what are the contents of a research paper

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How to Structure the Table of Contents for a Research Paper

4-minute read

• 16th July 2023

So you’ve made it to the important step of writing the table of contents for your paper. Congratulations on making it this far! Whether you’re writing a research paper or a dissertation , the table of contents not only provides the reader with guidance on where to find the sections of your paper, but it also signals that a quality piece of research is to follow. Here, we will provide detailed instructions on how to structure the table of contents for your research paper.

Steps to Create a Table of Contents

• Insert the table of contents after the title page.

Within the structure of your research paper , you should place the table of contents after the title page but before the introduction or the beginning of the content. If your research paper includes an abstract or an acknowledgements section , place the table of contents after it.

• List all the paper’s sections and subsections in chronological order.

Depending on the complexity of your paper, this list will include chapters (first-level headings), chapter sections (second-level headings), and perhaps subsections (third-level headings). If you have a chapter outline , it will come in handy during this step. You should include the bibliography and all appendices in your table of contents. If you have more than a few charts and figures (more often the case in a dissertation than in a research paper), you should add them to a separate list of charts and figures that immediately follows the table of contents. (Check out our FAQs below for additional guidance on items that should not be in your table of contents.)

• Paginate each section.

Label each section and subsection with the page number it begins on. Be sure to do a check after you’ve made your final edits to ensure that you don’t need to update the page numbers.

• Format your table of contents.

The way you format your table of contents will depend on the style guide you use for the rest of your paper. For example, there are table of contents formatting guidelines for Turabian/Chicago and MLA styles, and although the APA recommends checking with your instructor for formatting instructions (always a good rule of thumb), you can also create a table of contents for a research paper that follows APA style .

• Add hyperlinks if you like.

Depending on the word processing software you’re using, you may also be able to hyperlink the sections of your table of contents for easier navigation through your paper. (Instructions for this feature are available for both Microsoft Word and Google Docs .)

To summarize, the following steps will help you create a clear and concise table of contents to guide readers through your research paper:

1. Insert the table of contents after the title page.

2. List all the sections and subsections in chronological order.

3. Paginate each section.

4. Format the table of contents according to your style guide.

5. Add optional hyperlinks.

If you’d like help formatting and proofreading your research paper , check out some of our services. You can even submit a sample for free . Best of luck writing your research paper table of contents!

What is a table of contents?

A table of contents is a listing of each section of a document in chronological order, accompanied by the page number where the section begins. A table of contents gives the reader an overview of the contents of a document, as well as providing guidance on where to find each section.

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What should I include in my table of contents?

If your paper contains any of the following sections, they should be included in your table of contents:

●  Chapters, chapter sections, and subsections

●  Introduction

●  Conclusion

●  Appendices

●  Bibliography

Although recommendations may differ among institutions, you generally should not include the following in your table of contents:

●  Title page

●  Abstract

●  Acknowledgements

●  Forward or preface

If you have several charts, figures, or tables, consider creating a separate list for them that will immediately follow the table of contents. Also, you don’t need to include the table of contents itself in your table of contents.

Is there more than one way to format a table of contents?

Yes! In addition to following any recommendations from your instructor or institution, you should follow the stipulations of your style guide .

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Writing Research Papers

• Research Paper Structure

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

Major Sections of a Research Paper in APA Style

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

What is this paper called and who wrote it? – the first page of the paper; this includes the name of the paper, a “running head”, authors, and institutional affiliation of the authors.  The institutional affiliation is usually listed in an Author Note that is placed towards the bottom of the title page.  In some cases, the Author Note also contains an acknowledgment of any funding support and of any individuals that assisted with the research project.

One-paragraph summary of the entire study – typically no more than 250 words in length (and in many cases it is well shorter than that), the Abstract provides an overview of the study.

Introduction

What is the topic and why is it worth studying? – the first major section of text in the paper, the Introduction commonly describes the topic under investigation, summarizes or discusses relevant prior research (for related details, please see the Writing Literature Reviews section of this website), identifies unresolved issues that the current research will address, and provides an overview of the research that is to be described in greater detail in the sections to follow.

What did you do? – a section which details how the research was performed.  It typically features a description of the participants/subjects that were involved, the study design, the materials that were used, and the study procedure.  If there were multiple experiments, then each experiment may require a separate Methods section.  A rule of thumb is that the Methods section should be sufficiently detailed for another researcher to duplicate your research.

What did you find? – a section which describes the data that was collected and the results of any statistical tests that were performed.  It may also be prefaced by a description of the analysis procedure that was used. If there were multiple experiments, then each experiment may require a separate Results section.

What is the significance of your results? – the final major section of text in the paper.  The Discussion commonly features a summary of the results that were obtained in the study, describes how those results address the topic under investigation and/or the issues that the research was designed to address, and may expand upon the implications of those findings.  Limitations and directions for future research are also commonly addressed.

List of articles and any books cited – an alphabetized list of the sources that are cited in the paper (by last name of the first author of each source).  Each reference should follow specific APA guidelines regarding author names, dates, article titles, journal titles, journal volume numbers, page numbers, book publishers, publisher locations, websites, and so on (for more information, please see the Citing References in APA Style page of this website).

Tables and Figures

Graphs and data (optional in some cases) – depending on the type of research being performed, there may be Tables and/or Figures (however, in some cases, there may be neither).  In APA style, each Table and each Figure is placed on a separate page and all Tables and Figures are included after the References.   Tables are included first, followed by Figures.   However, for some journals and undergraduate research papers (such as the B.S. Research Paper or Honors Thesis), Tables and Figures may be embedded in the text (depending on the instructor’s or editor’s policies; for more details, see "Deviations from APA Style" below).

Supplementary information (optional) – in some cases, additional information that is not critical to understanding the research paper, such as a list of experiment stimuli, details of a secondary analysis, or programming code, is provided.  This is often placed in an Appendix.

Variations of Research Papers in APA Style

Although the major sections described above are common to most research papers written in APA style, there are variations on that pattern.  These variations include: 

• Literature reviews – when a paper is reviewing prior published research and not presenting new empirical research itself (such as in a review article, and particularly a qualitative review), then the authors may forgo any Methods and Results sections. Instead, there is a different structure such as an Introduction section followed by sections for each of the different aspects of the body of research being reviewed, and then perhaps a Discussion section. 
• Multi-experiment papers – when there are multiple experiments, it is common to follow the Introduction with an Experiment 1 section, itself containing Methods, Results, and Discussion subsections. Then there is an Experiment 2 section with a similar structure, an Experiment 3 section with a similar structure, and so on until all experiments are covered.  Towards the end of the paper there is a General Discussion section followed by References.  Additionally, in multi-experiment papers, it is common for the Results and Discussion subsections for individual experiments to be combined into single “Results and Discussion” sections.

Departures from APA Style

In some cases, official APA style might not be followed (however, be sure to check with your editor, instructor, or other sources before deviating from standards of the Publication Manual of the American Psychological Association).  Such deviations may include:

• Placement of Tables and Figures  – in some cases, to make reading through the paper easier, Tables and/or Figures are embedded in the text (for example, having a bar graph placed in the relevant Results section). The embedding of Tables and/or Figures in the text is one of the most common deviations from APA style (and is commonly allowed in B.S. Degree Research Papers and Honors Theses; however you should check with your instructor, supervisor, or editor first). 
• Incomplete research – sometimes a B.S. Degree Research Paper in this department is written about research that is currently being planned or is in progress. In those circumstances, sometimes only an Introduction and Methods section, followed by References, is included (that is, in cases where the research itself has not formally begun).  In other cases, preliminary results are presented and noted as such in the Results section (such as in cases where the study is underway but not complete), and the Discussion section includes caveats about the in-progress nature of the research.  Again, you should check with your instructor, supervisor, or editor first.
• Class assignments – in some classes in this department, an assignment must be written in APA style but is not exactly a traditional research paper (for instance, a student asked to write about an article that they read, and to write that report in APA style). In that case, the structure of the paper might approximate the typical sections of a research paper in APA style, but not entirely.  You should check with your instructor for further guidelines.

Workshops and Downloadable Resources

• For in-person discussion of the process of writing research papers, please consider attending this department’s “Writing Research Papers” workshop (for dates and times, please check the undergraduate workshops calendar).

Downloadable Resources

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

Further Resources

How-To Videos     

• Writing Research Paper Videos

APA Journal Article Reporting Guidelines

• Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 3.
• Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 26.  

External Resources

• Formatting APA Style Papers in Microsoft Word
• How to Write an APA Style Research Paper from Hamilton University
• WikiHow Guide to Writing APA Research Papers
• Sample APA Formatted Paper with Comments
• Sample APA Formatted Paper
• Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 41-60).  Washington, DC: American Psychological Association.

2 geller, e. (2018).  how to write an apa-style research report . [instructional materials]. , prepared by s. c. pan for ucsd psychology.

Back to top  

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

How to Write a Research Paper: Parts of the Paper

• Choosing Your Topic
• Citation & Style Guides This link opens in a new window
• Critical Thinking
• Evaluating Information
• Parts of the Paper
• Writing Tips from UNC-Chapel Hill
• Librarian Contact

Parts of the Research Paper Papers should have a beginning, a middle, and an end. Your introductory paragraph should grab the reader's attention, state your main idea, and indicate how you will support it. The body of the paper should expand on what you have stated in the introduction. Finally, the conclusion restates the paper's thesis and should explain what you have learned, giving a wrap up of your main ideas.

1. The Title The title should be specific and indicate the theme of the research and what ideas it addresses. Use keywords that help explain your paper's topic to the reader. Try to avoid abbreviations and jargon. Think about keywords that people would use to search for your paper and include them in your title.

2. The Abstract The abstract is used by readers to get a quick overview of your paper. Typically, they are about 200 words in length (120 words minimum to  250 words maximum). The abstract should introduce the topic and thesis, and should provide a general statement about what you have found in your research. The abstract allows you to mention each major aspect of your topic and helps readers decide whether they want to read the rest of the paper. Because it is a summary of the entire research paper, it is often written last. 

3. The Introduction The introduction should be designed to attract the reader's attention and explain the focus of the research. You will introduce your overview of the topic,  your main points of information, and why this subject is important. You can introduce the current understanding and background information about the topic. Toward the end of the introduction, you add your thesis statement, and explain how you will provide information to support your research questions. This provides the purpose and focus for the rest of the paper.

4. Thesis Statement Most papers will have a thesis statement or main idea and supporting facts/ideas/arguments. State your main idea (something of interest or something to be proven or argued for or against) as your thesis statement, and then provide your supporting facts and arguments. A thesis statement is a declarative sentence that asserts the position a paper will be taking. It also points toward the paper's development. This statement should be both specific and arguable. Generally, the thesis statement will be placed at the end of the first paragraph of your paper. The remainder of your paper will support this thesis.

Students often learn to write a thesis as a first step in the writing process, but often, after research, a writer's viewpoint may change. Therefore a thesis statement may be one of the final steps in writing. 

Examples of Thesis Statements from Purdue OWL

5. The Literature Review The purpose of the literature review is to describe past important research and how it specifically relates to the research thesis. It should be a synthesis of the previous literature and the new idea being researched. The review should examine the major theories related to the topic to date and their contributors. It should include all relevant findings from credible sources, such as academic books and peer-reviewed journal articles. You will want  to:

• Explain how the literature helps the researcher understand the topic.
• Try to show connections and any disparities between the literature.
• Identify new ways to interpret prior research.
• Reveal any gaps that exist in the literature.

More about writing a literature review. . .

6. The Discussion ​The purpose of the discussion is to interpret and describe what you have learned from your research. Make the reader understand why your topic is important. The discussion should always demonstrate what you have learned from your readings (and viewings) and how that learning has made the topic evolve, especially from the short description of main points in the introduction.Explain any new understanding or insights you have had after reading your articles and/or books. Paragraphs should use transitioning sentences to develop how one paragraph idea leads to the next. The discussion will always connect to the introduction, your thesis statement, and the literature you reviewed, but it does not simply repeat or rearrange the introduction. You want to: 

• Demonstrate critical thinking, not just reporting back facts that you gathered.
• If possible, tell how the topic has evolved over the past and give it's implications for the future.
• Fully explain your main ideas with supporting information.
• Explain why your thesis is correct giving arguments to counter points.

