how to write an abstract for a psychology research paper

How to Write an Abstract APA Format

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Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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An APA abstract is a brief, comprehensive summary of the contents of an article, research paper, dissertation, or report.

It is written in accordance with the guidelines of the American Psychological Association (APA), which is a widely used format in social and behavioral sciences. 

An APA abstract summarizes, usually in one paragraph of between 150–250 words, the major aspects of a research paper or dissertation in a prescribed sequence that includes:

• The rationale: the overall purpose of the study, providing a clear context for the research undertaken.
• Information regarding the method and participants: including materials/instruments, design, procedure, and data analysis.
• Main findings or trends: effectively highlighting the key outcomes of the hypotheses.
• Interpretations and conclusion(s): solidify the implications of the research.
• Keywords related to the study: assist the paper’s discoverability in academic databases.

The abstract should stand alone, be “self-contained,” and make sense to the reader in isolation from the main article.

The purpose of the abstract is to give the reader a quick overview of the essential information before reading the entire article. The abstract is placed on its own page, directly after the title page and before the main body of the paper.

Although the abstract will appear as the very first part of your paper, it’s good practice to write your abstract after you’ve drafted your full paper, so that you know what you’re summarizing.

Note : This page reflects the latest version of the APA Publication Manual (i.e., APA 7), released in October 2019.

Structure of the Abstract

[NOTE: DO NOT separate the components of the abstract – it should be written as a single paragraph. This section is separated to illustrate the abstract’s structure.]

1) The Rationale

One or two sentences describing the overall purpose of the study and the research problem(s) you investigated. You are basically justifying why this study was conducted.

• What is the importance of the research?
• Why would a reader be interested in the larger work?
• For example, are you filling a gap in previous research or applying new methods to take a fresh look at existing ideas or data?
• Women who are diagnosed with breast cancer can experience an array of psychosocial difficulties; however, social support, particularly from a spouse, has been shown to have a protective function during this time. This study examined the ways in which a woman’s daily mood, pain, and fatigue, and her spouse’s marital satisfaction predict the woman’s report of partner support in the context of breast cancer.
• The current nursing shortage, high hospital nurse job dissatisfaction, and reports of uneven quality of hospital care are not uniquely American phenomena.
• Students with special educational needs and disabilities (SEND) are more likely to exhibit behavioral difficulties than their typically developing peers. The aim of this study was to identify specific risk factors that influence variability in behavior difficulties among individuals with SEND.

2) The Method

Information regarding the participants (number, and population). One or two sentences outlining the method, explaining what was done and how. The method is described in the present tense.

• Pretest data from a larger intervention study and multilevel modeling were used to examine the effects of women’s daily mood, pain, and fatigue and average levels of mood, pain, and fatigue on women’s report of social support received from her partner, as well as how the effects of mood interacted with partners’ marital satisfaction.
• This paper presents reports from 43,000 nurses from more than 700 hospitals in the United States, Canada, England, Scotland, and Germany in 1998–1999.
• The study sample comprised 4,228 students with SEND, aged 5–15, drawn from 305 primary and secondary schools across England. Explanatory variables were measured at the individual and school levels at baseline, along with a teacher-reported measure of behavior difficulties (assessed at baseline and the 18-month follow-up).

3) The Results

One or two sentences indicating the main findings or trends found as a result of your analysis. The results are described in the present or past tense.

• Results show that on days in which women reported higher levels of negative or positive mood, as well as on days they reported more pain and fatigue, they reported receiving more support. Women who, on average, reported higher levels of positive mood tended to report receiving more support than those who, on average, reported lower positive mood. However, average levels of negative mood were not associated with support. Higher average levels of fatigue but not pain were associated with higher support. Finally, women whose husbands reported higher levels of marital satisfaction reported receiving more partner support, but husbands’ marital satisfaction did not moderate the effect of women’s mood on support.
• Nurses in countries with distinctly different healthcare systems report similar shortcomings in their work environments and the quality of hospital care. While the competence of and relation between nurses and physicians appear satisfactory, core problems in work design and workforce management threaten the provision of care.
• Hierarchical linear modeling of data revealed that differences between schools accounted for between 13% (secondary) and 15.4% (primary) of the total variance in the development of students’ behavior difficulties, with the remainder attributable to individual differences. Statistically significant risk markers for these problems across both phases of education were being male, eligibility for free school meals, being identified as a bully, and lower academic achievement. Additional risk markers specific to each phase of education at the individual and school levels are also acknowledged.

4) The Conclusion / Implications

A brief summary of your conclusions and implications of the results, described in the present tense. Explain the results and why the study is important to the reader.

• For example, what changes should be implemented as a result of the findings of the work?
• How does this work add to the body of knowledge on the topic?

Implications of these findings are discussed relative to assisting couples during this difficult time in their lives.

• Resolving these issues, which are amenable to managerial intervention, is essential to preserving patient safety and care of consistently high quality.
• Behavior difficulties are affected by risks across multiple ecological levels. Addressing any one of these potential influences is therefore likely to contribute to the reduction in the problems displayed.

The above examples of abstracts are from the following papers:

Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J. A., Busse, R., Clarke, H., … & Shamian, J. (2001). Nurses’ reports on hospital care in five countries . Health affairs, 20(3) , 43-53.

Boeding, S. E., Pukay-Martin, N. D., Baucom, D. H., Porter, L. S., Kirby, J. S., Gremore, T. M., & Keefe, F. J. (2014). Couples and breast cancer: Women’s mood and partners’ marital satisfaction predicting support perception . Journal of Family Psychology, 28(5) , 675.

Oldfield, J., Humphrey, N., & Hebron, J. (2017). Risk factors in the development of behavior difficulties among students with special educational needs and disabilities: A multilevel analysis . British journal of educational psychology, 87(2) , 146-169.

5) Keywords

APA style suggests including a list of keywords at the end of the abstract. This is particularly common in academic articles and helps other researchers find your work in databases.

Keywords in an abstract should be selected to help other researchers find your work when searching an online database. These keywords should effectively represent the main topics of your study. Here are some tips for choosing keywords:

Core Concepts: Identify the most important ideas or concepts in your paper. These often include your main research topic, the methods you’ve used, or the theories you’re discussing.

