studies involving them can be easily replicated . This means the results can be confirmed by other researchers, strengthening certainty in the findings.

Weaknesses of questionnaires:

Biased samples: Questionnaires handed out to people at random will select for participants who actually have the time and are willing to complete the questionnaire. As such, the responses may be biased towards those of people who e.g. have a lot of spare time.
Dishonest answers: Participants may lie in their responses – particularly if the true answer is something they are embarrassed or ashamed of (e.g. on controversial topics or taboo topics like sex)
Misunderstanding/differences in interpretation: Different participants may interpret the same question differently. For example, the “are you religious?” example above could be interpreted by one person to mean they go to church every Sunday and pray daily, whereas another person may interpret religious to mean a vague belief in the supernatural.
Less detail: Interviews may be better suited for detailed information – especially on sensitive topics – than questionnaires. For example, participants are unlikely to write detailed descriptions of private experiences in a questionnaire handed to them on the street.

In an interview , participants are asked questions in person. For example, Bowlby interviewed 44 children when studying the effects of maternal deprivation.

Interviews can be either structured or unstructured :

Structured interview: Questions are standardised and pre-set. The interviewer asks all participants the same questions in the same order.
Unstructured interview: The interviewer discusses a topic with the participant in a less structured and more spontaneous way, pursuing avenues of discussion as they come up.

Interviews can also be a cross between the two – these are called semi-structured interviews .

Strengths of interviews:

More detail: Interviews – particularly unstructured interviews conducted by a skilled interviewer – enable researchers to delve deeper into topics of interest, for example by asking follow-up questions. Further, the personal touch of an interviewer may make participants more open to discussing personal or sensitive issues.
Replicability: Structured interviews are easily replicated because participants are all asked the same pre-set list of questions. This replicability means the results can be confirmed by other researchers, strengthening certainty in the findings.

Weaknesses of interviews:

Lack of quantifiable data: Although unstructured interviews enable researchers to delve deeper into interesting topics, this lack of structure may produce difficulties in comparing data between participants. For example, one interview may go down one avenue of discussion and another interview down a different avenue. This qualitative data may make objective or statistical analysis difficult.
Interviewer effects : The interviewer’s appearance or character may bias the participant’s answers. For example, a female participant may be less comfortable answering questions on sex asked by a male interviewer and and thus give different answers than if she were asked by a female interviewer.

Note: This topic is A level only, you don’t need to learn about case studies if you are taking the AS exam only.

Case studies are detailed investigations into an individual, a group of people, or an event. For example, the biopsychology page describes a case study of a young boy who had the left hemisphere of his brain removed and the effects this had on his language skills.

In a case study, researchers use many of the methods described above – observation , questionnaires , interviews – to gather data on a subject. However, because case studies are studies of a single subject, the data they provide is primarily qualitative rather than quantitative . This data is then used to build a case history of the subject. Researchers then interpret this case history to draw their conclusions.

Types of case study

Typical vs. unusual cases.

Most case studies focus on unusual individuals, groups, and events.

Longitudinal

Many case studies are longitudinal . This means they take place over an extended time period, with researchers checking in with the subject at various intervals. For example, the case study of the boy who had his left hemisphere removed collected data on the boy’s language skills at ages 2.5, 4, and 14 to see how he progressed.

Strengths of case studies:

Provides detailed qualitative data: Rather than focusing on one or two aspects of behaviour at a single point in time (e.g. in an experiment ), case studies produce detailed qualitative data.
Allows for investigation into issues that may be impractical or unethical to study otherwise. For example, it would be unethical to remove half a toddler’s brain just to experiment , but if such a procedure is medically necessary then researchers can use this opportunity to learn more about the brain.

Weaknesses of case studies:

Lack of scientific rigour: Because case studies are often single examples that cannot be replicated , the results may not be valid when applied to the general population.
Researcher bias: The small sample size of case studies also means researchers need to apply their own subjective interpretation when drawing conclusions from them. As such, these conclusions may be skewed by the researcher’s own bias and not be valid when applied more generally. This criticism is often directed at Freud’s psychoanalytic theory because it draws heavily on isolated case studies of individuals.

Scientific processes

This section looks at how science works more generally – in particular how scientific studies are organised and reported . It also covers ways of evaluating a scientific study.

Study features and design

Studies will usually have an aim . The aim of a study is a description of what the researchers are investigating and why . For example, “to investigate the effect of SSRIs on symptoms of depression” or “to understand the effect uniforms have on obedience to authority”.

Studies seek to test a hypothesis . The experimental/alternate hypothesis of a study is a testable prediction of what the researchers expect to happen.

E.g. “That SSRIs will reduce symptoms of depression” or “subjects are more likely to comply when orders are issued by someone wearing a uniform”.
E.g. “That SSRIs have no effect on symptoms on depression” or “subject conformity will be the same when orders are issued by someone wearing a uniform as when orders are issued by someone bot wearing a uniform”

Either the experimental/alternate hypothesis or the null hypothesis will be supported by the results of the experiment.

It’s often not possible or practical to conduct research on everyone your study is supposed to apply to. So, researchers use sampling to select participants for their study.

E.g. all humans, all women, all men, all children, etc.
E.g. 10,000 humans, 200 women from the USA, children at a certain school

For example, the target population (i.e. who the results apply to) of Asch’s conformity experiments is all humans – but Asch didn’t conduct the experiment on that many people! Instead, Asch recruited 123 males and generalised the findings from this sample to the rest of the population.

Researchers choose from different sampling techniques – each has strengths and weaknesses.

