authentic research report example

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Chapter 11: Presenting Your Research

Writing a Research Report in American Psychological Association (APA) Style

Learning Objectives

• Identify the major sections of an APA-style research report and the basic contents of each section.
• Plan and write an effective APA-style research report.

In this section, we look at how to write an APA-style empirical research report , an article that presents the results of one or more new studies. Recall that the standard sections of an empirical research report provide a kind of outline. Here we consider each of these sections in detail, including what information it contains, how that information is formatted and organized, and tips for writing each section. At the end of this section is a sample APA-style research report that illustrates many of these principles.

Sections of a Research Report

Title page and abstract.

An APA-style research report begins with a  title page . The title is centred in the upper half of the page, with each important word capitalized. The title should clearly and concisely (in about 12 words or fewer) communicate the primary variables and research questions. This sometimes requires a main title followed by a subtitle that elaborates on the main title, in which case the main title and subtitle are separated by a colon. Here are some titles from recent issues of professional journals published by the American Psychological Association.

• Sex Differences in Coping Styles and Implications for Depressed Mood
• Effects of Aging and Divided Attention on Memory for Items and Their Contexts
• Computer-Assisted Cognitive Behavioural Therapy for Child Anxiety: Results of a Randomized Clinical Trial
• Virtual Driving and Risk Taking: Do Racing Games Increase Risk-Taking Cognitions, Affect, and Behaviour?

Below the title are the authors’ names and, on the next line, their institutional affiliation—the university or other institution where the authors worked when they conducted the research. As we have already seen, the authors are listed in an order that reflects their contribution to the research. When multiple authors have made equal contributions to the research, they often list their names alphabetically or in a randomly determined order.

In some areas of psychology, the titles of many empirical research reports are informal in a way that is perhaps best described as “cute.” They usually take the form of a play on words or a well-known expression that relates to the topic under study. Here are some examples from recent issues of the Journal Psychological Science .

• “Smells Like Clean Spirit: Nonconscious Effects of Scent on Cognition and Behavior”
• “Time Crawls: The Temporal Resolution of Infants’ Visual Attention”
• “Scent of a Woman: Men’s Testosterone Responses to Olfactory Ovulation Cues”
• “Apocalypse Soon?: Dire Messages Reduce Belief in Global Warming by Contradicting Just-World Beliefs”
• “Serial vs. Parallel Processing: Sometimes They Look Like Tweedledum and Tweedledee but They Can (and Should) Be Distinguished”
• “How Do I Love Thee? Let Me Count the Words: The Social Effects of Expressive Writing”

Individual researchers differ quite a bit in their preference for such titles. Some use them regularly, while others never use them. What might be some of the pros and cons of using cute article titles?

For articles that are being submitted for publication, the title page also includes an author note that lists the authors’ full institutional affiliations, any acknowledgments the authors wish to make to agencies that funded the research or to colleagues who commented on it, and contact information for the authors. For student papers that are not being submitted for publication—including theses—author notes are generally not necessary.

The  abstract  is a summary of the study. It is the second page of the manuscript and is headed with the word  Abstract . The first line is not indented. The abstract presents the research question, a summary of the method, the basic results, and the most important conclusions. Because the abstract is usually limited to about 200 words, it can be a challenge to write a good one.

Introduction

The  introduction  begins on the third page of the manuscript. The heading at the top of this page is the full title of the manuscript, with each important word capitalized as on the title page. The introduction includes three distinct subsections, although these are typically not identified by separate headings. The opening introduces the research question and explains why it is interesting, the literature review discusses relevant previous research, and the closing restates the research question and comments on the method used to answer it.

The Opening

The  opening , which is usually a paragraph or two in length, introduces the research question and explains why it is interesting. To capture the reader’s attention, researcher Daryl Bem recommends starting with general observations about the topic under study, expressed in ordinary language (not technical jargon)—observations that are about people and their behaviour (not about researchers or their research; Bem, 2003 [1] ). Concrete examples are often very useful here. According to Bem, this would be a poor way to begin a research report:

Festinger’s theory of cognitive dissonance received a great deal of attention during the latter part of the 20th century (p. 191)

The following would be much better:

The individual who holds two beliefs that are inconsistent with one another may feel uncomfortable. For example, the person who knows that he or she enjoys smoking but believes it to be unhealthy may experience discomfort arising from the inconsistency or disharmony between these two thoughts or cognitions. This feeling of discomfort was called cognitive dissonance by social psychologist Leon Festinger (1957), who suggested that individuals will be motivated to remove this dissonance in whatever way they can (p. 191).

After capturing the reader’s attention, the opening should go on to introduce the research question and explain why it is interesting. Will the answer fill a gap in the literature? Will it provide a test of an important theory? Does it have practical implications? Giving readers a clear sense of what the research is about and why they should care about it will motivate them to continue reading the literature review—and will help them make sense of it.

Breaking the Rules

Researcher Larry Jacoby reported several studies showing that a word that people see or hear repeatedly can seem more familiar even when they do not recall the repetitions—and that this tendency is especially pronounced among older adults. He opened his article with the following humourous anecdote:

A friend whose mother is suffering symptoms of Alzheimer’s disease (AD) tells the story of taking her mother to visit a nursing home, preliminary to her mother’s moving there. During an orientation meeting at the nursing home, the rules and regulations were explained, one of which regarded the dining room. The dining room was described as similar to a fine restaurant except that tipping was not required. The absence of tipping was a central theme in the orientation lecture, mentioned frequently to emphasize the quality of care along with the advantages of having paid in advance. At the end of the meeting, the friend’s mother was asked whether she had any questions. She replied that she only had one question: “Should I tip?” (Jacoby, 1999, p. 3)

Although both humour and personal anecdotes are generally discouraged in APA-style writing, this example is a highly effective way to start because it both engages the reader and provides an excellent real-world example of the topic under study.

The Literature Review

Immediately after the opening comes the  literature review , which describes relevant previous research on the topic and can be anywhere from several paragraphs to several pages in length. However, the literature review is not simply a list of past studies. Instead, it constitutes a kind of argument for why the research question is worth addressing. By the end of the literature review, readers should be convinced that the research question makes sense and that the present study is a logical next step in the ongoing research process.

Like any effective argument, the literature review must have some kind of structure. For example, it might begin by describing a phenomenon in a general way along with several studies that demonstrate it, then describing two or more competing theories of the phenomenon, and finally presenting a hypothesis to test one or more of the theories. Or it might describe one phenomenon, then describe another phenomenon that seems inconsistent with the first one, then propose a theory that resolves the inconsistency, and finally present a hypothesis to test that theory. In applied research, it might describe a phenomenon or theory, then describe how that phenomenon or theory applies to some important real-world situation, and finally suggest a way to test whether it does, in fact, apply to that situation.

Looking at the literature review in this way emphasizes a few things. First, it is extremely important to start with an outline of the main points that you want to make, organized in the order that you want to make them. The basic structure of your argument, then, should be apparent from the outline itself. Second, it is important to emphasize the structure of your argument in your writing. One way to do this is to begin the literature review by summarizing your argument even before you begin to make it. “In this article, I will describe two apparently contradictory phenomena, present a new theory that has the potential to resolve the apparent contradiction, and finally present a novel hypothesis to test the theory.” Another way is to open each paragraph with a sentence that summarizes the main point of the paragraph and links it to the preceding points. These opening sentences provide the “transitions” that many beginning researchers have difficulty with. Instead of beginning a paragraph by launching into a description of a previous study, such as “Williams (2004) found that…,” it is better to start by indicating something about why you are describing this particular study. Here are some simple examples:

Another example of this phenomenon comes from the work of Williams (2004).

Williams (2004) offers one explanation of this phenomenon.

An alternative perspective has been provided by Williams (2004).

We used a method based on the one used by Williams (2004).

Finally, remember that your goal is to construct an argument for why your research question is interesting and worth addressing—not necessarily why your favourite answer to it is correct. In other words, your literature review must be balanced. If you want to emphasize the generality of a phenomenon, then of course you should discuss various studies that have demonstrated it. However, if there are other studies that have failed to demonstrate it, you should discuss them too. Or if you are proposing a new theory, then of course you should discuss findings that are consistent with that theory. However, if there are other findings that are inconsistent with it, again, you should discuss them too. It is acceptable to argue that the  balance  of the research supports the existence of a phenomenon or is consistent with a theory (and that is usually the best that researchers in psychology can hope for), but it is not acceptable to  ignore contradictory evidence. Besides, a large part of what makes a research question interesting is uncertainty about its answer.

The Closing

The  closing  of the introduction—typically the final paragraph or two—usually includes two important elements. The first is a clear statement of the main research question or hypothesis. This statement tends to be more formal and precise than in the opening and is often expressed in terms of operational definitions of the key variables. The second is a brief overview of the method and some comment on its appropriateness. Here, for example, is how Darley and Latané (1968) [2] concluded the introduction to their classic article on the bystander effect:

These considerations lead to the hypothesis that the more bystanders to an emergency, the less likely, or the more slowly, any one bystander will intervene to provide aid. To test this proposition it would be necessary to create a situation in which a realistic “emergency” could plausibly occur. Each subject should also be blocked from communicating with others to prevent his getting information about their behaviour during the emergency. Finally, the experimental situation should allow for the assessment of the speed and frequency of the subjects’ reaction to the emergency. The experiment reported below attempted to fulfill these conditions. (p. 378)

Thus the introduction leads smoothly into the next major section of the article—the method section.

The  method section  is where you describe how you conducted your study. An important principle for writing a method section is that it should be clear and detailed enough that other researchers could replicate the study by following your “recipe.” This means that it must describe all the important elements of the study—basic demographic characteristics of the participants, how they were recruited, whether they were randomly assigned, how the variables were manipulated or measured, how counterbalancing was accomplished, and so on. At the same time, it should avoid irrelevant details such as the fact that the study was conducted in Classroom 37B of the Industrial Technology Building or that the questionnaire was double-sided and completed using pencils.

The method section begins immediately after the introduction ends with the heading “Method” (not “Methods”) centred on the page. Immediately after this is the subheading “Participants,” left justified and in italics. The participants subsection indicates how many participants there were, the number of women and men, some indication of their age, other demographics that may be relevant to the study, and how they were recruited, including any incentives given for participation.

After the participants section, the structure can vary a bit. Figure 11.1 shows three common approaches. In the first, the participants section is followed by a design and procedure subsection, which describes the rest of the method. This works well for methods that are relatively simple and can be described adequately in a few paragraphs. In the second approach, the participants section is followed by separate design and procedure subsections. This works well when both the design and the procedure are relatively complicated and each requires multiple paragraphs.

What is the difference between design and procedure? The design of a study is its overall structure. What were the independent and dependent variables? Was the independent variable manipulated, and if so, was it manipulated between or within subjects? How were the variables operationally defined? The procedure is how the study was carried out. It often works well to describe the procedure in terms of what the participants did rather than what the researchers did. For example, the participants gave their informed consent, read a set of instructions, completed a block of four practice trials, completed a block of 20 test trials, completed two questionnaires, and were debriefed and excused.

In the third basic way to organize a method section, the participants subsection is followed by a materials subsection before the design and procedure subsections. This works well when there are complicated materials to describe. This might mean multiple questionnaires, written vignettes that participants read and respond to, perceptual stimuli, and so on. The heading of this subsection can be modified to reflect its content. Instead of “Materials,” it can be “Questionnaires,” “Stimuli,” and so on.

The  results section  is where you present the main results of the study, including the results of the statistical analyses. Although it does not include the raw data—individual participants’ responses or scores—researchers should save their raw data and make them available to other researchers who request them. Several journals now encourage the open sharing of raw data online.

Although there are no standard subsections, it is still important for the results section to be logically organized. Typically it begins with certain preliminary issues. One is whether any participants or responses were excluded from the analyses and why. The rationale for excluding data should be described clearly so that other researchers can decide whether it is appropriate. A second preliminary issue is how multiple responses were combined to produce the primary variables in the analyses. For example, if participants rated the attractiveness of 20 stimulus people, you might have to explain that you began by computing the mean attractiveness rating for each participant. Or if they recalled as many items as they could from study list of 20 words, did you count the number correctly recalled, compute the percentage correctly recalled, or perhaps compute the number correct minus the number incorrect? A third preliminary issue is the reliability of the measures. This is where you would present test-retest correlations, Cronbach’s α, or other statistics to show that the measures are consistent across time and across items. A final preliminary issue is whether the manipulation was successful. This is where you would report the results of any manipulation checks.

The results section should then tackle the primary research questions, one at a time. Again, there should be a clear organization. One approach would be to answer the most general questions and then proceed to answer more specific ones. Another would be to answer the main question first and then to answer secondary ones. Regardless, Bem (2003) [3] suggests the following basic structure for discussing each new result:

• Remind the reader of the research question.
• Give the answer to the research question in words.
• Present the relevant statistics.
• Qualify the answer if necessary.
• Summarize the result.

Notice that only Step 3 necessarily involves numbers. The rest of the steps involve presenting the research question and the answer to it in words. In fact, the basic results should be clear even to a reader who skips over the numbers.

The  discussion  is the last major section of the research report. Discussions usually consist of some combination of the following elements:

• Summary of the research
• Theoretical implications
• Practical implications
• Limitations
• Suggestions for future research

The discussion typically begins with a summary of the study that provides a clear answer to the research question. In a short report with a single study, this might require no more than a sentence. In a longer report with multiple studies, it might require a paragraph or even two. The summary is often followed by a discussion of the theoretical implications of the research. Do the results provide support for any existing theories? If not, how  can  they be explained? Although you do not have to provide a definitive explanation or detailed theory for your results, you at least need to outline one or more possible explanations. In applied research—and often in basic research—there is also some discussion of the practical implications of the research. How can the results be used, and by whom, to accomplish some real-world goal?

The theoretical and practical implications are often followed by a discussion of the study’s limitations. Perhaps there are problems with its internal or external validity. Perhaps the manipulation was not very effective or the measures not very reliable. Perhaps there is some evidence that participants did not fully understand their task or that they were suspicious of the intent of the researchers. Now is the time to discuss these issues and how they might have affected the results. But do not overdo it. All studies have limitations, and most readers will understand that a different sample or different measures might have produced different results. Unless there is good reason to think they  would have, however, there is no reason to mention these routine issues. Instead, pick two or three limitations that seem like they could have influenced the results, explain how they could have influenced the results, and suggest ways to deal with them.

Most discussions end with some suggestions for future research. If the study did not satisfactorily answer the original research question, what will it take to do so? What  new  research questions has the study raised? This part of the discussion, however, is not just a list of new questions. It is a discussion of two or three of the most important unresolved issues. This means identifying and clarifying each question, suggesting some alternative answers, and even suggesting ways they could be studied.

Finally, some researchers are quite good at ending their articles with a sweeping or thought-provoking conclusion. Darley and Latané (1968) [4] , for example, ended their article on the bystander effect by discussing the idea that whether people help others may depend more on the situation than on their personalities. Their final sentence is, “If people understand the situational forces that can make them hesitate to intervene, they may better overcome them” (p. 383). However, this kind of ending can be difficult to pull off. It can sound overreaching or just banal and end up detracting from the overall impact of the article. It is often better simply to end when you have made your final point (although you should avoid ending on a limitation).

The references section begins on a new page with the heading “References” centred at the top of the page. All references cited in the text are then listed in the format presented earlier. They are listed alphabetically by the last name of the first author. If two sources have the same first author, they are listed alphabetically by the last name of the second author. If all the authors are the same, then they are listed chronologically by the year of publication. Everything in the reference list is double-spaced both within and between references.

Appendices, Tables, and Figures

Appendices, tables, and figures come after the references. An  appendix  is appropriate for supplemental material that would interrupt the flow of the research report if it were presented within any of the major sections. An appendix could be used to present lists of stimulus words, questionnaire items, detailed descriptions of special equipment or unusual statistical analyses, or references to the studies that are included in a meta-analysis. Each appendix begins on a new page. If there is only one, the heading is “Appendix,” centred at the top of the page. If there is more than one, the headings are “Appendix A,” “Appendix B,” and so on, and they appear in the order they were first mentioned in the text of the report.

After any appendices come tables and then figures. Tables and figures are both used to present results. Figures can also be used to illustrate theories (e.g., in the form of a flowchart), display stimuli, outline procedures, and present many other kinds of information. Each table and figure appears on its own page. Tables are numbered in the order that they are first mentioned in the text (“Table 1,” “Table 2,” and so on). Figures are numbered the same way (“Figure 1,” “Figure 2,” and so on). A brief explanatory title, with the important words capitalized, appears above each table. Each figure is given a brief explanatory caption, where (aside from proper nouns or names) only the first word of each sentence is capitalized. More details on preparing APA-style tables and figures are presented later in the book.