Specificity: Your keywords should be specific to your research. For example, suppose your paper is about the effects of climate change on bird migration patterns in a specific region. In that case, your keywords might include “climate change,” “bird migration,” and the region’s name.

Consistency with Paper: Make sure your keywords are consistent with the terms you’ve used in your paper. For example, if you use the term “adolescent” rather than “teen” in your paper, choose “adolescent” as your keyword, not “teen.”

Jargon and Acronyms: Avoid using too much-specialized jargon or acronyms in your keywords, as these might not be understood or used by all researchers in your field.

Synonyms: Consider including synonyms of your keywords to capture as many relevant searches as possible. For example, if your paper discusses “post-traumatic stress disorder,” you might include “PTSD” as a keyword.

Remember, keywords are a tool for others to find your work, so think about what terms other researchers might use when searching for papers on your topic.

The Abstract SHOULD NOT contain:

Lengthy background or contextual information: The abstract should focus on your research and findings, not general topic background.

Undefined jargon, abbreviations,  or acronyms: The abstract should be accessible to a wide audience, so avoid highly specialized terms without defining them.

Citations: Abstracts typically do not include citations, as they summarize original research.

Incomplete sentences or bulleted lists: The abstract should be a single, coherent paragraph written in complete sentences.

New information not covered in the paper: The abstract should only summarize the paper’s content.

Subjective comments or value judgments: Stick to objective descriptions of your research.

Excessive details on methods or procedures: Keep descriptions of methods brief and focused on main steps.

Speculative or inconclusive statements: The abstract should state the research’s clear findings, not hypotheses or possible interpretations.

• Any illustration, figure, table, or references to them . All visual aids, data, or extensive details should be included in the main body of your paper, not in the abstract. 
• Elliptical or incomplete sentences should be avoided in an abstract . The use of ellipses (…), which could indicate incomplete thoughts or omitted text, is not appropriate in an abstract.

APA Style for Abstracts

An APA abstract must be formatted as follows:

Include the running head aligned to the left at the top of the page (professional papers only) and page number. Note, student papers do not require a running head. On the first line, center the heading “Abstract” and bold (do not underlined or italicize). Do not indent the single abstract paragraph (which begins one line below the section title). Double-space the text. Use Times New Roman font in 12 pt. Set one-inch (or 2.54 cm) margins. If you include a “keywords” section at the end of the abstract, indent the first line and italicize the word “Keywords” while leaving the keywords themselves without any formatting.

Example APA Abstract Page

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Further Information

• APA 7th Edition Abstract and Keywords Guide
• Example APA Abstract
• How to Write a Good Abstract for a Scientific Paper or Conference Presentation
• How to Write a Lab Report
• Writing an APA paper

How long should an APA abstract be?

An APA abstract should typically be between 150 to 250 words long. However, the exact length may vary depending on specific publication or assignment guidelines. It is crucial that it succinctly summarizes the essential elements of the work, including purpose, methods, findings, and conclusions.

Where does the abstract go in an APA paper?

In an APA formatted paper, the abstract is placed on its own page, directly after the title page and before the main body of the paper. It’s typically the second page of the document. It starts with the word “Abstract” (centered and not in bold) at the top of the page, followed by the text of the abstract itself.

What are the 4 C’s of abstract writing?

The 4 C’s of abstract writing are an approach to help you create a well-structured and informative abstract. They are:

Conciseness: An abstract should briefly summarize the key points of your study. Stick to the word limit (typically between 150-250 words for an APA abstract) and avoid unnecessary details.

Clarity: Your abstract should be easy to understand. Avoid jargon and complex sentences. Clearly explain the purpose, methods, results, and conclusions of your study.

Completeness: Even though it’s brief, the abstract should provide a complete overview of your study, including the purpose, methods, key findings, and your interpretation of the results.

Cohesion: The abstract should flow logically from one point to the next, maintaining a coherent narrative about your study. It’s not just a list of disjointed elements; it’s a brief story of your research from start to finish.

What is the abstract of a psychology paper?

An abstract in a psychology paper serves as a snapshot of the paper, allowing readers to quickly understand the purpose, methodology, results, and implications of the research without reading the entire paper. It is generally between 150-250 words long.

Writing an Abstract for Your Research Paper

Definition and Purpose of Abstracts

An abstract is a short summary of your (published or unpublished) research paper, usually about a paragraph (c. 6-7 sentences, 150-250 words) long. A well-written abstract serves multiple purposes:

• an abstract lets readers get the gist or essence of your paper or article quickly, in order to decide whether to read the full paper;
• an abstract prepares readers to follow the detailed information, analyses, and arguments in your full paper;
• and, later, an abstract helps readers remember key points from your paper.

It’s also worth remembering that search engines and bibliographic databases use abstracts, as well as the title, to identify key terms for indexing your published paper. So what you include in your abstract and in your title are crucial for helping other researchers find your paper or article.

If you are writing an abstract for a course paper, your professor may give you specific guidelines for what to include and how to organize your abstract. Similarly, academic journals often have specific requirements for abstracts. So in addition to following the advice on this page, you should be sure to look for and follow any guidelines from the course or journal you’re writing for.

The Contents of an Abstract

Abstracts contain most of the following kinds of information in brief form. The body of your paper will, of course, develop and explain these ideas much more fully. As you will see in the samples below, the proportion of your abstract that you devote to each kind of information—and the sequence of that information—will vary, depending on the nature and genre of the paper that you are summarizing in your abstract. And in some cases, some of this information is implied, rather than stated explicitly. The Publication Manual of the American Psychological Association , which is widely used in the social sciences, gives specific guidelines for what to include in the abstract for different kinds of papers—for empirical studies, literature reviews or meta-analyses, theoretical papers, methodological papers, and case studies.

Here are the typical kinds of information found in most abstracts:

• the context or background information for your research; the general topic under study; the specific topic of your research
• the central questions or statement of the problem your research addresses
• what’s already known about this question, what previous research has done or shown
• the main reason(s) , the exigency, the rationale , the goals for your research—Why is it important to address these questions? Are you, for example, examining a new topic? Why is that topic worth examining? Are you filling a gap in previous research? Applying new methods to take a fresh look at existing ideas or data? Resolving a dispute within the literature in your field? . . .
• your research and/or analytical methods
• your main findings , results , or arguments
• the significance or implications of your findings or arguments.