Sampling techniques

Random sampling.

The random sampling method involves selecting participants from a target population at random – such as by drawing names from a hat or using a computer program to select them. This method means each member of the population has an equal chance of being selected and thus is not subject to any bias.

Strengths of random sampling:

Unbiased: Selecting participants by random chance reduces the likelihood that researcher bias will skew the results of the study.
Representative: If participants are selected at random – particularly if the sample size is large – it is likely that the sample will be representative of the population as a whole. For example, if the ratio of men:women in a population is 50:50 and participants are selected at random, it is likely that the sample will also have a ratio of men to women that is 50:50.

Weaknesses of random sampling:

Impractical: It’s often impractical/impossible to include all members of a target population for selection. For example, it wouldn’t be feasible for a study on women to include the name of every woman on the planet for selection. But even if this was done, the randomly selected women may not agree to take part in the study anyway.

Systematic sampling

The systematic sampling method involves selecting participants from a target population by selecting them at pre-set intervals. For example, selecting every 50th person from a list, or every 7th, or whatever the interval is.

Strengths of systematic sampling:

Unbiased and representative: Like random sampling , selecting participants according to a numerical interval provides an objective means of selecting participants that prevents researcher bias being able to skew the sample. Further, because the sampling method is independent of any particular characteristic (besides the arbitrary characteristic of the participant’s order in the list) this sample is likely to be representative of the population as a whole.

Weaknesses of systematic sampling:

Unexpected bias: Some characteristics could occur more or less frequently at certain intervals, making a sample that is selected based on that interval biased. For example, houses tend to be have even numbers on one side of a road and odd numbers on the other. If one side of the road is more expensive than the other and you select every 4th house, say, then you will only select even numbers from one side of the road – and this sample may not be representative of the road as a whole.

Stratified sampling

The stratified sampling method involves dividing the population into relevant groups for study, working out what percentage of the population is in each group, and then randomly sampling the population according to these percentages.

For example, let’s say 20% of the population is aged 0-18, and 50% of the population is aged 19-65, and 30% of the population is aged >65. A stratified sample of 100 participants would randomly select 20x 0-18 year olds, 50x 19-65 year olds, and 30x people over 65.

Strengths of stratified sampling:

Representative: The stratification is deliberately designed to yield a sample that is representative of the population as a whole. You won’t get people with certain characteristics being over- or under-represented within the sample.
Unbiased: Because participants within each group are selected randomly , researcher bias is unable to skew who is included in the study.

Weaknesses of stratified sampling:

Requires knowledge of population breakdown: Researchers need to accurately gauge what percentage of the population falls into what group. If the researchers get these percentages wrong, the sample will be biased and some groups will be over- or under-represented.

Opportunity and volunteer sampling

The opportunity and volunteer sampling methods:

E.g. Approaching people in the street and asking them to complete a questionnaire.
E.g. Placing an advert online inviting people to complete a questionnaire.

Strengths of opportunity and volunteer sampling:

Quick and easy: Approaching participants ( opportunity sampling) or inviting participants ( volunteer sampling) is quick and straightforward. You don’t have to spend time compiling details of the target population (like in e.g. random or systematic sampling ), nor do you have to spend time dividing participants according to relevant categories (like in stratified sampling ).
May be the only option: With natural experiments – where a variable changes as a result of something outside the researchers’ control – opportunity sampling may be the only viable sampling method. For example, researchers couldn’t randomly sample 10 cities from all the cities in the world and change the drug laws in those cities to see the effects – they don’t have that kind of power. However, if a city is naturally changing its drug laws anyway, researchers could use opportunity sampling to study that city for research.

Weaknesses of opportunity and volunteer sampling:

Unrepresentative: The pool of participants will likely be biased towards certain kinds of people. For example, if you conduct opportunity sampling on a weekday at 10am, this sample will likely exclude people who are at work. Similarly, volunteer sampling is likely to exclude people who are too busy to take part in the study.

Independent vs. dependent variables

If the study involves an experiment , the researchers will alter an independent variable to measure its effects on a dependent variable :

E.g. In Bickman’s study of the effects of uniforms on obedience , the independent variable was the uniform of the person giving orders.
E.g. In Bickman’s study of the effects of uniforms on obedience , the dependent variable was how many people followed the orders.

Extraneous and confounding variables

In addition to the variables actually being investigated ( independent and dependent ), there may be additional (unwanted) variables in the experiment. These additional variables are called extraneous variables .

Researchers must control for extraneous variables to prevent them from skewing the results and leading to false conclusions. When extraneous variables are not properly controlled for they are known as confounding variables .

For example, if you’re studying the effect of caffeine on reaction times, it might make sense to conduct all experiments at the same time of day to prevent this extraneous variable from confounding the results. Reaction times change throughout the day and so if you test one group of subjects at 3pm and another group right before they go to bed, you may falsely conclude that the second group had slower reaction times.

Operationalisation of variables

Operationalisation of variables is where researchers clearly and measurably define the variables in their study.

For example, an experiment on the effects of sleep ( independent variable ) on anxiety ( dependent variable ) would need to clearly operationalise each variable. Sleep could be defined by number of hours spent in bed, but anxiety is a bit more abstract and so researchers would need to operationalise (i.e. define) anxiety such that it can be quantified in a measurable and objective way.

If variables are not properly operationalised, the experiment cannot be properly replicated , experimenters’ subjective interpretations may skew results, and the findings may not be valid .