7. The Conclusion A concluding paragraph is a brief summary of your main ideas and restates the paper's main thesis, giving the reader the sense that the stated goal of the paper has been accomplished. What have you learned by doing this research that you didn't know before? What conclusions have you drawn? You may also want to suggest further areas of study, improvement of research possibilities, etc. to demonstrate your critical thinking regarding your research.

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• Next: Research >>
• Last Updated: Feb 13, 2024 8:35 AM
• URL: https://libguides.ucc.edu/research_paper

• Research guides

Writing an Educational Research Paper

Research paper sections, customary parts of an education research paper.

There is no one right style or manner for writing an education paper. Content aside, the writing style and presentation of papers in different educational fields vary greatly. Nevertheless, certain parts are common to most papers, for example:

Title/Cover Page

Contains the paper's title, the author's name, address, phone number, e-mail, and the day's date.

Not every education paper requires an abstract. However, for longer, more complex papers abstracts are particularly useful. Often only 100 to 300 words, the abstract generally provides a broad overview and is never more than a page. It describes the essence, the main theme of the paper. It includes the research question posed, its significance, the methodology, and the main results or findings. Footnotes or cited works are never listed in an abstract. Remember to take great care in composing the abstract. It's the first part of the paper the instructor reads. It must impress with a strong content, good style, and general aesthetic appeal. Never write it hastily or carelessly.

Introduction and Statement of the Problem

A good introduction states the main research problem and thesis argument. What precisely are you studying and why is it important? How original is it? Will it fill a gap in other studies? Never provide a lengthy justification for your topic before it has been explicitly stated.

Limitations of Study

Indicate as soon as possible what you intend to do, and what you are not going to attempt. You may limit the scope of your paper by any number of factors, for example, time, personnel, gender, age, geographic location, nationality, and so on.

Methodology

Discuss your research methodology. Did you employ qualitative or quantitative research methods? Did you administer a questionnaire or interview people? Any field research conducted? How did you collect data? Did you utilize other libraries or archives? And so on.

Literature Review

The research process uncovers what other writers have written about your topic. Your education paper should include a discussion or review of what is known about the subject and how that knowledge was acquired. Once you provide the general and specific context of the existing knowledge, then you yourself can build on others' research. The guide Writing a Literature Review will be helpful here.

Main Body of Paper/Argument

This is generally the longest part of the paper. It's where the author supports the thesis and builds the argument. It contains most of the citations and analysis. This section should focus on a rational development of the thesis with clear reasoning and solid argumentation at all points. A clear focus, avoiding meaningless digressions, provides the essential unity that characterizes a strong education paper.

After spending a great deal of time and energy introducing and arguing the points in the main body of the paper, the conclusion brings everything together and underscores what it all means. A stimulating and informative conclusion leaves the reader informed and well-satisfied. A conclusion that makes sense, when read independently from the rest of the paper, will win praise.

Works Cited/Bibliography

See the Citation guide .

Education research papers often contain one or more appendices. An appendix contains material that is appropriate for enlarging the reader's understanding, but that does not fit very well into the main body of the paper. Such material might include tables, charts, summaries, questionnaires, interview questions, lengthy statistics, maps, pictures, photographs, lists of terms, glossaries, survey instruments, letters, copies of historical documents, and many other types of supplementary material. A paper may have several appendices. They are usually placed after the main body of the paper but before the bibliography or works cited section. They are usually designated by such headings as Appendix A, Appendix B, and so on.

• << Previous: Choosing a Topic
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• Last Updated: Dec 5, 2023 2:26 PM
• Subjects: Education
• Tags: education , education_paper , education_research_paper

Scientific and Scholarly Writing

• Literature Searches
• Tracking and Citing References

Parts of a Scientific & Scholarly Paper

Introduction.

• Writing Effectively
• Where to Publish?
• Capstone Resources

Different sections are needed in different types of scientific papers (lab reports, literature reviews, systematic reviews, methods papers, research papers, etc.). Projects that overlap with the social sciences or humanities may have different requirements. Generally, however, you'll need to include:

INTRODUCTION (Background)

METHODS SECTION (Materials and Methods)

What is a title

Titles have two functions: to identify the main topic or the message of the paper and to attract readers.

The title will be read by many people. Only a few will read the entire paper, therefore all words in the title should be chosen with care. Too short a title is not helpful to the potential reader. Too long a title can sometimes be even less meaningful. Remember a title is not an abstract. Neither is a title a sentence.

What makes a good title?

A good title is accurate, complete, and specific. Imagine searching for your paper in PubMed. What words would you use?

• Use the fewest possible words that describe the contents of the paper.
• Avoid waste words like "Studies on", or "Investigations on".
• Use specific terms rather than general.
• Use the same key terms in the title as the paper.
• Watch your word order and syntax.

The abstract is a miniature version of your paper. It should present the main story and a few essential details of the paper for readers who only look at the abstract and should serve as a clear preview for readers who read your whole paper. They are usually short (250 words or less).

The goal is to communicate:

•  What was done?
•  Why was it done?
•  How was it done?
•  What was found?

A good abstract is specific and selective. Try summarizing each of the sections of your paper in a sentence two. Do the abstract last, so you know exactly what you want to write.

• Use 1 or more well developed paragraphs.
• Use introduction/body/conclusion structure.
• Present purpose, results, conclusions and recommendations in that order.
• Make it understandable to a wide audience.
• << Previous: Tracking and Citing References
• Next: Writing Effectively >>
• Last Updated: Mar 13, 2024 12:20 PM
• URL: https://libraryguides.umassmed.edu/scientific-writing

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13.1 Formatting a Research Paper

Learning objectives.

• Identify the major components of a research paper written using American Psychological Association (APA) style.
• Apply general APA style and formatting conventions in a research paper.

In this chapter, you will learn how to use APA style , the documentation and formatting style followed by the American Psychological Association, as well as MLA style , from the Modern Language Association. There are a few major formatting styles used in academic texts, including AMA, Chicago, and Turabian:

• AMA (American Medical Association) for medicine, health, and biological sciences
• APA (American Psychological Association) for education, psychology, and the social sciences
• Chicago—a common style used in everyday publications like magazines, newspapers, and books
• MLA (Modern Language Association) for English, literature, arts, and humanities
• Turabian—another common style designed for its universal application across all subjects and disciplines

While all the formatting and citation styles have their own use and applications, in this chapter we focus our attention on the two styles you are most likely to use in your academic studies: APA and MLA.

If you find that the rules of proper source documentation are difficult to keep straight, you are not alone. Writing a good research paper is, in and of itself, a major intellectual challenge. Having to follow detailed citation and formatting guidelines as well may seem like just one more task to add to an already-too-long list of requirements.

Following these guidelines, however, serves several important purposes. First, it signals to your readers that your paper should be taken seriously as a student’s contribution to a given academic or professional field; it is the literary equivalent of wearing a tailored suit to a job interview. Second, it shows that you respect other people’s work enough to give them proper credit for it. Finally, it helps your reader find additional materials if he or she wishes to learn more about your topic.

Furthermore, producing a letter-perfect APA-style paper need not be burdensome. Yes, it requires careful attention to detail. However, you can simplify the process if you keep these broad guidelines in mind:

• Work ahead whenever you can. Chapter 11 “Writing from Research: What Will I Learn?” includes tips for keeping track of your sources early in the research process, which will save time later on.
• Get it right the first time. Apply APA guidelines as you write, so you will not have much to correct during the editing stage. Again, putting in a little extra time early on can save time later.
• Use the resources available to you. In addition to the guidelines provided in this chapter, you may wish to consult the APA website at http://www.apa.org or the Purdue University Online Writing lab at http://owl.english.purdue.edu , which regularly updates its online style guidelines.

General Formatting Guidelines

This chapter provides detailed guidelines for using the citation and formatting conventions developed by the American Psychological Association, or APA. Writers in disciplines as diverse as astrophysics, biology, psychology, and education follow APA style. The major components of a paper written in APA style are listed in the following box.

These are the major components of an APA-style paper:

Body, which includes the following:

• Headings and, if necessary, subheadings to organize the content
• In-text citations of research sources
• References page

All these components must be saved in one document, not as separate documents.

The title page of your paper includes the following information:

• Title of the paper
• Author’s name
• Name of the institution with which the author is affiliated
• Header at the top of the page with the paper title (in capital letters) and the page number (If the title is lengthy, you may use a shortened form of it in the header.)

List the first three elements in the order given in the previous list, centered about one third of the way down from the top of the page. Use the headers and footers tool of your word-processing program to add the header, with the title text at the left and the page number in the upper-right corner. Your title page should look like the following example.

The next page of your paper provides an abstract , or brief summary of your findings. An abstract does not need to be provided in every paper, but an abstract should be used in papers that include a hypothesis. A good abstract is concise—about one hundred fifty to two hundred fifty words—and is written in an objective, impersonal style. Your writing voice will not be as apparent here as in the body of your paper. When writing the abstract, take a just-the-facts approach, and summarize your research question and your findings in a few sentences.

In Chapter 12 “Writing a Research Paper” , you read a paper written by a student named Jorge, who researched the effectiveness of low-carbohydrate diets. Read Jorge’s abstract. Note how it sums up the major ideas in his paper without going into excessive detail.

Write an abstract summarizing your paper. Briefly introduce the topic, state your findings, and sum up what conclusions you can draw from your research. Use the word count feature of your word-processing program to make sure your abstract does not exceed one hundred fifty words.

Depending on your field of study, you may sometimes write research papers that present extensive primary research, such as your own experiment or survey. In your abstract, summarize your research question and your findings, and briefly indicate how your study relates to prior research in the field.

Margins, Pagination, and Headings

APA style requirements also address specific formatting concerns, such as margins, pagination, and heading styles, within the body of the paper. Review the following APA guidelines.

Use these general guidelines to format the paper:

• Set the top, bottom, and side margins of your paper at 1 inch.
• Use double-spaced text throughout your paper.
• Use a standard font, such as Times New Roman or Arial, in a legible size (10- to 12-point).
• Use continuous pagination throughout the paper, including the title page and the references section. Page numbers appear flush right within your header.
• Section headings and subsection headings within the body of your paper use different types of formatting depending on the level of information you are presenting. Additional details from Jorge’s paper are provided.

Begin formatting the final draft of your paper according to APA guidelines. You may work with an existing document or set up a new document if you choose. Include the following:

• Your title page
• The abstract you created in Note 13.8 “Exercise 1”
• Correct headers and page numbers for your title page and abstract

APA style uses section headings to organize information, making it easy for the reader to follow the writer’s train of thought and to know immediately what major topics are covered. Depending on the length and complexity of the paper, its major sections may also be divided into subsections, sub-subsections, and so on. These smaller sections, in turn, use different heading styles to indicate different levels of information. In essence, you are using headings to create a hierarchy of information.

The following heading styles used in APA formatting are listed in order of greatest to least importance:

• Section headings use centered, boldface type. Headings use title case, with important words in the heading capitalized.
• Subsection headings use left-aligned, boldface type. Headings use title case.
• The third level uses left-aligned, indented, boldface type. Headings use a capital letter only for the first word, and they end in a period.
• The fourth level follows the same style used for the previous level, but the headings are boldfaced and italicized.
• The fifth level follows the same style used for the previous level, but the headings are italicized and not boldfaced.

Visually, the hierarchy of information is organized as indicated in Table 13.1 “Section Headings” .

Table 13.1 Section Headings

A college research paper may not use all the heading levels shown in Table 13.1 “Section Headings” , but you are likely to encounter them in academic journal articles that use APA style. For a brief paper, you may find that level 1 headings suffice. Longer or more complex papers may need level 2 headings or other lower-level headings to organize information clearly. Use your outline to craft your major section headings and determine whether any subtopics are substantial enough to require additional levels of headings.