Sample APA-Style Research Report

Figures 11.2, 11.3, 11.4, and 11.5 show some sample pages from an APA-style empirical research report originally written by undergraduate student Tomoe Suyama at California State University, Fresno. The main purpose of these figures is to illustrate the basic organization and formatting of an APA-style empirical research report, although many high-level and low-level style conventions can be seen here too.

Key Takeaways

• An APA-style empirical research report consists of several standard sections. The main ones are the abstract, introduction, method, results, discussion, and references.
• The introduction consists of an opening that presents the research question, a literature review that describes previous research on the topic, and a closing that restates the research question and comments on the method. The literature review constitutes an argument for why the current study is worth doing.
• The method section describes the method in enough detail that another researcher could replicate the study. At a minimum, it consists of a participants subsection and a design and procedure subsection.
• The results section describes the results in an organized fashion. Each primary result is presented in terms of statistical results but also explained in words.
• The discussion typically summarizes the study, discusses theoretical and practical implications and limitations of the study, and offers suggestions for further research.
• Practice: Look through an issue of a general interest professional journal (e.g.,  Psychological Science ). Read the opening of the first five articles and rate the effectiveness of each one from 1 ( very ineffective ) to 5 ( very effective ). Write a sentence or two explaining each rating.
• Practice: Find a recent article in a professional journal and identify where the opening, literature review, and closing of the introduction begin and end.
• Practice: Find a recent article in a professional journal and highlight in a different colour each of the following elements in the discussion: summary, theoretical implications, practical implications, limitations, and suggestions for future research.

Long Descriptions

Figure 11.1 long description: Table showing three ways of organizing an APA-style method section.

In the simple method, there are two subheadings: “Participants” (which might begin “The participants were…”) and “Design and procedure” (which might begin “There were three conditions…”).

In the typical method, there are three subheadings: “Participants” (“The participants were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”).

In the complex method, there are four subheadings: “Participants” (“The participants were…”), “Materials” (“The stimuli were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”). [Return to Figure 11.1]

• Bem, D. J. (2003). Writing the empirical journal article. In J. M. Darley, M. P. Zanna, & H. R. Roediger III (Eds.),  The compleat academic: A practical guide for the beginning social scientist  (2nd ed.). Washington, DC: American Psychological Association. ↵
• Darley, J. M., & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility.  Journal of Personality and Social Psychology, 4 , 377–383. ↵

A type of research article which describes one or more new empirical studies conducted by the authors.

The page at the beginning of an APA-style research report containing the title of the article, the authors’ names, and their institutional affiliation.

A summary of a research study.

The third page of a manuscript containing the research question, the literature review, and comments about how to answer the research question.

An introduction to the research question and explanation for why this question is interesting.

A description of relevant previous research on the topic being discusses and an argument for why the research is worth addressing.

The end of the introduction, where the research question is reiterated and the method is commented upon.

The section of a research report where the method used to conduct the study is described.

The main results of the study, including the results from statistical analyses, are presented in a research article.

Section of a research report that summarizes the study's results and interprets them by referring back to the study's theoretical background.

Part of a research report which contains supplemental material.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Uncomplicated Reviews of Educational Research Methods

• Writing a Research Report

.pdf version of this page

This review covers the basic elements of a research report. This is a general guide for what you will see in journal articles or dissertations. This format assumes a mixed methods study, but you can leave out either quantitative or qualitative sections if you only used a single methodology.

This review is divided into sections for easy reference. There are five MAJOR parts of a Research Report:

1.    Introduction 2.    Review of Literature 3.    Methods 4.    Results 5.    Discussion

As a general guide, the Introduction, Review of Literature, and Methods should be about 1/3 of your paper, Discussion 1/3, then Results 1/3.

Section 1 : Cover Sheet (APA format cover sheet) optional, if required.

Section 2: Abstract (a basic summary of the report, including sample, treatment, design, results, and implications) (≤ 150 words) optional, if required.

Section 3 : Introduction (1-3 paragraphs) •    Basic introduction •    Supportive statistics (can be from periodicals) •    Statement of Purpose •    Statement of Significance

Section 4 : Research question(s) or hypotheses •    An overall research question (optional) •    A quantitative-based (hypotheses) •    A qualitative-based (research questions) Note: You will generally have more than one, especially if using hypotheses.

Section 5: Review of Literature ▪    Should be organized by subheadings ▪    Should adequately support your study using supporting, related, and/or refuting evidence ▪    Is a synthesis, not a collection of individual summaries

Section 6: Methods ▪    Procedure: Describe data gathering or participant recruitment, including IRB approval ▪    Sample: Describe the sample or dataset, including basic demographics ▪    Setting: Describe the setting, if applicable (generally only in qualitative designs) ▪    Treatment: If applicable, describe, in detail, how you implemented the treatment ▪    Instrument: Describe, in detail, how you implemented the instrument; Describe the reliability and validity associated with the instrument ▪    Data Analysis: Describe type of procedure (t-test, interviews, etc.) and software (if used)

Section 7: Results ▪    Restate Research Question 1 (Quantitative) ▪    Describe results ▪    Restate Research Question 2 (Qualitative) ▪    Describe results

Section 8: Discussion ▪    Restate Overall Research Question ▪    Describe how the results, when taken together, answer the overall question ▪    ***Describe how the results confirm or contrast the literature you reviewed

Section 9: Recommendations (if applicable, generally related to practice)

Section 10: Limitations ▪    Discuss, in several sentences, the limitations of this study. ▪    Research Design (overall, then info about the limitations of each separately) ▪    Sample ▪    Instrument/s ▪    Other limitations

Section 11: Conclusion (A brief closing summary)

Section 12: References (APA format)

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About research rundowns.

Research Rundowns was made possible by support from the Dewar College of Education at Valdosta State University .

• Experimental Design
• What is Educational Research?
• Writing Research Questions
• Mixed Methods Research Designs
• Qualitative Coding & Analysis
• Qualitative Research Design
• Correlation
• Effect Size
• Instrument, Validity, Reliability
• Mean & Standard Deviation
• Significance Testing (t-tests)
• Steps 1-4: Finding Research
• Steps 5-6: Analyzing & Organizing
• Steps 7-9: Citing & Writing

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Scientific Reports

What this handout is about.

This handout provides a general guide to writing reports about scientific research you’ve performed. In addition to describing the conventional rules about the format and content of a lab report, we’ll also attempt to convey why these rules exist, so you’ll get a clearer, more dependable idea of how to approach this writing situation. Readers of this handout may also find our handout on writing in the sciences useful.

Background and pre-writing

Why do we write research reports.

You did an experiment or study for your science class, and now you have to write it up for your teacher to review. You feel that you understood the background sufficiently, designed and completed the study effectively, obtained useful data, and can use those data to draw conclusions about a scientific process or principle. But how exactly do you write all that? What is your teacher expecting to see?

To take some of the guesswork out of answering these questions, try to think beyond the classroom setting. In fact, you and your teacher are both part of a scientific community, and the people who participate in this community tend to share the same values. As long as you understand and respect these values, your writing will likely meet the expectations of your audience—including your teacher.

So why are you writing this research report? The practical answer is “Because the teacher assigned it,” but that’s classroom thinking. Generally speaking, people investigating some scientific hypothesis have a responsibility to the rest of the scientific world to report their findings, particularly if these findings add to or contradict previous ideas. The people reading such reports have two primary goals:

• They want to gather the information presented.
• They want to know that the findings are legitimate.

Your job as a writer, then, is to fulfill these two goals.

How do I do that?

Good question. Here is the basic format scientists have designed for research reports:

• Introduction

Methods and Materials

This format, sometimes called “IMRAD,” may take slightly different shapes depending on the discipline or audience; some ask you to include an abstract or separate section for the hypothesis, or call the Discussion section “Conclusions,” or change the order of the sections (some professional and academic journals require the Methods section to appear last). Overall, however, the IMRAD format was devised to represent a textual version of the scientific method.

The scientific method, you’ll probably recall, involves developing a hypothesis, testing it, and deciding whether your findings support the hypothesis. In essence, the format for a research report in the sciences mirrors the scientific method but fleshes out the process a little. Below, you’ll find a table that shows how each written section fits into the scientific method and what additional information it offers the reader.

Thinking of your research report as based on the scientific method, but elaborated in the ways described above, may help you to meet your audience’s expectations successfully. We’re going to proceed by explicitly connecting each section of the lab report to the scientific method, then explaining why and how you need to elaborate that section.

Although this handout takes each section in the order in which it should be presented in the final report, you may for practical reasons decide to compose sections in another order. For example, many writers find that composing their Methods and Results before the other sections helps to clarify their idea of the experiment or study as a whole. You might consider using each assignment to practice different approaches to drafting the report, to find the order that works best for you.

What should I do before drafting the lab report?

The best way to prepare to write the lab report is to make sure that you fully understand everything you need to about the experiment. Obviously, if you don’t quite know what went on during the lab, you’re going to find it difficult to explain the lab satisfactorily to someone else. To make sure you know enough to write the report, complete the following steps:

• What are we going to do in this lab? (That is, what’s the procedure?)
• Why are we going to do it that way?
• What are we hoping to learn from this experiment?
• Why would we benefit from this knowledge?
• Consult your lab supervisor as you perform the lab. If you don’t know how to answer one of the questions above, for example, your lab supervisor will probably be able to explain it to you (or, at least, help you figure it out).
• Plan the steps of the experiment carefully with your lab partners. The less you rush, the more likely it is that you’ll perform the experiment correctly and record your findings accurately. Also, take some time to think about the best way to organize the data before you have to start putting numbers down. If you can design a table to account for the data, that will tend to work much better than jotting results down hurriedly on a scrap piece of paper.
• Record the data carefully so you get them right. You won’t be able to trust your conclusions if you have the wrong data, and your readers will know you messed up if the other three people in your group have “97 degrees” and you have “87.”
• Consult with your lab partners about everything you do. Lab groups often make one of two mistakes: two people do all the work while two have a nice chat, or everybody works together until the group finishes gathering the raw data, then scrams outta there. Collaborate with your partners, even when the experiment is “over.” What trends did you observe? Was the hypothesis supported? Did you all get the same results? What kind of figure should you use to represent your findings? The whole group can work together to answer these questions.
• Consider your audience. You may believe that audience is a non-issue: it’s your lab TA, right? Well, yes—but again, think beyond the classroom. If you write with only your lab instructor in mind, you may omit material that is crucial to a complete understanding of your experiment, because you assume the instructor knows all that stuff already. As a result, you may receive a lower grade, since your TA won’t be sure that you understand all the principles at work. Try to write towards a student in the same course but a different lab section. That student will have a fair degree of scientific expertise but won’t know much about your experiment particularly. Alternatively, you could envision yourself five years from now, after the reading and lectures for this course have faded a bit. What would you remember, and what would you need explained more clearly (as a refresher)?

Once you’ve completed these steps as you perform the experiment, you’ll be in a good position to draft an effective lab report.

Introductions

How do i write a strong introduction.

For the purposes of this handout, we’ll consider the Introduction to contain four basic elements: the purpose, the scientific literature relevant to the subject, the hypothesis, and the reasons you believed your hypothesis viable. Let’s start by going through each element of the Introduction to clarify what it covers and why it’s important. Then we can formulate a logical organizational strategy for the section.

The inclusion of the purpose (sometimes called the objective) of the experiment often confuses writers. The biggest misconception is that the purpose is the same as the hypothesis. Not quite. We’ll get to hypotheses in a minute, but basically they provide some indication of what you expect the experiment to show. The purpose is broader, and deals more with what you expect to gain through the experiment. In a professional setting, the hypothesis might have something to do with how cells react to a certain kind of genetic manipulation, but the purpose of the experiment is to learn more about potential cancer treatments. Undergraduate reports don’t often have this wide-ranging a goal, but you should still try to maintain the distinction between your hypothesis and your purpose. In a solubility experiment, for example, your hypothesis might talk about the relationship between temperature and the rate of solubility, but the purpose is probably to learn more about some specific scientific principle underlying the process of solubility.

For starters, most people say that you should write out your working hypothesis before you perform the experiment or study. Many beginning science students neglect to do so and find themselves struggling to remember precisely which variables were involved in the process or in what way the researchers felt that they were related. Write your hypothesis down as you develop it—you’ll be glad you did.

As for the form a hypothesis should take, it’s best not to be too fancy or complicated; an inventive style isn’t nearly so important as clarity here. There’s nothing wrong with beginning your hypothesis with the phrase, “It was hypothesized that . . .” Be as specific as you can about the relationship between the different objects of your study. In other words, explain that when term A changes, term B changes in this particular way. Readers of scientific writing are rarely content with the idea that a relationship between two terms exists—they want to know what that relationship entails.

Not a hypothesis:

“It was hypothesized that there is a significant relationship between the temperature of a solvent and the rate at which a solute dissolves.”

Hypothesis:

“It was hypothesized that as the temperature of a solvent increases, the rate at which a solute will dissolve in that solvent increases.”

Put more technically, most hypotheses contain both an independent and a dependent variable. The independent variable is what you manipulate to test the reaction; the dependent variable is what changes as a result of your manipulation. In the example above, the independent variable is the temperature of the solvent, and the dependent variable is the rate of solubility. Be sure that your hypothesis includes both variables.

Justify your hypothesis

You need to do more than tell your readers what your hypothesis is; you also need to assure them that this hypothesis was reasonable, given the circumstances. In other words, use the Introduction to explain that you didn’t just pluck your hypothesis out of thin air. (If you did pluck it out of thin air, your problems with your report will probably extend beyond using the appropriate format.) If you posit that a particular relationship exists between the independent and the dependent variable, what led you to believe your “guess” might be supported by evidence?

Scientists often refer to this type of justification as “motivating” the hypothesis, in the sense that something propelled them to make that prediction. Often, motivation includes what we already know—or rather, what scientists generally accept as true (see “Background/previous research” below). But you can also motivate your hypothesis by relying on logic or on your own observations. If you’re trying to decide which solutes will dissolve more rapidly in a solvent at increased temperatures, you might remember that some solids are meant to dissolve in hot water (e.g., bouillon cubes) and some are used for a function precisely because they withstand higher temperatures (they make saucepans out of something). Or you can think about whether you’ve noticed sugar dissolving more rapidly in your glass of iced tea or in your cup of coffee. Even such basic, outside-the-lab observations can help you justify your hypothesis as reasonable.

Background/previous research

This part of the Introduction demonstrates to the reader your awareness of how you’re building on other scientists’ work. If you think of the scientific community as engaging in a series of conversations about various topics, then you’ll recognize that the relevant background material will alert the reader to which conversation you want to enter.

Generally speaking, authors writing journal articles use the background for slightly different purposes than do students completing assignments. Because readers of academic journals tend to be professionals in the field, authors explain the background in order to permit readers to evaluate the study’s pertinence for their own work. You, on the other hand, write toward a much narrower audience—your peers in the course or your lab instructor—and so you must demonstrate that you understand the context for the (presumably assigned) experiment or study you’ve completed. For example, if your professor has been talking about polarity during lectures, and you’re doing a solubility experiment, you might try to connect the polarity of a solid to its relative solubility in certain solvents. In any event, both professional researchers and undergraduates need to connect the background material overtly to their own work.

Organization of this section

Most of the time, writers begin by stating the purpose or objectives of their own work, which establishes for the reader’s benefit the “nature and scope of the problem investigated” (Day 1994). Once you have expressed your purpose, you should then find it easier to move from the general purpose, to relevant material on the subject, to your hypothesis. In abbreviated form, an Introduction section might look like this:

“The purpose of the experiment was to test conventional ideas about solubility in the laboratory [purpose] . . . According to Whitecoat and Labrat (1999), at higher temperatures the molecules of solvents move more quickly . . . We know from the class lecture that molecules moving at higher rates of speed collide with one another more often and thus break down more easily [background material/motivation] . . . Thus, it was hypothesized that as the temperature of a solvent increases, the rate at which a solute will dissolve in that solvent increases [hypothesis].”

Again—these are guidelines, not commandments. Some writers and readers prefer different structures for the Introduction. The one above merely illustrates a common approach to organizing material.

How do I write a strong Materials and Methods section?

As with any piece of writing, your Methods section will succeed only if it fulfills its readers’ expectations, so you need to be clear in your own mind about the purpose of this section. Let’s review the purpose as we described it above: in this section, you want to describe in detail how you tested the hypothesis you developed and also to clarify the rationale for your procedure. In science, it’s not sufficient merely to design and carry out an experiment. Ultimately, others must be able to verify your findings, so your experiment must be reproducible, to the extent that other researchers can follow the same procedure and obtain the same (or similar) results.

