three types of research studies

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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 3, 2023 3:14 PM
• URL: https://guides.lib.berkeley.edu/researchmethods

Research Study Types

There are many different types of research studies, and each has distinct strengths and weaknesses. In general, randomized trials and cohort studies provide the best information when looking at the link between a certain factor (like diet) and a health outcome (like heart disease).

Laboratory and Animal Studies

These are studies done in laboratories on cells, tissue, or animals.

• Strengths: Laboratories provide strictly controlled conditions and are often the genesis of scientific ideas that go on to have a broad impact on human health. They can help understand the mechanisms of disease.
• Weaknesses: Laboratory and animal studies are only a starting point. Animals or cells are not a substitute for humans.

Cross-Sectional Surveys

These studies examine the incidence of a certain outcome (disease or other health characteristic) in a specific group of people at one point in time. Surveys are often sent to participants to gather data about the outcome of interest.

• Strengths: Inexpensive and easy to perform.
• Weaknesses: Can only establish an association in that one specific time period.

Case-Control Studies

These studies look at the characteristics of one group of people who already have a certain health outcome (the cases) and compare them with a similar group of people who do not have the outcome (the controls). An example may be looking at a group of people with heart disease and another group without heart disease who are similar in age, sex, and economic status, and comparing their intakes of fruits and vegetables to see if this exposure could be associated with heart disease risk.

• Strengths: Case-control studies can be done quickly and relatively cheaply.
• Weaknesses: Not ideal for studying diet because they gather information from the past, which can be difficult for most people to recall accurately. Furthermore, people with illnesses often recall past behaviors differently from those without illness. This opens such studies to potential inaccuracy and bias in the information they gather.

Cohort Studies

These are observational studies that follow large groups of people over a long period of time, years or even decades, to find associations of an exposure(s) with disease outcomes. Researchers regularly gather information from the people in the study on several variables (like meat intake, physical activity level, and weight). Once a specified amount of time has elapsed, the characteristics of people in the group are compared to test specific hypotheses (such as a link between high versus low intake of carotenoid-rich foods and glaucoma, or high versus low meat intake and prostate cancer).

• Strengths: Participants are not required to change their diets or lifestyle as may be with randomized controlled studies. Study sizes may be larger than other study types. They generally provide more reliable information than case-control studies because they don’t rely on information from the past. Cohort studies gather information from participants at the beginning and throughout the study, long before they may develop the disease being studied. As a group, many of these types of studies have provided valuable information about the link between lifestyle factors and disease.
• Weaknesses: A longer duration of following participants make these studies time-consuming and expensive. Results cannot suggest cause-and-effect, only associations. Evaluation of dietary intake is self-reported.

Two of the largest and longest-running cohort studies of diet are the Harvard-based Nurses’ Health Study and the Health Professionals Follow-up Study.

If you follow nutrition news, chances are you have come across findings from a cohort called the Nurses’ Health Study . The Nurses’ Health Study (NHS) began in 1976, spearheaded by researchers from the Channing Laboratory at the Brigham and Women’s Hospital, Harvard Medical School, and the Harvard T.H. Chan School of Public Health, with funding from the National Institutes of Health. It gathered registered nurses ages 30-55 years from across the U.S. to respond to a series of questionnaires. Nurses were specifically chosen because of their ability to complete the health-related, often very technical, questionnaires thoroughly and accurately. They showed motivation to participate in the long-term study that required ongoing questionnaires every two years. Furthermore, the group provided blood, urine, and other samples over the course of the study.

The NHS is a prospective cohort study, meaning a group of people who are followed forward in time to examine lifestyle habits or other characteristics to see if they develop a disease, death, or some other indicated outcome. In comparison, a retrospective cohort study would specify a disease or outcome and look back in time at the group to see if there were common factors leading to the disease or outcome. A benefit of prospective studies over retrospective studies is greater accuracy in reporting details, such as food intake, that is not distorted by the diagnosis of illness.

To date, there are three NHS cohorts: NHS original cohort, NHS II, and NHS 3. Below are some features unique to each cohort.

NHS – Original Cohort

• Started in 1976 by Frank Speizer, M.D.
• Participants: 121,700 married women, ages 30 to 55 in 1976.
• Outcomes studied: Impact of contraceptive methods and smoking on breast cancer; later this was expanded to observe other lifestyle factors and behaviors in relation to 30 diseases.
• A food frequency questionnaire was added in 1980 to collect information on dietary intake, and continues to be collected every four years.
• Started in 1989 by Walter Willett, M.D., M.P.H., Dr.P.H., and colleagues.
• Participants: 116,430 single and married women, ages 25 to 42 in 1989.
• Outcomes studied: Impact on women’s health of oral contraceptives initiated during adolescence, diet and physical activity in adolescence, and lifestyle risk factors in a younger population than the NHS Original Cohort. The wide range of diseases examined in the original NHS is now also being studied in NHSII.
• The first food frequency questionnaire was collected in 1991, and is collected every four years.
• Started in 2010 by Jorge Chavarro, M.D., Sc.M., Sc.D, Walter Willett, M.D., M.P.H., Dr.P.H., Janet Rich-Edwards, Sc.D., M.P.H, and Stacey Missmer, Sc.D.
• Participants: Expanded to include not just registered nurses but licensed practical nurses (LPN) and licensed vocational nurses (LVN), ages 19 to 46. Enrollment is currently open.
• Inclusion of more diverse population of nurses, including male nurses and nurses from Canada.
• Outcomes studied: Dietary patterns, lifestyle, environment, and nursing occupational exposures that may impact men’s and women’s health; the impact of new hormone preparations and fertility/pregnancy on women’s health; relationship of diet in adolescence on breast cancer risk.

From these three cohorts, extensive research has been published regarding the association of diet, smoking, physical activity levels, overweight and obesity, oral contraceptive use, hormone therapy, endogenous hormones, dietary factors, sleep, genetics, and other behaviors and characteristics with various diseases. In 2016, in celebration of the 40 th  Anniversary of NHS, the  American Journal of Public Health’s  September issue  was dedicated to featuring the many contributions of the Nurses’ Health Studies to public health.

Growing Up Today Study (GUTS)

In 1996, recruitment began for a new cross-generational cohort called  GUTS (Growing Up Today Study) —children of nurses from the NHS II. GUTS is composed of 27,802 girls and boys who were between the ages of 9 and 17 at the time of enrollment. As the entire cohort has entered adulthood, they complete annual questionnaires including information on dietary intake, weight changes, exercise level, substance and alcohol use, body image, and environmental factors. Researchers are looking at conditions more common in young adults such as asthma, skin cancer, eating disorders, and sports injuries.

Randomized Trials

Like cohort studies, these studies follow a group of people over time. However, with randomized trials, the researchers intervene with a specific behavior change or treatment (such as following a specific diet or taking a supplement) to see how it affects a health outcome. They are called “randomized trials” because people in the study are randomly assigned to either receive or not receive the intervention. This randomization helps researchers determine the true effect the intervention has on the health outcome. Those who do not receive the intervention or labelled the “control group,” which means these participants do not change their behavior, or if the study is examining the effects of a vitamin supplement, the control group participants receive a placebo supplement that contains no active ingredients.