Pilot studies

A pilot study is basically a practice run of the proposed research project. Researchers will use a small number of participants and run through the procedure with them. The purpose of this is to identify any problems or areas for improvement in the study design before conducting the research in full. A pilot study may also give an early indication of whether the results will be statistically significant .

For example, if a task is too easy for participants, or it’s too obvious what the real purpose of an experiment is, or questions in a questionnaire are ambiguous, then the results may not be valid . Conducting a pilot study first may save time and money as it enables researchers to identify and address such issues before conducting the full study on thousands of participants.

Study reporting

Features of a psychological report.

The report of a psychological study (research paper) typically contains the following sections in the following order:

Title: A short and clear description of the research.
Abstract: A summary of the research. This typically includes the aim and hypothesis , methods, results, and conclusion.
Introduction: Funnel technique: Broad overview of the context (e.g. current theories, previous studies, etc.) before focusing in on this particular study, why it was conducted, its aims and hypothesis .
Study design: This will explain what method was used (e.g. experiment or observation ), how the study was designed (e.g. independent groups or repeated measures ), and identification and operationalisation of variables .
Participants: A description of the target population to be studied, the sampling method , how many participants were included.
Equipment used: A description of any special equipment used in the study and how it was used.
Standardised procedure: A detailed step-by-step description of how the study was conducted. This allows for the study to be replicated by other researchers.
Controls : An explanation of how extraneous variables were controlled for so as to generate accurate results.
Results: A presentation of the key findings from the data collected. This is typically written summaries of the raw data ( descriptive statistics ), which may also be presented in tables , charts, graphs , etc. The raw data itself is typically included in appendices.
Discussion: An explanation of what the results mean and how they relate to the experimental hypothesis (supporting or contradicting it), any issues with how results were generated, how the results fit with other research, and suggestions for future research.
Conclusion: A short summary of the key findings from the study.
Book: Milgram, S., 2010. Obedience to Authority . 1st ed. Pinter & Martin.
Journal article: Bandura, A., Ross, D. and Ross, S., 1961. Transmission of Aggression through Imitation of Aggressive Models . The Journal of Abnormal and Social Psychology, 63(3), pp.575-582.
Appendices: This is where you put any supporting materials that are too detailed or long to include in the main report. For example, the raw data collected from a study, or the complete list of questions in a questionnaire .

Peer review

Peer review is a way of assessing the scientific credibility of a research paper before it is published in a scientific journal. The idea with peer review is to prevent false ideas and bad research from being accepted as fact.

It typically works as follows: The researchers submit their paper to the journal they want it to be published in, and the editor of that journal sends the paper to expert reviewers (i.e. psychologists who are experts in that area – the researchers’ ‘peers’) who evaluate the paper’s scientific validity. The reviewers may accept the paper as it is, accept it with a few changes, reject it and suggest revisions and resubmission at a later date, or reject it completely.

There are several different methods of peer review:

Open review: The researchers and the reviewers are known to each other.
Single-blind: The researchers do not know the names of the reviewers. This prevents the researchers from being able to influence the reviewer. This is the most common form of peer review.
Double-blind: The researchers do not know the names of the reviewers, and the reviewers do not know the names of the researchers. This additionally prevents the reviewer’s bias towards the researcher from influencing their decision whether to accept their paper or not.

Criticisms of peer review:

Bias: There are several ways peer review can be subject to bias. For example, academic research (particularly in niche areas) takes place among a fairly small circle of people who know each other and so these relationships may affect publication decisions. Further, many academics are funded by organisations and companies that may prefer certain ideas to be accepted as scientifically legitimate, and so this funding may produce conflicts of interest.
Doesn’t always prevent fraudulent/bad research from being published: There are many examples of fraudulent research passing peer review and being published (see this Wikipedia page for examples).
Prevents progress of new ideas: Reviewers of papers are typically older and established academics who have made their careers within the current scientific paradigm. As such, they may reject new or controversial ideas simply because they go against the current paradigm rather than because they are unscientific.
Plagiarism: In single-blind and double-blind peer reviews, the reviewer may use their anonymity to reject or delay a paper’s publication and steal the good ideas for themself.
Slow: Peer review can mean it takes months or even years between the researcher submitting a paper and its publication.

Study evaluation

In psychological studies, ethical issues are questions of what is morally right and wrong. An ethically-conducted study will protect the health and safety of the participants involved and uphold their dignity, privacy, and rights.

To provide guidance on this, the British Psychological Association has published a code of human research ethics :

Participants are told the project’s aims , the data being collected, and any risks associated with participation.
Participants have the right to withdraw or modify their consent at any time.
Researchers can use incentives (e.g. money) to encourage participation, but these incentives can’t be so big that they would compromise a participant’s freedom of choice.
Researchers must consider the participant’s ability to consent (e.g. age, mental ability, etc.)
Prior (general) consent: Informing participants that they will be deceived without telling them the nature of the deception. However, this may affect their behaviour as they try to guess the real nature of the study.
Retrospective consent: Informing participants that they were deceived after the study is completed and asking for their consent. The problem with this is that if they don’t consent then it’s too late.
Presumptive consent: Asking people who aren’t participating in the study if they would be willing to participate in the study. If these people would be willing to give consent, then it may be reasonable to assume that those taking part in the study would also give consent.
Confidentiality: Personal data obtained about participants should not be disclosed (unless the participant agreed to this in advance). Any data that is published will not be publicly identifiable as the participant’s.
Debriefing: Once data gathering is complete, researchers must explain all relevant details of the study to participants – especially if deception was involved. If a study might have harmed the individual (e.g. its purpose was to induce a negative mood), it is ethical for the debrief to address this harm (e.g. by inducing a happy mood) so that the participant does not leave the study in a worse state than when they entered.