Working with the document you developed in Note 13.11 “Exercise 2” , begin setting up the heading structure of the final draft of your research paper according to APA guidelines. Include your title and at least two to three major section headings, and follow the formatting guidelines provided above. If your major sections should be broken into subsections, add those headings as well. Use your outline to help you.

Because Jorge used only level 1 headings, his Exercise 3 would look like the following:

Citation Guidelines

In-text citations.

Throughout the body of your paper, include a citation whenever you quote or paraphrase material from your research sources. As you learned in Chapter 11 “Writing from Research: What Will I Learn?” , the purpose of citations is twofold: to give credit to others for their ideas and to allow your reader to follow up and learn more about the topic if desired. Your in-text citations provide basic information about your source; each source you cite will have a longer entry in the references section that provides more detailed information.

In-text citations must provide the name of the author or authors and the year the source was published. (When a given source does not list an individual author, you may provide the source title or the name of the organization that published the material instead.) When directly quoting a source, it is also required that you include the page number where the quote appears in your citation.

This information may be included within the sentence or in a parenthetical reference at the end of the sentence, as in these examples.

Epstein (2010) points out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Here, the writer names the source author when introducing the quote and provides the publication date in parentheses after the author’s name. The page number appears in parentheses after the closing quotation marks and before the period that ends the sentence.

Addiction researchers caution that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (Epstein, 2010, p. 137).

Here, the writer provides a parenthetical citation at the end of the sentence that includes the author’s name, the year of publication, and the page number separated by commas. Again, the parenthetical citation is placed after the closing quotation marks and before the period at the end of the sentence.

As noted in the book Junk Food, Junk Science (Epstein, 2010, p. 137), “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive.”

Here, the writer chose to mention the source title in the sentence (an optional piece of information to include) and followed the title with a parenthetical citation. Note that the parenthetical citation is placed before the comma that signals the end of the introductory phrase.

David Epstein’s book Junk Food, Junk Science (2010) pointed out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Another variation is to introduce the author and the source title in your sentence and include the publication date and page number in parentheses within the sentence or at the end of the sentence. As long as you have included the essential information, you can choose the option that works best for that particular sentence and source.

Citing a book with a single author is usually a straightforward task. Of course, your research may require that you cite many other types of sources, such as books or articles with more than one author or sources with no individual author listed. You may also need to cite sources available in both print and online and nonprint sources, such as websites and personal interviews. Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.2 “Citing and Referencing Techniques” and Section 13.3 “Creating a References Section” provide extensive guidelines for citing a variety of source types.

Writing at Work

APA is just one of several different styles with its own guidelines for documentation, formatting, and language usage. Depending on your field of interest, you may be exposed to additional styles, such as the following:

• MLA style. Determined by the Modern Languages Association and used for papers in literature, languages, and other disciplines in the humanities.
• Chicago style. Outlined in the Chicago Manual of Style and sometimes used for papers in the humanities and the sciences; many professional organizations use this style for publications as well.
• Associated Press (AP) style. Used by professional journalists.

References List

The brief citations included in the body of your paper correspond to the more detailed citations provided at the end of the paper in the references section. In-text citations provide basic information—the author’s name, the publication date, and the page number if necessary—while the references section provides more extensive bibliographical information. Again, this information allows your reader to follow up on the sources you cited and do additional reading about the topic if desired.

The specific format of entries in the list of references varies slightly for different source types, but the entries generally include the following information:

• The name(s) of the author(s) or institution that wrote the source
• The year of publication and, where applicable, the exact date of publication
• The full title of the source
• For books, the city of publication
• For articles or essays, the name of the periodical or book in which the article or essay appears
• For magazine and journal articles, the volume number, issue number, and pages where the article appears
• For sources on the web, the URL where the source is located

The references page is double spaced and lists entries in alphabetical order by the author’s last name. If an entry continues for more than one line, the second line and each subsequent line are indented five spaces. Review the following example. ( Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.3 “Creating a References Section” provides extensive guidelines for formatting reference entries for different types of sources.)

In APA style, book and article titles are formatted in sentence case, not title case. Sentence case means that only the first word is capitalized, along with any proper nouns.

Key Takeaways

• Following proper citation and formatting guidelines helps writers ensure that their work will be taken seriously, give proper credit to other authors for their work, and provide valuable information to readers.
• Working ahead and taking care to cite sources correctly the first time are ways writers can save time during the editing stage of writing a research paper.
• APA papers usually include an abstract that concisely summarizes the paper.
• APA papers use a specific headings structure to provide a clear hierarchy of information.
• In APA papers, in-text citations usually include the name(s) of the author(s) and the year of publication.
• In-text citations correspond to entries in the references section, which provide detailed bibliographical information about a source.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

• Research Guides

BSCI 1510L Literature and Stats Guide: 3.2 Components of a scientific paper

• 1 What is a scientific paper?
• 2 Referencing and accessing papers
• 2.1 Literature Cited
• 2.2 Accessing Scientific Papers
• 2.3 Traversing the web of citations
• 2.4 Keyword Searches
• 3 Style of scientific writing
• 3.1 Specific details regarding scientific writing

3.2 Components of a scientific paper

• 4 For further information
• Appendix A: Calculation Final Concentrations
• 1 Formulas in Excel
• 2 Basic operations in Excel
• 3 Measurement and Variation
• 3.1 Describing Quantities and Their Variation
• 3.2 Samples Versus Populations
• 3.3 Calculating Descriptive Statistics using Excel
• 4 Variation and differences
• 5 Differences in Experimental Science
• 5.1 Aside: Commuting to Nashville
• 5.2 P and Detecting Differences in Variable Quantities
• 5.3 Statistical significance
• 5.4 A test for differences of sample means: 95% Confidence Intervals
• 5.5 Error bars in figures
• 5.6 Discussing statistics in your scientific writing
• 6 Scatter plot, trendline, and linear regression
• 7 The t-test of Means
• 8 Paired t-test
• 9 Two-Tailed and One-Tailed Tests
• 10 Variation on t-tests: ANOVA
• 11 Reporting the Results of a Statistical Test
• 12 Summary of statistical tests
• 1 Objectives
• 2 Project timeline
• 3 Background
• 4 Previous work in the BSCI 111 class
• 5 General notes about the project
• 6 About the paper
• 7 References

Nearly all journal articles are divided into the following major sections: abstract, introduction, methods, results, discussion, and references.  Usually the sections are labeled as such, although often the introduction (and sometimes the abstract) is not labeled.  Sometimes alternative section titles are used.  The abstract is sometimes called the "summary", the methods are sometimes called "materials and methods", and the discussion is sometimes called "conclusions".   Some journals also include the minor sections of "key words" following the abstract, and "acknowledgments" following the discussion.  In some journals, the sections may be divided into subsections that are given descriptive titles.  However, the general division into the six major sections is nearly universal.

3.2.1 Abstract

The abstract is a short summary (150-200 words or less) of the important points of the paper.  It does not generally include background information.  There may be a very brief statement of the rationale for conducting the study.  It describes what was done, but without details.  It also describes the results in a summarized way that usually includes whether or not the statistical tests were significant.  It usually concludes with a brief statement of the importance of the results.  Abstracts do not include references.  When writing a paper, the abstract is always the last part to be written.

The purpose of the abstract is to allow potential readers of a paper to find out the important points of the paper without having to actually read the paper.  It should be a self-contained unit capable of being understood without the benefit of the text of the article . It essentially serves as an "advertisement" for the paper that readers use to determine whether or not they actually want to wade through the entire paper or not.  Abstracts are generally freely available in electronic form and are often presented in the results of an electronic search.  If searchers do not have electronic access to the journal in which the article is published, the abstract is the only means that they have to decide whether to go through the effort (going to the library to look up the paper journal, requesting a reprint from the author, buying a copy of the article from a service, requesting the article by Interlibrary Loan) of acquiring the article.  Therefore it is important that the abstract accurately and succinctly presents the most important information in the article.

3.2.2 Introduction

The introduction provides the background information necessary to understand why the described experiment was conducted.  The introduction should describe previous research on the topic that has led to the unanswered questions being addressed by the experiment and should cite important previous papers that form the background for the experiment.  The introduction should also state in an organized fashion the goals of the research, i.e. the particular, specific questions that will be tested in the experiments.  There should be a one-to-one correspondence between questions raised in the introduction and points discussed in the conclusion section of the paper.  In other words, do not raise questions in the introduction unless you are going to have some kind of answer to the question that you intend to discuss at the end of the paper. 

You may have been told that every paper must have a hypothesis that can be clearly stated.  That is often true, but not always.  If your experiment involves a manipulation which tests a specific hypothesis, then you should clearly state that hypothesis.  On the other hand, if your experiment was primarily exploratory, descriptive, or measurative, then you probably did not have an a priori hypothesis, so don't pretend that you did and make one up.  (See the discussion in the introduction to Experiment 4 for more on this.)  If you state a hypothesis in the introduction, it should be a general hypothesis and not a null or alternative hypothesis for a statistical test.  If it is necessary to explain how a statistical test will help you evaluate your general hypothesis, explain that in the methods section. 

A good introduction should be fairly heavy with citations.  This indicates to the reader that the authors are informed about previous work on the topic and are not working in a vacuum.  Citations also provide jumping-off points to allow the reader to explore other tangents to the subject that are not directly addressed in the paper.  If the paper supports or refutes previous work, readers can look up the citations and make a comparison for themselves. 

"Do not get lost in reviewing background information. Remember that the Introduction is meant to introduce the reader to your research, not summarize and evaluate all past literature on the subject (which is the purpose of a review paper). Many of the other studies you may be tempted to discuss in your Introduction are better saved for the Discussion, where they become a powerful tool for comparing and interpreting your results. Include only enough background information to allow your reader to understand why you are asking the questions you are and why your hyptheses are reasonable ones. Often, a brief explanation of the theory involved is sufficient. …

Write this section in the past or present tense, never in the future. " (Steingraber et al. 1985)

3.2.3 Methods (taken verbatim from Steingraber et al. 1985)

The function of this section is to describe all experimental procedures, including controls. The description should be complete enough to enable someone else to repeat your work. If there is more than one part to the experiment, it is a good idea to describe your methods and present your results in the same order in each section. This may not be the same order in which the experiments were performed -it is up to you to decide what order of presentation will make the most sense to your reader.

1. Explain why each procedure was done, i.e., what variable were you measuring and why? Example:

Difficult to understand : First, I removed the frog muscle and then I poured Ringer’s solution on it. Next, I attached it to the kymograph.

Improved: I removed the frog muscle and poured Ringer’s solution on it to prevent it from drying out. I then attached the muscle to the kymograph in order to determine the minimum voltage required for contraction.

2. Experimental procedures and results are narrated in the past tense (what you did, what you found, etc.) whereas conclusions from your results are given in the present tense.

3. Mathematical equations and statistical tests are considered mathematical methods and should be described in this section along with the actual experimental work.

4. Use active rather than passive voice when possible.  [Note: see Section 3.1.4 for more about this.]  Always use the singular "I" rather than the plural "we" when you are the only author of the paper.  Throughout the paper, avoid contractions, e.g. did not vs. didn’t.

5. If any of your methods is fully described in a previous publication (yours or someone else’s), you can cite that instead of describing the procedure again.

Example: The chromosomes were counted at meiosis in the anthers with the standard acetocarmine technique of Snow (1955).

3.2.4 Results (with excerpts from Steingraber et al. 1985)

The function of this section is to summarize general trends in the data without comment, bias, or interpretation. The results of statistical tests applied to your data are reported in this section although conclusions about your original hypotheses are saved for the Discussion section.

Tables and figures should be used when they are a more efficient way to convey information than verbal description. They must be independent units, accompanied by explanatory captions that allow them to be understood by someone who has not read the text. Do not repeat in the text the information in tables and figures, but do cite them, with a summary statement when that is appropriate.  Example:

Incorrect: The results are given in Figure 1.