• Strengths: Considered the “gold standard” and best for determining the effectiveness of an intervention (e.g., dietary pattern, supplement) on an endpoint such as cancer or heart disease. Conducted in a highly controlled setting with limited variables that could affect the outcome. They determine cause-and-effect relationships.
• Weaknesses: High cost, potentially low long-term compliance with prescribed diets, and possible ethical issues. Due to expense, the study size may be small.

Meta-Analyses and Systematic Reviews

A meta-analysis collects data from several previous studies on one topic to analyze and combine the results using statistical methods to provide a summary conclusion. Meta-analyses are usually conducted using randomized controlled trials and cohort studies that have higher quality of evidence than other designs. A systematic review also examines past literature related to a specific topic and design, analyzing the quality of studies and results but may not pool the data. Sometimes a systematic review is followed by conducting a meta-analysis if the quality of the studies is good and the data can be combined.

• Strengths: Inexpensive and provides a general comprehensive summary of existing research on a topic. This can create an explanation or assumption to be used for further investigation.
• Weaknesses: Prone to selection bias, as the authors can choose or exclude certain studies, which can change the resulting outcome. Combining data that includes lower-quality studies can also skew the results.

A primer on systematic review and meta-analysis in diabetes research

Terms of use.

The contents of this website are for educational purposes and are not intended to offer personal medical advice. You should seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read on this website. The Nutrition Source does not recommend or endorse any products.

Scientific Research and Methodology : An introduction to quantitative research and statistics

3 types of study designs.

You have learnt how to ask a RQ. In this chapter , you will learn to:

• identify and describe the types of quantitative research studies.
• compare and contrast experimental and observational studies.
• describe and identify the directionality in observational studies.
• describe and identify true experimental and quasi-experimental studies.
• explain external and internal validity.

3.1 Introduction

Chapter  2 introduced four types of research questions: descriptive, relational, repeated-measures and correlational. This chapter discusses the types of research studies needed to answer descriptive, relational and repeated-measures RQs, while Chaps.  4 to  10 discuss the details of designing these studies and collecting the data. Many of the ideas also apply to correlational studies.

3.2 Three types of study designs

The RQ implies what data must be collected from the individuals in the study (the response and explanatory variables)... but the data can be collected in many different ways. Different types of studies are used to answer different types of RQs:

• descriptive studies (Sect.  3.3 ) answer descriptive RQs;
• observational studies (Sect.  3.4 ) answer RQs with a comparison, that do not have an intervention; and
• experimental studies (Sect.  3.5 ) answer RQs with a comparison, that do have an intervention.

Observational and experimental studies are sometimes collectively called analytical studies .

3.3 Descriptive studies

Descriptive studies answer descriptive RQs (Fig.  3.1 ).

Definition 3.1 (Descriptive study) Descriptive studies answer descriptive research questions, and do not study relationships between variables.

FIGURE 3.1: A descriptive study, used to answer a descriptive RQ

Example 3.1 (Descriptive study) A study in Hong Kong determined the percentage of people wearing face-masks under different circumstances ( L. Y. Lee et al. 2020 ) . This is a descriptive study, where the population is 'residents of Hong Kong', and the outcome is (for example) 'the percentage who wear face masks when taking care of family members with fever'.

We do not explicitly discuss descriptive studies further, as the necessary ideas are present in the discussion of observational and experimental studies.

3.4 Observational studies

Observational studies (Fig.  3.2 ) answer relational and repeated-measures RQs without an intervention. They are commonly-used, and sometimes are the only study design possible.

Definition 3.2 (Observational study) Observational studies answer research questions with a comparison, but without an intervention.

FIGURE 3.2: An observational study

Definition 3.3 (Condition) Conditions : The conditions are the values of the comparison that those in the observational study experience, but are not imposed by the researchers.

Example 3.2 (Observational study) Consider again this one-tailed, decision-making RQ (see Sect.  2.8 ):

Among Australian teens with a common cold, is the average duration of cold symptoms shorter for teens taking a daily dose of echinacea compared to teens taking no medication?

This RQ has a between-individuals comparison, so is a relational RQ. If the researchers do not impose the taking of echinacea (that is, the individuals make this decision themselves), the study is observational. The two conditions are 'taking echinacea', and 'not taking echinacea' (Fig.  3.3 ).

FIGURE 3.3: Observational studies with a between-individuals comparison. The dashed lines indicate steps not under the control of the researchers

Example 3.3 (Within-individuals relational study) D. A. Levitsky, Halbmaier, and Mrdjenovic ( 2004 ) conducted a study where the weights of university students were recorded both at the beginning university, and then after \(12\) weeks. The comparison is within individuals, and the study is a repeated measures (paired) study. Since the researchers do not impose anything on the students (they just measure their weight at two time points), there is no intervention (Fig.  3.4 ). The outcome is the average weight. The response variable is the weight of individuals. The within-individuals comparison is the number of weeks after university started ( \(0\) and \(12\) ).

FIGURE 3.4: Observational studies with a within-individuals comparison

3.5 Experimental studies

Experimental studies (Fig.  3.5 ), or experiments , are used to study relationships with an intervention , and are commonly-used. Well-designed experimental studies can establish a cause-and-effect relationship between the response and explanatory variables. However, using experimental studies is not always possible.

Definition 3.4 (Experiment) Experimental studies (or experiments ) answer RQs answer research questions with a comparison with an intervention.

Definition 3.5 (Treatments) The treatments are the values of the comparison that the researchers impose upon the individuals in the experimental study.

In an experimental study , the unit of analysis (Def.  2.14 ) is the smallest collection of units of observations that can be randomly allocated to separate treatments.

FIGURE 3.5: An experimental study

Example 3.4 (Within-individuals experimental study) Consider this estimation RQ:

For obese men over \(60\) , what is the average increase in heart rate after walking \(400\)  m?

This RQ uses a within-individuals comparison (before and after walking \(400\)  m) so is a repeated-measures (and paired) RQ. There is an intervention if researchers impose the \(400\)  m walk on the subjects. The outcome is the average heart rate. The response variable is the heart rate for each individual man.

FIGURE 3.6: Experimental studies with a within-individuals comparison

Between -individuals experimental studies can be either true experiments (Sect.  3.5.1 or quasi-experiments (Sect.  3.5.2 ); see Table  3.1 .

3.5.1 True experimental studies

True experiments are commonly used to answer relational RQs (with a between-individuals comparison), but are not always possible. An example of a true experiment is a randomised controlled trial , often used in drug trials.

Definition 3.6 (True experiment) In a true experiment , the researchers:

• allocate treatments to groups of individuals (i.e., determine the values of the explanatory for the individuals), and
• determine who or what individuals are in those groups.

While these may not happen in these explicit steps, they can happen conceptually .