Reliability

Study results are reliable if the same results can be consistently replicated under the same circumstances. If results are inconsistent then the study is unreliable.

Note: Just because a study is reliable, its results are not automatically valid . A broken tape measure may reliably (i.e. consistently) record a person’s height as 200m, but that doesn’t mean this measurement is accurate.

There are several ways researchers can assess a study’s reliability:

Test-retest

Test-retest is when you give the same test to the same person on two different occasions. If the results are the same or similar both times, this suggests they are reliable.

For example, if your study used scales to measure participants’ weight, you would expect the scales to record the same (or a very similar) weight for the same person in the morning as in the evening. If the scales said the person weighed 100kg more later that same day, the scales (and therefore the results of the study) would be unreliable.

Inter-observer

Inter-observer reliability is a way to test the reliability of observational studies .

For example, if your study required observers to assess participants’ anxiety levels, you would expect different observers to grade the same behaviour in the same way. If one observer rated a participant’s behaviour a 3 for anxiety, and another observer rated the exact same behaviour an 8, the results would be unreliable.

Inter-observer reliability can be assessed mathematically by looking for correlation between observers’ scores. Inter-observer reliability can be improved by setting clearly defined behavioural categories .

Study results are valid if they accurately measure what they are supposed to. There are several ways researchers can assess a study’s validity:

E.g. let’s say you come up with a new test to measure participants’ intelligence levels. If participants scoring highly on your test also scored highly on a standardised IQ test and vice versa, that would suggest your test has concurrent validity because participants’ scores are correlated with a known accurate test.
E.g. a study that measures participants’ intelligence levels by asking them when their birthday is would not have face validity. Getting participants to complete a standardised IQ test would have greater face validity.
E.g. let’s say your study was supposed to measure aggression levels in response to someone annoying. If the study was conducted in a lab and the participant knew they were taking part in a study, the results probably wouldn’t have much ecological validity because of the unrealistic environment.
E.g. a study conducted in 1920 that measured participants’ attitudes towards social issues may have low temporal validity because societal attitudes have changed since then.

Control of extraneous variables

There are several different types of extraneous variables that can reduce the validity of a study. A well-conducted psychological study will control for these extraneous variables so that they do not skew the results.

Demand characteristics

Demand characteristics are extraneous variables where the demands of a study make participants behave in ways they wouldn’t behave outside of the study. This reduces the study’s ecological validity .

For example, if a participant guesses the purpose of an experiment they are taking part in, they may try to please the researcher by behaving in the ‘right’ way rather than the way they would naturally. Alternatively, the participant might rebel against the study and deliberately try to sabotage it (e.g. by deliberately giving wrong answers).

In some study designs, researchers can control for demand characteristics using single- blind methods. For example, a drug trial could give half the participants the actual drug and the other half a placebo but not tell participants which treatment they received. This way, both groups will have equal demand characteristics and so any differences between them should be down to the drug itself.

Investigator effects

Investigator effects are another extraneous variable where the characteristics of the researcher affect the participant’s behaviour. Again, this reduces the study’s ecological validity .

Many characteristics – e.g. the researcher’s age, gender, accent, what they’re wearing – could potentially influence the participant’s responses. For example, in an interview about sex, females may feel less comfortable answering questions asked by a male interviewer and thus give different answers than if they were asked by a female. The researcher’s biases may also come across in their body language or tone of voice, affecting the participant’s responses.

In some study designs, researchers can control for demand characteristics using double- blind methods. In a double-blind drug trial, for example, neither the participants nor the researchers know which participants get the actual drug and which get the placebo. This way, the researcher is unable to give any clues (consciously or unconsciously) to participants that would affect their behaviour.

Participant variables

Participant variables are differences between participants. These can be controlled for by random allocation .

For example, in an experiment on the effect of caffeine on reaction times, participants would be randomly allocated into either the caffeine group or the non-caffeine group. A non -random allocation method, such as allocating caffeine to men and placebo to women, could mean variables in the allocation method (in this case gender) skew the results. When participants are randomly allocated, any extraneous variables (e.g. gender in this case) will be allocated evenly between each group and so not skew the results of one group more than the other.

Situational variables

Situational variables are the environment the experiment is conducted in. These can be controlled for by standardisation .

For example, all the tests of caffeine on reaction times would be conducted in the same room, at the same time of day, using the same equipment, and so on to prevent these features of the environment from skewing the results.

In a repeated measures experiment, researchers may use counterbalancing to control for the order in which tasks are completed.

For example, half of participants would do task A followed by task B, and the other half would do task B followed by task A.

Implications of psychological research for the economy

Psychological research often has practical applications in real life. The following are some examples of how psychological findings may affect the economy:

Attachment : Bowlby’s maternal deprivation hypothesis suggests that periods of extended separation between mother and child before age 3 are harmful to the child’s psychological development. And if mothers stay at home during this period, they can’t go out to work. However, some more recent research challenges Bowlby’s conclusions, suggesting that substitutes (e.g. the father , or nursery care) can care for the child, allowing the mother to go back to work sooner and remain economically active.
Depression : Psychological research has found effective therapies for treating depression, such as cognitive behavioural therapy and SSRIs. The benefits of such therapies – if they are effective – are likely to outweigh the costs because they enable the person to return to work and pay taxes, as well avoiding long-term costs to the health service.
OCD : Similar to above: Drug therapies (e.g. SSRIs) and behavioural approaches (e.g. CBT) may alleviate OCD symptoms, enabling OCD sufferers to return to work, pay taxes, and avoid reliance on healthcare services.
Memory : Public money is required to fund police investigations. Psychological tools, such as the cognitive interview , have improved the accuracy of eyewitness testimonies, which equates to more efficient use of police time and resources.