Correct: Temperature was directly proportional to metabolic rate (Fig. 1).

Please note that the entire word "Figure" is almost never written in an article.  It is nearly always abbreviated as "Fig." and capitalized.  Tables are cited in the same way, although Table is not abbreviated.

Whenever possible, use a figure instead of a table. Relationships between numbers are more readily grasped when they are presented graphically rather than as columns in a table.

Data may be presented in figures and tables, but this may not substitute for a verbal summary of the findings. The text should be understandable by someone who has not seen your figures and tables.

1. All results should be presented, including those that do not support the hypothesis.

2. Statements made in the text must be supported by the results contained in figures and tables.

3. The results of statistical tests can be presented in parentheses following a verbal description.

Example: Fruit size was significantly greater in trees growing alone (t = 3.65, df = 2, p < 0.05).

Simple results of statistical tests may be reported in the text as shown in the preceding example.  The results of multiple tests may be reported in a table if that increases clarity. (See Section 11 of the Statistics Manual for more details about reporting the results of statistical tests.)  It is not necessary to provide a citation for a simple t-test of means, paired t-test, or linear regression.  If you use other tests, you should cite the text or reference you followed to do the test.  In your materials and methods section, you should report how you did the test (e.g. using the statistical analysis package of Excel). 

It is NEVER appropriate to simply paste the results from statistical software into the results section of your paper.  The output generally reports more information than is required and it is not in an appropriate format for a paper.

3.2.4.1 Tables

• Do not repeat information in a table that you are depicting in a graph or histogram; include a table only if it presents new information.
• It is easier to compare numbers by reading down a column rather than across a row. Therefore, list sets of data you want your reader to compare in vertical form.
• Provide each table with a number (Table 1, Table 2, etc.) and a title. The numbered title is placed above the table .
• Please see Section 11 of the Excel Reference and Statistics Manual for further information on reporting the results of statistical tests.

3.2.4.2. Figures

• These comprise graphs, histograms, and illustrations, both drawings and photographs. Provide each figure with a number (Fig. 1, Fig. 2, etc.) and a caption (or "legend") that explains what the figure shows. The numbered caption is placed below the figure .  Figure legend = Figure caption.
• Figures submitted for publication must be "photo ready," i.e., they will appear just as you submit them, or photographically reduced. Therefore, when you graduate from student papers to publishable manuscripts, you must learn to prepare figures that will not embarrass you. At the present time, virtually all journals require manuscripts to be submitted electronically and it is generally assumed that all graphs and maps will be created using software rather than being created by hand.  Nearly all journals have specific guidelines for the file types, resolution, and physical widths required for figures.  Only in a few cases (e.g. sketched diagrams) would figures still be created by hand using ink and those figures would be scanned and labeled using graphics software.  Proportions must be the same as those of the page in the journal to which the paper will be submitted. 
• Graphs and Histograms: Both can be used to compare two variables. However, graphs show continuous change, whereas histograms show discrete variables only.  You can compare groups of data by plotting two or even three lines on one graph, but avoid cluttered graphs that are hard to read, and do not plot unrelated trends on the same graph. For both graphs, and histograms, plot the independent variable on the horizontal (x) axis and the dependent variable on the vertical (y) axis. Label both axes, including units of measurement except in the few cases where variables are unitless, such as absorbance.
• Drawings and Photographs: These are used to illustrate organisms, experimental apparatus, models of structures, cellular and subcellular structure, and results of procedures like electrophoresis. Preparing such figures well is a lot of work and can be very expensive, so each figure must add enough to justify its preparation and publication, but good figures can greatly enhance a professional article, as your reading in biological journals has already shown.

3.2.5 Discussion (taken from Steingraber et al. 1985)

The function of this section is to analyze the data and relate them to other studies. To "analyze" means to evaluate the meaning of your results in terms of the original question or hypothesis and point out their biological significance.

1. The Discussion should contain at least:

• the relationship between the results and the original hypothesis, i.e., whether they support the hypothesis, or cause it to be rejected or modified
• an integration of your results with those of previous studies in order to arrive at explanations for the observed phenomena
• possible explanations for unexpected results and observations, phrased as hypotheses that can be tested by realistic experimental procedures, which you should describe

2. Trends that are not statistically significant can still be discussed if they are suggestive or interesting, but cannot be made the basis for conclusions as if they were significant.

3. Avoid redundancy between the Results and the Discussion section. Do not repeat detailed descriptions of the data and results in the Discussion. In some journals, Results and Discussions are joined in a single section, in order to permit a single integrated treatment with minimal repetition. This is more appropriate for short, simple articles than for longer, more complicated ones.

4. End the Discussion with a summary of the principal points you want the reader to remember. This is also the appropriate place to propose specific further study if that will serve some purpose, but do not end with the tired cliché that "this problem needs more study." All problems in biology need more study. Do not close on what you wish you had done, rather finish stating your conclusions and contributions.

3.2.6 Title

The title of the paper should be the last thing that you write.  That is because it should distill the essence of the paper even more than the abstract (the next to last thing that you write). 

The title should contain three elements:

1. the name of the organism studied;

2. the particular aspect or system studied;

3. the variable(s) manipulated.

Do not be afraid to be grammatically creative. Here are some variations on a theme, all suitable as titles:

THE EFFECT OF TEMPERATURE ON GERMINATION OF ZEA MAYS

DOES TEMPERATURE AFFECT GERMINATION OF ZEA MAYS?

TEMPERATURE AND ZEA MAYS GERMINATION: IMPLICATIONS FOR AGRICULTURE

Sometimes it is possible to include the principal result or conclusion in the title:

HIGH TEMPERATURES REDUCE GERMINATION OF ZEA MAYS

Note for the BSCI 1510L class: to make your paper look more like a real paper, you can list all of the other group members as co-authors.  However, if you do that, you should list you name first so that we know that you wrote it.

3.2.7 Literature Cited

Please refer to section 2.1 of this guide.

• << Previous: 3.1 Specific details regarding scientific writing
• Next: 4 For further information >>
• Last Updated: Apr 19, 2023 2:37 PM
• URL: https://researchguides.library.vanderbilt.edu/bsci1510L

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• 27 March 2024

Tweeting your research paper boosts engagement but not citations

• Bianca Nogrady

You can also search for this author in PubMed   Google Scholar

Even before complaints about X’s declining quality, posting a paper on the social-media platform did not lead to a boost in citations. Credit: Matt Cardy/Getty

Posting about a research paper on social-media platform X (formerly known as Twitter) doesn’t translate into a bump in citations, according to a study that looked at 550 papers.

The finding comes as scientists are moving away from the platform in the wake of changes after its 2022 purchase by entrepreneur Elon Musk.

An international group of 11 researchers, who by the end of the experiment had between them nearly 230,000 followers on X, examined whether there was evidence that posting about a paper would increase its citation rate.

“There certainly is a correlation, and that’s been found in a lot of papers. But very few people have ever looked to see whether there’s any experimental causation,” says Trevor Branch, a marine ecologist at the University of Washington in Seattle and lead author on the paper, published in PLoS ONE last week 1 .

Every month for ten months, each researcher was allocated a randomly selected primary research article or review from a journal of their choice to post about on their personal account. Four randomly chosen articles from the same edition of the journal served as controls, which the researchers did not post about. They conducted the experiment in the period before Elon Musk took ownership of what was then known as Twitter and complaints of its declining quality increased.

‘Nail in the coffin’

Three years after the initial posts, the team compared the citation rates for the 110 posted articles with those of the 440 control articles, and found no significant difference. The researchers did acknowledge that their followers might not have been numerous enough to detect a statistically significant effect on citations.

The rate of daily downloads for the posted papers was nearly fourfold higher on the day that they were shared, compared with controls. Shared papers also had significantly higher accumulated Altmetric scores both 30 days and three years after the initial post. Calculated by London-based technology company Digital Science, an Altmetric score, says Branch, is a measure of how many people have looked at a paper and are talking about it, but it’s not a reliable indicator of a paper’s scientific worth. “It’s thoroughly biased by how many people with large followings tweet about it,” he says.

The findings echo those of information scientist Stefanie Haustein at the University of Ottawa, whose 2013 study 2 found a low correlation between posts and citations.

Haustein says the problem with using posts as a metric is that, even a decade ago, there was a lot of noise in the signal.

“We actually showed that a lot of the counts on Twitter you would get were bots, it wasn’t even humans,” says Haustein, who wasn’t involved in the new study.

She says the more recent departure of scientists from the platform has been the final nail in the coffin of the idea that posting could increase citations.

doi: https://doi.org/10.1038/d41586-024-00922-y

Branch, T. A. et al. PLoS ONE 19 , e0292201 (2024).

Article   PubMed   Google Scholar  

Haustein, S., Peters, I., Sugimoto, C. R., Thelwall, M. & Larivière, V. J. Assoc. Inf. Sci. Technol. 65, 656–669 (2014).

Article   Google Scholar  

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Home » APA Table of Contents – Format and Example

APA Table of Contents – Format and Example

Table of Contents

APA Table of Contents

The APA (American Psychological Association) Table of Contents is a structured outline that provides an overview of the content of a research paper or manuscript. It is typically included in the front matter of the document and lists the major sections and subsections of the paper, along with their page numbers. The Table of Contents is an important organizational tool that helps readers navigate the document and locate specific information quickly and easily.

How to Make APA Table of Contents

Here are the steps you can follow:

• Create a new page for the table of contents. The page number should be the Roman numeral “i”.
• Center the title “Table of Contents” at the top of the page.
• List all the headings and subheadings in your paper in order. Be sure to include all major sections and subsections.
• Align the page numbers to the right margin of the page.
• Use dot leaders to connect the headings to their respective page numbers. Dot leaders are a row of dots that help guide the reader’s eye from the heading to the page number.

I. Introduction ……………………………………………………… i

II. Literature Review ……………………………………………….. 1

A. Subheading ………………………………………………………… 2

B. Subheading ………………………………………………………… 3

III. Methodology ………………………………………………………….. 4

A. Participants ………………………………………………………… 5

B. Procedure …………………………………………………………… 6

IV. Results ……………………………………………………………………. 8

V. Discussion ……………………………………………………………… 10

VI. Conclusion …………………………………………………………….. 12

How to Make APA Table of Contents in MS Words

To create an APA table of contents in Microsoft Word, follow these steps:

• Start by typing out your document in Microsoft Word.
• Once you have finished typing your document, place your cursor at the beginning of your document.
• Click on the “References” tab in the top menu bar.
• Click on the “Table of Contents” option on the left-hand side of the menu bar.
• Choose one of the APA table of contents styles from the drop-down menu that appears. There are two options for an APA table of contents: “APA 6th Edition” and “APA 7th Edition.”
• Once you have chosen your preferred APA table of contents style, click on it to insert it into your document.
• Now you need to format your headings to be included in the table of contents. Select the heading you want to include in your table of contents.
• Click on the “Styles” option in the top menu bar.
• Choose the appropriate heading style from the drop-down menu that appears. You can choose from “Heading 1,” “Heading 2,” “Heading 3,” etc.
• Repeat the previous two steps for each heading you want to include in the table of contents.
• Once you have formatted all the headings, go back to the “References” tab in the top menu bar.
• Select the “Update Table” option from the drop-down menu that appears.
• Choose whether you want to update the page numbers only or the entire table of contents.
• Click “OK” to update your table of contents.

Your APA table of contents is now complete!

APA Table of Contents Format

Here’s the general format for creating a table of contents in APA style:

• Start a new page after the title page and abstract.
• Type “Table of Contents” at the top of the page, centered.
• List all the major sections of your paper, including the introduction, body, and conclusion.
• Indent each level of subheading, using either the tab key or your word processor’s formatting tools.
• Use the same font and size for the table of contents as you did for the rest of the paper.
• Align page numbers on the right side of the page, directly after each section and sub-section.
• Include any appendices and references in the table of contents, if applicable.