Here’s a real-world example of the importance of reproducibility. In 1989, physicists Stanley Pons and Martin Fleischman announced that they had discovered “cold fusion,” a way of producing excess heat and power without the nuclear radiation that accompanies “hot fusion.” Such a discovery could have great ramifications for the industrial production of energy, so these findings created a great deal of interest. When other scientists tried to duplicate the experiment, however, they didn’t achieve the same results, and as a result many wrote off the conclusions as unjustified (or worse, a hoax). To this day, the viability of cold fusion is debated within the scientific community, even though an increasing number of researchers believe it possible. So when you write your Methods section, keep in mind that you need to describe your experiment well enough to allow others to replicate it exactly.

With these goals in mind, let’s consider how to write an effective Methods section in terms of content, structure, and style.

Sometimes the hardest thing about writing this section isn’t what you should talk about, but what you shouldn’t talk about. Writers often want to include the results of their experiment, because they measured and recorded the results during the course of the experiment. But such data should be reserved for the Results section. In the Methods section, you can write that you recorded the results, or how you recorded the results (e.g., in a table), but you shouldn’t write what the results were—not yet. Here, you’re merely stating exactly how you went about testing your hypothesis. As you draft your Methods section, ask yourself the following questions:

• How much detail? Be precise in providing details, but stay relevant. Ask yourself, “Would it make any difference if this piece were a different size or made from a different material?” If not, you probably don’t need to get too specific. If so, you should give as many details as necessary to prevent this experiment from going awry if someone else tries to carry it out. Probably the most crucial detail is measurement; you should always quantify anything you can, such as time elapsed, temperature, mass, volume, etc.
• Rationale: Be sure that as you’re relating your actions during the experiment, you explain your rationale for the protocol you developed. If you capped a test tube immediately after adding a solute to a solvent, why did you do that? (That’s really two questions: why did you cap it, and why did you cap it immediately?) In a professional setting, writers provide their rationale as a way to explain their thinking to potential critics. On one hand, of course, that’s your motivation for talking about protocol, too. On the other hand, since in practical terms you’re also writing to your teacher (who’s seeking to evaluate how well you comprehend the principles of the experiment), explaining the rationale indicates that you understand the reasons for conducting the experiment in that way, and that you’re not just following orders. Critical thinking is crucial—robots don’t make good scientists.
• Control: Most experiments will include a control, which is a means of comparing experimental results. (Sometimes you’ll need to have more than one control, depending on the number of hypotheses you want to test.) The control is exactly the same as the other items you’re testing, except that you don’t manipulate the independent variable-the condition you’re altering to check the effect on the dependent variable. For example, if you’re testing solubility rates at increased temperatures, your control would be a solution that you didn’t heat at all; that way, you’ll see how quickly the solute dissolves “naturally” (i.e., without manipulation), and you’ll have a point of reference against which to compare the solutions you did heat.

Describe the control in the Methods section. Two things are especially important in writing about the control: identify the control as a control, and explain what you’re controlling for. Here is an example:

“As a control for the temperature change, we placed the same amount of solute in the same amount of solvent, and let the solution stand for five minutes without heating it.”

Structure and style

Organization is especially important in the Methods section of a lab report because readers must understand your experimental procedure completely. Many writers are surprised by the difficulty of conveying what they did during the experiment, since after all they’re only reporting an event, but it’s often tricky to present this information in a coherent way. There’s a fairly standard structure you can use to guide you, and following the conventions for style can help clarify your points.

• Subsections: Occasionally, researchers use subsections to report their procedure when the following circumstances apply: 1) if they’ve used a great many materials; 2) if the procedure is unusually complicated; 3) if they’ve developed a procedure that won’t be familiar to many of their readers. Because these conditions rarely apply to the experiments you’ll perform in class, most undergraduate lab reports won’t require you to use subsections. In fact, many guides to writing lab reports suggest that you try to limit your Methods section to a single paragraph.
• Narrative structure: Think of this section as telling a story about a group of people and the experiment they performed. Describe what you did in the order in which you did it. You may have heard the old joke centered on the line, “Disconnect the red wire, but only after disconnecting the green wire,” where the person reading the directions blows everything to kingdom come because the directions weren’t in order. We’re used to reading about events chronologically, and so your readers will generally understand what you did if you present that information in the same way. Also, since the Methods section does generally appear as a narrative (story), you want to avoid the “recipe” approach: “First, take a clean, dry 100 ml test tube from the rack. Next, add 50 ml of distilled water.” You should be reporting what did happen, not telling the reader how to perform the experiment: “50 ml of distilled water was poured into a clean, dry 100 ml test tube.” Hint: most of the time, the recipe approach comes from copying down the steps of the procedure from your lab manual, so you may want to draft the Methods section initially without consulting your manual. Later, of course, you can go back and fill in any part of the procedure you inadvertently overlooked.
• Past tense: Remember that you’re describing what happened, so you should use past tense to refer to everything you did during the experiment. Writers are often tempted to use the imperative (“Add 5 g of the solid to the solution”) because that’s how their lab manuals are worded; less frequently, they use present tense (“5 g of the solid are added to the solution”). Instead, remember that you’re talking about an event which happened at a particular time in the past, and which has already ended by the time you start writing, so simple past tense will be appropriate in this section (“5 g of the solid were added to the solution” or “We added 5 g of the solid to the solution”).
• Active: We heated the solution to 80°C. (The subject, “we,” performs the action, heating.)
• Passive: The solution was heated to 80°C. (The subject, “solution,” doesn’t do the heating–it is acted upon, not acting.)

Increasingly, especially in the social sciences, using first person and active voice is acceptable in scientific reports. Most readers find that this style of writing conveys information more clearly and concisely. This rhetorical choice thus brings two scientific values into conflict: objectivity versus clarity. Since the scientific community hasn’t reached a consensus about which style it prefers, you may want to ask your lab instructor.

How do I write a strong Results section?

Here’s a paradox for you. The Results section is often both the shortest (yay!) and most important (uh-oh!) part of your report. Your Materials and Methods section shows how you obtained the results, and your Discussion section explores the significance of the results, so clearly the Results section forms the backbone of the lab report. This section provides the most critical information about your experiment: the data that allow you to discuss how your hypothesis was or wasn’t supported. But it doesn’t provide anything else, which explains why this section is generally shorter than the others.

Before you write this section, look at all the data you collected to figure out what relates significantly to your hypothesis. You’ll want to highlight this material in your Results section. Resist the urge to include every bit of data you collected, since perhaps not all are relevant. Also, don’t try to draw conclusions about the results—save them for the Discussion section. In this section, you’re reporting facts. Nothing your readers can dispute should appear in the Results section.

Most Results sections feature three distinct parts: text, tables, and figures. Let’s consider each part one at a time.

This should be a short paragraph, generally just a few lines, that describes the results you obtained from your experiment. In a relatively simple experiment, one that doesn’t produce a lot of data for you to repeat, the text can represent the entire Results section. Don’t feel that you need to include lots of extraneous detail to compensate for a short (but effective) text; your readers appreciate discrimination more than your ability to recite facts. In a more complex experiment, you may want to use tables and/or figures to help guide your readers toward the most important information you gathered. In that event, you’ll need to refer to each table or figure directly, where appropriate:

“Table 1 lists the rates of solubility for each substance”

“Solubility increased as the temperature of the solution increased (see Figure 1).”

If you do use tables or figures, make sure that you don’t present the same material in both the text and the tables/figures, since in essence you’ll just repeat yourself, probably annoying your readers with the redundancy of your statements.

Feel free to describe trends that emerge as you examine the data. Although identifying trends requires some judgment on your part and so may not feel like factual reporting, no one can deny that these trends do exist, and so they properly belong in the Results section. Example:

“Heating the solution increased the rate of solubility of polar solids by 45% but had no effect on the rate of solubility in solutions containing non-polar solids.”

This point isn’t debatable—you’re just pointing out what the data show.

As in the Materials and Methods section, you want to refer to your data in the past tense, because the events you recorded have already occurred and have finished occurring. In the example above, note the use of “increased” and “had,” rather than “increases” and “has.” (You don’t know from your experiment that heating always increases the solubility of polar solids, but it did that time.)

You shouldn’t put information in the table that also appears in the text. You also shouldn’t use a table to present irrelevant data, just to show you did collect these data during the experiment. Tables are good for some purposes and situations, but not others, so whether and how you’ll use tables depends upon what you need them to accomplish.

Tables are useful ways to show variation in data, but not to present a great deal of unchanging measurements. If you’re dealing with a scientific phenomenon that occurs only within a certain range of temperatures, for example, you don’t need to use a table to show that the phenomenon didn’t occur at any of the other temperatures. How useful is this table?

As you can probably see, no solubility was observed until the trial temperature reached 50°C, a fact that the text part of the Results section could easily convey. The table could then be limited to what happened at 50°C and higher, thus better illustrating the differences in solubility rates when solubility did occur.

As a rule, try not to use a table to describe any experimental event you can cover in one sentence of text. Here’s an example of an unnecessary table from How to Write and Publish a Scientific Paper , by Robert A. Day:

As Day notes, all the information in this table can be summarized in one sentence: “S. griseus, S. coelicolor, S. everycolor, and S. rainbowenski grew under aerobic conditions, whereas S. nocolor and S. greenicus required anaerobic conditions.” Most readers won’t find the table clearer than that one sentence.

When you do have reason to tabulate material, pay attention to the clarity and readability of the format you use. Here are a few tips:

• Number your table. Then, when you refer to the table in the text, use that number to tell your readers which table they can review to clarify the material.
• Give your table a title. This title should be descriptive enough to communicate the contents of the table, but not so long that it becomes difficult to follow. The titles in the sample tables above are acceptable.
• Arrange your table so that readers read vertically, not horizontally. For the most part, this rule means that you should construct your table so that like elements read down, not across. Think about what you want your readers to compare, and put that information in the column (up and down) rather than in the row (across). Usually, the point of comparison will be the numerical data you collect, so especially make sure you have columns of numbers, not rows.Here’s an example of how drastically this decision affects the readability of your table (from A Short Guide to Writing about Chemistry , by Herbert Beall and John Trimbur). Look at this table, which presents the relevant data in horizontal rows:

It’s a little tough to see the trends that the author presumably wants to present in this table. Compare this table, in which the data appear vertically:

The second table shows how putting like elements in a vertical column makes for easier reading. In this case, the like elements are the measurements of length and height, over five trials–not, as in the first table, the length and height measurements for each trial.

• Make sure to include units of measurement in the tables. Readers might be able to guess that you measured something in millimeters, but don’t make them try.
• Don’t use vertical lines as part of the format for your table. This convention exists because journals prefer not to have to reproduce these lines because the tables then become more expensive to print. Even though it’s fairly unlikely that you’ll be sending your Biology 11 lab report to Science for publication, your readers still have this expectation. Consequently, if you use the table-drawing option in your word-processing software, choose the option that doesn’t rely on a “grid” format (which includes vertical lines).

How do I include figures in my report?

Although tables can be useful ways of showing trends in the results you obtained, figures (i.e., illustrations) can do an even better job of emphasizing such trends. Lab report writers often use graphic representations of the data they collected to provide their readers with a literal picture of how the experiment went.

When should you use a figure?

Remember the circumstances under which you don’t need a table: when you don’t have a great deal of data or when the data you have don’t vary a lot. Under the same conditions, you would probably forgo the figure as well, since the figure would be unlikely to provide your readers with an additional perspective. Scientists really don’t like their time wasted, so they tend not to respond favorably to redundancy.

If you’re trying to decide between using a table and creating a figure to present your material, consider the following a rule of thumb. The strength of a table lies in its ability to supply large amounts of exact data, whereas the strength of a figure is its dramatic illustration of important trends within the experiment. If you feel that your readers won’t get the full impact of the results you obtained just by looking at the numbers, then a figure might be appropriate.

Of course, an undergraduate class may expect you to create a figure for your lab experiment, if only to make sure that you can do so effectively. If this is the case, then don’t worry about whether to use figures or not—concentrate instead on how best to accomplish your task.

Figures can include maps, photographs, pen-and-ink drawings, flow charts, bar graphs, and section graphs (“pie charts”). But the most common figure by far, especially for undergraduates, is the line graph, so we’ll focus on that type in this handout.

At the undergraduate level, you can often draw and label your graphs by hand, provided that the result is clear, legible, and drawn to scale. Computer technology has, however, made creating line graphs a lot easier. Most word-processing software has a number of functions for transferring data into graph form; many scientists have found Microsoft Excel, for example, a helpful tool in graphing results. If you plan on pursuing a career in the sciences, it may be well worth your while to learn to use a similar program.

Computers can’t, however, decide for you how your graph really works; you have to know how to design your graph to meet your readers’ expectations. Here are some of these expectations:

• Keep it as simple as possible. You may be tempted to signal the complexity of the information you gathered by trying to design a graph that accounts for that complexity. But remember the purpose of your graph: to dramatize your results in a manner that’s easy to see and grasp. Try not to make the reader stare at the graph for a half hour to find the important line among the mass of other lines. For maximum effectiveness, limit yourself to three to five lines per graph; if you have more data to demonstrate, use a set of graphs to account for it, rather than trying to cram it all into a single figure.
• Plot the independent variable on the horizontal (x) axis and the dependent variable on the vertical (y) axis. Remember that the independent variable is the condition that you manipulated during the experiment and the dependent variable is the condition that you measured to see if it changed along with the independent variable. Placing the variables along their respective axes is mostly just a convention, but since your readers are accustomed to viewing graphs in this way, you’re better off not challenging the convention in your report.
• Label each axis carefully, and be especially careful to include units of measure. You need to make sure that your readers understand perfectly well what your graph indicates.
• Number and title your graphs. As with tables, the title of the graph should be informative but concise, and you should refer to your graph by number in the text (e.g., “Figure 1 shows the increase in the solubility rate as a function of temperature”).
• Many editors of professional scientific journals prefer that writers distinguish the lines in their graphs by attaching a symbol to them, usually a geometric shape (triangle, square, etc.), and using that symbol throughout the curve of the line. Generally, readers have a hard time distinguishing dotted lines from dot-dash lines from straight lines, so you should consider staying away from this system. Editors don’t usually like different-colored lines within a graph because colors are difficult and expensive to reproduce; colors may, however, be great for your purposes, as long as you’re not planning to submit your paper to Nature. Use your discretion—try to employ whichever technique dramatizes the results most effectively.
• Try to gather data at regular intervals, so the plot points on your graph aren’t too far apart. You can’t be sure of the arc you should draw between the plot points if the points are located at the far corners of the graph; over a fifteen-minute interval, perhaps the change occurred in the first or last thirty seconds of that period (in which case your straight-line connection between the points is misleading).
• If you’re worried that you didn’t collect data at sufficiently regular intervals during your experiment, go ahead and connect the points with a straight line, but you may want to examine this problem as part of your Discussion section.
• Make your graph large enough so that everything is legible and clearly demarcated, but not so large that it either overwhelms the rest of the Results section or provides a far greater range than you need to illustrate your point. If, for example, the seedlings of your plant grew only 15 mm during the trial, you don’t need to construct a graph that accounts for 100 mm of growth. The lines in your graph should more or less fill the space created by the axes; if you see that your data is confined to the lower left portion of the graph, you should probably re-adjust your scale.
• If you create a set of graphs, make them the same size and format, including all the verbal and visual codes (captions, symbols, scale, etc.). You want to be as consistent as possible in your illustrations, so that your readers can easily make the comparisons you’re trying to get them to see.

How do I write a strong Discussion section?

The discussion section is probably the least formalized part of the report, in that you can’t really apply the same structure to every type of experiment. In simple terms, here you tell your readers what to make of the Results you obtained. If you have done the Results part well, your readers should already recognize the trends in the data and have a fairly clear idea of whether your hypothesis was supported. Because the Results can seem so self-explanatory, many students find it difficult to know what material to add in this last section.

Basically, the Discussion contains several parts, in no particular order, but roughly moving from specific (i.e., related to your experiment only) to general (how your findings fit in the larger scientific community). In this section, you will, as a rule, need to:

Explain whether the data support your hypothesis

• Acknowledge any anomalous data or deviations from what you expected

Derive conclusions, based on your findings, about the process you’re studying

• Relate your findings to earlier work in the same area (if you can)

Explore the theoretical and/or practical implications of your findings

Let’s look at some dos and don’ts for each of these objectives.