Your abstract should be intelligible on its own, without a reader’s having to read your entire paper. And in an abstract, you usually do not cite references—most of your abstract will describe what you have studied in your research and what you have found and what you argue in your paper. In the body of your paper, you will cite the specific literature that informs your research.

When to Write Your Abstract

Although you might be tempted to write your abstract first because it will appear as the very first part of your paper, it’s a good idea to wait to write your abstract until after you’ve drafted your full paper, so that you know what you’re summarizing.

What follows are some sample abstracts in published papers or articles, all written by faculty at UW-Madison who come from a variety of disciplines. We have annotated these samples to help you see the work that these authors are doing within their abstracts.

Choosing Verb Tenses within Your Abstract

The social science sample (Sample 1) below uses the present tense to describe general facts and interpretations that have been and are currently true, including the prevailing explanation for the social phenomenon under study. That abstract also uses the present tense to describe the methods, the findings, the arguments, and the implications of the findings from their new research study. The authors use the past tense to describe previous research.

The humanities sample (Sample 2) below uses the past tense to describe completed events in the past (the texts created in the pulp fiction industry in the 1970s and 80s) and uses the present tense to describe what is happening in those texts, to explain the significance or meaning of those texts, and to describe the arguments presented in the article.

The science samples (Samples 3 and 4) below use the past tense to describe what previous research studies have done and the research the authors have conducted, the methods they have followed, and what they have found. In their rationale or justification for their research (what remains to be done), they use the present tense. They also use the present tense to introduce their study (in Sample 3, “Here we report . . .”) and to explain the significance of their study (In Sample 3, This reprogramming . . . “provides a scalable cell source for. . .”).

Sample Abstract 1

From the social sciences.

Reporting new findings about the reasons for increasing economic homogamy among spouses

Gonalons-Pons, Pilar, and Christine R. Schwartz. “Trends in Economic Homogamy: Changes in Assortative Mating or the Division of Labor in Marriage?” Demography , vol. 54, no. 3, 2017, pp. 985-1005.

Sample Abstract 2

From the humanities.

Analyzing underground pulp fiction publications in Tanzania, this article makes an argument about the cultural significance of those publications

Emily Callaci. “Street Textuality: Socialism, Masculinity, and Urban Belonging in Tanzania’s Pulp Fiction Publishing Industry, 1975-1985.” Comparative Studies in Society and History , vol. 59, no. 1, 2017, pp. 183-210.

Sample Abstract/Summary 3

From the sciences.

Reporting a new method for reprogramming adult mouse fibroblasts into induced cardiac progenitor cells

Lalit, Pratik A., Max R. Salick, Daryl O. Nelson, Jayne M. Squirrell, Christina M. Shafer, Neel G. Patel, Imaan Saeed, Eric G. Schmuck, Yogananda S. Markandeya, Rachel Wong, Martin R. Lea, Kevin W. Eliceiri, Timothy A. Hacker, Wendy C. Crone, Michael Kyba, Daniel J. Garry, Ron Stewart, James A. Thomson, Karen M. Downs, Gary E. Lyons, and Timothy J. Kamp. “Lineage Reprogramming of Fibroblasts into Proliferative Induced Cardiac Progenitor Cells by Defined Factors.” Cell Stem Cell , vol. 18, 2016, pp. 354-367.

Sample Abstract 4, a Structured Abstract

Reporting results about the effectiveness of antibiotic therapy in managing acute bacterial sinusitis, from a rigorously controlled study

Note: This journal requires authors to organize their abstract into four specific sections, with strict word limits. Because the headings for this structured abstract are self-explanatory, we have chosen not to add annotations to this sample abstract.

Wald, Ellen R., David Nash, and Jens Eickhoff. “Effectiveness of Amoxicillin/Clavulanate Potassium in the Treatment of Acute Bacterial Sinusitis in Children.” Pediatrics , vol. 124, no. 1, 2009, pp. 9-15.

“OBJECTIVE: The role of antibiotic therapy in managing acute bacterial sinusitis (ABS) in children is controversial. The purpose of this study was to determine the effectiveness of high-dose amoxicillin/potassium clavulanate in the treatment of children diagnosed with ABS.

METHODS : This was a randomized, double-blind, placebo-controlled study. Children 1 to 10 years of age with a clinical presentation compatible with ABS were eligible for participation. Patients were stratified according to age (<6 or ≥6 years) and clinical severity and randomly assigned to receive either amoxicillin (90 mg/kg) with potassium clavulanate (6.4 mg/kg) or placebo. A symptom survey was performed on days 0, 1, 2, 3, 5, 7, 10, 20, and 30. Patients were examined on day 14. Children’s conditions were rated as cured, improved, or failed according to scoring rules.

RESULTS: Two thousand one hundred thirty-five children with respiratory complaints were screened for enrollment; 139 (6.5%) had ABS. Fifty-eight patients were enrolled, and 56 were randomly assigned. The mean age was 6630 months. Fifty (89%) patients presented with persistent symptoms, and 6 (11%) presented with nonpersistent symptoms. In 24 (43%) children, the illness was classified as mild, whereas in the remaining 32 (57%) children it was severe. Of the 28 children who received the antibiotic, 14 (50%) were cured, 4 (14%) were improved, 4(14%) experienced treatment failure, and 6 (21%) withdrew. Of the 28children who received placebo, 4 (14%) were cured, 5 (18%) improved, and 19 (68%) experienced treatment failure. Children receiving the antibiotic were more likely to be cured (50% vs 14%) and less likely to have treatment failure (14% vs 68%) than children receiving the placebo.

CONCLUSIONS : ABS is a common complication of viral upper respiratory infections. Amoxicillin/potassium clavulanate results in significantly more cures and fewer failures than placebo, according to parental report of time to resolution.” (9)

Some Excellent Advice about Writing Abstracts for Basic Science Research Papers, by Professor Adriano Aguzzi from the Institute of Neuropathology at the University of Zurich:

Academic and Professional Writing

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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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Writing Research Papers

• Formatting Research Papers

Research papers written in APA style should follow the formatting rules specified in the Publication Manual of the American Psychological Association .  Most research papers that are written for psychology courses at UCSD, including the B.S. Degree Research Paper and the Honors Thesis, have to follow APA format.  Here we discuss the formatting of research papers according to APA style.