Here is an example of an APA-formatted table of contents:

Table of Contents Format

Introduction ………………………………………. 1

Literature Review ………………………………… 2

Methods ………………………………………….. 6

Participants ……………………………………. 6

Procedure ……………………………………….. 8

Results ………………………………………….. 10

Discussion ………………………………………. 15

Appendices ………………………………………. 20

References ………………………………………. 21

APA Table of Contents Example

Here is an example of an APA-style table of contents:

I. Introduction ……………………………………………………………………. 1

II. Literature Review …………………………………………………………….. 3

A. Background………………………………………………………………… 3

B. Theoretical Framework ………………………………………………… 5

C. Empirical Studies………………………………………………………… 7

III. Methodology …………………………………………………………………. 10

A. Research Design ………………………………………………………… 10

B. Participants ……………………………………………………………….. 11

C. Materials ………………………………………………………………….. 12 ‘

D. Procedure …………………………………………………………………. 14

IV. Results …………………………………………………………………………. 16

V. Discussion ……………………………………………………………………… 19

A. Summary of Findings …………………………………………………. 19

B. Implications ………………………………………………………………. 21

C. Limitations and Future Directions ………………………………… 23

VI. Conclusion ……………………………………………………………………. 25

VII. References …………………………………………………………………… 27

VIII. Appendices ………………………………………………………………….. 31

When to use APA Table of Contents

You should use an APA TOC when:

• You are writing a research paper or a thesis that is more than 5 pages in length.
• Your document has multiple headings and subheadings that require organization and clarification for the reader.
• You want to make it easy for readers to find specific sections or information within your document.
• You want to comply with the APA style guidelines for formatting and referencing.
• Your document contains complex information that requires a clear structure to make it more comprehensible for the reader.

Advantages of APA Table of Contents

The American Psychological Association (APA) style table of contents has several advantages, including:

• Easy navigation: A well-organized table of contents makes it easy for readers to find the information they need quickly and easily. This is especially important in longer documents such as academic papers, theses, and dissertations.
• Standardized formatting: The APA style table of contents follows a standardized formatting style that is familiar to many academic readers. This makes it easier for readers to understand the structure and organization of the document.
• Consistency : By using the APA style table of contents, authors can ensure that the document is consistent and follows a clear organizational structure. This can help readers to better understand the content and stay focused on the main points.
• Professional appearance : A well-formatted APA style table of contents can enhance the professional appearance of the document. This is particularly important in academic and research settings where a professional appearance can increase the credibility of the work.
• Compliance with academic standards : Many academic institutions require the use of the APA style for academic papers, theses, and dissertations. By using the APA style table of contents, authors can ensure that their work complies with these academic standards.

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This paper is in the following e-collection/theme issue:

Published on 28.3.2024 in Vol 26 (2024)

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

Authors of this article:

Original Paper

• Yiyang Sheng 1 , MSc   ; 
• Raymond Bond 2 , PhD   ; 
• Rajesh Jaiswal 3 , PhD   ; 
• John Dinsmore 4 , PhD   ; 
• Julie Doyle 1 , PhD  

1 NetwellCASALA, Dundalk Institution of Technology, Dundalk, Ireland

2 School of Computing, Ulster University, Jordanstown, United Kingdom

3 School of Enterprise Computing and Digital Transformation, Technological University Dublin, Dublin, Ireland

4 Trinity Centre for Practice and Healthcare Innovation, School of Nursing and Midwifery, Trinity College Dublin, Dublin, Ireland

Corresponding Author:

Yiyang Sheng, MSc

NetwellCASALA

Dundalk Institution of Technology

Dublin Road, PJ Carrolls Building, Dundalk Institute of Technology

Co.Louth, Ireland

Dundalk, A91 K584

Phone: 353 894308214

Email: [email protected]

Background: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change.

Objective: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months.

Methods: Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered.

Results: Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants’ outcomes (eg, reduce symptom exacerbation and increase physical activity).

Conclusions: The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.

Introduction

According to the United Nations, the number of people aged ≥65 years is growing faster than all other age groups [ 1 ]. The worldwide population of people aged ≥65 years will increase from approximately 550 million in 2000 to 973 million in 2030 [ 2 ]. Furthermore, by 2050, approximately 16% of the world’s population will be aged >65 years, whereas 426 million people will be aged >80 years [ 1 ]. Living longer is a great benefit to today’s society. However, this comes with several challenges. Aging can be associated with many health problems, including multimorbidity (ie, the presence of ≥2 chronic conditions) [ 3 ]. The prevalence rate of multimorbidity among older adults is estimated to be between 55% and 98%, and the factors associated with multimorbidity are older age, female sex, and low socioeconomic status [ 4 ]. In the United States, almost 75% of older adults have multimorbidity [ 5 ], and it was estimated that 50 million people in the European Union were living with multimorbidity in 2015 [ 6 ]. Likewise, the prevalence rate of multimorbidity is 69.3% among older adults in China [ 5 ].

Home-based self-management for chronic health conditions involves actions and behaviors that protect and promote good health care practices comprising the management of physical, emotional, and social care [ 7 ]. Engaging in self-management can help older adults understand and manage their health conditions, prevent illness, and promote wellness [ 7 , 8 ]. However, self-management for older adults with multimorbidity is a long-term, complex, and challenging mission [ 9 , 10 ]. There are numerous self-care tasks to engage in, which can be very complicated, especially for people with multiple chronic health conditions. Furthermore, the severity of the disease can negatively impact a person’s ability to engage in self-management [ 10 ].

Digital home-based health technologies have the potential to support better engagement with self-management interventions, such as the monitoring of symptom and well-being parameters as well as medication adherence [ 10 , 11 ]. Such technologies can help older adults understand their disease or diseases, respond to changes, and communicate with health care providers [ 12 - 14 ]. Furthermore, digital health technologies can be tailored to individual motivations and personal needs [ 13 ], which can improve sustained use [ 15 ] and result in people feeling supported [ 16 ]. Digital self-management can also create better opportunities for adoption and adherence in the long term compared with paper booklet self-management [ 16 ]. Moreover, digital health technologies, such as small wearable monitoring devices, can increase the frequency of symptom monitoring for patients with minimal stress compared with symptom monitoring with manual notifications [ 17 ].

A large body of research implements data mining and machine learning algorithms using data acquired from home-based health care data sets. Data mining techniques, such as data visualization, clustering, classification, and prediction, to name a few, can help researchers understand users, behaviors, and health care phenomena by identifying novel, interesting patterns. These techniques can also be used to build predictive models [ 18 - 21 ]. In addition, data mining techniques can help in designing health care management systems and tracking the state of a person’s chronic disease, resulting in appropriate interventions and a reduction in hospital admissions [ 18 , 22 ]. Vast amounts of data can be generated when users interact with digital health technologies, which provides an opportunity to understand chronic illnesses as well as elucidate how users engage with digital health technologies in the real world. Armstrong et al [ 23 ] used the k-means algorithm to identify previously unknown patterns of clinical characteristics in home care rehabilitation services. The authors used k-means cluster analysis to analyze data from 150,253 clients and discovered new insights into the clients’ characteristics and their needs, which led to more appropriate rehabilitation services for home care clients. Madigan and Curet [ 22 ] used classification and regression trees to investigate a home-based health care data set that comprised 580 patients who had 3 specific conditions: chronic obstructive pulmonary disease (COPD), heart failure (HF), and hip replacement. They found that data mining methods identified the dependencies and interactions that influence the results, thereby improving the accuracy of risk adjustment methods and establishing practical benchmarks [ 22 ]. Other research [ 24 ] has developed a flow diagram of a proposed platform by using machine learning methods to analyze multiple health care data sets, including medical images as well as diagnostic and voice records. The authors believe that the system could help people in resource-limited areas, which have lower ratios of physicians and hospitals, to diagnose diseases such as breast cancer, heart disease (HD), diabetes, and liver disease at a lower cost and in less time than local hospitals. In the study, the accuracy of disease detection was >95% [ 24 ].

There are many different approaches to clustering analysis of health care data sets, such as k-means, density-based spatial clustering of applications with noise, agglomerative hierarchical clustering, self-organizing maps, partitioning around medoids algorithm, hybrid hierarchical clustering, and so on [ 25 - 28 ]. K-means clustering is 1 of the most commonly used clustering or unsupervised machine learning algorithms [ 19 , 29 ], and it is relatively easy to implement and relatively fast [ 30 - 32 ]. In addition, k-means has been used in research studies related to chronic health conditions such as diabetes [ 33 ], COPD [ 34 , 35 ], and HF [ 36 ]; for example, a cloud-based framework with k-means clustering technique has been used for the diagnosis of diabetes and was found to be more efficient and suitable for handling extensive data sets in cloud computing platforms than hierarchical clustering [ 32 ]. Violán et al [ 37 ] analyzed data from 408,994 patients aged 45 to 64 years with multimorbidity using k-means clustering to ascertain multimorbidity patterns. The authors stratified the k-means clustering analysis by sex, and 6 multimorbidity patterns were found for each sex. They also suggest that clusters identified by multimorbidity patterns obtained using nonhierarchical clustering analysis (eg, k-means and k-medoids) are more consistent with clinical practice [ 37 ].

The majority of data mining studies on chronic health conditions focus on the diseases themselves and their symptoms; there is less exploration of the patterns of engagement of persons with multimorbidity with digital health technologies. However, data mining and machine learning are excellent ways to understand users’ engagement patterns with digital health technologies. A study by McCauley et al [ 38 ] compared clustering analysis of the user interaction event log data from a reminiscence mobile app that was designed for people living with dementia. In addition to performing quantitative user interaction log analysis, the authors also gathered data on the qualitative experience of users. The study showed the benefits of using data mining to analyze the user log data with complementary qualitative data analysis [ 38 ]. This is a research challenge where both quantitative and qualitative methods can be combined to fully understand users; for example, the quantitative analysis of the user event data can tell us about use patterns, the preferred times of day to use the app, the feature use, and so on, but qualitative data (eg, user interviews) are necessary to understand why these use patterns exist.

The aim of this study was to analyze how older adults with multimorbidity engage with digital symptom and health monitoring over a period of approximately 12 months using a digital health platform. In this study, user log data of engagement with digital health technology and user interview qualitative data were examined to explore the patterns of engagement. K-means clustering was used to analyze the user log data. The study had four research questions: (1) How do clusters differ in terms of participant characteristics such as age, sex, and health conditions? (2) How do clusters differ in terms of patterns of engagement, such as the number of days a week participants take readings (eg, weight and blood pressure [BP])? (3) How do engagement rates with the different devices correlate with each other (determined by analyzing the weekly submissions of every parameter and the interviews of participants)? and (4) How do engagement rates affect participants’ health condition symptoms, such as BP, blood glucose (BG) level, weight, peripheral oxygen saturation (SpO 2 ) level, and physical activity (PA)?

The study was a proof-of-concept trial with an action research design and mixed methods approach. Action research is a period of investigation that “describes, interprets, and explains social situations while executing a change intervention aimed at improvement and involvement” [ 39 ]. An action research approach supports the generation of solutions to practical problems while using methods to understand the contexts of care as well as the needs and experiences of participants.

Recruitment and Sample

Although 120 participants consented to take part across Ireland and Belgium, this paper reports on data from 60 Irish older adults with multiple chronic health conditions (≥2 of the following: COPD, HF, HD, and diabetes). Participants were recruited through purposive sampling and from multiple sources, including through health care organizations (general practitioner clinics and specialist clinics), relevant older adult networks, chronic disease support groups, social media, and local newspaper advertising. Recruitment strategies included the use of study flyers and advertisements as well as giving talks and platform demonstrations.