Example 3.5 (True experiment) The echinacea study (Sect.  2.7 ) could be designed as a true experiment . The researchers would allocate individuals to one of two groups, and then decide which group took echinacea and which group did not (Fig.  3.7 ).

These steps may happen implicitly: researchers may allocate each person at random to one of the two groups (echinacea; no echinacea). This is still a true experiment, since the researchers could decide to switch which group receives echinacea; ultimately, the decision is still made by the researchers.

FIGURE 3.7: True experimental studies: researchers allocate individuals to groups, and treatments to groups

3.5.2 Quasi-experimental studies

Quasi-experiments are similar to true experiments (i.e., answer relational RQs) where treatments are allocated to groups that already exist (e.g., may be naturally occurring).

Definition 3.7 (Quasi-experiment) In a quasi-experiment , the researchers:

• allocate treatments to groups of individuals (i.e., allocate the values of the explanatory variable to the individuals), but
• do not determine who or what individuals are in those groups.

Example 3.6 (Quasi-experiments) The echinacea study (Sect.  2.7 ) could be designed as a quasi-experiment. The researchers could find (not create ) two existing groups of people (say, from Suburbs A and B), then decide to allocate people in Suburb A to take echinacea, and people in Suburb B to not take echinacea (Fig.  3.8 ).

FIGURE 3.8: Quasi-experimental studies: researchers do not allocate individuals to groups, but do allocate treatments to groups. The dashed lines indicate steps not under the control of the researchers.

Example 3.7 (Quasi-experiments) A researcher wants to examine the effect of an alcohol awareness program ( M. MacDonald 2008 ) on the average amount of alcohol consumed per student in a university Orientation Week. She runs the program at University A only, then compares the average amount of alcohol consumed per person at two universities (A and B).

This study is a quasi-experiment since the researcher did not (and can not) determine the groups: the students (not the researcher) would have chosen University A or University B for many reasons. However, the researcher did decide whether to allocate the program to University A or University B.

3.6 Comparing study types

Different RQs require different study designs. In experimental studies, researchers create differences in the values of the explanatory variable through allocation, and then note the effect this has on the values of the response variable. In observational studies, researchers observe differences in the values of the explanatory variable, and observe the values of the response variable.

Importantly, only well-designed true experiments can show cause-and-effect . Nonetheless, well-designed observational and quasi-experimental studies can provide evidence to support cause-and-effect conclusions, especially when supported by other evidence. Although only experimental studies can show cause-and-effect, experimental studies are often not possible for ethical, financial, practical or logistical reasons.

The advantages and disadvantages of each study type are discussed later (Sect.  9.2 ), after these study types are discussed in greater detail in the following chapters.

Example 3.8 (Cause and effect) Many studies report that the bacteria in the gut of people on the autism spectrum is different than the bacteria in the gut of people not on the autism spectrum ( Kang et al. ( 2019 ) , Ho et al. ( 2020 ) ), and suggest the bacteria may contribute whether a person is autistic. These studies were observational, so the suggestion of a cause-and-effect relationship may be inaccurate .

Other studies ( Yap et al. 2021 ) suggest that people on the autism spectrum are more likely to be 'picky eaters', which contributes to the differences in gut bacteria.

The animation below compares observational, quasi-experimental and true experimental designs.

FIGURE 3.9: The three main study designs

3.7 Directionality

Analytical research studies (observational; experimental) can be classified by their directionality (Table  3.2 ):

• Forward direction (Sect.  3.7.1 ): The values of the explanatory variable are obtained, and the study determines what values of the response variable occur in the future. All experimental studies have a forward direction.
• Backward direction (Sect.  3.7.2 ): The values of the response variable are obtained, then the study determines what values of the explanatory variable occurred in the past.
• No direction (Sect.  3.7.3 ): The values of the response and explanatory variables are obtained at the same time.

Directionality is important for understanding cause-and-effect relationships. If the comparison occurs before the outcome is observed, a cause-and-effect relationship may be possible. That is, studies with a forward direction are more likely to provide evidence of causality.

Example 3.9 (Directionality) In South Australia in 1988--1989, \(25\) cases of legionella infections (an unusually high number) were investigated ( O’Connor et al. 2007 ) . All \(25\) cases were gardeners.

Researchers compared \(25\) people with legionella infections with \(75\) similar people without the infection. The use of potting mix in the previous four weeks was associated with an increase in the risk of contracting illness of about \(4.7\) times.

This study has a backward direction : people were identified with an infection, and then the researchers looked back at past activities.

Research studies are sometimes described as 'prospective' or 'retrospective', but these terms can be misleading ( Ranganathan and Aggarwal 2018 ) and their use not recommended ( Vandenbroucke et al. 2014 ) .

Experimental studies always have a forward direction. Observational studies may have any directionality, and are sometimes given different names accordingly.

3.7.1 Forward-directional studies

All experimental studies have a forward direction, and include randomised controlled trials (RCTs) and clinical trials .

Observational studies with a forward direction are often called cohort studies . Both experimental studies and cohort studies can be expensive and tricky: tracking a group of individuals (a cohort ) into the future is not always easy, and the ability to track some individuals into the future may be lost ( drop outs ) as (for example) plants or animals die, or people move or decide to no longer participate, etc. Forward-directional observational studies:

• may add support to cause-and-effect conclusions, since the comparison occurs before the outcome (only well-designed experimental studies can establish cause-and-effect).
• can examine many different outcomes in one study, since the outcome(s) occur in the future.
• can be problematic for rare outcomes, as the outcome of interest may not appear (or may appear rarely) in the future.

Example 3.10 (Forward study) Chih et al. ( 2018 ) studied dogs and cats who had been recommended to receive intermittent nasogastric tube (NGT) aspiration for up to \(36\)  hrs. Some pet owners did not give permission for NGT, while some did; thus, whether the animal received NGT was not determined by the researchers (so this study is observational). The researchers then observed whether the animals developed hypochloremic metabolic alkalosis (HCMA) in the next \(36\)  hrs.

Since the explanatory variable (whether NGT was used or not) was recorded at the start of the study, and the response variable (whether HCMA was observed or not) was determined within the following \(36\)  hrs, this study has a forward direction .

3.7.2 Backward-directional studies

Observational studies with a backward direction are often called case-control studies. Researchers find individuals with specific values of the response variable (the cases and the controls), and determine values of the explanatory variable from the past. Case-control studies:

• only allow one outcome to be studied, since individuals are chosen to be in the study based on the value of the response variable of interest.
• are useful for rare outcomes: the researchers can purposely select large numbers with the rare outcome of interest.
• do not effectively eliminate other explanations for the relationship between the response and explanatory variables (called confounding ; Def.  6.5 ).
• may suffer from selection bias (Sect.  5.10 ), as researchers try to locate individuals with a rare outcome.
• may suffer from recall bias (Sect.  10.2.2 ) when the individuals are people: accurately recalling the past can be unreliable.

Example 3.11 (Backwards study) A study ( Pamphlett 2012 ) examined patients with and without sporadic motor neurone disease (SMND), and asked about past exposure to metals.