How to Format a Research Paper in APA Style

For the most accurate and comprehensive information on formatting papers in APA style, we recommend referring directly to the Publication Manual of the American Psychological Association. Reputable online sources (e.g., the official APA Style website and the Purdue University Online Writing Lab’s guide to APA style) are also recommended. 

According to the Publication Manual, the major sections and components of APA style research papers should adhere to the following guidelines.  Note that how closely these guidelines are followed may vary depending on the course and instructor.  

General Formatting Rules

• Papers should have at least 1-in. margins on all sides. 1
• All text should be double spaced . 1
• Times New Roman, 12 point font is preferred. 1
• All lines of text should be flush-left and should not be justified, except where noted in the Manual. 1
• The first line of every paragraph should be indented. Exceptions to the indenting rule are the Abstract, quotations, titles and headings, as well as Tables and Figures. 1
• Pages should be numbered at the top right, with the title page numbered page 1, the Abstract numbered page 2, and the text starting on page 3. 1
• An abbreviated title called the Running Head should be placed at the top of each page, flush-left in uppercase letters. 1
• Two spaces should be used after punctuation marks at the end of each sentence (in other words, there should be two spaces after the period that ends each sentence). 2

Formatting the Title Page

• The title should be typed in the upper half of the title page, centered, and with the first letters of all but minor words capitalized. 3
• The name(s) of the author(s) should be typed below the title and followed with the institutional affiliation(s) of the author(s). 3
• An Author Note should appear below the aforementioned items. The Author Note can have up to four paragraphs.  These respectively describe the author(s)’ departmental and institutional affiliation, any changes in affiliation, acknowledgments, and contact information. 3

Formatting the Abstract

• The Abstract typically should not exceed 250 words. 4
• The Abstract should be placed on a separate page, with the label Abstract appearing at the top center of that page and followed by the text of the Abstract. 4
• The Abstract should not be indented. 4

Formatting the Main Body of Text

• The main body of text should begin on a separate page after the Abstract. 5
• It should begin with the Introduction section. 5
• The Introduction section should be titled with the title of the research paper and not the word “Introduction.” The title should appear at the top of the page, centered, and should not be bolded. 5
• The remainder of the text should be flush-left, with each new paragraph indented except where noted above (see General Formatting Rules ). 5
• Each of the subsequent sections of the paper should be prefaced with a heading. APA guidelines specify different heading formats (for more information on Levels of Headings , see below). 5

Formatting References

• The references section should begin on a separate page after the main body of text. 6
• It should begin with the word “References” placed at the top of the page and centered. 6
• All references should be listed in alphabetical order by the last name of the first author of each reference. 6
• All references should be double-spaced and should use a hanging indent format wherein the first line of each reference is flush-left and all subsequent lines of that reference are indented (with that pattern repeating for each reference). 6
• All references should use the appropriate APA reference format (for more information, please see the Citing References section of this website). 6

Levels of Headings in APA Style

As of the sixth edition of the Publication Manual of the American Psychological Association (released in 2010), the five possible levels of heading in APA-formatted manuscripts are: 7

• Level 1: centered, bold, on a separate line, and the first letters of all but minor words capitalized.
• Level 2: flush-left, bold, on a separate line, and the first letters of all but minor words capitalized.
• Level 3: indented, bold, as a paragraph heading (the first part of a paragraph; regular text follows on the same line), and in lowercase letters ending with a period.
• Level 4: indented, bold, italicized, as a paragraph heading (the first part of a paragraph; regular text follows on the same line), and in lowercase letters ending with a period.
• Level 5: indented, not bold, italicized, as a paragraph heading (the first part of a paragraph; regular text follows on the same line), and in lowercase letters ending with a period.

Depending on the structure of your research paper, some or all of the five levels of headings may be used.  The headings have a “hierarchical nested structure” where Level 1 is the highest and Level 5 is the lowest.  For example, you may have a research paper which uses all five levels of heading as follows:

Downloadable Resources

• How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
• Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – empirical research) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos     

• Writing Research Paper Videos

External Resources

• APA Style Guide from the Purdue University Online Writing Lab (OWL)
• APA Tutorial on the Basics of APA Style
• EasyBib Guide to Writing and Citing in APA Format
• Sample APA Formatted Paper
• Sample APA Formatted Paper with Comments
• Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 228-229).  Washington, DC: American Psychological Association.

2 vandenbos, g. r. (ed). (2010). (pp. 87-88). , 3 vandenbos, g. r. (ed). (2010). (pp. 23-25). , 4 vandenbos, g. r. (ed). (2010). (pp. 25-27)., 5 vandenbos, g. r. (ed). (2010). (pp. 41-49). , 6 vandenbos, g. r. (ed). (2010). (pp. 37-38, 49-51). , 7 vandenbos, g. r. (ed). (2010). (p. 62). .

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• Research Paper Structure
• Using Databases and Finding References
• What Types of References Are Appropriate?
• Evaluating References and Taking Notes
• Citing References
• Writing a Literature Review
• Writing Process and Revising
• Improving Scientific Writing
• Academic Integrity and Avoiding Plagiarism
• Writing Research Papers Videos

How to Write an Abstract?

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• First Online: 24 October 2021

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• Samiran Nundy 4 ,
• Atul Kakar 5 &
• Zulfiqar A. Bhutta 6  

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An abstract is a crisp, short, powerful, and self-contained summary of a research manuscript used to help the reader swiftly determine the paper’s purpose. Although the abstract is the first paragraph of the manuscript it should be written last when all the other sections have been addressed.

Research is formalized curiosity. It is poking and prying with a purpose. — Zora Neale Hurston, American Author, Anthropologist and Filmmaker (1891–1960)

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Writing the Abstract

Abstract and Keywords

Additional Commentaries

1 what is an abstract.

An abstract is usually a standalone document that informs the reader about the details of the manuscript to follow. It is like a trailer to a movie, if the trailer is good, it stimulates the audience to watch the movie. The abstract should be written from scratch and not ‘cut –and-pasted’ [ 1 ].

2 What is the History of the Abstract?

An abstract, in the form of a single paragraph, was first published in the Canadian Medical Association Journal in 1960 with the idea that the readers may not have enough time to go through the whole paper, and the first abstract with a defined structure was published in 1991 [ 2 ]. The idea sold and now most original articles and reviews are required to have a structured abstract. The abstract attracts the reader to read the full manuscript [ 3 ].