Sources of Data

The data set was collected during the Integrated Technology Systems for Proactive Patient Centred Care (ProACT) project proof-of-concept trial. As the trial was a proof-of-concept of a novel digital health platform, the main goal was to understand how the platform worked or did not work, rather than whether it worked. Thus, to determine sample size, a pragmatic approach was taken in line with two important factors: (1) Is the sample size large enough to provide a reliable analysis of the ecosystem? and (2) Is the sample size small enough to be financially feasible? The literature suggests that overall sample size in proof-of-concept digital health trials is low. A review of 1030 studies on technical interventions for management of chronic disease that focused on HF (436 studies), stroke (422 studies), and COPD (172 studies) suggested that robust sample sizes were 17 for COPD, 19 for HF, and 21 for stroke [ 40 ]. Full details on the study protocol can be found in the study by Dinsmore et al [ 41 ].

Participants used a suite of sensor devices (ie, BP monitors, weight scales, glucometers, pulse oximeters, and activity watches) and a tablet app to monitor their health conditions and well-being. All participants received a smartwatch to measure PA levels and sleep, a BP monitor to measure BP and pulse rate, and a weight scale. A BG meter was provided to participants with diabetes, and a pulse oximeter was provided to those with COPD to measure SpO 2 levels. In addition, all participants received an iPad with a custom-designed app, the ProACT CareApp, that allowed users to view their data, provide self-report (SR) data on symptoms that could not be easily captured through a sensor (eg, breathlessness and edema) and well-being (eg, mood and satisfaction with social life), receive targeted education based on their current health status, set PA goals, and share their data with others. The ProACT platform was designed and developed following an extensive user-centered design process. This involved interviews, focus groups, co-design sessions (hands-on design activities with participants), and usability testing before the platform’s deployment in the trial. A total of 58 people with multimorbidity and 106 care network participants, including informal carers, formal carers, and health care professionals, took part in this process. Findings from the user-centered design process have been published elsewhere [ 42 , 43 ]. More detailed information about the full ProACT platform and the CareApp used by participants can be found in the study by Doyle et al [ 44 ].

The study took place between April 1, 2018, and June 30, 2019. Participants in the trial typically participated for 12 months, although some stayed on for 14 months and others for 9 months (in the case of those who entered the trial later). One of the trial objectives was to understand real-world engagement. Therefore, participants were asked to take readings with the devices and provide SR data in the ProACT CareApp whenever they wished (not necessarily daily). As part of the trial, participants were assisted by technical help desk staff who responded to questions about the technology, and home visits were conducted as needed to resolve issues. In addition, a clinical triage service monitored the participants’ readings and contacted them in instances of abnormal parameter values (eg, high BP and low SpO 2 levels) [ 45 ]. Participants also received a monthly check-in telephone call from 1 of the triage nurses.

Table 1 outlines the types of health and well-being metrics that were collected, as well as the collection method and the number of participants who collected that type of data. The health and well-being metrics were determined from the interviews and focus groups held with health care professionals during the design of the ProACT platform to determine the most important symptom and well-being parameters to monitor across the health conditions of interest [ 42 ]. Off-the-shelf digital devices manufactured by 2 providers, Withings and iHealth, were used during the trial. Data from these providers were extracted into a custom platform called Context-Aware Broker and Inference Engine–Subject Information Management System (CABIE-SIMS), which includes a data aggregator for storing health and well-being data. All devices require the user to interact with them in some way. However, some devices needed more interaction than others (eg, taking a BG reading involved several steps, but PA and sleep only required participants to open the activity watch app to sync the relevant data). The activity watch was supposed to synchronize automatically without user interaction. However, inconsistencies with syncing meant that users were advised to open the Withings app to sync their data. The CABIE-SIMS platform would display the readings in near real time, apart from PA data, which were collected at regular intervals throughout the day, whereas sleep data were gathered every morning. Table 1 lists the types of data that were collected and the number of participants who collected them. In addition, semistructured interviews were conducted with all participants at 4 time points throughout the trial to understand their experience of using the ProACT platform. Although a full qualitative thematic analysis was outside the scope of this study and was reported on elsewhere [ 44 ], interview transcripts for participants of interest to the analysis presented in this paper were reviewed as part of this study to provide an enhanced understanding of the results.

a SpO 2 : peripheral oxygen saturation.

b HF: heart failure.

c ProACT: Integrated Technology Systems for Proactive Patient Centred Care.

d CABIE-SIMS: Context-Aware Broker and Inference Engine–Subject Information Management System.

e COPD: chronic obstructive pulmonary disease.

Data Analysis Methods

The original data set in the CABIE-SIMS platform was formatted using the JSON format. As a first step, a JSON-to-CSV file converter was used to make the data set more accessible for data analysis. The main focus was on dealing with duplicate data and missing data during the data cleaning phase. Data duplication might occur when a user uploads their SpO 2 reading 3 times in 2 minutes as a result of mispressing the button. In such cases, only 1 record was added to the cleaned data file. As for missing data, the data set file comprised “N/A” (not available) values for all missing data.

The cleaned data set was preprocessed using Microsoft Excel, the R programming language (R Foundation for Statistical Computing), and RStudio (Posit Software, PBC). The preprocessed data set included participants’ details (ID, sex, age, and chronic health conditions) and the number of days of weekly submissions of every parameter (BP, pulse rate, SpO 2 level, BG level, weight, PA, SR data, and sleep). All analyses (including correlation analysis, principal component analysis [PCA], k-means clustering, 2-tailed t test, and 1-way ANOVA) were implemented in the R programming language and RStudio.

After performing Shapiro-Wilk normality tests on the data submitted each week, we found that the data were not normally distributed. Therefore, Spearman correlation was used to check the correlation among the parameters. Correlation analysis and PCA were used to determine which portions of the data would be included in the k-means clustering. Correlation analysis determined which characteristics or parameters should be selected, and PCA determined the number of dimensions that should be selected as features for clustering. In the clustering process, the weekly submission of each parameter was considered as an independent variable for the discovery of participant clusters, and the outcome of the clustering was a categorical taxonomy that was used to label the 3 discovered clusters. Similarly, the Shapiro-Wilk test was conducted to check the normality of the variables in each group. It was found that most of the variables in each group were normally distributed, and only the weight data submission records of cluster 3, the PA data submission records of cluster 2, the SR data submission records of cluster 3, and the sleep data submission records of cluster 1 were not normally distributed. Therefore, the 2-tailed t test and 1-way ANOVA were used to compare different groups of variables. The 2-tailed t test was used to compare 2 groups of variables, whereas 1-way ANOVA was used to compare ≥2 groups of variables. P values >.05 indicated that there were no statistically significant differences among the groups of variables [ 46 ].

As for the qualitative data from the interviews, we performed keyword searches after a review of the entire interview; for example, when the data analysis was related to BP and weight monitoring, a search with the keywords “blood pressure,” “weight,” or “scale” was performed to identify relevant information. In addition, when the aim was to understand the impact of digital health care technology, we focused on specific questions in the second interview, such as “Has it had any impact on the management of your health?”

Ethical Considerations

Ethics approval was received from 3 ethics committees: the Health Service Executive North East Area Research Ethics Committee, the School of Health and Science Research Ethics Committee at Dundalk Institute of Technology, and the Faculty of Health Sciences Research Ethics Committee at Trinity College Dublin. All procedures were in line with the European Union’s General Data Protection Regulation for research projects, with the platform and trial methods and procedures undergoing data protection impact assessments. Written informed consent was obtained on an individual basis from participants in accordance with legal and ethics guidelines after a careful explanation of the study and the provision of patient information and informed consent forms in plain language. All participants were informed of their right to withdraw from the study at any time without having to provide a reason. Participants were not compensated for their time. Data stored within the CABIE-SIMS platform were identifiable because they were shared (with the participant’s consent) with the clinical triage teams and health care professionals. This was clearly outlined in the participant information leaflet and consent form. However, the data set that was extracted for the purpose of the analysis presented in this paper was pseudonymized.

Participants

A total of 60 older adults were enrolled in the study. The average age of participants was 74 (SD 6.4; range 65-92) years; 60% (36) were male individuals, and 40% (24/60) were female individuals. The most common combination of health conditions was diabetes and HD (30/60, 50%), which was followed by COPD and HD (16/60, 27%); HF and HD (7/60, 12%); diabetes and COPD (3/60, 5%); diabetes and HF (1/60, 2%); COPD and HF (1/60, 2%); HF, HD, and COPD (1/60, 2%); and COPD, HD, and diabetes (1/60, 2%). Of the 60 participants, 11 (18%) had HF, 55 (92%) had HD, 22 (37%) had COPD, and 31 (52%) had diabetes. Over the course of the trial, of the 60 participants, 8 (13%) withdrew, and 3 (5%) died. However, this study included data from all participants in the beginning, as long as the participant had at least 1 piece of data. Hence, of the 60 participants, we included 56 (93%) in our analysis, whereas 4 (7%) were excluded because no data were recorded.

Correlation of Submission Parameters

To help determine which distinct use characteristics or parameters (such as the weekly frequency of BP data submissions) should be selected as features for clustering, the correlations among the parameters were calculated. Figure 1 shows the correlation matrix for all parameter weekly submissions (days). In this study, a moderate correlation (correlation coefficient between 0.3 to 0.7 and −0.7 to −0.3) [ 47 , 48 ] was chosen as the standard for selecting parameters. First, every participant received a BP monitor to measure BP, and pulse rate was collected as part of the BP measurement. Moreover, the correlation coefficient between BP and pulse rate was 0.93, a strong correlation. In this case, BP was selected for clustering rather than pulse rate. As for the other parameters, the correlations between BP and weight (0.51), PA (0.55), SR data (0.41), and sleep (0.55) were moderate, whereas the correlations between BP and SpO 2 level (0.05) and BG (0.24) were weak. In addition, the correlations between SpO 2 level and weight (−0.25), PA (0.16), SR data (0.29), and sleep (−0.24) were weak. Therefore, SpO 2 level was not selected for clustering. Likewise, the correlations between BG and weight (0.19), PA (0.2), SR data (−0.06), and sleep (0.25) were weak. Therefore, BG was not selected for clustering. Thus, BP, weight, PA, SR data, and sleep were selected for clustering.

PCA and Clustering

The fundamental question for k-means clustering is this: how many clusters (k) should be discovered? To determine the optimum number of clusters, we further investigated the data through visualization offered by PCA. As can be seen from Figure 2 , the first 2 principal components (PCs) explain 73.6% of the variation, which is an acceptably large percentage. However, after a check of individual contributions, we found that there were 3 participants—P038, P016, and P015—who contributed substantially to PC1 and PC2. After a check of the original data set, we found that P038 submitted symptom parameters only on 1 day, and P016 submitted symptom parameters only on 2 days. Conversely, P015 submitted parameters almost every day during the trial. Therefore, P038 and P016 were omitted from clustering.

After removing the outliers (P038 and P016), we found that the first 2 PCs explain 70.5% of the variation ( Figure 3 ), which is an acceptably large percentage.

The clusters were projected into 2 dimensions as shown in Figure 4 . Each subpart in Figure 4 shows a different number of clusters (k). When k=2, the data are obviously separated into 2 big clusters. Similarly, when k=3, the clusters are still separated very well into 3 clusters. When k=4, the clusters are well separated, but compared with the subpart with 3 clusters, 2 clusters are similar, whereas cluster 1, which only has 3 participants, is a relatively small cluster. When k=5, there is some overlap between cluster 1 and cluster 2. Likewise, Figure 5 shows the optimal number of clusters using the elbow method. In view of this, we determined that 3 clusters of participants separate the data set best. The 3 clusters can be labeled as the least engaged user group (cluster 1), the highly engaged user group (cluster 2), and the typical user group (cluster 3).

In the remainder of this section, we report on the examination of the clusters with respect to participant characteristics and the weekly submissions (days) of different parameters in a visual manner to reveal potential correlations and insights. Finally, we report on the examination of the correlations among all parameters by PCA.