This statement is usually a good way to begin the Discussion, since you can’t effectively speak about the larger scientific value of your study until you’ve figured out the particulars of this experiment. You might begin this part of the Discussion by explicitly stating the relationships or correlations your data indicate between the independent and dependent variables. Then you can show more clearly why you believe your hypothesis was or was not supported. For example, if you tested solubility at various temperatures, you could start this section by noting that the rates of solubility increased as the temperature increased. If your initial hypothesis surmised that temperature change would not affect solubility, you would then say something like,

“The hypothesis that temperature change would not affect solubility was not supported by the data.”

Note: Students tend to view labs as practical tests of undeniable scientific truths. As a result, you may want to say that the hypothesis was “proved” or “disproved” or that it was “correct” or “incorrect.” These terms, however, reflect a degree of certainty that you as a scientist aren’t supposed to have. Remember, you’re testing a theory with a procedure that lasts only a few hours and relies on only a few trials, which severely compromises your ability to be sure about the “truth” you see. Words like “supported,” “indicated,” and “suggested” are more acceptable ways to evaluate your hypothesis.

Also, recognize that saying whether the data supported your hypothesis or not involves making a claim to be defended. As such, you need to show the readers that this claim is warranted by the evidence. Make sure that you’re very explicit about the relationship between the evidence and the conclusions you draw from it. This process is difficult for many writers because we don’t often justify conclusions in our regular lives. For example, you might nudge your friend at a party and whisper, “That guy’s drunk,” and once your friend lays eyes on the person in question, she might readily agree. In a scientific paper, by contrast, you would need to defend your claim more thoroughly by pointing to data such as slurred words, unsteady gait, and the lampshade-as-hat. In addition to pointing out these details, you would also need to show how (according to previous studies) these signs are consistent with inebriation, especially if they occur in conjunction with one another. To put it another way, tell your readers exactly how you got from point A (was the hypothesis supported?) to point B (yes/no).

Acknowledge any anomalous data, or deviations from what you expected

You need to take these exceptions and divergences into account, so that you qualify your conclusions sufficiently. For obvious reasons, your readers will doubt your authority if you (deliberately or inadvertently) overlook a key piece of data that doesn’t square with your perspective on what occurred. In a more philosophical sense, once you’ve ignored evidence that contradicts your claims, you’ve departed from the scientific method. The urge to “tidy up” the experiment is often strong, but if you give in to it you’re no longer performing good science.

Sometimes after you’ve performed a study or experiment, you realize that some part of the methods you used to test your hypothesis was flawed. In that case, it’s OK to suggest that if you had the chance to conduct your test again, you might change the design in this or that specific way in order to avoid such and such a problem. The key to making this approach work, though, is to be very precise about the weakness in your experiment, why and how you think that weakness might have affected your data, and how you would alter your protocol to eliminate—or limit the effects of—that weakness. Often, inexperienced researchers and writers feel the need to account for “wrong” data (remember, there’s no such animal), and so they speculate wildly about what might have screwed things up. These speculations include such factors as the unusually hot temperature in the room, or the possibility that their lab partners read the meters wrong, or the potentially defective equipment. These explanations are what scientists call “cop-outs,” or “lame”; don’t indicate that the experiment had a weakness unless you’re fairly certain that a) it really occurred and b) you can explain reasonably well how that weakness affected your results.

If, for example, your hypothesis dealt with the changes in solubility at different temperatures, then try to figure out what you can rationally say about the process of solubility more generally. If you’re doing an undergraduate lab, chances are that the lab will connect in some way to the material you’ve been covering either in lecture or in your reading, so you might choose to return to these resources as a way to help you think clearly about the process as a whole.

This part of the Discussion section is another place where you need to make sure that you’re not overreaching. Again, nothing you’ve found in one study would remotely allow you to claim that you now “know” something, or that something isn’t “true,” or that your experiment “confirmed” some principle or other. Hesitate before you go out on a limb—it’s dangerous! Use less absolutely conclusive language, including such words as “suggest,” “indicate,” “correspond,” “possibly,” “challenge,” etc.

Relate your findings to previous work in the field (if possible)

We’ve been talking about how to show that you belong in a particular community (such as biologists or anthropologists) by writing within conventions that they recognize and accept. Another is to try to identify a conversation going on among members of that community, and use your work to contribute to that conversation. In a larger philosophical sense, scientists can’t fully understand the value of their research unless they have some sense of the context that provoked and nourished it. That is, you have to recognize what’s new about your project (potentially, anyway) and how it benefits the wider body of scientific knowledge. On a more pragmatic level, especially for undergraduates, connecting your lab work to previous research will demonstrate to the TA that you see the big picture. You have an opportunity, in the Discussion section, to distinguish yourself from the students in your class who aren’t thinking beyond the barest facts of the study. Capitalize on this opportunity by putting your own work in context.

If you’re just beginning to work in the natural sciences (as a first-year biology or chemistry student, say), most likely the work you’ll be doing has already been performed and re-performed to a satisfactory degree. Hence, you could probably point to a similar experiment or study and compare/contrast your results and conclusions. More advanced work may deal with an issue that is somewhat less “resolved,” and so previous research may take the form of an ongoing debate, and you can use your own work to weigh in on that debate. If, for example, researchers are hotly disputing the value of herbal remedies for the common cold, and the results of your study suggest that Echinacea diminishes the symptoms but not the actual presence of the cold, then you might want to take some time in the Discussion section to recapitulate the specifics of the dispute as it relates to Echinacea as an herbal remedy. (Consider that you have probably already written in the Introduction about this debate as background research.)

This information is often the best way to end your Discussion (and, for all intents and purposes, the report). In argumentative writing generally, you want to use your closing words to convey the main point of your writing. This main point can be primarily theoretical (“Now that you understand this information, you’re in a better position to understand this larger issue”) or primarily practical (“You can use this information to take such and such an action”). In either case, the concluding statements help the reader to comprehend the significance of your project and your decision to write about it.

Since a lab report is argumentative—after all, you’re investigating a claim, and judging the legitimacy of that claim by generating and collecting evidence—it’s often a good idea to end your report with the same technique for establishing your main point. If you want to go the theoretical route, you might talk about the consequences your study has for the field or phenomenon you’re investigating. To return to the examples regarding solubility, you could end by reflecting on what your work on solubility as a function of temperature tells us (potentially) about solubility in general. (Some folks consider this type of exploration “pure” as opposed to “applied” science, although these labels can be problematic.) If you want to go the practical route, you could end by speculating about the medical, institutional, or commercial implications of your findings—in other words, answer the question, “What can this study help people to do?” In either case, you’re going to make your readers’ experience more satisfying, by helping them see why they spent their time learning what you had to teach them.

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

American Psychological Association. 2010. Publication Manual of the American Psychological Association . 6th ed. Washington, DC: American Psychological Association.

Beall, Herbert, and John Trimbur. 2001. A Short Guide to Writing About Chemistry , 2nd ed. New York: Longman.

Blum, Deborah, and Mary Knudson. 1997. A Field Guide for Science Writers: The Official Guide of the National Association of Science Writers . New York: Oxford University Press.

Booth, Wayne C., Gregory G. Colomb, Joseph M. Williams, Joseph Bizup, and William T. FitzGerald. 2016. The Craft of Research , 4th ed. Chicago: University of Chicago Press.

Briscoe, Mary Helen. 1996. Preparing Scientific Illustrations: A Guide to Better Posters, Presentations, and Publications , 2nd ed. New York: Springer-Verlag.

Council of Science Editors. 2014. Scientific Style and Format: The CSE Manual for Authors, Editors, and Publishers , 8th ed. Chicago & London: University of Chicago Press.

Davis, Martha. 2012. Scientific Papers and Presentations , 3rd ed. London: Academic Press.

Day, Robert A. 1994. How to Write and Publish a Scientific Paper , 4th ed. Phoenix: Oryx Press.

Porush, David. 1995. A Short Guide to Writing About Science . New York: Longman.

Williams, Joseph, and Joseph Bizup. 2017. Style: Lessons in Clarity and Grace , 12th ed. Boston: Pearson.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Home » Research Report – Example, Writing Guide and Types

Research Report – Example, Writing Guide and Types

Table of Contents

Research Report

Definition:

Research Report is a written document that presents the results of a research project or study, including the research question, methodology, results, and conclusions, in a clear and objective manner.

The purpose of a research report is to communicate the findings of the research to the intended audience, which could be other researchers, stakeholders, or the general public.

Components of Research Report

Components of Research Report are as follows:

Introduction

The introduction sets the stage for the research report and provides a brief overview of the research question or problem being investigated. It should include a clear statement of the purpose of the study and its significance or relevance to the field of research. It may also provide background information or a literature review to help contextualize the research.

Literature Review

The literature review provides a critical analysis and synthesis of the existing research and scholarship relevant to the research question or problem. It should identify the gaps, inconsistencies, and contradictions in the literature and show how the current study addresses these issues. The literature review also establishes the theoretical framework or conceptual model that guides the research.

Methodology

The methodology section describes the research design, methods, and procedures used to collect and analyze data. It should include information on the sample or participants, data collection instruments, data collection procedures, and data analysis techniques. The methodology should be clear and detailed enough to allow other researchers to replicate the study.

The results section presents the findings of the study in a clear and objective manner. It should provide a detailed description of the data and statistics used to answer the research question or test the hypothesis. Tables, graphs, and figures may be included to help visualize the data and illustrate the key findings.

The discussion section interprets the results of the study and explains their significance or relevance to the research question or problem. It should also compare the current findings with those of previous studies and identify the implications for future research or practice. The discussion should be based on the results presented in the previous section and should avoid speculation or unfounded conclusions.

The conclusion summarizes the key findings of the study and restates the main argument or thesis presented in the introduction. It should also provide a brief overview of the contributions of the study to the field of research and the implications for practice or policy.

The references section lists all the sources cited in the research report, following a specific citation style, such as APA or MLA.

The appendices section includes any additional material, such as data tables, figures, or instruments used in the study, that could not be included in the main text due to space limitations.

Types of Research Report

Types of Research Report are as follows:

Thesis is a type of research report. A thesis is a long-form research document that presents the findings and conclusions of an original research study conducted by a student as part of a graduate or postgraduate program. It is typically written by a student pursuing a higher degree, such as a Master’s or Doctoral degree, although it can also be written by researchers or scholars in other fields.

Research Paper

Research paper is a type of research report. A research paper is a document that presents the results of a research study or investigation. Research papers can be written in a variety of fields, including science, social science, humanities, and business. They typically follow a standard format that includes an introduction, literature review, methodology, results, discussion, and conclusion sections.

Technical Report

A technical report is a detailed report that provides information about a specific technical or scientific problem or project. Technical reports are often used in engineering, science, and other technical fields to document research and development work.

Progress Report

A progress report provides an update on the progress of a research project or program over a specific period of time. Progress reports are typically used to communicate the status of a project to stakeholders, funders, or project managers.

Feasibility Report

A feasibility report assesses the feasibility of a proposed project or plan, providing an analysis of the potential risks, benefits, and costs associated with the project. Feasibility reports are often used in business, engineering, and other fields to determine the viability of a project before it is undertaken.

Field Report

A field report documents observations and findings from fieldwork, which is research conducted in the natural environment or setting. Field reports are often used in anthropology, ecology, and other social and natural sciences.

Experimental Report

An experimental report documents the results of a scientific experiment, including the hypothesis, methods, results, and conclusions. Experimental reports are often used in biology, chemistry, and other sciences to communicate the results of laboratory experiments.

Case Study Report

A case study report provides an in-depth analysis of a specific case or situation, often used in psychology, social work, and other fields to document and understand complex cases or phenomena.

Literature Review Report

A literature review report synthesizes and summarizes existing research on a specific topic, providing an overview of the current state of knowledge on the subject. Literature review reports are often used in social sciences, education, and other fields to identify gaps in the literature and guide future research.

Research Report Example

Following is a Research Report Example sample for Students:

Title: The Impact of Social Media on Academic Performance among High School Students

This study aims to investigate the relationship between social media use and academic performance among high school students. The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The findings indicate that there is a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students. The results of this study have important implications for educators, parents, and policymakers, as they highlight the need for strategies that can help students balance their social media use and academic responsibilities.

Introduction:

Social media has become an integral part of the lives of high school students. With the widespread use of social media platforms such as Facebook, Twitter, Instagram, and Snapchat, students can connect with friends, share photos and videos, and engage in discussions on a range of topics. While social media offers many benefits, concerns have been raised about its impact on academic performance. Many studies have found a negative correlation between social media use and academic performance among high school students (Kirschner & Karpinski, 2010; Paul, Baker, & Cochran, 2012).

Given the growing importance of social media in the lives of high school students, it is important to investigate its impact on academic performance. This study aims to address this gap by examining the relationship between social media use and academic performance among high school students.

Methodology:

The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The questionnaire was developed based on previous studies and was designed to measure the frequency and duration of social media use, as well as academic performance.

The participants were selected using a convenience sampling technique, and the survey questionnaire was distributed in the classroom during regular school hours. The data collected were analyzed using descriptive statistics and correlation analysis.

The findings indicate that the majority of high school students use social media platforms on a daily basis, with Facebook being the most popular platform. The results also show a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students.

Discussion:

The results of this study have important implications for educators, parents, and policymakers. The negative correlation between social media use and academic performance suggests that strategies should be put in place to help students balance their social media use and academic responsibilities. For example, educators could incorporate social media into their teaching strategies to engage students and enhance learning. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. Policymakers could develop guidelines and policies to regulate social media use among high school students.

Conclusion:

In conclusion, this study provides evidence of the negative impact of social media on academic performance among high school students. The findings highlight the need for strategies that can help students balance their social media use and academic responsibilities. Further research is needed to explore the specific mechanisms by which social media use affects academic performance and to develop effective strategies for addressing this issue.

Limitations:

One limitation of this study is the use of convenience sampling, which limits the generalizability of the findings to other populations. Future studies should use random sampling techniques to increase the representativeness of the sample. Another limitation is the use of self-reported measures, which may be subject to social desirability bias. Future studies could use objective measures of social media use and academic performance, such as tracking software and school records.

Implications:

The findings of this study have important implications for educators, parents, and policymakers. Educators could incorporate social media into their teaching strategies to engage students and enhance learning. For example, teachers could use social media platforms to share relevant educational resources and facilitate online discussions. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. They could also engage in open communication with their children to understand their social media use and its impact on their academic performance. Policymakers could develop guidelines and policies to regulate social media use among high school students. For example, schools could implement social media policies that restrict access during class time and encourage responsible use.

References:

• Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237-1245.
• Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Journal of the Research Center for Educational Technology, 8(1), 1-19.
• Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17(10), 652-657.
• Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958.

Note*: Above mention, Example is just a sample for the students’ guide. Do not directly copy and paste as your College or University assignment. Kindly do some research and Write your own.

Applications of Research Report

Research reports have many applications, including:

• Communicating research findings: The primary application of a research report is to communicate the results of a study to other researchers, stakeholders, or the general public. The report serves as a way to share new knowledge, insights, and discoveries with others in the field.
• Informing policy and practice : Research reports can inform policy and practice by providing evidence-based recommendations for decision-makers. For example, a research report on the effectiveness of a new drug could inform regulatory agencies in their decision-making process.
• Supporting further research: Research reports can provide a foundation for further research in a particular area. Other researchers may use the findings and methodology of a report to develop new research questions or to build on existing research.
• Evaluating programs and interventions : Research reports can be used to evaluate the effectiveness of programs and interventions in achieving their intended outcomes. For example, a research report on a new educational program could provide evidence of its impact on student performance.
• Demonstrating impact : Research reports can be used to demonstrate the impact of research funding or to evaluate the success of research projects. By presenting the findings and outcomes of a study, research reports can show the value of research to funders and stakeholders.
• Enhancing professional development : Research reports can be used to enhance professional development by providing a source of information and learning for researchers and practitioners in a particular field. For example, a research report on a new teaching methodology could provide insights and ideas for educators to incorporate into their own practice.

How to write Research Report

Here are some steps you can follow to write a research report:

• Identify the research question: The first step in writing a research report is to identify your research question. This will help you focus your research and organize your findings.
• Conduct research : Once you have identified your research question, you will need to conduct research to gather relevant data and information. This can involve conducting experiments, reviewing literature, or analyzing data.
• Organize your findings: Once you have gathered all of your data, you will need to organize your findings in a way that is clear and understandable. This can involve creating tables, graphs, or charts to illustrate your results.
• Write the report: Once you have organized your findings, you can begin writing the report. Start with an introduction that provides background information and explains the purpose of your research. Next, provide a detailed description of your research methods and findings. Finally, summarize your results and draw conclusions based on your findings.
• Proofread and edit: After you have written your report, be sure to proofread and edit it carefully. Check for grammar and spelling errors, and make sure that your report is well-organized and easy to read.
• Include a reference list: Be sure to include a list of references that you used in your research. This will give credit to your sources and allow readers to further explore the topic if they choose.
• Format your report: Finally, format your report according to the guidelines provided by your instructor or organization. This may include formatting requirements for headings, margins, fonts, and spacing.