3 What are the Qualities of a Good Abstract?

The quality of information in an abstract can be summarized by four ‘C’s. It should be:

C: Condensed

C: Critical

4 What are the Types of Abstract?

Before writing the abstract, you need to check with the journal website about which type of abstract it requires, with its length and style in the ‘Instructions to Authors’ section.

The abstract types can be divided into:

Descriptive: Usually written for psychology, social science, and humanities papers. It is about 50–100 words long. No conclusions can be drawn from this abstract as it describes the major points in the paper.

Informative: The majority of abstracts for science-related manuscripts are informative and are surrogates for the research done. They are single paragraphs that provide the reader an overview of the research paper and are about 100–150 words in length. Conclusions can be drawn from the abstracts and in the recommendations written in the last line.

Critical: This type of abstract is lengthy and about 400–500 words. In this, the authors’ own research is discussed for reliability, judgement, and validation. A comparison is also made with similar studies done earlier.

Highlighting: This is rarely used in scientific writing. The style of the abstract is to attract more readers. It is not a balanced or complete overview of the article with which it is published.

Structured: A structured abstract contains information under subheadings like background, aims, material and methods, results, conclusion, and recommendations (Fig. 15.1 ). Most leading journals now carry these.

Example of a structured abstract (with permission editor CMRP)

5 What is the Purpose of an Abstract?

An abstract is written to educate the reader about the study that follows and provide an overview of the science behind it. If written well it also attracts more readers to the article. It also helps the article getting indexed. The fate of a paper both before and after publication often depends upon its abstract. Most readers decide if a paper is worth reading on the basis of the abstract. Additionally, the selection of papers in systematic reviews is often dependent upon the abstract.

6 What are the Steps of Writing an Abstract?

An abstract should be written last after all the other sections of an article have been addressed. A poor abstract may turn off the reader and they may cause indexing errors as well. The abstract should state the purpose of the study, the methodology used, and summarize the results and important conclusions. It is usually written in the IMRAD format and is called a structured abstract [ 4 , 5 ].

I: The introduction in the opening line should state the problem you are addressing.

M: Methodology—what method was chosen to finish the experiment?

R: Results—state the important findings of your study.

D: Discussion—discuss why your study is important.

Mention the following information:

Important results with the statistical information ( p values, confidence intervals, standard/mean deviation).

Arrange all information in a chronological order.

Do not repeat any information.

The last line should state the recommendations from your study.

The abstract should be written in the past tense.

7 What are the Things to Be Avoided While Writing an Abstract?

Cut and paste information from the main text

Hold back important information

Use abbreviations

Tables or Figures

Generalized statements

Arguments about the study

8 What are Key Words?

These are important words that are repeated throughout the manuscript and which help in the indexing of a paper. Depending upon the journal 3–10 key words may be required which are indexed with the help of MESH (Medical Subject Heading).

9 How is an Abstract Written for a Conference Different from a Journal Paper?

The basic concept for writing abstracts is the same. However, in a conference abstract occasionally a table or figure is allowed. A word limit is important in both of them. Many of the abstracts which are presented in conferences are never published in fact one study found that only 27% of the abstracts presented in conferences were published in the next five years [ 6 ].

Table 15.1 gives a template for writing an abstract.

10 What are the Important Recommendations of the International Committees of Medical Journal of Editors?

The recommendations are [ 7 ]:

An abstract is required for original articles, metanalysis, and systematic reviews.

A structured abstract is preferred.

The abstract should mention the purpose of the scientific study, how the procedure was carried out, the analysis used, and principal conclusion.

Clinical trials should be reported according to the CONSORT guidelines.

The trials should also mention the funding and the trial number.

The abstract should be accurate as many readers have access only to the abstract.

11 Conclusions

An Abstract should be written last after all the other sections of the manuscript have been completed and with due care and attention to the details.

It should be structured and written in the IMRAD format.

For many readers, the abstract attracts them to go through the complete content of the article.

The abstract is usually followed by key words that help to index the paper.

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Preparing a manuscript for submission to a medical journal. Available on http://www.icmje.org/recommendations/browse/manuscript-preparation/preparing-for-submission.html . Accessed 10 May 2020.

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Samiran Nundy

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Institute for Global Health and Development, The Aga Khan University, South Central Asia, East Africa and United Kingdom, Karachi, Pakistan

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Nundy, S., Kakar, A., Bhutta, Z.A. (2022). How to Write an Abstract?. In: How to Practice Academic Medicine and Publish from Developing Countries?. Springer, Singapore. https://doi.org/10.1007/978-981-16-5248-6_15

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How to Write an Abstract for a Research Paper

Writing Informative Abstracts

Informative abstracts state in one paragraph the essence of a whole paper about a study or a research project. That one paragraph must mention all the main points or parts of the paper: a description of the study or project, its methods, the results, and the conclusions. Here is an example of the abstract accompanying a seven-page essay that appeared in 2002 in  The Journal of Clinical Psychology :

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The relationship between boredom proneness and health-symptom reporting was examined. Undergraduate students (N = 200) completed the Boredom Proneness Scale and the Hopkins Symptom Checklist. A multiple analysis of covariance indicated that individuals with high boredom-proneness total scores reported significantly higher ratings on all five sub-scales of the Hopkins Symptom Checklist (Obsessive–Compulsive, Somatization, Anxiety, Interpersonal Sensitivity, and Depression). The results suggest that boredom proneness may be an important element to consider when assessing symptom reporting. Implications for determining the effects of boredom proneness on psychological- and physical-health symptoms, as well as the application in clinical settings, are discussed. —Jennifer Sommers and Stephen J. Vodanovich, (adsbygoogle = window.adsbygoogle || []).push({}); “Boredom Proneness”

The first sentence states the nature of the study being reported. The next summarizes the method used to investigate the problem, and the following one gives the results: students who, according to specific tests, are more likely to be bored are also more likely to have certain medical or psychological symptoms. The last two sentences indicate that the paper discusses those results and examines the conclusion and its implications.

Writing Descriptive Abstracts

Descriptive abstracts are usually much briefer than informative abstracts and provide much less information. Rather than summarizing the entire paper, a descriptive abstract functions more as a teaser, providing a quick overview that invites the reader to read the whole. Descriptive abstracts usually do not give or discuss results or set out the conclusion or its implications. A descriptive abstract of the boredom-proneness essay might simply include the first sentence from the informative abstract plus a final sentence of its own:

The relationship between boredom proneness and health-symptom reporting was examined. The findings and their application in clinical settings are discussed.