Features of science

Theory construction and hypothesis testing.

Science works by making empirical observations of the world, formulating hypotheses /theories that explain these observations, and repeatedly testing these hypotheses /theories via experimentation.

E.g. A tape measure provides a more objective measurement of something compared to a researcher’s guess. Similarly, a set of scales is a more objective way of determining which of two objects is heavier than a researcher lifting each up and giving their opinion.
E.g. Burger (2009) replicated Milgram’s experiments with similar results.
E.g. The hypothesis that “water boils at 100°c” could be falsified by an experiment where you heated water to 999°c and it didn’t boil. In contrast, “everything doubles in size every 10 seconds” could not be falsified by any experiment because whatever equipment you used to measure everything would also double in size.
Freud’s psychodynamic theories are often criticised for being unfalsifiable: There’s not really any observations that could disprove them because every possible behaviour (e.g. crying or not crying) could be explained as the result of some unconscious thought process.

Paradigm shifts

Philosopher Thomas Kuhn argues that science is not as unbiased and objective as it seems. Instead, the majority of scientists just accept the existing scientific theories (i.e. the existing paradigm) as true and then find data that supports these theories while ignoring/rejecting data that refutes them.

Rarely, though, minority voices are able to successfully challenge the existing paradigm and replace it with a new one. When this happens it is a paradigm shift . An example of a paradigm shift in science is that from Newtonian gravity to Einstein’s theory of general relativity.

Data handling and analysis

Types of data, quantitative vs. qualitative.

Data from studies can be quantitative or qualitative :

Quantitative: Numerical
Qualitative: Non-numerical

For example, some quantitative data in the Milgram experiment would be how many subjects delivered a lethal shock. In contrast, some qualitative data would be asking the subjects afterwards how they felt about delivering the lethal shock.

Strengths of quantitative data / weaknesses of qualitative data:

Can be compared mathematically and scientifically: Quantitative data enables researchers to mathematically and objectively analyse data. For example, mood ratings of 7 and 6 can be compared objectively, whereas qualitative assessments such as ‘sad’ and ‘unhappy’ are hard to compare scientifically.

Weaknesses of quantitative data / strengths of qualitative data:

Less detailed: In reducing data to numbers and narrow definitions, quantitative data may miss important details and context.

Content analysis

Although the detail of qualitative data may be valuable, this level of detail can also make it hard to objectively or mathematically analyse. Content analysis is a way of analysing qualitative data. The process is as follows:

E.g. A bunch of unstructured interviews on the topic of childhood
E.g. Discussion of traumatic events, happy memories, births, and deaths
E.g. Researchers listen to the unstructured interviews and count how often traumatic events are mentioned
Statistical analysis is carried out on this data

Primary vs. secondary

Researchers can produce primary data or use secondary data to achieve the research aims of their study:

Primary data: Original data collected for the study
Secondary data: Data from another study previously conducted

Meta-analysis

A meta-analysis is a study of studies. It involves taking several smaller studies within a certain research area and using statistics to identify similarities and trends within those studies to create a larger study.

We have looked at some examples of meta-analyses elsewhere in the course such as Van Ijzendoorn’s meta-analysis of several strange situation studies and Grootheest et al’s meta-analysis of twin studies on OCD .

A good meta-analysis is often more reliable than a regular study because it is based on a larger data set, and any issues with one single study will be balanced out by the other studies.

Descriptive statistics

Measures of central tendency: mean, median, mode.

Mean , median , and mode are measures of central tendency . In other words, they are ways of reducing large data sets into averages .

The mean is calculated by adding all the numbers in a set together and dividing the total by the number of numbers.

Example set: 22, 78, 3, 33, 90
22+78+3+33+90=226
The mean is 45.2
Uses all data in the set.
Accurate: Provides a precise number based on all the data in a set.

Weaknesses:

E.g.: 1, 3, 2, 5, 9, 4, 913 <- the mean is 133.9, but the 913 could be a measurement error or something and thus the mean is not representative of the data set

The median is calculated by arranging all the numbers in a set from smallest to biggest and then finding the number in the middle. Note: If the total number of numbers is odd, you just pick the middle one. But if the total number of numbers is even, you take the mid-point between the two numbers in the middle.

Example set: 20, 66, 85, 45, 18, 13, 90, 28, 9
9, 13, 18, 20, 28 , 45, 66, 85, 90
The median is 28
Won’t be skewed by freak scores (unlike the mean).
E.g.: 1, 1, 3 , 9865, 67914 <- 3 is not really representative of the larger numbers in the set.
Less accurate/sensitive than the mean.

The mode is calculated by counting which is the most commonly occurring number in a set.

Example set: 7, 7, 20 , 16, 1, 20 , 25, 16, 20 , 9
There are two 7’s, but three 20’s
The mode is 20
Makes more sense for presenting the central tendency in data sets with whole numbers. For example, the average number of limbs for a human being will have a mean of something like 3.99, but a mode of 4.
Does not use all the data in a set.
A data set may have more than one mode.

Measures of dispersion: Range and standard deviation

Range and standard deviation are measures of dispersion . In other words, they quantify how much scores in a data set vary .

The range is calculated by subtracting the smallest number in the data set from the largest number.