The response variable (whether or not the respondent had SMND) is assessed when the study begins, and whether or not they had exposure to metals (explanatory variable) is determined from the past . This observational study has a backward direction.

3.7.3 Non-directional studies

Non-directional observational studies are called cross-sectional studies. Cross-sectional studies:

• are good for findings associations between variables (which may or may not be causation).
• are generally quicker and cheaper than other types of studies.
• are not useful for studying rare outcomes.

Example 3.12 (Non-directional study) A study ( J. Russell et al. 2014 ) asked older Australian their opinions of their own food security, and recorded their living arrangements. Individuals' responses to both both the response variable and explanatory variable were gathered at the same time. This observational study is non-directional .

3.8 Internal validity

Well-designed studies allow the researchers to focus on the relationship of interest, and to eliminate other possible explanations for changes in the value of the response variable apart from this relationship. A study which does this is said to have good internally validity .

Definition 3.8 (Internal validity) Internally validity refers to the extent to which a cause-and-effect relationship can be established in a study, that cannot be otherwise explained.

A study with high internal validity shows that the changes in the response variable can be attributed to changes in the explanatory variables; other explanations have been ruled out.

Studies with high internal validity show that changes in the response variable can confidently be related to changes in the explanatory variable in the group that was studied ; the possibility of other explanations has been minimised.

In contrast, studies with low internal validity leave open other possibilities, apart from changes in value of the explanatory variable, to explain changes in the value of the response variable. Experimental studies usually have higher internal validity than observational studies.

Ideally, all studies should be designed to be internally valid as far as possible. For this reason, internal validity is studied in more detail in Chaps.  7 (for experimental studies) and 8 (for observational studies).

Example 3.13 (Low internal validity) In a review of studies that used double-fortified salt to manage iodine and iron deficiencies ( L. M. Larson et al. 2021 ) , one conclusion was (p. 265):

Internal validity of the efficacy trials was generally weak [...] because of issues around selection bias, unaccounted confounders, and participant withdrawals.

One of many potential threats to internal validity is that the groups being compared are initially different; for example, the group receiving echinacea is younger (on average) than the group receiving no medication. This is a form of confounding (Def.  6.5 ).

The comparison groups are often compared at the start of the study: the groups being compared should be as similar as possible, so that any differences in the outcome cannot be attributed to pre-existing difference in the groups.

Example 3.14 (Baseline characteristics) In a study of treating depression in adults ( Danielsson et al. 2014 ) , three treatments were compared: exercise, basic body awareness therapy, or advice.

If any differences in the outcomes of patients receiving the different treatments were found, the researchers need to be confident that the differences were due to the treatment. For this reason, the three groups were compared to ensure the groups were similar in terms of average ages, percentage of women, taking of anti-depressants, and many other aspects.

An internally valid study requires studies to be carefully designed, as discussed in Chaps.  7 and  8 . In general, well-designed experimental studies are more likely to be internally valid than observational studies.

3.9 External validity

Apart from being internally valid, the conclusions from the study of a sample should apply to the intended population . This is called external validity .

A study is externally valid if the results of the study are likely to generalise to the rest of the population , beyond just those studied in the sample. To be externally valid, a study first needs to be internally valid, since the results must at least be sound for the group under study before being extended to other members of the population.

Using a random sample helps ensure external validity. In addition, the use of inclusion and exclusion criteria (Sect.  2.2.1 ) helps clarify to whom or what the results may apply.

Definition 3.9 (External validity) External validity refers to the ability to generalise the results to the rest of the population, beyond just those in the sample studied. For a study to be truly externally valid, the sample must be a random sample (Chap.  5 ) from the population.

External validity does not mean that the results apply more widely than the intended population.

Example 3.15 (External validity) Suppose the population in a study is Californian university students . The sample comprises the Californian university students actually studied by the researchers. The study is externally valid if the sample is a random sample from the population of all Californian university students.

The results will not necessarily apply to university students outside of Californian (though they may), or all Californian residents. However, this is irrelevant for external validity . External validity concerns how the sample represents the intended population in the RQ, which is Californian university students . The study is not concerned with all Californian residents, or with non-Californian university students.

3.10 The importance of design

Choosing the type of study is only one part of research design. Planning the data collection process, and actually collecting the data, is still required. Sometimes, data may be already available (called secondary data ), and sometimes it may need collecting (called primary data ).

Either way, how the data are obtained is important. The design phase is concerned with planning the best approach to obtaining the data, to ensure the study is internally and externally valid, as far as possible.

Designing a study to maximise internal validity means:

• identifying what else might influence the values of the response variable, apart from the explanatory variable (Chap.  6 ); and
• designing the study to be effective (Chaps.  7 and  8 ).

Designing a study to maximise external validity means:

• identifying who or what to study, since the whole population cannot be studied (Chap.  5 ); and
• determining how many individuals to study. (We need to learn more before we can answer this critical question in Chap.  30 .)

The details of how the data will be collected (Chap.  10 ) and ethical issues (Chap.  4 ) must also be considered. Furthermore, the limitations of the study must be communicated (Chap.  9 ).

The following short (humourous) video demonstrates the importance of understanding the design!

3.11 Chapter summary

Three types of research studies are: Descriptive studies (for descriptive RQs), observational studies (for relational or repeated-measures RQs without an intervention), and experimental (for relational or repeated-measures RQs with an intervention).

Observational studies can be classified as having a forward direction (cohort studies), backward direction (case-control studies), or no direction (cross-sectional studies). Experimental studies always have a forward direction. Relational RQs with an intervention can be classified as true experiments or quasi-experiments . Cause-and-effect conclusions can only be made from well-designed true experiments .

Ideally studies should be designed to be internally and externally valid. In general, experimental studies have better internal validity than observational studies.

The following short videos may help explain some of these concepts:

3.12 Quick review questions

• A study ( Fraboni et al. 2018 ) examined the 'red-light running behaviour of cyclists in Italy'. This study is most likely to be: an observational study a quasi-experimental study an experimental study
• When the results of studying a sample apply to the wider population of interest, the study is called: internally valid externally valid
• In a quasi-experiment, the researchers allocate treatments to groups that they cannot manipulate. True or false? TRUE FALSE
• What is the difference between an true experiment and a quasi-experiment?

Which of the following are true?

• True experiments generally have a higher internal validity than observational studies. TRUE FALSE
• Internal validity refers to the strength of the inferences made from the study. TRUE FALSE
• External validity refers to the ability to generalise the results to other groups apart from those studied. TRUE FALSE
• Inclusion and exclusion criteria can be used to clarify the internal validity. TRUE FALSE
• Observational studies have a higher external validity than experimental studies. TRUE FALSE

3.13 Exercises

Selected answers are available in App.  E .

Exercise 3.1 Consider this RQ ( McLinn et al. 1994 ) :

In children with acute otitis media, what is the difference in the average duration of symptoms when treated with cefuroxime compared to amoxicillin?