Participant Characteristics

As seen in Figure 6 , the distribution of age within the 3 clusters is similar, with the P value of the 1-way ANOVA being .93, because all participants in this trial were older adults. However, the median age in the cluster 3 box plot is slightly higher than the median ages in the box plots of the other 2 clusters, and the average age of cluster 2 participants (74.1 years) is lower than that of cluster 1 (74.6 years) and cluster 3 (74.8 years; Table 2 ) participants. As Table 2 shows, 6 (26%) of the 23 female participants are in cluster 1 compared with 7 (23%) of the 31 male participants. However, the male participants in cluster 2 (10/31, 32%) and cluster 3 (14/31, 45%) represent higher proportions of total male participants compared with female participants in cluster 2 (7/23, 30%) and cluster 3 (10/23, 43%). Figure 7 shows the proportion of the 4 chronic health conditions within the 3 clusters. Cluster 1 has the largest proportion of participants with COPD and the smallest proportion of participants with diabetes. Moreover, cluster 3 has the smallest proportion of participants with HF (3/24, 13%; Table 2 ).

a COPD: chronic obstructive pulmonary disease.

Participant Engagement Outcomes

Cluster 2 has the longest average enrollment time at 352 days compared with cluster 3 at 335 days and cluster 1 at 330 days. As seen in Figure 8 , the overall distribution of the BP data weekly submissions is different, with the P value of the 1-way ANOVA being 8.4 × 10 −9 . The frequency of BP data weekly submissions (days) of cluster 2 exceeds the frequencies of cluster 1 and cluster 3, which means that participants in cluster 2 have a higher frequency of BP data submissions than those in the other 2 clusters. The median and maximum of cluster 3 are higher than those of cluster 1, but the minimum of cluster 3 is lower than that of cluster 1. Likewise, as seen in Table 3 , the mean and SD of cluster 1 (mean 2.5, SD 1.4) are smaller than those of cluster 3 (mean 2.9, SD 2.9).

As Figure 9 shows, the overall distribution of the weekly submissions of weight data is different, with the P value of the 1-way ANOVA being 1.4 × 10 −13 , because the participants in cluster 2 submitted weight parameters more frequently than those in cluster 1 and cluster 3. In addition, similar to the BP data submissions, the median of cluster 3 is higher than that of cluster 1. As seen in Figure 9 , there are 3 outliers in cluster 2. The top outlier is P015, who submitted a weight reading almost every day. During the trial, this participant mentioned many times in the interviews that his goal was to lose weight and that he used the scale to check his progress:

I’ve set out to reduce my weight. The doctor has been saying to me you know there’s where you are and you should be over here. So, I’ve been using the weighing thing just to clock, to track reduction of weight. [P015]

The other 2 outliers are P051 and P053, both of whom mentioned taking their weight measurements as part of their daily routine:

Once I get up in the morning the first thing is I weigh myself. That is, the day starts off with the weight, right. [P053]

Although their frequency of weekly weight data submissions is lower than that of all other participants in cluster 2, it is still higher than that of most of the participants in the other 2 clusters.

In Table 3 , it can be observed that the average frequency of weekly submissions of PA and sleep data for every cluster is higher than the frequencies of other variables, and the SDs are relatively low. This is likely because participants only needed to open the Withings app once a day to ensure the syncing of data. However, the overall distributions of PA and sleep data submissions are different in Figure 10 and Figure 11 , with the P values of the 1-way ANOVA being 1.1 × 10 −9 and 3.7 × 10 −10 , respectively. Moreover, as Figure 10 and Figure 11 show, there are still some outliers who have a low frequency of submissions, and the box plot of cluster 1 is lower than the box plots of cluster 2 and cluster 3 in both figures. The reasons for the low frequency of submissions can mostly be explained by (1) technical issues, including internet connection issues, devices not syncing, and devices needing to be paired again; (2) participants forgetting to put the watch back on after taking it off; and (3) participants stopping using the devices (eg, some participants do not like wearing the watch while sleeping or when they go on holiday):

I was without my watch there for the last month or 3 or 4 weeks [owing to technical issues], and I missed it very badly because everything I look at the watch to tell the time, I was looking at my steps. [P042]

I don’t wear it, I told them I wouldn’t wear the watch at night, I don’t like it. [P030]

Unlike in the case of other variables, the submission of SR data through the ProACT CareApp required participants to reflect on each question and their status before selecting the appropriate answer. Participants had different questions to answer based on their health conditions; for example, participants with HF and COPD were asked to answer symptom-related questions, whereas those with diabetes were not. All participants were presented with general well-being and mood questions. Therefore, for some participants, self-reporting could possibly take more time than using the health monitoring devices. As shown in Table 3 , the frequency of average weekly submissions of SR data within the 3 clusters is relatively small and the SDs are large, which means that the frequency of SR data submissions is lower than that of other variables. Furthermore, there were approximately 5 questions asked daily about general well-being, and some participants would skip the questions if they thought the question was unnecessary or not relevant:

Researcher: And do you answer your daily questions? P027: Yeah, once a week.

Researcher: Once a week, okay. P027: But they’re the same.

As Figure 12 shows, the distribution of SR data submissions is different, with the P value of the 1-way ANOVA being .001. In Figure 12 , the median of cluster 2 is higher than the medians of the other 2 clusters, and compared with other variables, but unlike other parameters, cluster 2 also has some participants who had very low SR data submission rates (close to 0). SR data is the only parameter where cluster 1 has a higher median than cluster 3.

a Lowest submission rate across the clusters.

b Highest submission rate across the clusters.

The Correlation Among the Weekly Submissions of Different Parameters

As seen in Figure 13 , the arrows of BP and weight point to the same side of the plot, which shows a strong correlation. Likewise, PA and sleep also have a strong correlation. As noted previously, the strong correlation between PA and sleep is because the same device collected these 2 measurements, and participants only needed to sync the data once a day. By contrast, BP and weight were collected by 2 different devices but are strongly correlated. During interviews, many participants mentioned that their daily routine with the ProACT platform involved taking both BP and weight readings:

Usually in the morning when I get out of the bed, first, I go into the bathroom, wash my hands and come back, then weigh myself, do my blood pressure, do my bloods. [P008]

I now have a routine that I let the system read my watch first thing, then I do my blood pressure thing and then I do the weight. [P015]

As I said, it’s keeping me in line with my, when I dip my finger, my weight, my blood pressure. [P040]

I use it in the morning and at night for putting in the details of blood pressure in the morning and then the blood glucose at night. Yes, there’s nothing else, is there? Oh, every morning the [weight] scales. [P058]

By contrast, as shown in Figure 13 , SR data have a weak correlation with other parameters, for reasons noted earlier.

Parameter Variation Over Time

Analysis was conducted to determine any differences among the clusters in terms of symptom and well-being parameter changes over the course of the trial. Table 4 provides a description of each cluster in this regard. As Figure 14 shows, the box plot of cluster 2 is comparatively short in every time period of the trial, and the medians of cluster 2 and cluster 3 are more stable than the median of cluster 1. In addition, the median of cluster 1 is increasing over time, whereas the medians of cluster 2 and cluster 3 are decreasing and within the normal systolic BP of older adults [ 49 ] ( Figure 14 ). As can be seen in Table 5 , cluster 2 has a P value of .51 for systolic BP and a P value of .52 for diastolic BP, which are higher than the P values of cluster 1 ( P =.19 and P =.16, respectively) and cluster 3 ( P =.27 and P =.35, respectively). Therefore, participants in cluster 2, as highly engaged users, have more stable B P values than those in the other 2 clusters. By contrast, participants in cluster 1, as the least engaged users, have the most unstable B P values.

As seen in Figure 15 , the median of cluster 2 is relatively higher than the medians of the other 2 clusters. The median of cluster 3 is increasing over time. In the second and third time periods of the trial, the box plot of cluster 1 is comparatively short. Normal SpO 2 levels are between 95% and 100%, but older adults may have SpO 2 levels closer to 95% [ 50 ]. In addition, for patients with COPD, SpO 2 levels range between 88% and 92% [ 51 ]. In this case, there is not much difference in terms of SpO 2 levels, and most of the SpO 2 levels are between 90% and 95% in this study. However, the SpO 2 levels of cluster 1 and cluster 2 were maintained at a relatively high level during the trial. As for cluster 3, the SpO 2 levels were comparatively low but relatively the same as those in the other 2 clusters in the later period of the trial. Therefore, the SpO 2 levels of cluster 3 ( P =.25) are relatively unstable compared with those of cluster 1 ( P =.66) and cluster 2 ( P =.59). As such, there is little correlation between SpO 2 levels and engagement with digital health monitoring.

In relation to BG, Figure 16 shows that the box plot of cluster 2 is relatively lower than the box plots of the other 2 clusters in the second and third time periods. Moreover, the medians of cluster 2 and cluster 3 are lower than those of cluster 1 in the second and third time periods. The BG levels in cluster 2 and cluster 3 decreased at later periods of the trial compared with the beginning of the trial, but those in cluster 1 increased. Cluster 3 ( P =.25), as the typical user group, had more significant change than cluster 1 ( P =.50) and cluster 2 ( P =.41). Overall, participants with a higher engagement rate had better BG control.

In relation to weight, Figure 17 shows that the box plot of cluster 2 is lower than the box plots of the other 2 clusters and comparatively short. As Table 5 shows, the P value of cluster 2 weight data is .72, which is higher than the P values of cluster 1 (.47) and cluster 3 (.61). Therefore, participants in cluster 2 had a relatively stable weight during the trial. In addition, as seen in Figure 17 , the median weight of cluster 1 participants is decreasing, whereas that of cluster 3 participants is increasing. It is well known that there are many factors that can influence body weight, such as PA, diet, environmental factors, and so on. [ 52 ]. In this case, engagement with digital health and well-being monitoring may help control weight but the impact is not significant.

As Table 5 shows, the P value of cluster 2 PA (.049) is lower than .05, which means that there are significant differences among the 3 time slots in cluster 2. However, the median of cluster 2 PA, as seen in Figure 18 , is still higher than the medians of the other 2 clusters. In cluster 2, approximately 50% of daily PA (steps) consists of >2500 steps. Overall, participants with a higher engagement rate also had a higher level of PA.

a BP: blood pressure.

b BG: blood glucose.

c SR: self-report.

d PA: physical activity.

b SpO 2 : peripheral oxygen saturation.

c BG: blood glucose.

Principal Findings

Digital health technologies hold great promise to help older adults with multimorbidity to improve health management and health outcomes. However, such benefits can only be realized if users engage with the technology. The aim of this study was to explore the engagement patterns of older adults with multimorbidity with digital self-management by using data mining to analyze users’ weekly submission data. Three clusters were identified: cluster 1 (the least engaged user group), cluster 2 (the highly engaged user group), and cluster 3 (the typical user group). The subsequent analysis focused on how the clusters differ in terms of participant characteristics, patterns of engagement, and stabilization of health condition symptoms and well-being parameters over time, as well as how engagement rates with the different devices correlate with each other.

The key findings from the study are as follows:

• There is no significant difference in participants’ characteristics among the clusters in general. The highly engaged group had the lowest average age ( Table 4 ), and there was no significant difference with regard to sex and health conditions among these clusters. The least engaged user group had fewer male participants and participants with diabetes.
• There are 3 main factors influencing the correlations among the submission rates of different parameters. The first concerns whether the same device was used to submit the parameters, the second concerns the number of manual operations required to submit the parameter, and the third concerns the daily routine of the participants.
• Increased engagement with devices may improve the participants’ health and well-being outcomes (eg, symptoms and PA levels). However, the difference between the highly engaged user group and the typical user group was relatively minimal compared with the difference between the highly engaged user group and the least engaged user group.

Each of these findings is discussed in further detail in the following subsections.