Example set: 59, 8, 7, 84, 9, 49, 14, 75, 88, 11
The largest number is 88
The smallest number is 7
The range is 81
Easy and quick to calculate: You just subtract one number from another
Accounts for freak scores (highest and lowest)
Can be skewed by freak scores: The difference between the biggest and smallest numbers can be skewed by a single anomalous result or error, which may give an exaggerated impression of the data distribution compared to standard deviation .
4, 4, 5, 5, 5, 6, 6, 7, 19
4, 16, 16, 17, 17, 17, 18, 19 19

Standard deviation

The standard deviation (σ) is a measure of how much numbers in a data set deviate from the mean (average). It is calculated as follows:

Example data set: 59, 79, 43, 42, 81, 100, 38, 54, 92, 62
Calculate the mean (65)
-6, 14, -22, -23, 16, 35, -27, -11, 27, -3
36, 196, 484, 529, 256, 1225, 729, 121, 729, 9
36+196+484+529+256+1225+729+121+729+9=4314
4314/10=431.4
√431.4=20.77
The standard deviation is 20.77

Note: This method of standard deviation is based on the entire population. There is a slightly different method for calculating based on a sample where instead of dividing by the number of numbers in the second to last step, you divide by the number of numbers-1 (in this case 4314/9=479.333). This gives a standard deviation of 21.89.

Is less skewed by freak scores: Standard deviation measures the average difference from the mean and so is less likely to be skewed by a single freak score (compared to the range ).
Takes longer to calculate than the range .

Percentages

A percentage (%) describes how much out of 100 something occurs. It is calculated as follows:

Example: 63 out of a total of 82 participants passed the test
63/82=0.768
0.768*100=76.8
76.8% of participants passed the test

Percentage change

To calculate a percentage change, work out the difference between the original number and the after number, divide that difference by the original number, then multiply the result by 100:

Example: He got 80 marks on the test but after studying he got 88 marks on the test
His test score increased by 10% after studying

Normal and skewed distributions

Normal distribution.

A data set that has a normal distribution will have the majority of scores on or near the mean average. A normal distribution is also symmetrical: There are an equal number of scores above the mean as below it. In a normal distribution, scores become rarer and rarer the more they deviate from the mean.

An example of a normal distribution is IQ scores. As you can see from the histogram below, there are as many IQ scores below the mean as there are above the mean :

When plotted on a histogram , data that follows a normal distribution will form a bell-shaped curve like the one above.

Skewed distribution

positive skew and negative skew histograms

Skewed distributions are caused by outliers: Freak scores that throw off the mean . Skewed distributions can be positive or negative :

Mean > Median > Mode
Mean < Median < Mode

Correlation

Correlation refers to how closely related two (or more) things are related. For example, hot weather and ice cream sales may be positively correlated: When hot weather goes up, so do ice cream sales.

Correlations are measured mathematically using correlation coefficients (r). A correlation coefficient will be anywhere between +1 and -1:

r=+1 means two things are perfectly positively correlated: When one goes up , so does the other by the same amount
r=-1 means two things perfectly negatively correlated: When one goes up , the other goes down by the same amount
r=0 means two things are not correlated at all: A change in one is totally independent of a change in the other

The following scattergrams illustrate various correlation coefficients:

correlation coefficient scatter graph examples

Presentation of data

For example, the behavioural categories table above presents the raw data of each student in this made-up study. But in the results section, researchers might include another table that compares average anxiety rating scores for males and females.

Scattergrams

For example, each dot on the correlation scattergram opposite could represent a student. The x-axis could represent the number of hours the student studied, and the y-axis could represent the student’s test score.

For example, the results of Loftus and Palmer’s study into the effects of different leading questions on memory could be presented using the bar chart above. It’s not like there are categories in-between ‘contacted’ and ‘hit’, so the bars have gaps between them (unlike a histogram ).

A histogram is a bit like a bar chart but is used to illustrate continuous or interval data (rather than discrete data or whole numbers).

Because the data on the x axis is continuous, there are no gaps between the bars.

For example, the line graph above illustrates 3 different people’s progression in a strength training program over time.

For example, the frequency with which different attachment styles occurred in Ainsworth’s strange situation could be represented by the pie chart opposite.

Inferential testing

Probability and significance.

The point of inferential testing is to see whether a study’s results are statistically significant , i.e. whether any observed effects are as a result of whatever is being studied rather than just random chance.

For example, let’s say you are studying whether flipping a coin outdoors increases the likelihood of getting heads. You flip the coin 100 times and get 52 heads and 48 tails. Assuming a baseline expectation of 50:50, you might take these results to mean that flipping the coin outdoors does increase the likelihood of getting heads. However, from 100 coin flips, a ratio of 52:48 between heads and tails is not very significant and could have occurred due to luck. So, the probability that this difference in heads and tails is because you flipped the coin outside (rather than just luck) is low.

Probability is denoted by the symbol p . The lower the p value, the more statistically significant your results are. You can never get a p value of 0, though, so researchers will set a threshold at which point the results are considered statistically significant enough to reject the null hypothesis . In psychology, this threshold is usually <0.05, which means there is a less than 5% chance the observed effect is due to luck and a >95% chance it is a real effect.

Type 1 and type 2 errors

When interpreting statistical significance, there are two types of errors:

E.g. The p threshold is <0.05, but the researchers’ results are among the 5% of fluke outcomes that look significant but are just due to luck
E.g. The p threshold is set too low (e.g. <0.01), and the data falls short (e.g. p=<0.02)

Increasing the sample size reduces the likelihood of type 1 and type 2 errors.