Writing Proposal Abstracts

Proposal abstracts contain the same basic information as informative abstracts, but their purpose is very different. You prepare proposal abstracts to persuade someone to let you write on a topic, pursue a project, conduct an experiment, or present a paper at a scholarly conference. This kind of abstract is not written to introduce a longer piece but rather to stand alone, and often the abstract is written before the paper itself. Titles and other aspects of the proposal deliberately reflect the theme of the proposed work, and you may use the future tense, rather than the past, to describe work not yet completed. Here is a possible proposal for doing research on boredom:

Undergraduate students will complete the Boredom Proneness Scale and the Hopkins Symptom Checklist. A multiple analysis of covariance will be performed to determine the relationship between boredom-proneness total scores and ratings on the five sub-scales of the Hopkins Symptom Checklist (Obsessive–Compulsive, Somatization, Anxiety, Interpersonal Sensitivity, and Depression).

Key Features of a Research Paper Abstract

• A summary of basic information . An informative abstract includes enough information to substitute for the report itself, a descriptive abstract offers only enough information to let the audience decide whether to read further, and a proposal abstract gives an overview of the planned work.
• Objective description . Abstracts present information on the contents of a report or a proposed study; they do not present arguments about or personal perspectives on those contents. The informative abstract on boredom proneness, for example, offers only a tentative conclusion: “The results suggest that boredom proneness may be an important element to consider.”
• Brevity . Although the length of abstracts may vary, journals and organizations often restrict them to 120–200 words—meaning you must carefully select and edit your words.

A Brief Guide to Writing Abstracts

Consider the rhetorical situation.

• Purpose : Are you giving a brief but thorough overview of a completed study? Only enough information to create interest? Or a proposal for a planned study or presentation?
• Audience : For whom are you writing this abstract? What information about your project will your readers need?
• Stance : Whatever your stance in the longer work, your abstract must be objective.
• Media/Design : How will you set your abstract off from the rest of the text? If you are publishing it online, will you devote a single page to it? What format does your audience require?

Generating Ideas and Text

Write the paper first, the abstract last. You can then use the finished work as the guide for the abstract, which should follow the same basic structure. Exception: You may need to write a proposal abstract months before the work it describes will be complete.

Copy and paste key statements. If you’ve already written the work, highlight your thesis, objective, or purpose; basic information on your methods; your results; and your conclusion. Copy and paste those sentences into a new document to create a rough version of your abstract.

Pare down the information to key ideas. Summarize the report, editing out any nonessential words and details. In your first sentence, introduce the overall scope of your study. Also include any other information that seems crucial to understanding your paper. Avoid phrases that add unnecessary words, such as “It is concluded that.” In general, you probably won’t want to use “I”; an abstract should cover ideas, not say what you think or will do.

Conform to any requirements. In general, an informative abstract should be at most 10 percent as long as the original and no longer than the maximum length allowed. Descriptive abstracts should be shorter still, and proposal abstracts should conform to the requirements of the organization calling for the proposal.

By now your writing is almost complete; you’ve come a long way, but you’re not finished yet! Now it’s time to revise the research paper.

Back to  How To Write A Research Paper .

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The abstract is the first section in a psychological report or journal. It includes a summary of the aims, hypothesis, method, results and conclusions, and thus provides an overview of the entire report.

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How to Write an APA Methods Section | With Examples

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

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

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

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Table of contents

Structuring an apa methods section.

Participants

Example of an APA methods section

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

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

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

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

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

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

Prevent plagiarism. Run a free check.

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

Participant or subject characteristics

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

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

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

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

Sampling procedures

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

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

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

Sample size and power

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

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

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

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

Primary and secondary measures

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

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

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

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

For each instrument used, report measures of the following:

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

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

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

Quality of measurements

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

These may include:

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

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

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

Data collection methods and research design

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

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

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

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

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

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

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

Data diagnostics

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

This includes the following:

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

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

Analytic strategies

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

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

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

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

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

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

Religiosity

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

Trust in Science

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

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

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

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

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

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

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

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

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

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

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

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

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How to Write an APA Research Paper

Psychology/neuroscience 201, v iew in pdf format.

An APA-style paper includes the following sections: title page, abstract, introduction, method, results, discussion, and references. Your paper may also include one or more tables and/or figures. Different types of information about your study are addressed in each of the sections, as described below.

General formatting rules are as follows:

Do not put page breaks in between the introduction, method, results, and discussion sections.

The title page, abstract, references, table(s), and figure(s) should be on their own pages. The entire paper should be written in the past tense, in a 12-point font, double-spaced, and with one-inch margins all around.

(see sample on p. 41 of APA manual)

• Title should be between 10-12 words and should reflect content of paper (e.g., IV and DV).
• Title, your name, and Hamilton College are all double-spaced (no extra spaces)
• Create a page header using the “View header” function in MS Word. On the title page, the header should include the following: Flush left: Running head: THE RUNNING HEAD SHOULD BE IN ALL CAPITAL LETTERS. The running head is a short title that appears at the top of pages of published articles. It should not exceed 50 characters, including punctuation and spacing. (Note: on the title page, you actually write the words “Running head,” but these words do not appear on subsequent pages; just the actual running head does. If you make a section break between the title page and the rest of the paper you can make the header different for those two parts of the manuscript). Flush right, on same line: page number. Use the toolbox to insert a page number, so it will automatically number each page.

Abstract (labeled, centered, not bold)

No more than 120 words, one paragraph, block format (i.e., don’t indent), double-spaced.

• State topic, preferably in one sentence. Provide overview of method, results, and discussion.