Purpose of Research Report

The purpose of a research report is to communicate the results of a research study to a specific audience, such as peers in the same field, stakeholders, or the general public. The report provides a detailed description of the research methods, findings, and conclusions.

Some common purposes of a research report include:

• Sharing knowledge: A research report allows researchers to share their findings and knowledge with others in their field. This helps to advance the field and improve the understanding of a particular topic.
• Identifying trends: A research report can identify trends and patterns in data, which can help guide future research and inform decision-making.
• Addressing problems: A research report can provide insights into problems or issues and suggest solutions or recommendations for addressing them.
• Evaluating programs or interventions : A research report can evaluate the effectiveness of programs or interventions, which can inform decision-making about whether to continue, modify, or discontinue them.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies.

When to Write Research Report

A research report should be written after completing the research study. This includes collecting data, analyzing the results, and drawing conclusions based on the findings. Once the research is complete, the report should be written in a timely manner while the information is still fresh in the researcher’s mind.

In academic settings, research reports are often required as part of coursework or as part of a thesis or dissertation. In this case, the report should be written according to the guidelines provided by the instructor or institution.

In other settings, such as in industry or government, research reports may be required to inform decision-making or to comply with regulatory requirements. In these cases, the report should be written as soon as possible after the research is completed in order to inform decision-making in a timely manner.

Overall, the timing of when to write a research report depends on the purpose of the research, the expectations of the audience, and any regulatory requirements that need to be met. However, it is important to complete the report in a timely manner while the information is still fresh in the researcher’s mind.

Characteristics of Research Report

There are several characteristics of a research report that distinguish it from other types of writing. These characteristics include:

• Objective: A research report should be written in an objective and unbiased manner. It should present the facts and findings of the research study without any personal opinions or biases.
• Systematic: A research report should be written in a systematic manner. It should follow a clear and logical structure, and the information should be presented in a way that is easy to understand and follow.
• Detailed: A research report should be detailed and comprehensive. It should provide a thorough description of the research methods, results, and conclusions.
• Accurate : A research report should be accurate and based on sound research methods. The findings and conclusions should be supported by data and evidence.
• Organized: A research report should be well-organized. It should include headings and subheadings to help the reader navigate the report and understand the main points.
• Clear and concise: A research report should be written in clear and concise language. The information should be presented in a way that is easy to understand, and unnecessary jargon should be avoided.
• Citations and references: A research report should include citations and references to support the findings and conclusions. This helps to give credit to other researchers and to provide readers with the opportunity to further explore the topic.

Advantages of Research Report

Research reports have several advantages, including:

• Communicating research findings: Research reports allow researchers to communicate their findings to a wider audience, including other researchers, stakeholders, and the general public. This helps to disseminate knowledge and advance the understanding of a particular topic.
• Providing evidence for decision-making : Research reports can provide evidence to inform decision-making, such as in the case of policy-making, program planning, or product development. The findings and conclusions can help guide decisions and improve outcomes.
• Supporting further research: Research reports can provide a foundation for further research on a particular topic. Other researchers can build on the findings and conclusions of the report, which can lead to further discoveries and advancements in the field.
• Demonstrating expertise: Research reports can demonstrate the expertise of the researchers and their ability to conduct rigorous and high-quality research. This can be important for securing funding, promotions, and other professional opportunities.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies. Producing a high-quality research report can help ensure compliance with these requirements.

Limitations of Research Report

Despite their advantages, research reports also have some limitations, including:

• Time-consuming: Conducting research and writing a report can be a time-consuming process, particularly for large-scale studies. This can limit the frequency and speed of producing research reports.
• Expensive: Conducting research and producing a report can be expensive, particularly for studies that require specialized equipment, personnel, or data. This can limit the scope and feasibility of some research studies.
• Limited generalizability: Research studies often focus on a specific population or context, which can limit the generalizability of the findings to other populations or contexts.
• Potential bias : Researchers may have biases or conflicts of interest that can influence the findings and conclusions of the research study. Additionally, participants may also have biases or may not be representative of the larger population, which can limit the validity and reliability of the findings.
• Accessibility: Research reports may be written in technical or academic language, which can limit their accessibility to a wider audience. Additionally, some research may be behind paywalls or require specialized access, which can limit the ability of others to read and use the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• v.5(7); 2019 Jul

Authenticity and inauthenticity in narrative identity

Joshua a. wilt.

a Case Western Reserve University, USA

Sarah Thomas

b PBS-SoCal (KOCE-TV), USA

Dan P. McAdams

c Northwestern University, USA

Self-reported authenticity is related to higher well-being, however, employing self-report questionnaires to measure authenticity may be limited in that they do not capture the lived experience of authenticity. We employ a narrative identity approach to the study of authenticity to potentially better capture some of the idiosyncratic richness and nuance of authentic experience. In Study 1, 87 undergraduates wrote descriptions of three separate memories: one in which they felt authentic, one in which they felt inauthentic, and a vivid, emotional memory. Thematic analysis identified five dimensions of authenticity (relational authenticity, resisting external pressures, expression of true self, contentment, owning one's actions) and 4 dimensions of inauthenticity (phoniness, suppression, self-denigration, and conformity). In study 2, 103 undergraduates provided written descriptions of authentic and inauthentic experiences. Scenes were coded for the dimensions of authenticity and inauthenticity listed above, and those categories were related to self-report scales assessing authenticity and related constructs (autonomy, honesty, Machiavellianism). Correlational and factor extension results suggested that narratives themes showed evidence of both convergent and discriminant validity. Implications for narrative and self-report approaches to authenticity are discussed.

1. Introduction

The idea of finding and expressing one's true nature, or being authentic , has been exalted through the ages by philosophers, social theorists, and psychologists alike ( Robinson et al., 2012 ). The converse, denying and subjugating one's real identity, or being inauthentic , is portrayed in a more negative light ( Nehamas, 1999 ). This consensus of attitudes toward authenticity and inauthenticity may stem from the view that authenticity is an honest and therefore morally superior way of being (e.g., Christy et al., 2016 ; Gino et al., 2015 ; Strohminger et al., 2017 ). Research also suggests that authenticity is beneficial; studies consistently document associations between authentic functioning and higher levels of self-esteem, psychological well-being, positive affect, relationship quality, and a host of other indicators of psychosocial adaptation ( Erickson and Whatron, 1997 ; Goldman and Kernis, 2002 ; Harter, 2002 ; Schlegel et al., 2016 ; Wood et al., 2008 ).

The studies cited above relied on self-report scales to measure various aspects of authenticity and inauthenticity (e.g., Kernis et al., 2006 ; Wood et al., 2008 ). These scales treat authenticity as a trait-level individual difference, that is, they quantify the extent to which people believe they are being authentic versus inauthentic across a number of different dimensions. We believe that the narrative identity perspective ( McAdams, 2008 ) may complement the trait approach to authenticity by offering a compelling framework from which to explore individuals' lived-experience of authenticity and inauthenticity. According to this approach, the psychological construction of life-stories brings together one's remembered past and imagined future into a narrative identity that provides life with some degree of unity and purpose ( McAdams, 1996 ). Constructing life stories involves autobiographical reasoning, which involves making connections between experiences and aspects of the self ( McLean and Fournier, 2008 ). It is possible that, during the process of autobiographical reasoning, individuals could evaluate whether their lived-experience was consistent with their core sense of self, or whether the experience felt phony or fake with reference to the self. That is, people could form judgments about whether their ways of being in different situations in their lives were authentic or inauthentic.

In this paper, across two studies, we aim to (i) develop a coding system for authentic and inauthentic themes, and (ii) relate those themes to existing self-report measures of authenticity and inauthenticity. Achieving these aims may show areas of convergence and divergence between self-report and narrative approaches to authenticity.

1.1. Conceptualization and measurement of trait authenticity and inauthenticity

Authenticity has been an eminent concern across the history of personality psychology. Humanistic psychologists (e.g., Maslow, 1968 ; Rogers, 1961 ) characterized the true self as being an innate set of attributes that may be discovered on the journey to self-realization and optimal functioning. Psychodynamic perspectives noted the importance of honest self-appraisal (acknowledging one's strengths and weaknesses) as crucial to achieving a balanced psychological mindset ( Horney, 1950 ). Personality developmental approaches to authenticity have emphasized that owning one's inner psychological characteristics (thoughts, emotions, desires) and expressing oneself in ways consistent with that inner life are fundamental to healthy maturation ( Harter, 2002 ). Despite such interest, efforts to measure authenticity and related constructs have emerged only relatively recently.

Kernis et al. (2006) developed a multicomponent self-report measure of authenticity, the Authenticity Inventory 3 (AI3), that was based on a review of philosophical approaches to the true self (e.g., Aristotle, Kierkegaard, Sartre). The four components identified were Awareness, or knowing and trusting in one's motives, feelings, desires, and self-relevant cognitions; Unbiased Processing, or objectivity with respect to one's positive and negative self-aspects; Behavior, which measures the extent to which one behaves in accord with one's values; and Relational Orientation, which measures valuing and striving for openness, sincerity, and truthfulness in one's close relationships.

A three-factor model of authenticity proposed by Wood et al. (2008) , meant to encompass previous philosophical and psychological approaches to authenticity, is operationalized by the Authenticity Scale. This self-report measure consists of Authentic-Living, or living in accordance with one's own values and beliefs; Accepting External Influences, or the belief that one must conform to the expectations of others; and Self-alienation, or feeling out of touch with the true-self.

Scores from each of the measures have shown good evidence of internal consistency as well as validity for predicting multiple domains of well-being across several studies (e.g., Boyraz et al., 2014 ; Kernis et al., 2008 ). Other reliable and valid self-report measures of authenticity have been developed for more specific purposes, for Example, to assess authenticity in different social contexts ( Robinson et al., 2012 ), in relationships ( Gan and Chen, 2017 ; Wang, 2016 ), and as a short-term state (e.g., degree of perceived authenticity over a 30-minute period) as opposed to a more enduring trait ( Lenton et al., 2016 ; Sedikides et al., 2017 ). Thus, there are a wide array of self-report inventories of authenticity that may be applied flexibly for different research aims.

Self-report measures of authenticity have notable strengths, including their previously noted psychometric characteristics. They also consist of a relatively small number of items and require a short amount of time to complete, which allows for efficient data collection on a large scale. Further, as the self may have privileged knowledge regarding a person's perceived “true self” (as compared to peers or observers), self-reports could reflect a fundamentally important source of data on authenticity ( Fleeson and Wilt, 2010 ).

Self-report authenticity questionnaires may also carry some disadvantages that are highly relevant to authenticity. For instance, people may miss the subjective details related to how people understand their own lives in the framework of their society and culture ( McAdams, 1996 ). Furthermore, the scope of self-report questionnaires is limited by the content of questions, and thus such measures may not capture individuals' experiences of authenticity ( Schlegel and Hicks, 2011 ). Social desirability and self-enhancement biases may be particularly likely with a construct as socially valued as authenticity. Additionally, general limitations of self-report scales include the possibility that individuals may not understand the questions or the response options, and that people may interpret the questions and response options differently.

1.2. A narrative identity approach to assessing authenticity

Narrative identity is typically studied by collecting a select set of scenes from individuals' life-stories memories that portray a personally significant event ( McAdams, 2008 ). Narrative identity researchers aim to quantify important characteristics of stories (e.g., narrative complexity, different themes, affective tone) and have developed a myriad of valid measures of a number of these qualities ( McAdams and Manczak, 2015 ). The current project examined life-story scenes that are specifically related to authenticity and inauthenticity and attempted to create a reliable and valid system for coding authenticity/inauthenticity themes. This approach is grounded in notions of authenticity as being an experience in which people evaluate themselves in relation to aspects of their identity, namely against aspects of their “true self” identity ( Vannini and Franzese, 2008 ). That is, authenticity and inauthenticity emerge as one self-reflects on whether their behaviors, goals, and feelings in specific circumstances were congruent or not with their “true self” concept.

One previous project examined authenticity and inauthenticity in narratives of when participants felt “most me” or “least me” ( Lenton et al., 2013 , p. 280). Narratives were coded for self-aspects, experiential themes, emotions, and needs that were theoretically related to authenticity- and inauthenticity-based reviews of the literature. The “most me” narratives were characterized by higher levels (as compared to the “least me” narratives) of ideal-self attributes, themes such as fun, familiarity, and achievement, by higher levels of positive emotions, and by higher reports of need-satisfaction in the domains of self-esteem, relatedness, autonomy, competence, security, meaning, popularity, and physical thriving. In contrast, the “least me” narratives contained relatively more themes of doing as expected, feeling judged, facing difficulty, isolation, failing one's own or others' standards, and illness, and by higher levels of negative emotions. Furthermore, upon reflection, participants reported more nostalgia and positive emotions when thinking about the “most me” scene. Another study found that different groups report varying levels of affect, need satisfaction, and self-consciousness in their “most me” and “least me” scenes ( Lenton et al., 2014 ).

1.3. Overview of studies

The results presented in Lenton et al. (2013 , 2014) went beyond self-report research by capturing experiences of authenticity as expressed in life-stories. Yet the narrative coding also included the limitation of coding themes that had been previously identified in the literature. Therefore, those studies may not capture unique aspects of authenticity that emerge from a bottom-up approach to coding participants' lived-experience. That was the aim of Study 1, described next. 1 In Study 2, we relate aspects of authenticity/inauthenticity that emerged in Study 1 to self-reported authenticity and other potentially relevant traits. Study 2 answers questions about whether people who think of themselves as authentic/inauthentic describe their experiences in ways that reflect their traits. These studies occur in a particular sociohistorical context, particularly, they involve American undergraduate students at a midwestern university in the early 21 st century. Cultural influences undoubtedly influence notions of authenticity and inauthenticity of our study participants. Therefore, though generalizability to other contexts cannot be assumed, we hope to provide an approach that can be generalized to other populations in the future. All materials, data, and analyses are provided at https://osf.io/u9fea/?view_only=5bb21c2ba8d442218ed4f750c6c2a4c1 .

2.1. Study 1

We collected descriptions of life-story scenes that were experienced as authentic, inauthentic, and emotionally vivid. We coded themes present in the scenes using a bottom-up approach. In order to identify the themes that were uniquely prevalent in the authentic and inauthentic scenes, we compared the frequencies of themes in these scenes to the same themes in the emotionally vivid scene.

2.1.1. Participants

Study 1 consisted of 87 undergraduate students (54% men), aged between 18 and 22 ( M = 18.66, SD = .93), at a large, private Midwestern university who participated in partial fulfillment of the research participation requirement of an introductory psychology course. Ethnicities were: White (41%), Asian-American (36%), Hispanic or Latino (9%), Black (4%), Multiracial (4%), and “other” (5%). All methods were approved by the Northwestern University Institutional Review Board (IRB).

2.1.2. Procedure

Participants were recruited to the study through the introductory psychology student participant pool. Individuals deciding to participate in the study navigated to a secure website affiliated with the university where they gave informed consent. Then, participants completed written narratives of scenes in the past year of their lives that were remembered as authentic, inauthentic, and emotionally vivid. The authenticity prompt directed participants to describe “an authentic scene, a scene in which you were most like your true self.” The inauthenticity prompt directed participants to describe and “an inauthentic scene, a scene in which you were least like your true self.” Prompts were comparable to those used in Lenton et al. (2013) and were kept short because longer and more descriptive prompts could be more likely to bias participant responses toward the description contained in the prompt. The instructions for the emotionally vivid memory were to describe “a particularly emotionally vivid scene, a scene which you experienced as very memorable.” For each scene, participants were asked to describe what happened, where and when the event took place, who was involved, thoughts and feelings during the event, and what the event says about “you and your personality.”

2.1.3. Coding procedure

The purpose of the coding procedure was to identify and classify the phenomenology of authentic and inauthentic experiences. In the first part of the coding procedure, we employed an approach similar to grounded theory ( Creswell, 2007 ; Strauss and Corbin, 1998 ) to inductively derive themes from the data. Two undergraduate research assistants (coders) read the authenticity and inauthenticity scenes independently and generated lists of adjectives and phrases to describe the content of each scene. Coders were instructed to generate descriptors of the protagonist's behavior and mental state (e.g., thoughts, feelings, goals, etc.), the physical location of the scene (e.g., at school, at home, etc.), and the people present in the scene (e.g., friends, family, etc.). The two coders' lists were combined and duplicate descriptors were deleted. In all, a total of 108 unique descriptors were identified (see Appendix ). In the second part of the coding procedure, two independent, undergraduate coders rated whether each of the 108 descriptors identified in the first part of the coding procedure were present or absent in the authentic scene, inauthentic scene, and emotionally vivid scene. 2 In order to illustrate and clarify the coding procedure, we provide in the Appendices an Example of one participant's authentic, inauthentic, and emotionally vivid scenes and the characteristics that were noted as present in each scene during in the second part of the coding procedure.