Key maths skills made easy!

psychology research methods maths skills revision guide

Types of statistical test

Note: The inferential tests below are needed for A level only, if you are taking the AS exam , you only need to know the sign test .

There are several different types of inferential test in addition to the sign test . Which inferential test is best for a study will depend on the following three criteria:

Whether you are looking for a difference or a correlation
E.g. at the competition there were 8 runners, 12 swimmers, and 6 long jumpers (it’s not like there are in-between measurements between ‘swimmer’ and ‘runner’)
E.g. First, second, and third place in a race
E.g. Ranking your mood on a scale of 1-10
E.g. Weights in kg
E.g. Heights in cm
E.g. Times in seconds
Whether the experimental design is related (i.e. repeated measures ) or unrelated (i.e. independent groups )

The following table shows which inferential test is appropriate according to these criteria:

Note: You won’t have to work out all these tests from scratch, but you may need to:

Say which of the statistical tests is appropriate (i.e. based on whether it’s a difference or correlation; whether the data is nominal, ordinal, or interval; and whether the data is related or unrelated).
Identify the critical value from a critical values table and use this to say whether a result (which will be given to you in the exam) is statistically significant.

The sign test

The sign test is a way to calculate the statistical significance of differences between related pairs (e.g. before and after in a repeated measures experiment ) of nominal data. If the observed value (s) is equal or less than the critical value (cv), the results are statistically significant.

Example: Let’s say we ran an experiment on 10 participants to see whether they prefer movie A or movie B .

n = 9 (because even though there are 10 participants, one participant had no change so we exclude them from our calculation)
In this case our experimental hypothesis is two-tailed: Participants may prefer movie A or movie B
(The null hypothesis is that participants like both movies equally)
In this case, let’s say it’s 0.1
The experimental hypothesis is two-tailed
So, in this example, our critical value (cv) is 1
In this example, there are 2 As, so our observed value (s) is 2
In this example, the observed value (2) is greater than the critical value (1) and so the results are not statistically significant. This means we must accept the null hypothesis and reject the experimental hypothesis .

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Experimental methods in psychology, aims and hypotheses, directional and non-directional, sampling methods, experimental designs, ethics; including the role of the british psychological society’s code of ethics, correlational analysis: positive, negative and zero correlations, naturalistic observation, questionnaire construction; including the use of open and closed questions, design of interviews, case studies, pilot study, reliability, features of science, objectivity and the empirical method, psychological report writing, primary and secondary data, including meta-analysis, descriptive statistics: measures of central tendency (mean, median and mode), presentation and display of quantitative data: graphs, tables, scatter grams and bar charts, measures of dispersion (range and standard deviation), calculating percentages, quantitative data analysis - normal and skewed distributions, sign test – inferential statistics, statistical (inferential) testing, qualitative data analysis, the role of peer review in the scientific process.

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PSYCH 3002 - Research Methods in Psychology