• Is the comparison a within- or between-individuals comparison?
• Is this RQ relational, repeated-measures or correlational?
• Is there likely an intervention?
• Is the RQ an estimation or decision-making RQ?
• Is the study observational or experimental? If observational, what is the direction ? If experiment, is this a quasi-experiment or true experiment?

Exercise 3.2 A study ( Khair et al. 2015 ) studied the time needed for organic waste to turn into compost. For some batches of compost, earthworms were added. In other batches, earthworms were not added to the waste.

One RQs asked whether the composting times for waste with and without earthworms was the same or not.

• Is there an intervention?

Exercise 3.3 In a study on the shear strength of recycled concrete beams ( Gonzalez-Fonteboa and Martinez-Abella 2007 ) , beams were divided into three groups. Different loads were then applied to each group, and the shear strength needed to fracture the beams was measured. Is this a quasi-experiment or a true experiment ? Explain.

Exercise 3.4 A research study compared the use of two different education programs to reduce the percentage of patients experiencing ventilator-associated pneumonia (VAP). Paramedics from two cities were chosen to participate. Paramedics in City A were chosen to receive Program 1, and paramedics in the other city to receive Program 2.

Exercise 3.5 A nursing study aimed to compare 'the effectiveness of alternating pressure air mattresses vs. overlays, to prevent pressure ulcers' ( Manzano et al. ( 2013 ) , p. 2099). Patients were provided with either alternating pressure air overlays (in 2001) or alternating pressure air mattresses (in 2006). The number of pressure ulcers were recorded.

This study is experimental, because the researchers provided the mattresses. Is this a true experiment or quasi -experiment? Explain.

Exercise 3.6 Consider this initial RQ (based on Friedmann and Thomas ( 1985 ) ), that clearly requires a lot of refining: 'Are people with pets healthier?' To answer this RQ:

• Briefly describe useful and practical definitions for P, O and C.
• Briefly describe an experimental study to answer the RQ.
• Briefly describe an observational study to answer the RQ.

Exercise 3.7 Consider this journal article extract ( Sacks et al. ( 2009 ) , p. 859):

We randomly assigned \(811\) overweight adults to one of four diets [...] The diets consisted of similar foods and met guidelines for cardiovascular health [...] The primary outcome was the change in body weight after \(2\) years in [...] comparisons of low fat versus high fat and average protein versus high protein and in the comparison of highest and lowest carbohydrate content.

• What is the between -individuals comparison?
• What is the within -individuals comparison?
• Is this study observational or experimental? Why?
• Is this study a quasi-experiment or a true experiment? Why?
• What are the units of analysis?
• What are the units of observation?
• What is the response variable?
• What is the explanatory variable?

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Introduction to Research Methods in Psychology

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.

There are several different research methods in psychology , each of which can help researchers learn more about the way people think, feel, and behave. If you're a psychology student or just want to know the types of research in psychology, here are the main ones as well as how they work.

Three Main Types of Research in Psychology

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Psychology research can usually be classified as one of three major types.

1. Causal or Experimental Research

When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change.

An example of this type of research in psychology would be changing the length of a specific mental health treatment and measuring the effect on study participants.

2. Descriptive Research

Descriptive research seeks to depict what already exists in a group or population. Three types of psychology research utilizing this method are:

• Case studies
• Observational studies

An example of this psychology research method would be an opinion poll to determine which presidential candidate people plan to vote for in the next election. Descriptive studies don't try to measure the effect of a variable; they seek only to describe it.

3. Relational or Correlational Research

A study that investigates the connection between two or more variables is considered relational research. The variables compared are generally already present in the group or population.

For example, a study that looks at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference.

Theory vs. Hypothesis in Psychology Research

People often confuse the terms theory and hypothesis or are not quite sure of the distinctions between the two concepts. If you're a psychology student, it's essential to understand what each term means, how they differ, and how they're used in psychology research.

A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted.

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.

While the terms are sometimes used interchangeably in everyday use, the difference between a theory and a hypothesis is important when studying experimental design.

Some other important distinctions to note include:

• A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.

The Effect of Time on Research Methods in Psychology

There are two types of time dimensions that can be used in designing a research study:

• Cross-sectional research takes place at a single point in time. All tests, measures, or variables are administered to participants on one occasion. This type of research seeks to gather data on present conditions instead of looking at the effects of a variable over a period of time.
• Longitudinal research is a study that takes place over a period of time. Data is first collected at the beginning of the study, and may then be gathered repeatedly throughout the length of the study. Some longitudinal studies may occur over a short period of time, such as a few days, while others may take place over a period of months, years, or even decades.

The effects of aging are often investigated using longitudinal research.

Causal Relationships Between Psychology Research Variables

What do we mean when we talk about a “relationship” between variables? In psychological research, we're referring to a connection between two or more factors that we can measure or systematically vary.

One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.

A causal relationship is when one variable causes a change in another variable. These types of relationships are investigated by experimental research to determine if changes in one variable actually result in changes in another variable.

Correlational Relationships Between Psychology Research Variables

A correlation is the measurement of the relationship between two variables. These variables already occur in the group or population and are not controlled by the experimenter.

• A positive correlation is a direct relationship where, as the amount of one variable increases, the amount of a second variable also increases.
• In a negative correlation , as the amount of one variable goes up, the levels of another variable go down.

In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.

The most important concept is that correlation does not equal causation. Many popular media sources make the mistake of assuming that simply because two variables are related, a causal relationship exists.

Psychologists use descriptive, correlational, and experimental research designs to understand behavior . In:  Introduction to Psychology . Minneapolis, MN: University of Minnesota Libraries Publishing; 2010.

Caruana EJ, Roman M, Herandez-Sanchez J, Solli P. Longitudinal studies . Journal of Thoracic Disease. 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

University of Berkeley. Science at multiple levels . Understanding Science 101 . Published 2012.

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

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Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design  is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is research designed to provide a snapshot of the current state of affairs . Correlational research  is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research  is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median  is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot  is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991).  Multiple regression  is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable  is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship  is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991).  Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978).  Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings.  (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research  (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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1.9: Types of Research Studies and How To Interpret Them

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• Page ID 49296

• Alice Callahan, Heather Leonard, & Tamberly Powell
• Lane Community College via OpenOregon

The field of nutrition is dynamic, and our understanding and practices are always evolving. Nutrition scientists are continuously conducting new research and publishing their findings in peer-reviewed journals. This adds to scientific knowledge, but it’s also of great interest to the public, so nutrition research often shows up in the news and other media sources. You might be interested in nutrition research to inform your own eating habits, or if you work in a health profession, so that you can give evidence-based advice to others. Making sense of science requires that you understand the types of research studies used and their limitations.

The Hierarchy of Nutrition Evidence

Researchers use many different types of study designs depending on the question they are trying to answer, as well as factors such as time, funding, and ethical considerations. The study design affects how we interpret the results and the strength of the evidence as it relates to real-life nutrition decisions. It can be helpful to think about the types of studies within a pyramid representing a hierarchy of evidence, where  the  studies at the bottom of the pyramid usually give us the weakest evidence with the least relevance to real-life nutrition decisions, and the studies at the top offer the strongest evidence, with the most relevance to real-life nutrition  decisions .