2.1.3.1. Inter-coder agreement

We first examined whether coders agreed on the relative frequency of the different characteristics across each scene. We created variables reflecting the frequency of each characteristic occurred in each scene for each coder. For Example, for the authenticity scene, the number of times each characteristic occurred represented a row value in this variable. In order to assess inter-coder agreement, we computed intraclass correlations ( ICCs ; Shrout and Fleiss, 1979 ). For example, coder 1's authentic scene variable was correlated with coder 2's authentic scene variable.

2.1.3.2. Identifying unique characteristics

Characteristics that occur frequently in participants' authentic scene narratives and with high relative frequency compared to their occurrence in inauthentic and emotionally vivid narratives could be said to be uniquely definitive of authentic experiences. In order to quantify “uniqueness,” we relied on three criteria.

The first criterion of uniqueness was calculated as the total frequency of occurrence for characteristics in authentic and inauthentic scenes. The second criterion was the raw difference between frequency of occurrence between the target scene (authenticity or inauthenticity) and the sum of frequencies across other scenes. For the authenticity scene, this would equal the frequency of occurrence of the characteristic in the authenticity scene minus the sum of the frequencies in the inauthenticity and vivid scenes. This criterion is important because characteristics that occur frequently in the target scene may occur at a high rate in other scenes as well and therefore not be unique to the target scene. The third criterion was calculated by multiplying the raw difference (i.e., the value obtained for the second criterion) by the ratio of occurrence in the target scene compared to the sum of occurrence in the other scenes. For the authenticity scene, this value was obtained by taking the raw difference (i.e., the second criterion) and multiplying it by the quotient of the frequency of occurrence of the characteristic in the authenticity scene and the sum of the frequencies in the inauthenticity and vivid scenes. This weighted difference criterion weights the second criterion by the odds of occurrence for each characteristic in the target scene compared to the other scenes combined.

2.1.4. Results

We first assessed the intercoder agreement for the different scenes. Then we identified unique characteristics in each scene using the total frequency, raw difference, and weighted difference criteria described in the previous section. Finally, we clustered similar individual characteristics into broader, conceptually coherent categories.

2.1.4.1. Intercoder agreement

ICC s were uniformly high for the authentic (.95), inauthentic (.88), and vivid (.92) scenes, indicating that coders agreed on the relative frequency of each characteristic for each scene.

2.1.4.2. Unique characteristics

The top 30 most frequently occurring characteristics (and ties) for the authentic scene and inauthentic scene are shown in Tables  1 and ​ and2, 2 , respectively. These tables also show each characteristic's values for the absolute difference and ratio of occurrence criteria (along with the ranking of those characteristics based on each respective criterion). Most of the frequently occurring characteristics were also identified as highly unique based on the absolute difference and ratio criteria (indicated with italics in the table). Indeed, 23 of the most frequently occurring authentic characteristics and 19 of the most frequently occurring inauthentic characteristics were in the top 30 (and ties) based on the other criteria as well. We selected these criteria to be uniquely definitive of authentic and inauthentic experiences, respectively.

Table 1

The top 30 most frequently occurring characteristics (and ties) for the authentic scene.

Note. The Raw Difference was computed as the Authentic Scene frequency minus the sum of frequencies across other scenes. The Weighted Difference is equal to the Raw Difference divided by the sum of the frequencies in the other scenes. The numbers appearing in parentheses are the ranks of each characteristic according to the Raw Difference and Weighted Difference criteria, respectively. Characteristics in bold were in the top 30 for each of the criteria and were selected as uniquely definitive of authenticity scenes.

Table 2

The top 30 most frequently occurring characteristics (and ties) for the inauthentic scene.

Note. The Raw Difference was computed as the Inauthentic Scene frequency minus the sum of frequencies across other scenes. The Weighted Difference is equal to the Raw Difference divided by the sum of the frequencies in the other scenes. The. The Weighted Difference for “Going with the crowd/conformity is listed as “NA” because the sum of occurrence for this characteristic in the Authenticity and Emotionally Vivid scenes was equal to 0. The numbers appearing in parentheses are the ranks of each characteristic according to the Raw Difference and Weighted Difference criteria, respectively. Characteristics in bold were in the top 30 for each of the criteria and were selected as uniquely definitive of inauthenticity scenes.

2.1.4.3. Conceptual and thematic aggregation

Many of the characteristics identified as highly unique in authentic scenes (e.g, “trusting others,” and “understanding others”) and inauthentic scenes (e.g., “feeling phony/fake,” and “putting on an act.”) were deemed conceptually similar. We therefore sought to increase the parsimony of the results by clustering characteristics together. This was done by the first and second author independently, then discussed by both authors and again and discussed with the third author. Authors classified similar themes together and assigned a name to the cluster that represented the concept common among the individual themes (see Table 3 ).

Table 3

Conceptual and thematic aggregation of characteristics that were uniquely definitive of the authentic scene and inauthentic scene.

2.1.5. Discussion

This study represents a first step to examine themes occurring in narratives of authentic and inauthentic experiences. We identified individual themes that were conveyed at relatively high levels in life story scenes of authentic and inauthentic experiences as compared with scenes depicting an emotionally vivid experience.

For the authentic scene, the first category was named relational authenticity . This category is similar to the relational component of authenticity proposed by Kernis et al. (2006) , as it encompasses the values of openness and honesty in interpersonal interactions, as well as striving for empathy and understanding in relationships. Furthermore, authenticity in relational contexts has received increased empirical attention and has been shown as a robust predictor of relational health, such as more secure romantic attachment, higher caregiving to a partner, positive responses from interaction partners, and interpersonal trust ( Gouveia et al., 2016 ; Plasencia et al., 2016 ; Wickham, 2013 ). The second category, expression of true self , is similar to the authentic behavior component of Kernis et al. and the authentic living component of Wood et al. (2008) , as it reflects a genuine expression of one's true thoughts and feelings. The contentment category, reflecting comfort and enjoyment, is not well-represented by self-report authenticity measures; however, this theme is similar to the positive emotions theme that characterized the “most me” narratives in Lenton et al. (2013) . Ownership of actions reflects taking responsibility for behaviors and a sense of self-authorship of one's life, similar to the construct of autonomy in Self-Determination Theory ( Deci and Ryan, 1985 , 2000 ). Resisting external pressures, such as by disobeying authority and resisting outside influence seems to be the conceptual inverse of Wood et al.'s accepting external influence component.

For the inauthentic scene, the phoniness category reflects being “fake” or putting on an act that is inconsistent with one's true self. This component seems to capture the recognition that one is not behaving authentically and may therefore be thought of as the opposite pole of the expression of true self category. To the extent that being fake reflects perceptions of being out of touch with one's true self, this category may also be similar to the self-alienation component of Wood et al. (2008) . Conformity , encompassing themes such as “going with the crowd” and “living up to others' expectations” has the flavor of controlled behavior (low autonomy) along with the accepting external influence component of Wood et al. The suppression category reflects both a knowledge of one's true way of being and actively denying its expression through thought, feeling, and behavior. Thus, it seems this category captures both the awareness component of Kernis et al. (2006) and the denial of authentic behavior or authentic living. Finally, the self-denigration category encompasses derogatory thoughts and disgust directed toward the self. This category is not well-represented in self-report measures of authenticity but seems related to the negative emotions theme found in the “least me” scenes in Lenton et al. (2013) .

In sum, most of our categories shared conceptual space with existing self-report measures. Contentment and self-denigration do not have corresponding self-report scales, however, similar themes were observed in “most me” and “least me” scenes reported in Lenton et al. (2013) . Well-being and ill-being are typically seen as outcomes of authenticity and inauthenticity, respectively ( Boyraz et al., 2014 ), and so it may not be surprising that these categories have emerged as uniquely definitive of authentic and inauthentic experiences. All components from self-report measures were reflected in our categories of aggregated themes with the exception of the unbiased awareness component of Kernis et al. (2006) . However, single themes that uniquely defined the authenticity scene (“acceptance of imperfections/flaws”) and inauthenticity scene (“avoiding negative evaluation”) suggest that the unbiased awareness component may be relevant to participants' memories of authentic and inauthentic scenes.

2.2. Study 2

Our goals in Study 2 were twofold. First, we aimed to use the clusters of themes that were characteristic of authenticity and inauthenticity scenes as the basis for generating a systematic and reliable coding system for memories of authenticity and inauthenticity. Second, we aimed to examine the relations between our coding categories with self-report measures of authenticity, as well as with the traits of autonomy, honesty-humility and Machiavellianism.

The construct of autonomy from Self-Determination Theory ( Deci and Ryan, 2000 ) may be thought of as related to authenticity, as it is defined by experiencing one's behavior as congruent with inner values and interests as opposed to being controlled by external forces. Autonomy may be distinguished from classical views of authenticity by the lack of the assumption of a single “true self”; that is, from the perspective of SDT, any aspect of the self that feels autonomous is considered true and authentic.

Honesty-humility is a trait from the HEXACO model of personality that reflects individual differences in tendencies to approach others in fair, sincere, and modest ways as opposed to being deceptive and greedy ( Ashton and Lee, 2001 ). When included in factor analyses with other personality traits, honesty-humility loaded on a factor defined by authenticity and autonomy measures ( Maltby et al., 2012 ).

Machiavellianism is part of the “Dark Triad” of traits, including narcissism and subclinical psychopathy ( Paulhus and Williams, 2002 ), that reflects individual differences in tendencies to manipulate others, experience cold affect in interpersonal situations, have negative views of other people, and pursue selfish goals ( Rauthmann and Will, 2011 ). Though no studies (to our knowledge) have examined the relations between authenticity and Machiavellianism, studies have suggested that Machiavellianism is strongly, negatively associated with honesty-humility ( Jonason and McCain, 2012 ; Lee and Ashton, 2005 ) and thus might be expected to relate negatively with authenticity.

This study may be important for at least three reasons. First, it serves as a first step toward establishing the reliability and validity of a coding scheme for authentic and inauthentic experiences. Second, it begins to situate a narrative approach to authenticity within prominent trait approaches to authenticity (and to related traits) in the personality literature. Third, including measures of honesty-humility and Machiavellianism allowed us to explore whether people at different ends of socially desirable (i.e., honesty-humility) and undesirable (i.e., Machiavellianism) traits understand authenticity in different ways.

2.2.1. Participants

Participants were 103 undergraduate students (40% men) from the same university as in Study 1, ages 18 to 22 ( M = 18.79, SD = 1.06), who completed the study as partial fulfillment of the requirements of an introductory psychology course. Ethnicities were: White (58%), Asian-American (21%), Black (9%), Hispanic or Latino (6%), Multiracial (4%), and Native Hawaiian or other Pacific Islander (1%). The sample size was based upon practical limitations of the undergraduate subject pool. All methods were approved by the Northwestern university IRB.

2.2.2. Procedure

Participants were recruited to the study through the introductory psychology participant pool. Individuals deciding to participate in the study navigated to a secure website affiliated with the university where they gave informed consent. Then, participants completed an online questionnaire containing the self-report measures described below as well as the written narratives of authentic and inauthentic scenes. As in Study 1, the authenticity prompt directed participants to describe “an authentic scene, a scene in which you were most like your true self”, and the inauthenticity prompt directed participants to describe and “an inauthentic scene, a scene in which you were least like your true self.” For each scene, participants were asked to describe what happened, where and when the event took place, who was involved, thoughts and feelings during the event, and what the event says about “you and your personality.”

2.2.3. Self-report measures

2.2.3.1. authenticity inventory.

Participants completed Kernis et al. (2006) Authenticity Inventory (AI-3), which includes 45 items that assess four components of authenticity: awareness (e.g., I am in touch with my motives and desires); unbiased processing (e.g., I am very uncomfortable objectively considering my limitations and shortcomings (reverse scored)); behavior (e.g., I frequently pretend to enjoy something when in actuality I really don't (reverse scored)); and relational orientation (e.g., I want people with whom I am close to understand my weaknesses). Participants responded to items on a 6-point scale ranging from 1 (strongly disagree) to 6 (strongly agree). Alphas were acceptable for each subscale: for awareness, α = .83; for unbiased processing, α = .74; for behavior, α = .74; and for relational orientation, α = .79.

2.2.3.2. Authenticity Scale

Participants completed Wood et al.'s (2008) Authenticity scale, which includes 12 items that assess their tripartite model of authenticity: self-alienation (e.g., I don't know how I feel inside); accepting external influence (e.g., I usually do what other people tell me to do); and authentic living (e.g., I am true to myself if most situations). Participants responded to items on a 6-point scale ranging from 1 (strongly disagree) to 6 (strongly agree). Alphas were acceptable for each component: for self-alienation, α = .90; for accepting external influences, α = .84; and for authentic living, α = .83.

2.2.3.3. Autonomy

Participants completed the 7-item assessment of autonomy included in the Basic Psychological Needs Scale ( Gagné, 2003 ). Items assess the degree to which participants feel their need for autonomy is currently satisfied (e.g., I feel like I am free to decide for myself how to live my life). Participants responded to items on a 6-point scale ranging from 1 (strongly disagree) to 6 (strongly agree), and α = .72 for the scale was acceptable.

2.2.3.4. Honesty-humility

Participants completed the International Personality Item Pool (IPIP) assessment of honesty-humility (IPIP-HEXACO; Ashton et al., 2007 ; Goldberg et al., 2006 ). The IPIP-HEXACO honesty-humility scales assess the four facets of honesty-humility included in the HEXACO model of personality with 10 items for each facet: sincerity, (e.g. “Don't pretend to be more than I am”); fairness, (e.g., “Try to follow the rules”); greed avoidance (e.g. ”Don't strive for elegance in my appearance”); and modesty (e.g. “Am just an ordinary person”). Participants responded to items on a 6-point scale ranging from 1 (strongly disagree) to 6 (strongly agree. Alphas were acceptable for each facet: for sincerity, α = .87; for fairness, α = .85; for greed avoidance, α = .62; and for modesty, α = 83. The IPIP-HEXACO scales have convergent correlations of between .76 and .98 with the original HEXACO facet scales ( Ashton et al., 2007 ).

2.2.3.5. Machiavellianism

Machiavellianism was measured using the 20-item MACH IV scale developed by Christie and Geis (1970) . Each respondent was asked to indicate the extent of his or her agreement or disagreement with each of the 20 items (e.g., “It is wise to flatter important people.”) using a 6-point scale ranging from 1 (strongly disagree) to 6 (strongly agree). An α coefficient of .73 was obtained for this scale.

2.2.4. Coding procedure

We developed a content analysis system for coding the themes that were identified in Study 1 as uniquely descriptive of authentic and inauthentic experiences. Two coders blind to information about participants were required to determine (i) how much each authentic scene conveyed each of the themes that were uniquely descriptive of authentic scenes and (ii) how much each inauthentic scene conveyed each of the themes that were uniquely descriptive of inauthentic scenes. Themes uniquely descriptive of authentic experiences in Study 1 were: relational authenticity, expression of true self, contentment, ownership of actions, and resisting external pressures. Themes unique to inauthentic experiences were: phoniness, conformity, suppression, and self-denigration. Coders used the guidelines presented in the Appendix to code each respective theme. Inter-coder reliabilities were calculated as ICC s for the average of fixed coders ( Shrout and Fleiss, 1979 ) for all themes, and scores for each theme were calculated as the average of coders' ratings.

2.2.5. Reliability of coding categories

Our first goal of this study was to create reliable coding categories for themes that were uniquely definitive of authenticity and inauthenticity scenes. Based on the guidelines of Koo and Li (2016) , this goal was achieved for relational ( ICC = .75), contentment ( ICC = .83), ownership of actions ( ICC = .65), resisting external pressures ( ICC = .67), phoniness ( ICC = .75), conformity ( ICC = .75), and suppression ( ICC = .62) codes. The expression of true self theme from the authenticity scene ( ICC = .51) and self-denigration theme from the inauthenticity scene ( ICC = .51) had moderate interrater reliabilities. However, 70% of expression of true self codes were within one point of complete agreement (e.g., Coder 1 rating = 1, Coder 2 rating = 2) as were 89% of the suppression codes, suggesting that these categories were coded reliably. Nonetheless, results pertaining to these themes should be interpreted with some caution.

First, we present descriptive statistics for self-report and narrative variables. We then examine correlations among self-report variables and narrative themes. Finally, we conduct data reduction procedures on the self-report variables and relate the resultant factors to narrative themes.