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Research Methods In Psychology
Olivia Guy-Evans, MSc. Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.
PDF APA Handbook of Research Methods in Psychology
Chapter 12. Mixed Methods Research in Psychology ..... 235 Timothy C. Guetterman and Analay Perez Chapter 13. The Cases W ithin Trials (CWT) Method: An Example of a Mixed Methods Research Design ..... 257 Daniel B. Fishman Chapter 14. Resear ching With American Indian and Alaska Native Communities:
How to Write an APA Methods Section
Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods. In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample, measures, and procedures used.
How to Write a Methods Section for a Psychology Paper
To write your methods section in APA format, describe your participants, materials, study design, and procedures. Keep this section succinct, and always write in the past tense. The main heading of this section should be labeled "Method" and it should be centered, bolded, and capitalized. Each subheading within this section should be bolded ...
Research Methods in Psychology
Table of Contents. Chapter 1: The Science of Psychology. Chapter 2: Overview of the Scientific Method. Chapter 3: Research Ethics. Chapter 4: Psychological Measurement. Chapter 5: Experimental Research. Chapter 6: Non-experimental Research. Chapter 7: Survey Research. Chapter 8: Quasi-Experimental Research.
APA Handbook of Research Methods in Psychology
With significant new and updated content across dozens of chapters, the second edition of the APA Handbook of Research Methods in Psychology presents the most exhaustive treatment available of the techniques psychologists and others have developed to help them pursue a shared understanding of why humans think, feel, and behave the way they do. Across three volumes, the chapters in this ...
PDF APA Handbook of Research Methods in Psychology, Second Edition Sample Pages
Chapter˜12. Mixed Methods Research in Psychology ..... 235 Timothy C. Guetterman and Analay Perez Chapter˜13. The "Cases Within Trials" (CWT) Method: An Example of a Mixed-Methods Research Design ..... 257 Daniel B. Fishman Chapter˜14. Researching With American Indian and Alaska Native Communities:
APA Handbook of Research Methods in Psychology
Mixed Methods Research in Psychology Timothy C. Guetterman and Analay Perez; Chapter 13. The "Cases Within Trials" (CWT) Method: An Example of a Mixed-Methods Research Design Daniel B. Fishman; Chapter 14. Researching With American Indian and Alaska Native Communities: Pursuing Partnerships for Psychological Inquiry in Service to Indigenous ...
Ch 2: Psychological Research Methods
Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in "APA style") are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines ...
PDF PSYCHOLOGY RESEARCH METHODS
Rider University s Iorio Faculty Research Prize in 2015. She has enjoyed teaching research methods to and conducting research with undergradu-ates for over 20 years. Dr. Heath has also shown her support for student research by creating the American Psychology-Law Society Award for the Best Undergraduate Research Paper, awarded annually since 2006.
Writing Research Papers
Here you'll find details on multiple aspects of the research paper writing process, ranging from how the paper should be structured to how to write more effectively. Structure and Format - the critical components of each section of an APA-formatted research paper (Introduction, Methods, and on), as well as how those sections should be ...
Research Paper Structure
A complete research paper in APA style that is reporting on experimental research will typically contain a Title page, Abstract, Introduction, Methods, Results, Discussion, and References sections. 1 Many will also contain Figures and Tables and some will have an Appendix or Appendices. These sections are detailed as follows (for a more in ...
APA
APA 7th guidelines: Treat the authors, date, and title as before (with the title in italics). APA 7th requires an edition number after the title e.g. (4th ed) but this is not in italics. APA 7th no longer requires a "place of publication" - just the name of the publisher. APA 7th - For ebooks only use a DOI if it exists.
AQA A-Level Psychology Past Papers With Answers
The past papers are free to download for you to use as practice for your exams. Paper 1: Introductory Topics. Paper 2: Psychology in Context. Paper 3: Issues and Options. AS Psychology (7181): Paper 1. A-Level Psychology (7182): Paper 1. 72 Marks.
Psych./Neuro. 201 How to Write an APA Style Research Paper
An APA-style paper includes the following sections: title page, abstract, introduction, method, results, discussion, and references. Your paper may also include one or more tables and/or figures. Different types of information about your study are addressed in each of the sections, as described below. General formatting rules are as follows:
PSYC 212: Research Methods in Psychology: Writing Your Papers
Guidance for finding research and research methods in psychology Guidance from APA Style Website. Skip to Main Content. Libraries; Research and Course Guides; PSYC 212: Research Methods in Psychology ... Sample Research Paper. A look at the structure of a research paper. Journal Article Reporting Standards.
Research Methods in Psychology
Download this book. While Research Methods in Psychology is fairly traditional— making it easy for you to use with your existing courses — it also emphasizes a fundamental idea that is often lost on undergraduates: research methods are not a peripheral concern in our discipline; they are central. For questions about this textbook please ...
Free APA Journal Articles
Recently published articles from subdisciplines of psychology covered by more than 90 APA Journals™ publications. For additional free resources (such as article summaries, podcasts, and more), please visit the Highlights in Psychological Research page. Browse and read free articles from APA Journals across the field of psychology, selected by ...
Past Papers: Research Methods: Aqa A-level Psychology Resources
The participants returned to the psychology department the following week and repeated the test in the other condition. (a) Identify the type of experiment used in this study. (b) Identify the operationalised dependent variable in the study. (2 marks) The results of the study are given in Table 1 below.
Research Methods
Note: Unlike all other sections across the 3 exam papers, research methods is worth 48 marks instead of 24. Not only that, the other sections often include a few research methods questions, so this topic is the most important on the syllabus! ... The Journal of Abnormal and Social Psychology, 63(3), pp.575-582.
Research Methods
Paper 2 - Research Methods Paper 2 Research Methods AS/A Level Revision Notes (AQA) BACK TO STUDENT RESOURCES Research Methods Resources Quantitative And Qualitative Data: The Distinction Between Qualitative And Quantitative Data Collection Techniques Paper 2 - Psychology in Context | Research Methods | 30 Minutes Variables In Psychological Research Paper 2 - Psychology in […]
Psychology Journals
A guide to using APA PsycInfo, Google Scholar, determining if a journal is peer-reviewed, and limiting to empirical studies. Includes directions for creating a Zotero account, how to upload citations to Zotero, and how to download citations in APA format. List of high impact psychology journals generated by Journal Citation Reports
A-Level AQA Psychology Questions by Topic
A-Level Psychology past paper questions by topic for AQA. Also offering past papers and videos for Edexcel and OCR. ... Research Methods. Topic Mark Scheme; Case Studies: Mark Scheme: Content Analysis: Mark Scheme: Correlations: Mark Scheme: Experimental Methods: Mark Scheme: Interferential Testing: Mark Scheme: Observation Techniques: Mark ...

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Participant or subject characteristics

Sampling procedures

Sample size and power

Primary and secondary measures

Quality of measurements

Data collection methods and research design

Data diagnostics

Analytic strategies

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Ch 2: Psychological Research Methods

Introduction to the Scientific Method

The Scientific Process

Process of Scientific Research

Basic Principles of the Scientific Method

Applying the Scientific Method

Link to Learning

Ethics in Research

Research Involving Human Participants

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Research Involving Animal Subjects

Introduction to Approaches to Research

Watch It: More on Research

Descriptive Research

Naturalistic Observation

Case Studies

Think It Over

Longitudinal and Cross-Sectional Research

Correlational Research

Correlation Does Not Indicate Causation

Illusory Correlations

Experiments

Designing an Experiment

Independent and Dependent Variables

Selecting and Assigning Experimental Participants

Issues to Consider

Introduction to Statistical Thinking

Interpreting Experimental Findings

Reporting Research

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Reliability and Validity

Statistical Significance

Distributional Thinking

Finding Significance in Data

Drawing Conclusions from Statistics

Cause and Effect

How to Read Research

Putting It Together: Psychological Research

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Writing Research Papers

Major Sections of a Research Paper in APA Style

Introduction

Tables and Figures

Variations of Research Papers in APA Style

Departures from APA Style