The pyramid also represents a few other general ideas. There tend to be more studies published using the methods at the bottom of the pyramid, because they require less time, money, and other resources. When researchers want to test a new hypothesis , they often start with the study designs at the bottom of the pyramid , such as in vitro, animal, or observational studies. Intervention studies are more expensive and resource-intensive, so there are fewer of these types of studies conducted. But they also give us higher quality evidence, so they’re an important next step if observational and non-human studies have shown promising results. Meta-analyses and systematic reviews combine the results of many studies already conducted, so they help researchers summarize scientific knowledge on a topic.

Non-Human Studies: In Vitro & Animal Studies

The simplest form of nutrition research is an in vitro study . In vitro means “within glass,” (although plastic is used more commonly today) and these experiments are conducted within flasks, dishes, plates, and test tubes. These studies are performed on isolated cells or tissue samples, so they’re less expensive and time-intensive than animal or human studies. In vitro studies are vital for zooming in on biological mechanisms, to see how things work at the cellular or molecular level. However, these studies shouldn’t be used to draw conclusions about how things work in humans (or even animals), because we can’t assume that the results will apply to a whole, living organism.

Animal studies are one form of in vivo research, which translates to “within the living.” Rats and mice are the most common animals used in nutrition research. Animals are often used in research that would be unethical to conduct in humans. Another advantage of animal dietary studies is that researchers can control exactly what the animals eat. In human studies, researchers can tell subjects what to eat and even provide them with the food, but they may not stick to the planned diet. People are also not very good at estimating, recording, or reporting what they eat and in what quantities. In addition, animal studies typically do not cost as much as human studies.

There are some important limitations of animal research. First, an animal’s metabolism and physiology are different from humans. Plus, animal models of disease (cancer, cardiovascular disease, etc.), although similar, are different from human diseases. Animal research is considered preliminary, and while it can be very important to the process of building scientific understanding and informing the types of studies that should be conducted in humans, animal studies shouldn’t be considered relevant to real-life decisions about how people eat.

Observational Studies

Observational studies  in human nutrition collect information on people’s dietary patterns or nutrient intake and look for associations with health outcomes. Observational studies do not give participants a treatment or intervention; instead, they look at what they’re already doing and see how it relates to their health. These types of study designs can only identify  correlations  (relationships) between nutrition and health; they can’t show that one factor  causes  another. (For that, we need intervention studies, which we’ll discuss in a moment.) Observational studies that describe factors correlated with human health are also called  epidemiological studies . 1

One example of a nutrition hypothesis that has been investigated using observational studies is that eating a Mediterranean diet reduces the risk of developing cardiovascular disease. (A Mediterranean diet focuses on whole grains, fruits and vegetables, beans and other legumes, nuts, olive oil, herbs, and spices. It includes small amounts of animal protein (mostly fish), dairy, and red wine. 2 ) There are three main types of observational studies, all of which could be used to test hypotheses about the Mediterranean diet:

• Cohort studies follow a group of people (a cohort) over time, measuring factors such as diet and health outcomes. A cohort study of the Mediterranean diet would ask a group of people to describe their diet, and then researchers would track them over time to see if those eating a Mediterranean diet had a lower incidence of cardiovascular disease.
• Case-control studies compare a group of cases and controls, looking for differences between the two groups that might explain their different health outcomes. For example, researchers might compare a group of people with cardiovascular disease with a group of healthy controls to see whether there were more controls or cases that followed a Mediterranean diet.
• Cross-sectional studies collect information about a population of people at one point in time. For example, a cross-sectional study might compare the dietary patterns of people from different countries to see if diet correlates with the prevalence of cardiovascular disease in the different countries.

Prospective cohort studies, which enroll a cohort and follow them into the future, are usually considered the strongest type of observational study design. Retrospective studies look at what happened in the past, and they’re considered weaker because they rely on people’s memory of what they ate or how they felt in the past. There are several well-known examples of prospective cohort studies that have described important correlations between diet and disease:

• Framingham Heart Study : Beginning in 1948, this study has followed the residents of Framingham, Massachusetts to identify risk factors for heart disease.
• Health Professionals Follow-Up Study : This study started in 1986 and enrolled 51,529 male health professionals (dentists, pharmacists, optometrists, osteopathic physicians, podiatrists, and veterinarians), who complete diet questionnaires every 2 years.
• Nurses Health Studies : Beginning in 1976, these studies have enrolled three large cohorts of nurses with a total of 280,000 participants. Participants have completed detailed questionnaires about diet, other lifestyle factors (smoking and exercise, for example), and health outcomes.

Observational studies have the advantage of allowing researchers to study large groups of people in the real world, looking at the frequency and pattern of health outcomes and identifying factors that correlate with them. But even very large observational studies may not apply to the population as a whole. For example, the Health Professionals Follow-Up Study and the Nurses Health Studies include people with above-average knowledge of health. In many ways, this makes them ideal study subjects, because they may be more motivated to be part of the study and to fill out detailed questionnaires for years. However, the findings of these studies may not apply to people with less baseline knowledge of health.

We’ve already mentioned another important limitation of observational studies—that they can only determine correlation, not causation. A prospective cohort study that finds that people eating a Mediterranean diet have a lower incidence of heart disease can only show that the Mediterranean diet is correlated with lowered risk of heart disease. It can’t show that the Mediterranean diet directly prevents heart disease. Why? There are a huge number of factors that determine health outcomes such as heart disease, and other factors might explain a correlation found in an observational study. For example, people who eat a Mediterranean diet might also be the same kind of people who exercise more, sleep more, have higher income (fish and nuts can be expensive!), or be less stressed. These are called confounding factors ; they’re factors that can affect the outcome in question (i.e., heart disease) and also vary with the factor being studied (i.e., Mediterranean diet).

Intervention Studies

Intervention studies , also sometimes called experimental studies or clinical trials, include some type of treatment or change imposed by the researcher. Examples of interventions in nutrition research include asking participants to change their diet, take a supplement, or change the time of day that they eat. Unlike observational studies, intervention studies can provide evidence of cause and effect , so they are higher in the hierarchy of evidence pyramid.

The gold standard for intervention studies is the randomized controlled trial (RCT) . In an RCT, study subjects are recruited to participate in the study. They are then randomly assigned into one of at least two groups, one of which is a control group (this is what makes the study controlled ). In an RCT to study the effects of the Mediterranean diet on cardiovascular disease development, researchers might ask the control group to follow a low-fat diet (typically recommended for heart disease prevention) and the intervention group to eat a Mediterrean diet. The study would continue for a defined period of time (usually years to study an outcome like heart disease), at which point the researchers would analyze their data to see if more people in the control or Mediterranean diet had heart attacks or strokes. Because the treatment and control groups were randomly assigned, they should be alike in every other way except for diet, so differences in heart disease could be attributed to the diet. This eliminates the problem of confounding factors found in observational research, and it’s why RCTs can provide evidence of causation, not just correlation.