3.1. Descriptive statistics

Means and standard deviations for self-report and narrative variables are shown in Table 4 . Participants reported relatively higher scores on self-report variables scored in a desirable direction (e.g., authenticity, honesty) than those scored in an undesirable direction (e.g., inauthenticity, Machiavellianism). Although mean values were relatively low for narrative codes, this was likely because only one or two themes were most prevalent in each individual's scene. Supporting this interpretation, 70/103 participants scored at least 2.5 for one of the authenticity codes, and 90/103 participants scored at least 2.5 on one of the inauthenticity codes. Thus, the large majority of participants showed at least moderate levels of at least one of the themes in their authenticity and inauthenticity scenes.

Table 4

Means, standard deviations, and correlations for self-report measures (s1–s13).

Note. Descriptive statistics were calculated at the average item level for self-report variables. Correlations >.2 are significant at α = .05.

3.2. Correlations among variables

3.2.1. self-report variables.

Pearson correlations among variables are shown in Table 4 . As was to be expected, self-report scales scored in the direction of authenticity had strong positive correlations with each other (and autonomy) and strong negative correlations with scales scored in the direction of inauthenticity. Authenticity scales and autonomy also tended to correlate positively with honesty facets and negatively with Machiavellianism, whereas inauthenticity scales showed the opposite pattern of relations. Finally, honesty facets correlated negatively with Machiavellianism.

3.2.2. Narrative themes

Turning to the correlations between authenticity codes (see Table 5 ), relational authenticity was positively correlated with expression of true self and contentment, and ownership of actions was positively correlated with resisting pressures. Contentment was negatively correlated with ownership of actions. For inauthenticity codes, suppression was positively correlated with conformity and negatively correlated with self-denigration. Looking across authenticity and inauthenticity codes, ownership of actions was positively correlated with self-denigration. Overall, most bivariate correlations between pairs of narrative themes did not reach statistical significance, and magnitudes of those that did were small to moderate. This pattern of findings suggests that individual themes may be considered separately in relation to self-report variables.

Table 5

Means, standard deviations, and correlations for narrative codes (n1-n9).

Note. Descriptive statistics were calculated from the average of coders' ratings for narrative variables. Correlations >.2 are significant at α = .05.

3.2.3. Self-report variables and narrative themes

There were several statistically significant correlations between narrative codes and self-report variables (see Table 6 ). Relational authenticity in authentic scenes was positively associated with relational orientation from the Authenticity Inventory; people who view themselves as expressing their true selves in relationships described authenticity scenes in which they related to other people in honest, trusting, and understanding ways. Self-denigration in inauthentic scenes was positively associated with awareness from the Authenticity Inventory; people who profess to know their psychological make up (affect, behavior, thoughts, and motivations) tend to criticize themselves when depicting memories of inauthenticity. The negative associations between self-report variables reflecting higher authenticity, autonomy, and honesty and the phoniness code suggests that people who perceive themselves as less authentic emphasize discrepancies from their true selves when recounting inauthentic memories. People who report higher Machiavellianism also emphasized phoniness in inauthenticity memories; thus, people who report using others to get what they want narrated inauthenticity memories in which they acted in a fake manner.

Table 6

Correlations between self-report variables and narrative codes.

Note. Correlations >.2 are significant at α = .05. n1 = Relational authenticity; n2 = Expression of true self; n3 = Contentment; n4 = Ownership of actions; n5 = Resisting external pressures; n6 = Phoniness; n7 = Conformity; n8 = Suppression; n9 = Self-denigration.

3.3. Factor extension analysis

Factor extension analysis involves deriving factors from a given set of variables, and then finding the loadings of an additional (or extended) set of variables (i.e., variables not included in the original factor analysis) on those factors ( Dwyer, 1937 ; Horn, 1973 ). Factor extension analysis was desirable for these data because the self-report variables were highly intercorrelated. In order to conduct the factor extension analysis, we first needed to know how many factors to extract. The technique used to answer this question was Very Simple Structure (VSS) analysis ( Revelle and Rocklin, 1979 ), an exploratory method for determining the optimum number of interpretable factors to extract from a data set. This index can take values between 0 and 1 and is a measure of the goodness-of-fit of the factor solution for a given factorial complexity. The VSS value peaked at .91 for a two-factor solution for with a complexity of two.

A minimum residual factor analysis extracting 2 factors was carried out on the self-report scales and rotated using the oblimin rotation. Factor extension analysis determined what the loadings of the narrative codes would be on these factors (see Table 7 ). Factor 1 was defined at the positive pole by authenticity and autonomy scales, and at the negative pole by inauthenticity. Factor 2 was defined by the honesty scales at the positive pole and by Machiavellianism at the negative pole. The two factors were moderately, positively correlated ( r = .49). Using a cutoff of .2 for interpretation, factor extension results showed that the narrative codes of “ownership of actions” and “self-denigration” were positively related to the authenticity factor (Factor 1), and that the “phoniness” code was negatively related to both the authenticity and honesty factors (Factor 2). People who scored high on authenticity/autonomy tended to describe their authentic moments as times when they took ownership of their actions, and they described their inauthentic moments as times when they criticized themselves. People who reported themselves as highly inauthentic, as Machiavellian, and as dishonest described their inauthentic moments as containing relatively high degrees of phoniness.

Table 7

Factor extension analysis results.

Note. A minimum residual factor analysis extracting 2 factors was carried out on the self-report scales and rotated using oblimin rotation. Factor extension analysis determined what the loadings of the narrative codes would be on these factors. Numbers underneath columns “Factor 1” and “Factor 2” are factor loadings. “h2” = communality; “u2” = uniqueness.

3.4. Discussion

As noted in the discussion of Study 1, there is a great deal of conceptual overlap between our narrative coding scheme and existing self-report measures of authenticity and related constructs. This overlap is also observed in the empirical associations in Study 2, attesting to the convergent validity of both the self-report variables and narrative codes. Yet the degree of overlap is not nearly strong enough to consider the constructs redundant across levels of analysis. The magnitude and consistency of the associations suggests that trait approaches and narrative approaches to authenticity may each tap into unique information about a person's psyche; the narrative codes show evidence of both convergent and discriminant validity with trait measures. This pattern of results mirrors findings from other research examining relations between trait and narrative variables (e.g., Lodi-Smith et al., 2009 ; McAdams et al., 2004 ; Wilt et al., 2011 ).

4. Discussion and conclusions

Authenticity has been espoused as a central virtue by philosophers and psychologists alike, and recent trait approaches to measurement of authenticity have revealed compelling empirical associations between higher levels of authenticity and greater well-being. Based on the notion that authenticity may be relevant to narrative identity — the evolving life stories that connect one's past with the present and imagined future — our goals in the current research were to (i) identify authenticity and inauthenticity themes as expressed in life story scenes, (ii) create reliable coding categories for such these themes, and (iii) examine the relations between narrative themes and self-report assessments of authenticity and related constructs. These goals were largely realized across two studies of undergraduates, thus representing a first step toward the study of authenticity and inauthenticity in narrative identity.

4.1. Authenticity/inauthenticity in life-story scenes

The different components of authenticity and inauthenticity revealed through life story scenes coincided well with self-report measures. Though this might not seem surprising in hindsight, there are several reasons that our coding strategy could have identified themes lying outside of existing self-report frameworks (see Adler et al., 2017 ; Panattoni and McLean, 2018 ). Methodological reasons include lack of method variance, differences in operationalization of the constructs, and socially desirable responding to both self-reports and narrative prompts. Conceptual reasons include the possibility that generating narratives taps into a different mode of thought than does responding to self-report questions, that narrative measures may be more susceptible to unconscious influences than are self-report items, and that narratives are more contextualized to particular situations and influenced by a perceived audience than are self-report responses. Finally, our use of an inductive or data-driven approach to coding did not necessitate overlap with any existing theoretical frameworks of authenticity ( Syed and Nelson, 2015 ).

The conceptual correspondence seems to be good news for both self-report and narrative approaches to authenticity. Self-report measures need not expand to include content that was missed, and narrative approaches need not seek theoretical justification for content lying outside of self-report boundaries. However, the levels of assessment were not redundant; the moderate associations between narrative themes and self-report measures suggest that narrative assessments do not overlap completely with self-report scales.

Because authenticity has been a robust predictor of mental health, perhaps the most pressing question for future research is to determine whether narrative themes are related to mental health above and beyond self-report measures. There is good reason to think that this might be the case. A recent review concluded that narrative themes have incremental validity in predicting well-being both cross-sectionally and prospectively independently of dispositional personality traits ( Adler et al., 2016 ). Additionally, qualitative studies link authenticity to healthy behavior ( Conroy and de Visser, 2015 ) and quantitative studies show that expressing intrinsic values in memories is related to well-being ( Lekes et al., 2014 ).

Another direction for future research that may further integrate trait and narrative approaches to authenticity is to explore what types of life stories support (self-reported) authentic functioning. That is, how does narrative identity differ among people with higher and lower levels of self-reported authenticity? The current study reveals only limited information related to this question; specifically, our findings suggest that people with different levels of self-reported authenticity see different themes as indicative of authenticity/inauthenticity in life story scenes. For Example, our findings indicate that a person with higher self-reported relational authenticity is likely to view relational honesty as indicative of authenticity. Our findings have nothing to say about whether people with higher levels of self-reported relational authenticity tell life stories (when not primed to tell moments that are authentic) that convey themes of open, honest, and close relationships. It is easy to envision a study that tests these ideas. Scenes from a typical life story interview (e.g., high point, low point, turning point, important memories from different times of life) could be coded for the authenticity/inauthenticity themes identified in the current study, and these themes could be correlated with self-reported authenticity measures. It is worth noting that a study like this could not be conducted without first identifying valid coding categories for authenticity/inauthenticity, as was done in the current studies.

4.2. Limitations

There may be some concern that authenticity and inauthenticity were assessed through subjective methods of self-report and life-story scenes. Therefore, we cannot make claims about more objective aspects of authenticity and inauthenticity that might be captured by measures such as experimentally manipulated reaction times (e.g., Schlegel et al., 2009 ). Though this is a potential limitation, subjectivity is at the core of a narrative identity perspective on authenticity. The narrative identity approach is authenticity centered on one's personal myth regarding what it means to be true (and false) to oneself. This standpoint is somewhat similar to that taken by Schlegel and colleagues (e.g., 2009 ; 2011 ; 2013 ) regarding the true self-concept. The authors remained neutral on the issue of the ontological reality, preferring instead to define the true self-concept as the cognitive schema and subjective beliefs and feelings what aspects of one's self-concept reflect true, core characteristics. This decision was based on the rationale that perceptions of reality can have important consequences regardless of the accuracy of the perceptions.

The researchers and coders bring unique perspectives and biases to extracting themes from qualitative data. Other labs may have identified different themes and clustered themes in different ways in Study 1. This would have resulted in different quantitative coding categories in Study 2. Indeed, the emergence of categories that were similar to dimensions of authenticity/inauthenticity that emerged in previous research may reflect some of our biases. Therefore, our coding systems is not meant to be exhaustive but rather one potentially useful way of examining authenticity in life-stories.

The generalizability of our findings may also be limited because we relied on samples of undergraduates from a Western, educated, industrialized, rich, and democratic (WEIRD) society ( Henrich et al., 2010 ). Potential limitations to generalizability based on age and culture are highly relevant for the concept of authenticity. Identity conflicts may be more salient for undergraduate participants than for older adults, and therefore older adults might have a more stable ideas about what constitutes authenticity and inauthenticity ( Schlegel et al., 2013 ). More stable notions of authenticity could result in stronger and possibly more reliable associations between scales attempting to measure authenticity-related constructs. Authenticity may also be more highly valued among Western as compared to non-Western nations ( Lenton et al., 2013 ; Slabu et al., 2014 ). Understandings of authenticity could therefore vary across cultures, which may have consequences for which themes emerged as uniquely defining of authenticity and inauthenticity; for instance, most themes that were uniquely definitive of authenticity in the current study were primarily concerned with the self, which may be due to the relative emphasis on self-enhancement in Western nations. Perhaps people from non-Western nations would place more emphasis on relational authenticity themes. Regardless of these limitations, narrative studies in their essence are aimed at developing an understanding of how a particular group of people understand themselves in a particular sociohistorical context ( McAdams and Pals, 2006 ).

Finally, our relatively small sample sizes may be considered a limitation. In Study 1, it is possible that different unique themes may have emerged with a greater sample size, or that the rank-ordering of unique themes could have changed. In Study 2, our power to detect small-to-medium effect sizes was not optimal. Though we recommend that future studies employ larger sample sizes, we are aware that logistical challenges to collecting and coding narratives would make doing so a lengthy and costly undertaking.

5. Conclusion

In the current studies, we found that for undergraduates in a Western society, authentic experiences entail expressing one's perceived true nature, being content and relaxed, taking ownership of one's choices, not giving in to external pressures, and having open and honest relationships. In contrast, inauthentic experiences involve being phony, conforming to others' expectations, suppressing one's emotions, and denigrating the self. Thus, life-story scenes characteristic of authenticity/inauthenticity turned out to be similar to the ideas emerging from prominent psychological and philosophical theories. Assessments of narrative authenticity/inauthenticity were related to but distinct from self-report assessments, opening the possibility for narrative approaches to reveal unique insight into the psychology of the true and false selves.

Declarations

Author contribution statement.

Joshua Wilt: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Sarah Thomas: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.

Dan McAdams: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interest statement

The authors declare no conflict of interest.

Additional information

Data associated with this study has been deposited at Open Science Framework under: https://osf.io/u9fea/?view_only=5bb21c2ba8d442218ed4f750c6c2a4c1 .

1 Both studies were part of larger projects and thus contained additional self-report measures that were not included in the current analyses. All study materials, all data, and all analyses for the current manuscript are available at: https://osf.io/u9fea/?view_only=5bb21c2ba8d442218ed4f750c6c2a4c1 .

2 Coders were instructed to work on only one type of scene at a time in order to reduce the likelihood that the coding of the participant's target scene (i.e., the scene being coded) was influenced by the content of the participant's other scenes. For instance, we did not want the content of a participant's authenticity scene to influence how a coder perceived the same participant's inauthenticity scene.

Appendix A. Unique descriptors of authentic and inauthentic scenes

Appendix b. example scenes.

Example authenticity scene. “I was with my friends at their dorm room. We were in the common room, just us, it was late and we were laughing about weird ticks that we have. I was with me boyfriend, and when it was my turn I said that I drool a lot while I'm asleep. At the time I was sleeping with my boyfriend but hadn't told him about my drooling. We all laughed, and then my boyfriend turned to me and said, “yeah you do drool a lot” and I felt so embarrassed and ashamed. But we were laughing about it at the same time and it just made me feel comfortable about myself. Then he went and said something equally as embarrassing and I felt better. I realized that everyone does something weird and gross and it was cool to talk about it, out in the open. I really felt like I could be myself around these people.”

This scene was coded as containing the characteristics of: “companionship”, “acting in a genuine way with others, “feeling true to oneself” “enjoyment” “feeling understood by others”, “being honest with others”, “receiving encouragement”, “being with friends”, “not being judged”, “embarrassment”, “being carefree”, “trusting others”, “revealing true feelings to others”, “being relaxed”, “being disinhibited”, and “acceptance of imperfections”.

Example inauthenticity scene. “ The first day at [university blinded] I was really desperate to meet people that would like me. At night I went to a dance party and felt that no one would like me unless I was “on”. So I started being funny and comical, people started laughing and soon I had people congregating around watching me tell jokes and be funny. I started saying that I liked music that I hated because these people liked it. I felt so fake! And I wanted to just be myself, but I knew that if I was myself people wouldn't like me. So I kept up being funny because I wanted to be the life of the party. Pretty soon everyone was talking about how funny and clever I was. As the party died down and people left I had a phone filled with new numbers, but I felt just as alone as when I walked in. I walked back to my room and thought that I really wouldn't like anyone that I had met that night personally.”

This scene was coded as containing the characteristics (presented in the order in which they were coded) of: “feeling phony”, “lying to others”, “being different from one's usual self”, “desire to make a good impression”, “striving for acceptance”, “acting to please others”, “being pessimistic”, and “being upset”.

Example emotionally vivid scene. “ I drove out of my high school for the very last time. It was the last day of school my senior year, and I was done with all of my tests. It was the last time I would drive out of the parking lot as a student. I felt so many emotions: accomplishment, fear, lots of sadness, yet happiness. I was one of the last ones to finish my exams and when I walked out the parking lot was empty. It was hot I just remembered wanting to stay there, and not leave. I thought back at my experiences and wanted to have the same great moments in college. At the end I remembered being afraid of were life was gonna take me next.”