Introduction

(Do not label as “Introduction.” Title of paper goes at the top of the page—not bold)

The introduction of an APA-style paper is the most difficult to write. A good introduction will summarize, integrate, and critically evaluate the empirical knowledge in the relevant area(s) in a way that sets the stage for your study and why you conducted it. The introduction starts out broad (but not too broad!) and gets more focused toward the end. Here are some guidelines for constructing a good introduction:

• Don’t put your readers to sleep by beginning your paper with the time-worn sentence, “Past research has shown (blah blah blah)” They’ll be snoring within a paragraph!  Try to draw your reader in by saying something interesting or thought-provoking right off the bat.  Take a look at articles you’ve read. Which ones captured your attention right away? How did the authors accomplish this task? Which ones didn’t?  Why not?  See if you can use articles you liked as a model. One way to begin (but not the only way) is to provide an example or anecdote illustrative of your topic area.
• Although you won’t go into the details of your study and hypotheses until the end of the intro, you should foreshadow your study a bit at the end of the first paragraph by stating your purpose briefly, to give your reader a schema for all the information you will present next.
• Your intro should be a logical flow of ideas that leads up to your hypothesis. Try to organize it in terms of the ideas rather than who did what when. In other words, your intro shouldn’t read like a story of “Schmirdley did such-and-such in 1991. Then Gurglehoff did something-or-other in 1993.  Then....(etc.)” First, brainstorm all of the ideas you think are necessary to include in your paper. Next, decide which ideas make sense to present first, second, third, and so forth, and think about how you want to transition between ideas. When an idea is complex, don’t be afraid to use a real-life example to clarify it for your reader. The introduction will end with a brief overview of your study and, finally, your specific hypotheses. The hypotheses should flow logically out of everything that’s been presented, so that the reader has the sense of, “Of course. This hypothesis makes complete sense, given all the other research that was presented.”
• When incorporating references into your intro, you do not necessarily need to describe every single study in complete detail, particularly if different studies use similar methodologies. Certainly you want to summarize briefly key articles, though, and point out differences in methods or findings of relevant studies when necessary. Don’t make one mistake typical of a novice APA-paper writer by stating overtly why you’re including a particular article (e.g., “This article is relevant to my study because…”). It should be obvious to the reader why you’re including a reference without your explicitly saying so.  DO NOT quote from the articles, instead paraphrase by putting the information in your own words.
• Be careful about citing your sources (see APA manual). Make sure there is a one-to-one correspondence between the articles you’ve cited in your intro and the articles listed in your reference section.
• Remember that your audience is the broader scientific community, not the other students in your class or your professor.  Therefore, you should assume they have a basic understanding of psychology, but you need to provide them with the complete information necessary for them to understand the research you are presenting.

Method (labeled, centered, bold)

The Method section of an APA-style paper is the most straightforward to write, but requires precision. Your goal is to describe the details of your study in such a way that another researcher could duplicate your methods exactly.

The Method section typically includes Participants, Materials and/or Apparatus, and Procedure sections. If the design is particularly complicated (multiple IVs in a factorial experiment, for example), you might also include a separate Design subsection or have a “Design and Procedure” section.

Note that in some studies (e.g., questionnaire studies in which there are many measures to describe but the procedure is brief), it may be more useful to present the Procedure section prior to the Materials section rather than after it.

Participants (labeled, flush left, bold)

Total number of participants (# women, # men), age range, mean and SD for age, racial/ethnic composition (if applicable), population type (e.g., college students). Remember to write numbers out when they begin a sentence.

• How were the participants recruited? (Don’t say “randomly” if it wasn’t random!) Were they compensated for their time in any way? (e.g., money, extra credit points)
• Write for a broad audience. Thus, do not write, “Students in Psych. 280...” Rather, write (for instance), “Students in a psychological statistics and research methods course at a small liberal arts college….”
• Try to avoid short, choppy sentences. Combine information into a longer sentence when possible.

Materials (labeled, flush left, bold)

Carefully describe any stimuli, questionnaires, and so forth. It is unnecessary to mention things such as the paper and pencil used to record the responses, the data recording sheet, the computer that ran the data analysis, the color of the computer, and so forth.

• If you included a questionnaire, you should describe it in detail. For instance, note how many items were on the questionnaire, what the response format was (e.g., a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree)), how many items were reverse-scored, whether the measure had subscales, and so forth. Provide a sample item or two for your reader.
• If you have created a new instrument, you should attach it as an Appendix.
• If you presented participants with various word lists to remember or stimuli to judge, you should describe those in detail here. Use subheadings to separate different types of stimuli if needed.  If you are only describing questionnaires, you may call this section “Measures.”

Apparatus (labeled, flush left, bold)

Include an apparatus section if you used specialized equipment for your study (e.g., the eye tracking machine) and need to describe it in detail.

Procedure (labeled, flush left, bold)

What did participants do, and in what order? When you list a control variable (e.g., “Participants all sat two feet from the experimenter.”), explain WHY you did what you did.  In other words, what nuisance variable were you controlling for? Your procedure should be as brief and concise as possible. Read through it. Did you repeat yourself anywhere? If so, how can you rearrange things to avoid redundancy? You may either write the instructions to the participants verbatim or paraphrase, whichever you deem more appropriate. Don’t forget to include brief statements about informed consent and debriefing.

Results (labeled, centered, bold)

In this section, describe how you analyzed the data and what you found. If your data analyses were complex, feel free to break this section down into labeled subsections, perhaps one section for each hypothesis.

• Include a section for descriptive statistics
• List what type of analysis or test you conducted to test each hypothesis.
• Refer to your Statistics textbook for the proper way to report results in APA style. A t-test, for example, is reported in the following format: t (18) = 3.57, p < .001, where 18 is the number of degrees of freedom (N – 2 for an independent-groups t test). For a correlation: r (32) = -.52, p < .001, where 32 is the number of degrees of freedom (N – 2 for a correlation). For a one-way ANOVA: F (2, 18) = 7.00, p < .001, where 2 represents the between and 18 represents df within Remember that if a finding has a p value greater than .05, it is “nonsignificant,” not “insignificant.” For nonsignificant findings, still provide the exact p values. For correlations, be sure to report the r 2 value as an assessment of the strength of the finding, to show what proportion of variability is shared by the two variables you’re correlating. For t- tests and ANOVAs, report eta 2 .
• Report exact p values to two or three decimal places (e.g., p = .042; see p. 114 of APA manual).  However, for p-values less than .001, simply put p < .001.
• Following the presentation of all the statistics and numbers, be sure to state the nature of your finding(s) in words and whether or not they support your hypothesis (e.g., “As predicted …”). This information can typically be presented in a sentence or two following the numbers (within the same paragraph). Also, be sure to include the relevant means and SDs.
• It may be useful to include a table or figure to represent your results visually. Be sure to refer to these in your paper (e.g., “As illustrated in Figure 1…”). Remember that you may present a set of findings either as a table or as a figure, but not as both. Make sure that your text is not redundant with your tables/figures. For instance, if you present a table of means and standard deviations, you do not need to also report these in the text. However, if you use a figure to represent your results, you may wish to report means and standard deviations in the text, as these may not always be precisely ascertained by examining the figure. Do describe the trends shown in the figure.
• Do not spend any time interpreting or explaining the results; save that for the Discussion section.