Imagine for a moment what would happen if the two groups weren’t randomly assigned. What if the researchers let study participants choose which diet they’d like to adopt for the study? They might, for whatever reason, end up with more overweight people who smoke and have high blood pressure in the low-fat diet group, and more people who exercised regularly and had already been eating lots of olive oil and nuts for years in the Mediterranean diet group. If they found that the Mediterranean diet group had fewer heart attacks by the end of the study, they would have no way of knowing if this was because of the diet or because of the underlying differences in the groups. In other words, without randomization, their results would be compromised by confounding factors, with many of the same limitations as observational studies.

In an RCT of a supplement, the control group would receive a placebo—a  “fake” treatment that contains no active ingredients, such as a sugar pill. The use of a placebo is necessary in medical research because of a phenomenon known as the placebo effect. The placebo effect results in a beneficial effect because of a subject’s belief in the treatment, even though there is no treatment actually being administered.

Blinding is a technique to prevent bias in intervention studies. In a study without blinding, the subject and the researchers both know what treatment the subject is receiving. This can lead to bias if the subject or researcher have expectations about the treatment working, so these types of trials are used less frequently. It’s best if a study is double-blind , meaning that neither the researcher nor the subject know what treatment the subject is receiving. It’s relatively simple to double-blind a study where subjects are receiving a placebo or treatment pill, because they could be formulated to look and taste the same. In a single-blind study , either the researcher or the subject knows what treatment they’re receiving, but not both. Studies of diets—such as the Mediterranean diet example—often can’t be double-blinded because the study subjects know whether or not they’re eating a lot of olive oil and nuts. However, the researchers who are checking participants’ blood pressure or evaluating their medical records could be blinded to their treatment group, reducing the chance of bias.

Like all studies, RCTs and other intervention studies do have some limitations. They can be difficult to carry on for long periods of time and require that participants remain compliant with the intervention. They’re also costly and often have smaller sample sizes. Furthermore, it is unethical to study certain interventions. (An example of an unethical intervention would be to advise one group of pregnant mothers to drink alcohol to determine its effects on pregnancy outcomes, because we know that alcohol consumption during pregnancy damages the developing fetus.)

VIDEO: “ Not all scientific studies are created equal ” by David H. Schwartz, YouTube (April 28, 2014), 4:26.

Meta-Analyses and Systematic Reviews

At the top of the hierarchy of evidence pyramid are systematic reviews and meta-analyses .  You can think of these as “studies of studies.” They attempt to combine all of the relevant studies that have been conducted on a research question and summarize their overall conclusions. Researchers conducting a  systematic review  formulate a research question and then systematically and independently identify, select, evaluate, and synthesize all high-quality evidence that relates to the research question. Since systematic reviews combine the results of many studies, they help researchers produce more reliable findings. A  meta-analysis  is a type of systematic review that goes one step further, combining the data from multiple studies and using statistics to summarize it, as if creating a mega-study from many smaller studies . 4

However, even systematic reviews and meta-analyses aren’t the final word on scientific questions. For one thing, they’re only as good as the studies that they include. The  Cochrane Collaboration  is an international consortium of researchers who conduct systematic reviews in order to inform evidence-based healthcare, including nutrition, and their reviews are among the most well-regarded and rigorous in science. For the most recent Cochrane review of the Mediterranean diet and cardiovascular disease, two authors independently reviewed studies published on this question. Based on their inclusion criteria, 30 RCTs with a total of 12,461 participants were included in the final analysis. However, after evaluating and combining the data, the authors concluded that “despite the large number of included trials, there is still uncertainty regarding the effects of a Mediterranean‐style diet on cardiovascular disease occurrence and risk factors in people both with and without cardiovascular disease already.” Part of the reason for this uncertainty is that different trials found different results, and the quality of the studies was low to moderate. Some had problems with their randomization procedures, for example, and others were judged to have unreliable data. That doesn’t make them useless, but it adds to the uncertainty about this question, and uncertainty pushes the field forward towards more and better studies. The Cochrane review authors noted that they found seven ongoing trials of the Mediterranean diet, so we can hope that they’ll add more clarity to this question in the future. 5

Science is an ongoing process. It’s often a slow process, and it contains a lot of uncertainty, but it’s our best method of building knowledge of how the world and human life works. Many different types of studies can contribute to scientific knowledge. None are perfect—all have limitations—and a single study is never the final word on a scientific question. Part of what advances science is that researchers are constantly checking each other’s work, asking how it can be improved and what new questions it raises.

Attributions:

• “Chapter 1: The Basics” from Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR , CC BY-NC-SA 4.0
• “ The Broad Role of Nutritional Science ,” section 1.3 from the book An Introduction to Nutrition (v. 1.0), CC BY-NC-SA 3.0

References:

• 1 Thiese, M. S. (2014). Observational and interventional study design types; an overview. Biochemia Medica , 24 (2), 199–210. https://doi.org/10.11613/BM.2014.022
• 2 Harvard T.H. Chan School of Public Health. (2018, January 16). Diet Review: Mediterranean Diet . The Nutrition Source. https://www.hsph.harvard.edu/nutritionsource/healthy-weight/diet-reviews/mediterranean-diet/
• 3 Ross, R., Gray, C. M., & Gill, J. M. R. (2015). Effects of an Injected Placebo on Endurance Running Performance. Medicine and Science in Sports and Exercise , 47 (8), 1672–1681. https://doi.org/10.1249/MSS.0000000000000584
• 4 Hooper, A. (n.d.). LibGuides: Systematic Review Resources: Systematic Reviews vs Other Types of Reviews . Retrieved February 7, 2020, from //libguides.sph.uth.tmc.edu/c.php?g=543382&p=5370369
• 5 Rees, K., Takeda, A., Martin, N., Ellis, L., Wijesekara, D., Vepa, A., Das, A., Hartley, L., & Stranges, S. (2019). Mediterranean‐style diet for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews , 3 . doi.org/10.1002/14651858.CD009825.pub3
• Figure 2.3. The hierarchy of evidence by Alice Callahan, is licensed under CC BY 4.0
• Research lab photo by National Cancer Institute on Unsplas h ; mouse photo by vaun0815 on Unsplash
• Figure 2.4. “Placebo effect example” by Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR

Research Methods In Psychology

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.