This scene was coded as containing the characteristics (presented in the order in which they were coded) of: “sadness”, “happiness”, “accomplishment”, “being at school”, “fear”, and “nervousness”.

Appendix C. Authenticity and inauthenticity coding guidelines

Relational authenticity. Key characteristics of scenes containing “relational authenticity” (identified in Study 1) are: acting in a genuine way with others, companionship, feeling understood by others, honest with others, revealing true feelings to others, trusting others, and understanding others . Relational authenticity reflects the degree to which the protagonist feels connected to others. The scene conveys the sense that others are trustworthy and expressions of honesty are welcomed with understanding and in a non-judgmental way. The protagonist feels a strong sense of companionship with others in the scene. Scenes were coded on a scale from 1 (very low) to 4 (very high) based on the degree to which they conveyed relational authenticity: “The scene conveyed relational authenticity to a...degree”.

Expression of true self. Key characteristics scenes containing the theme “expression of true self” are: expression of true thoughts, feeling true to oneself, genuineness, and revealing one's true self . This code reflects the degree to which the protagonist feels true to him/herself. The scene conveys the sense that one's actions and feelings are accurate reflections of one's true nature. The protagonist feels that the scene is a genuine portrayal of one's real self. Scenes were coded on a scale from 1 (very low) to 4 (very high) based on the degree to which the protagonist expressed his or her true self: “The scene expresses the protagonist's true self to a...degree''.

Contentment. Key characteristics of scenes containing the theme “contentment” are enjoyment, feeling comfortable, happiness, and relaxation . This code reflects the degree to which the protagonist feels content in his/her surroundings. The scene conveys a sense of comfort and happiness. The protagonist feels relaxed and natural in the situation or event described in the scene. Scenes were coded on a scale from 1 (very low) to 4 (very high), based on the degree to which they convey contentment: “The scene conveyed feelings of contentment to a...degree.''

Ownership of actions. Key characteristics of scenes containing the theme “ownership of actions” are accepting responsibility, acting in accordance with one's beliefs and values, and taking ownership of choices . This code reflects the degree to which the protagonist feels ownership of his/her actions. The scene conveys the sense that actions were in accordance with the values of the protagonist. The protagonist accepts responsibility for his/her actions by identifying some value or value system and acting in accordance with those values. Scenes were coded on a scale from 1 (very low) to 4 (very high), based on the degree to which they conveyed that the protagonist accepted ownership of his/her actions. “The protagonist accepted ownership of his/her actions to a...degree.''

Resisting external pressures. Key characteristics of scenes containing the theme “resisting external pressures'' are disobeying authority and resisting influence . This code reflects the degree to which the protagonist resists external pressures to conform. The scene conveys the sense that the protagonist defies the influence of social pressures. The protagonist opposes authority figures and/or societal expectations. Scenes were coded on a scale from 1 (very low) to 4 (very high), based on the degree to which the protagonist resisted external pressures. “The protagonist resisted external pressures to a...degree.''

Phoniness. Key characteristics of scenes containing the theme “phoniness'' are different from usual self, feeling phony/fake, and putting on an act . This code reflects the degree to which the protagonist feels that he/she is being phony. The scene conveys the sense that the protagonist is putting on an act that is different from how he/she usually behaves (unless the difference is endorsed as authentic). The protagonist feels fake and/or contrived. Scenes were coded on a scale from 1 (very low) to 4 (very high), based on the degree to which the protagonist felt phony. “The protagonist conveys the sense of phoniness to a...degree.''

Conformity. Key characteristics of scenes containing the theme “conformity” are acting to please others, going with the crowd, being influenced by others' expectations, living up to others' expectations, concern with making a good impression, pleasing others, and striving for acceptance or to fit in . This code reflects the degree to which the protagonist conforms to the demands of others or society. The scene conveys a sense that the protagonist is striving to make a good impression, fit in, or is acting in order to please others. The protagonist attempts to gain acceptance by adjusting his/her behavior to conform to social/societal pressures. Scenes were coded on a scale from 1 (very low) to 4 (very high), based on the degree to which the protagonist conformed to external pressures. “The protagonist conforms to external pressures to a...degree.''

Suppression. Key characteristics of scenes containing the theme “suppression'' are denying, subverting, or changing one's emotions, holding in one's true beliefs, and lying to others . This code reflects the degree to which the protagonist suppresses his/her emotions, thoughts, and actions. The scene conveys the sense that the protagonist is actively withholding his/her opinions or lies to others in order to avoid revealing true beliefs and opinions. The protagonist actively avoids expressing his/her true self in order to avoid negative consequences. Scenes were coded on a scale from 1 (very low) to 4 (very high), based on the degree to which the protagonist suppresses true feelings, beliefs, and behavior. “The protagonist suppressed his/her true self to a...degree.''

Self-denigration. Key characteristics of scenes containing the theme “self-denigration'' are expressing disgust with oneself and self-criticism . This code reflects the degree to which the protagonist describes him/herself in derogatory terms. The scene conveys the sense that the protagonist does not approve of the way he/she acted and may be disgusted with him/herself. The protagonist is critical of him/herself. Scenes were coded on a scale from 1 (very low) to 4 (very high), based on the degree to which the protagonist was self-critical and self-denigrating. “The protagonist was self-critical and self-denigrating to a...degree.''

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Analyzing the target company's overview aids in gaining insights into its business model. It provides competitive advantage and helps in growth prospects. It forms the basis for financial statement analysis that serves as the reference point for investors. Thus, target company overview is instrumental in decision-making processes. However, sorting through extensive company data can take time and effort. Structuring vast information in a visually appealing manner can be challenging. That's why businesses need our professionally designed templates. This template provides a detailed snapshot and condenses extensive data into a structured format. Moreover, it helps make informed investment decisions by presenting vital financial indicators. It streamlines the analysis process and guides users through essential sections.

Template 7: Target Company Vertical and Horizontal Analysis Template

Vertical and horizontal analysis offers distinct perspectives on a company's financial performance. It helps in analyzing financial statements with the kind of depth and relevance that you need.  Contrary horizontal analysis, on the other hand, compares financial data across periods. It helps in revealing trends, patterns, and changes in performance. Thus, it aids in identifying growth or decline trends. Businesses need these analyses as they provide insights into revenue streams and financial health. Understanding these trends helps companies decide on budget allocation and resource management. Our template streamlines this process by offering a pre-designed format. It simplifies complex financial data, enabling insights.

Template 8: Valuation Equity Research Method Template

Understand the complexities of equity valuation with our template. This comprehensive resource condenses intricate valuation methods. This template simplifies complex financial concepts and helps businesses overcome challenges. It transforms valuation methodologies into visuals that aid conceptual clarity. Thus, it makes them accessible to professionals, investors, and analysts.

Template 9:  Market Research Report Development Workflow 

This template comprehensively depicts the market research process and helps businesses from data collection to analysis and classification. It showcases a strategic workflow for identifying new areas of opportunity.

This template offers a structured and visually engaging workflow and maximizes research efficiency.

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Template 10: Campaign Keyword Optimization Research Template

This PPT Template maximizes your campaign's success and showcases projected keyword improvement reports. It helps you evaluate the impact of campaign efforts on crucial metrics like impressions, clicks, and CTR. This tool is powerful for insightful discussions and seamless navigation through vital metrics. It empowers you to present and analyze campaign data effortlessly. It’s your gateway to informed decision-making and strategy refinement.

Want to harness the potential of this versatile tool to steer your paid advertising campaigns toward enhanced performance and success? Get it now!

Template 11: School Education Research Statement Report Template

This template is an indispensable resource for scholars, academicians, and researchers. Its one-page layout simplifies the daunting task of summarizing research findings. Use this template to showcase research background, objectives, and directions. It also empowers researchers to outline research ideas in a comprehensive manner.

RESEARCH IS AN INVALUABLE ASSET

Our templates serve as invaluable assets, aiding professionals in overcoming complexities. It simplifies analyses and engages audiences effectively. It helps organizations make informed decisions with ease. Download these templates now to streamline your presentations and conquer market challenges.

PS You can also explore our top proposal report templates for guidance and creating winning presentations.

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Research Report

The act of researching can be long-winded and difficult. Once you’ve gone through it, you’ll need something to help you record your findings. For many, the thought of coming up with a research report can be as daunting as the actual research itself. This can be a kind of business report if your research has a lot to do with business or it can be academic in nature. Either way, by scrolling down you will not only learn more about this kind of document but you’ll have a list of amazing templates to choose from as well.

Brand Research Report Template

• Google Docs
• Apple Pages

Size: A4, US

Academic Research Report Template

Investment Research Report Template

Equity Research Report Template

Corporate Research Report Template

Product Research Report Template

Research Progress Report Template

Market Research Report Template

Research Report Executive Summary Template

Size: 120 KB

Research Report Gantt Chart Template

Size: 31 KB

Company Research Report Template

Size: 64 KB

Business Research Report Template

Size: 39 KB

Monthly Research Report Template

Size: 59 KB

Survey Research Report Template

Size: 58 KB

Quantitative Research Report Template

Size: 144 KB

Research Paper Report Template

Size: 191 KB

Acknowledgement For Research Project Report

Size: 162 KB

Business Project Report Word Template

Printable Survey Report Template

Training Report Template to Edit

Formal Report Template MS Word

Research Report Cover Page Template

Free Download

Sample Equity Research

Size: 887 KB

Market Research Report

Size: 78 KB

Research Report Outline

Size: 41 KB

Action Research Report

Size: 52 KB

Company Research Report Example

Size: 506 KB

Psychology Research

Size: 84 KB

Final Research Report

Size: 359 KB

Sample of Research Report

Size: 87 KB

Statistics Report

Size: 32 KB

What Is a Research Report?

By definition, a research report is a document presented when reporting about the findings or results of a research or investigation about particular subjects or topics. In business, a research report is a document containing the results of business research. A fine example of this would be the  market reports  that firms write up on a monthly or periodic basis. As you may have already expected, in lieu of actually creating a report on your own, there’s always the option of downloading items like self-report research templates and market research report samples.

Tips for Writing Your Own Research Report

If you’re having trouble coming up your research report, know that you’ve come to the right place. Learning how to write a research report does not need to be overly difficult. All one needs to do is keep a few points in mind. Having said that, take a look at the following:

Tip 1: Decide on the Report Type

By now you’re aware that there are multiple kinds of research reports. Each one will correspond to a different topic, a different industry, and various other factors. Before you get started, you need to know which one you are going to be diving into. Otherwise, the effort of creating a quality report will be wasted on you.

Tip 2: Identify Your Goals

Knowing what your goals are when it comes to your research report can help immensely with how you will end up completing it. If you recognize that your goal is to do more market research , for example, then you’ll have a much easier time coming up with a market research report.

Tip 3: Keep Everything Organized

Although there is no need to strictly adhere to any kind of research report format, you must still do what you can to ensure that your report remains organized. Take a look at any research report example or research report template . See how those examples keep themselves organized and try to copy it as much as you can.

Tip 4: Do Not Neglect to Proofread

You need to check for possible mistakes or errors in spelling, grammar , and information. Though it can also be helpful if you had somebody else to proofread your work for you. This is because a different perspective can often find things that we may be blind to.

What are the different types of research reports?

First of all, there is a technical report , which happens to be more suited to the researchers’ target audience. Then you have the popular report, which is designed to be consumed by a more general or mainstream audience. It is meant to display findings in such a non-technical manner for easier readability.

What are the different components of a research report outline?

When it comes to the research report outline, you can expect the following components: first is the abstract, followed closely by the introduction. After that, you have the research methodology , the results, and then the discussion. The references come at the very end.

How can you end or conclude a research report?

There are a few ways you can choose to end any research report you may be writing. You can choose to go for a summary , for one. Either that or you may come up with an afterword or a true conclusion.

Like any other kind of report, there are a lot of factors to consider when dealing with research reports. Having read this article, we guarantee that you are now better off compared to before due to the new-found knowledge you’ve gained here. Now all you have to do is decide how to best apply that knowledge. Will you start creating report documents yourself or are you going to choose from our list of report examples ? Either way, you can’t go wrong with the resources at your disposal, so act now!

Report Generator

Text prompt

• Instructive
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Generate a report on the impact of technology in the classroom on student learning outcomes

Prepare a report analyzing the trends in student participation in sports and arts programs over the last five years at your school.

AI Index: State of AI in 13 Charts

In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.

This year’s AI Index — a 500-page report tracking 2023’s worldwide trends in AI — is out.

The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year’s report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.

Don’t have an afternoon to pore through the findings? Check out the high level here.

A Move Toward Open-Sourced

This past year, organizations released 149 foundation models, more than double the number released in 2022. Of these newly released models, 65.7% were open-source (meaning they can be freely used and modified by anyone), compared with only 44.4% in 2022 and 33.3% in 2021.

But At a Cost of Performance?

Closed-source models still outperform their open-sourced counterparts. On 10 selected benchmarks, closed models achieved a median performance advantage of 24.2%, with differences ranging from as little as 4.0% on mathematical tasks like GSM8K to as much as 317.7% on agentic tasks like AgentBench.

Biggest Players

Industry dominates AI, especially in building and releasing foundation models. This past year Google edged out other industry players in releasing the most models, including Gemini and RT-2. In fact, since 2019, Google has led in releasing the most foundation models, with a total of 40, followed by OpenAI with 20. Academia trails industry: This past year, UC Berkeley released three models and Stanford two.

Industry Dwarfs All

If you needed more striking evidence that corporate AI is the only player in the room right now, this should do it. In 2023, industry accounted for 72% of all new foundation models.

Prices Skyrocket

One of the reasons academia and government have been edged out of the AI race: the exponential increase in cost of training these giant models. Google’s Gemini Ultra cost an estimated $191 million worth of compute to train, while OpenAI’s GPT-4 cost an estimated $78 million. In comparison, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900.

What AI Race?

At least in terms of notable machine learning models, the United States vastly outpaced other countries in 2023, developing a total of 61 models in 2023. Since 2019, the U.S. has consistently led in originating the majority of notable models, followed by China and the UK.

Move Over, Human

As of 2023, AI has hit human-level performance on many significant AI benchmarks, from those testing reading comprehension to visual reasoning. Still, it falls just short on some benchmarks like competition-level math. Because AI has been blasting past so many standard benchmarks, AI scholars have had to create new and more difficult challenges. This year’s index also tracked several of these new benchmarks, including those for tasks in coding, advanced reasoning, and agentic behavior.

Private Investment Drops (But We See You, GenAI)

While AI private investment has steadily dropped since 2021, generative AI is gaining steam. In 2023, the sector attracted $25.2 billion, nearly ninefold the investment of 2022 and about 30 times the amount from 2019 (call it the ChatGPT effect). Generative AI accounted for over a quarter of all AI-related private investments in 2023.

U.S. Wins $$ Race

And again, in 2023 the United States dominates in AI private investment. In 2023, the $67.2 billion invested in the U.S. was roughly 8.7 times greater than the amount invested in the next highest country, China, and 17.8 times the amount invested in the United Kingdom. That lineup looks the same when zooming out: Cumulatively since 2013, the United States leads investments at $335.2 billion, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion.

Where is Corporate Adoption?

More companies are implementing AI in some part of their business: In surveys, 55% of organizations said they were using AI in 2023, up from 50% in 2022 and 20% in 2017. Businesses report using AI to automate contact centers, personalize content, and acquire new customers. 

Younger and Wealthier People Worry About Jobs

Globally, most people expect AI to change their jobs, and more than a third expect AI to replace them. Younger generations — Gen Z and millennials — anticipate more substantial effects from AI compared with older generations like Gen X and baby boomers. Specifically, 66% of Gen Z compared with 46% of boomer respondents believe AI will significantly affect their current jobs. Meanwhile, individuals with higher incomes, more education, and decision-making roles foresee AI having a great impact on their employment.

While the Commonwealth Worries About AI Products

When asked in a survey about whether AI products and services make you nervous, 69% of Aussies and 65% of Brits said yes. Japan is the least worried about their AI products at 23%.  

Regulation Rallies

More American regulatory agencies are passing regulations to protect citizens and govern the use of AI tools and data. For example, the Copyright Office and the Library of Congress passed copyright registration guidance concerning works that contained material generated by AI, while the Securities and Exchange Commission developed a cybersecurity risk management strategy, governance, and incident disclosure plan. The agencies to pass the most regulation were the Executive Office of the President and the Commerce Department. 

The AI Index was first created to track AI development. The index collaborates with such organizations as LinkedIn, Quid, McKinsey, Studyportals, the Schwartz Reisman Institute, and the International Federation of Robotics to gather the most current research and feature important insights on the AI ecosystem. 

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