Discussion (labeled, centered, bold)

The goal of the discussion section is to interpret your findings and place them in the broader context of the literature in the area. A discussion section is like the reverse of the introduction, in that you begin with the specifics and work toward the more general (funnel out). Some points to consider:

• Begin with a brief restatement of your main findings (using words, not numbers). Did they support the hypothesis or not? If not, why not, do you think? Were there any surprising or interesting findings? How do your findings tie into the existing literature on the topic, or extend previous research? What do the results say about the broader behavior under investigation? Bring back some of the literature you discussed in the Introduction, and show how your results fit in (or don’t fit in, as the case may be). If you have surprising findings, you might discuss other theories that can help to explain the findings. Begin with the assumption that your results are valid, and explain why they might differ from others in the literature.
• What are the limitations of the study? If your findings differ from those of other researchers, or if you did not get statistically significant results, don’t spend pages and pages detailing what might have gone wrong with your study, but do provide one or two suggestions. Perhaps these could be incorporated into the future research section, below.
• What additional questions were generated from this study? What further research should be conducted on the topic? What gaps are there in the current body of research? Whenever you present an idea for a future research study, be sure to explain why you think that particular study should be conducted. What new knowledge would be gained from it?  Don’t just say, “I think it would be interesting to re-run the study on a different college campus” or “It would be better to run the study again with more participants.” Really put some thought into what extensions of the research might be interesting/informative, and why.
• What are the theoretical and/or practical implications of your findings? How do these results relate to larger issues of human thoughts, feelings, and behavior? Give your readers “the big picture.” Try to answer the question, “So what?

Final paragraph: Be sure to sum up your paper with a final concluding statement. Don’t just trail off with an idea for a future study. End on a positive note by reminding your reader why your study was important and what it added to the literature.

References (labeled, centered, not bold)

Provide an alphabetical listing of the references (alphabetize by last name of first author). Double-space all, with no extra spaces between references. The second line of each reference should be indented (this is called a hanging indent and is easily accomplished using the ruler in Microsoft Word). See the APA manual for how to format references correctly.

Examples of references to journal articles start on p. 198 of the manual, and examples of references to books and book chapters start on pp. 202. Digital object identifiers (DOIs) are now included for electronic sources (see pp. 187-192 of APA manual to learn more).

Journal article example: [Note that only the first letter of the first word of the article title is capitalized; the journal name and volume are italicized. If the journal name had multiple words, each of the major words would be capitalized.] 

Ebner-Priemer, U. W., & Trull, T. J. (2009). Ecological momentary assessment of mood disorders and mood dysregulation. Psychological Assessment, 21, 463-475. doi:10.1037/a0017075

Book chapter example: [Note that only the first letter of the first word of both the chapter title and book title are capitalized.]

Stephan, W. G. (1985). Intergroup relations. In G. Lindzey & E. Aronson (Eds.), The handbook of social psychology (3 rd ed., Vol. 2, pp. 599-658). New York: Random House.

Book example: Gray, P. (2010). Psychology (6 th ed.). New York: Worth

Table There are various formats for tables, depending upon the information you wish to include. See the APA manual. Be sure to provide a table number and table title (the latter is italicized). Tables can be single or double-spaced.

Figure If you have more than one figure, each one gets its own page. Use a sans serif font, such as Helvetica, for any text within your figure. Be sure to label your x- and y-axes clearly, and make sure you’ve noted the units of measurement of the DV. Underneath the figure provide a label and brief caption (e.g., “Figure 1. Mean evaluation of job applicant qualifications as a function of applicant attractiveness level”). The figure caption typically includes the IVs/predictor variables and the DV. Include error bars in your bar graphs, and note what the bars represent in the figure caption: Error bars represent one standard error above and below the mean.

In-Text Citations: (see pp. 174-179 of APA manual) When citing sources in your paper, you need to include the authors’ names and publication date.

You should use the following formats:

• When including the citation as part of the sentence, use AND: “According to Jones and Smith (2003), the…”
• When the citation appears in parentheses, use “&”: “Studies have shown that priming can affect actual motor behavior (Jones & Smith, 2003; Klein, Bailey, & Hammer, 1999).” The studies appearing in parentheses should be ordered alphabetically by the first author’s last name, and should be separated by semicolons.
• If you are quoting directly (which you should avoid), you also need to include the page number.
• For sources with three or more authors, once you have listed all the authors’ names, you may write “et al.” on subsequent mentions. For example: “Klein et al. (1999) found that….” For sources with two authors, both authors must be included every time the source is cited. When a source has six or more authors, the first author’s last name and “et al.” are used every time the source is cited (including the first time). 

Secondary Sources

“Secondary source” is the term used to describe material that is cited in another source. If in his article entitled “Behavioral Study of Obedience” (1963), Stanley Milgram makes reference to the ideas of Snow (presented above), Snow (1961) is the primary source, and Milgram (1963) is the secondary source.

Try to avoid using secondary sources in your papers; in other words, try to find the primary source and read it before citing it in your own work. If you must use a secondary source, however, you should cite it in the following way:

Snow (as cited in Milgram, 1963) argued that, historically, the cause of most criminal acts... The reference for the Milgram article (but not the Snow reference) should then appear in the reference list at the end of your paper.

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How to Write a Methods Section for a Psychology Paper

Tips and Examples of an APA Methods Section

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

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

Verywell / Brianna Gilmartin 

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

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

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

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

What to Include in a Method Section

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

• Research design
• Participants
• Participant behavior

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

Components of a Method Section

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

Participants 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Tips for How to Write a Methods Section

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

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

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

A Word From Verywell

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

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

Frequently Asked Questions

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

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

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

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

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

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

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

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

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

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• Open access
• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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DOI : https://doi.org/10.1038/s41467-024-46346-0

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