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.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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• Volume 24, Issue 2
• Five tips for developing useful literature summary tables for writing review articles
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• http://orcid.org/0000-0003-0157-5319 Ahtisham Younas 1 , 2 ,
• http://orcid.org/0000-0002-7839-8130 Parveen Ali 3 , 4
• 1 Memorial University of Newfoundland , St John's , Newfoundland , Canada
• 2 Swat College of Nursing , Pakistan
• 3 School of Nursing and Midwifery , University of Sheffield , Sheffield , South Yorkshire , UK
• 4 Sheffield University Interpersonal Violence Research Group , Sheffield University , Sheffield , UK
• Correspondence to Ahtisham Younas, Memorial University of Newfoundland, St John's, NL A1C 5C4, Canada; ay6133{at}mun.ca

https://doi.org/10.1136/ebnurs-2021-103417

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Introduction

Literature reviews offer a critical synthesis of empirical and theoretical literature to assess the strength of evidence, develop guidelines for practice and policymaking, and identify areas for future research. 1 It is often essential and usually the first task in any research endeavour, particularly in masters or doctoral level education. For effective data extraction and rigorous synthesis in reviews, the use of literature summary tables is of utmost importance. A literature summary table provides a synopsis of an included article. It succinctly presents its purpose, methods, findings and other relevant information pertinent to the review. The aim of developing these literature summary tables is to provide the reader with the information at one glance. Since there are multiple types of reviews (eg, systematic, integrative, scoping, critical and mixed methods) with distinct purposes and techniques, 2 there could be various approaches for developing literature summary tables making it a complex task specialty for the novice researchers or reviewers. Here, we offer five tips for authors of the review articles, relevant to all types of reviews, for creating useful and relevant literature summary tables. We also provide examples from our published reviews to illustrate how useful literature summary tables can be developed and what sort of information should be provided.

Tip 1: provide detailed information about frameworks and methods

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Tabular literature summaries from a scoping review. Source: Rasheed et al . 3

The provision of information about conceptual and theoretical frameworks and methods is useful for several reasons. First, in quantitative (reviews synthesising the results of quantitative studies) and mixed reviews (reviews synthesising the results of both qualitative and quantitative studies to address a mixed review question), it allows the readers to assess the congruence of the core findings and methods with the adapted framework and tested assumptions. In qualitative reviews (reviews synthesising results of qualitative studies), this information is beneficial for readers to recognise the underlying philosophical and paradigmatic stance of the authors of the included articles. For example, imagine the authors of an article, included in a review, used phenomenological inquiry for their research. In that case, the review authors and the readers of the review need to know what kind of (transcendental or hermeneutic) philosophical stance guided the inquiry. Review authors should, therefore, include the philosophical stance in their literature summary for the particular article. Second, information about frameworks and methods enables review authors and readers to judge the quality of the research, which allows for discerning the strengths and limitations of the article. For example, if authors of an included article intended to develop a new scale and test its psychometric properties. To achieve this aim, they used a convenience sample of 150 participants and performed exploratory (EFA) and confirmatory factor analysis (CFA) on the same sample. Such an approach would indicate a flawed methodology because EFA and CFA should not be conducted on the same sample. The review authors must include this information in their summary table. Omitting this information from a summary could lead to the inclusion of a flawed article in the review, thereby jeopardising the review’s rigour.

Tip 2: include strengths and limitations for each article

Critical appraisal of individual articles included in a review is crucial for increasing the rigour of the review. Despite using various templates for critical appraisal, authors often do not provide detailed information about each reviewed article’s strengths and limitations. Merely noting the quality score based on standardised critical appraisal templates is not adequate because the readers should be able to identify the reasons for assigning a weak or moderate rating. Many recent critical appraisal checklists (eg, Mixed Methods Appraisal Tool) discourage review authors from assigning a quality score and recommend noting the main strengths and limitations of included studies. It is also vital that methodological and conceptual limitations and strengths of the articles included in the review are provided because not all review articles include empirical research papers. Rather some review synthesises the theoretical aspects of articles. Providing information about conceptual limitations is also important for readers to judge the quality of foundations of the research. For example, if you included a mixed-methods study in the review, reporting the methodological and conceptual limitations about ‘integration’ is critical for evaluating the study’s strength. Suppose the authors only collected qualitative and quantitative data and did not state the intent and timing of integration. In that case, the strength of the study is weak. Integration only occurred at the levels of data collection. However, integration may not have occurred at the analysis, interpretation and reporting levels.

Tip 3: write conceptual contribution of each reviewed article

While reading and evaluating review papers, we have observed that many review authors only provide core results of the article included in a review and do not explain the conceptual contribution offered by the included article. We refer to conceptual contribution as a description of how the article’s key results contribute towards the development of potential codes, themes or subthemes, or emerging patterns that are reported as the review findings. For example, the authors of a review article noted that one of the research articles included in their review demonstrated the usefulness of case studies and reflective logs as strategies for fostering compassion in nursing students. The conceptual contribution of this research article could be that experiential learning is one way to teach compassion to nursing students, as supported by case studies and reflective logs. This conceptual contribution of the article should be mentioned in the literature summary table. Delineating each reviewed article’s conceptual contribution is particularly beneficial in qualitative reviews, mixed-methods reviews, and critical reviews that often focus on developing models and describing or explaining various phenomena. Figure 2 offers an example of a literature summary table. 4

Tabular literature summaries from a critical review. Source: Younas and Maddigan. 4

Tip 4: compose potential themes from each article during summary writing

While developing literature summary tables, many authors use themes or subthemes reported in the given articles as the key results of their own review. Such an approach prevents the review authors from understanding the article’s conceptual contribution, developing rigorous synthesis and drawing reasonable interpretations of results from an individual article. Ultimately, it affects the generation of novel review findings. For example, one of the articles about women’s healthcare-seeking behaviours in developing countries reported a theme ‘social-cultural determinants of health as precursors of delays’. Instead of using this theme as one of the review findings, the reviewers should read and interpret beyond the given description in an article, compare and contrast themes, findings from one article with findings and themes from another article to find similarities and differences and to understand and explain bigger picture for their readers. Therefore, while developing literature summary tables, think twice before using the predeveloped themes. Including your themes in the summary tables (see figure 1 ) demonstrates to the readers that a robust method of data extraction and synthesis has been followed.

Tip 5: create your personalised template for literature summaries

Often templates are available for data extraction and development of literature summary tables. The available templates may be in the form of a table, chart or a structured framework that extracts some essential information about every article. The commonly used information may include authors, purpose, methods, key results and quality scores. While extracting all relevant information is important, such templates should be tailored to meet the needs of the individuals’ review. For example, for a review about the effectiveness of healthcare interventions, a literature summary table must include information about the intervention, its type, content timing, duration, setting, effectiveness, negative consequences, and receivers and implementers’ experiences of its usage. Similarly, literature summary tables for articles included in a meta-synthesis must include information about the participants’ characteristics, research context and conceptual contribution of each reviewed article so as to help the reader make an informed decision about the usefulness or lack of usefulness of the individual article in the review and the whole review.

In conclusion, narrative or systematic reviews are almost always conducted as a part of any educational project (thesis or dissertation) or academic or clinical research. Literature reviews are the foundation of research on a given topic. Robust and high-quality reviews play an instrumental role in guiding research, practice and policymaking. However, the quality of reviews is also contingent on rigorous data extraction and synthesis, which require developing literature summaries. We have outlined five tips that could enhance the quality of the data extraction and synthesis process by developing useful literature summaries.

• Aromataris E ,
• Rasheed SP ,

Twitter @Ahtisham04, @parveenazamali

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

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