Although the findings presented in this paper focus on engagement based on the ProACT trial participants’ use data, the interviews that were carried out as part of the trial identified additional potential factors of engagement. As reported in the study by Doyle et al [ 44 ], participants spoke about how they used the data to support their self-management (eg, taking action based on their data) and experienced various benefits, including increased knowledge of their health conditions and well-being, symptom optimization, reductions in weight, increased PA, and increased confidence to participate in certain activities as a result of health improvements. The peace of mind and encouragement provided by the clinical triage service as well as the technical support available were also identified during the interviews as potential factors positively impacting engagement [ 44 ]. In addition, the platform was found to be usable, and it imposed minimal burden on participants ( Table 1 ). These findings supplement the quantitative findings presented in this paper.

Age, Sex, Health Condition Types, and Engagement

In this study, the difference in engagement with health care technologies between the sex was not significant. Of the 23 female participants, 6 (26%) were part of the least engaged user group compared with 7 (23%) of the 31 male participants. Moreover, there were lower proportions of female participants in the highly engaged user group (7/23, 30%) and typical user group (10/23, 43%) compared with male participants (10/31, 32% and 14/31, 45%, respectively). Other research has found that engagement with mobile health technology for BP monitoring was independent of sex [ 53 ]. However, there are also some studies that show that female participants are more likely to engage with digital mental health care interventions [ 54 , 55 ]. Therefore, sex cannot be considered as a separate criterion when comparing engagement with health care technologies, and it was not found to have significant impact on engagement in this study. Regarding age, many studies have shown that younger people are more likely to use health care technologies than older adults [ 56 , 57 ]. Although all participants in our study are older adults, the highly engaged user group is the youngest group. However, there was no significant difference in age among the clusters, with some of the oldest users being part of cluster 3, the typical user cluster. Similarly, the health conditions of a participant did not significantly impact their level of engagement. Other research [ 53 ] found that participants who were highly engaged with health monitoring had higher rates of hypertension, chronic kidney disease, and hypercholesterolemia than those with lower engagement levels. Our findings indicate that the highly engaged user group had a higher proportion of participants with diabetes, and the least engaged user group had a higher proportion of participants with COPD. Further research is needed to understand why there might be differences in engagement depending on health conditions. In our study, participants with COPD also self-reported on certain symptoms, such as breathlessness, chest tightness, and sputum amount and color. Although engagement with specific questions was not explored, participants in cluster 1, the least engaged user group, self-reported more frequently than those in cluster 3, the typical user group. Our findings also indicate that participants monitoring BG level and BP experienced better symptom stabilization over time than those monitoring SpO 2 level. It has been noted that the expected benefits of technology (eg, increased safety and usefulness) and need for technology (eg, subjective health status and perception of need) are 2 important factors that can influence the acceptance and use of technology by older adults [ 58 ]. It is also well understood that engaging in monitoring BG level can help people with diabetes to better self-manage and make decisions about diet, exercise, and medication [ 59 ].

Factors Influencing Engagement

Many research studies use P values to show the level of similarity or difference among clusters [ 60 - 63 ]. For most of the engagement outcomes in this study, all clusters significantly differed, with 1-way ANOVA P <.001, with the exception being SR data ( P =.001). In addition, the 2-tailed t test P values showed that cluster 2 was significantly different from cluster 1 and cluster 3 in BP and weight data submission rates, whereas cluster 1 was significantly different from cluster 2 and cluster 3 in PA and sleep data submission rates. As for SR data submission rates, all 3 two-tailed t tests had P values >.001, meaning that there were no significant differences between any 2 of these clusters. Therefore, all 5 parameters used for clustering were separated into 3 groups based on the correlations of submission rates: 1 for BP and weight, 1 for PA and sleep, and 1 for SR data. PA and sleep data submission rates have a strong correlation because participants used the same device to record daily PA and sleeping conditions. SR data submission rates have a weak correlation with other parameters’ submission rates. Our previous research found that user retention in terms of submitting SR data was poorer than user retention in terms of using digital health devices, possibly because more manual operations are involved in the submission of SR data than other parameters or because the same questions were asked regularly, as noted by P027 in the Participant Engagement Outcomes subsection [ 64 ].

Other research that analyzed engagement with a diabetes support app found that user engagement was lower when more manual data entry was required [ 65 ]. In contrast to the other 2 groups of parameters, BP and weight data are collected using different devices. Whereas measuring BP requires using a BP monitor and manually synchronizing the data, measuring weight simply requires standing on the weight scale, and the data are automatically synchronized. Therefore, the manual operations involved in submitting BP and weight data are slightly different. However, the results showed a strong correlation between BP and weight because many participants preferred to measure both BP and weight together and incorporate taking these measurements into their daily routines. Research has indicated that if the use of a health care device becomes a regular routine, then participants will use it without consciously thinking about it [ 66 ]. Likewise, Yuan et al [ 67 ] note that integrating health apps into people’s daily activities and forming regular habits can increase people’s willingness to continue using the apps. However, participants using health care technology for long periods of time might become less receptive to exploring the system compared with using it based on the established methods to which they are accustomed [ 68 ]. In this study, many participants bundled their BP measurement with their weight measurement during their morning routine. Therefore, the engagement rates of interacting with these 2 devices were enhanced by each other. Future work could explore how to integrate additional measurements, such as monitoring SpO 2 level as well as self-reporting into this routine (eg, through prompting the user to submit these parameters while they are engaging with monitoring other parameters, such as BP and weight).

Relationship Between Engagement and Health and Well-Being Outcomes

Our third finding indicates that higher levels of engagement with digital health monitoring may result in better outcomes, such as symptom stabilization and increased PA levels. Milani et al [ 69 ] found that digital health care interventions can help people achieve BP control and improve hypertension control compared with usual care. In their study, users in the digital intervention group took an average of 4.2 readings a week. Compared with our study, this rate is lower than that of cluster 2 (5.7), the highly engaged user group, but higher than cluster 1 (2.5) and cluster 3 (2.9) rates. In our study, participants with a higher engagement rate experienced more stable BP, and for the majority of these participants (34/41, 83%), levels were maintained within the recommended thresholds of 140/90 mm Hg [ 70 ]. Many studies have shown that as engagement in digital diabetes interventions increases, patients will experience greater reductions in BG level compared with those with lower engagement [ 71 , 72 ]. However, in our study, BG levels in both the highly engaged user group (cluster 2) and the least engaged user group (cluster 1) increased in the later stages of the trial. Only the BG levels of the typical user group (cluster 3) decreased over time, which could be because the cluster 3 participants performed more PA in the later stages of the trial than during other time periods, as Figure 18 shows. Cluster 2, the highly engaged user group, maintained a relatively high level of PA during the trial period, although it continued to decline throughout the trial. Other research shows that more PA can also lead to better weight control and management [ 73 , 74 ], which could be 1 of the reasons why cluster 2 participants maintained their weight.

Limitations

There are some limitations to the research presented in this paper. First, although the sample size (n=60) was relatively large for a digital health study, the sample sizes for some parameters were small because not all participants monitored all parameters. Second, the participants were clustered based on weekly submissions of parameters only. If more features were included in clustering, such as submission intervals, participants could be grouped differently. It should also be pointed out that correlation is not a causality with respect to analyzing engagement rates with outcomes.

Conclusions

This study presents findings after the clustering of a data set that was generated from a longitudinal study of older adults using a digital health technology platform (ProACT) to self-manage multiple chronic health conditions. The highly engaged user group cluster (includes 17/54, 31% of users) had the lowest average age and highest frequency of submissions for every parameter. Engagement with digital health care technologies may also influence health and well-being outcomes (eg, symptoms and PA levels). The least engaged user group in our study had relatively poorer outcomes. However, the difference between the outcomes of the highly engaged user group and those of the typical user group is relatively small. There are 3 possible reasons for the correlations between the submission rates of parameters and devices. First, if 2 parameters are collected by the same device, they usually have a strong correlation, and users will engage with both equally. Second, the devices that involve fewer steps and parameters with less manual data entry will have a weak correlation with those devices that require more manual operations and data entry. Finally, participants’ daily routines also influence the correlations among devices; for example, in this study, many participants had developed a daily routine to weigh themselves after measuring their BP, which led to a strong correlation between BP and weight data submission rates. Future work should explore how to integrate the monitoring of additional parameters into a user’s routine and whether additional characteristics, such as the severity of disease or technical proficiency, impact engagement.

Acknowledgments

This work was part funded by the Integrated Technology Systems for Proactive Patient Centred Care (ProACT) project and has received funding from the European Union (EU)–funded Horizon 2020 research and innovation program (689996). This work was part funded by the EU’s INTERREG VA program, managed by the Special EU Programs Body through the Eastern Corridor Medical Engineering Centre (ECME) project. This work was part funded by the Scaling European Citizen Driven Transferable and Transformative Digital Health (SEURO) project and has received funding from the EU-funded Horizon 2020 research and innovation program (945449). This work was part funded by the COVID-19 Relief for Researchers Scheme set up by Ireland’s Higher Education Authority. The authors would like to sincerely thank all the participants of this research for their valuable time.

Conflicts of Interest

None declared.

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Abbreviations

Edited by T Leung, T de Azevedo Cardoso; submitted 05.02.23; peer-reviewed by B Chaudhry, M Peeples, A DeVito Dabbs; comments to author 12.09.23; revised version received 25.10.23; accepted 29.01.24; published 28.03.24.

©Yiyang Sheng, Raymond Bond, Rajesh Jaiswal, John Dinsmore, Julie Doyle. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Methodology

• Content Analysis | Guide, Methods & Examples

Content Analysis | Guide, Methods & Examples

Published on July 18, 2019 by Amy Luo . Revised on June 22, 2023.

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

• Books, newspapers and magazines
• Speeches and interviews
• Web content and social media posts
• Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding).  In both types, you categorize or “code” words, themes, and concepts within the texts and then analyze the results.

Table of contents

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis, other interesting articles.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyze.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects or concepts in a set of historical or contemporary texts.

Quantitative content analysis example

To research the importance of employment issues in political campaigns, you could analyze campaign speeches for the frequency of terms such as unemployment , jobs , and work  and use statistical analysis to find differences over time or between candidates.

In addition, content analysis can be used to make qualitative inferences by analyzing the meaning and semantic relationship of words and concepts.

Qualitative content analysis example

To gain a more qualitative understanding of employment issues in political campaigns, you could locate the word unemployment in speeches, identify what other words or phrases appear next to it (such as economy,   inequality or  laziness ), and analyze the meanings of these relationships to better understand the intentions and targets of different campaigns.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

• Finding correlations and patterns in how concepts are communicated
• Understanding the intentions of an individual, group or institution
• Identifying propaganda and bias in communication
• Revealing differences in communication in different contexts
• Analyzing the consequences of communication content, such as the flow of information or audience responses

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• Unobtrusive data collection

You can analyze communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

• Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

• Highly flexible

You can conduct content analysis at any time, in any location, and at low cost – all you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions, leading to various types of research bias and cognitive bias .

• Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct  research question .

Example research question for content analysis

Is there a difference in how the US media represents younger politicians compared to older ones in terms of trustworthiness?

Next, you follow these five steps.

1. Select the content you will analyze

Based on your research question, choose the texts that you will analyze. You need to decide:

• The medium (e.g. newspapers, speeches or websites) and genre (e.g. opinion pieces, political campaign speeches, or marketing copy)
• The inclusion and exclusion criteria (e.g. newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
• The parameters in terms of date range, location, etc.

If there are only a small amount of texts that meet your criteria, you might analyze all of them. If there is a large volume of texts, you can select a sample .

2. Define the units and categories of analysis

Next, you need to determine the level at which you will analyze your chosen texts. This means defining:

• The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
• The set of categories that you will use for coding. Categories can be objective characteristics (e.g. aged 30-40 ,  lawyer , parent ) or more conceptual (e.g. trustworthy , corrupt , conservative , family oriented ).

Your units of analysis are the politicians who appear in each article and the words and phrases that are used to describe them. Based on your research question, you have to categorize based on age and the concept of trustworthiness. To get more detailed data, you also code for other categories such as their political party and the marital status of each politician mentioned.

3. Develop a set of rules for coding

Coding involves organizing the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

In considering the category “younger politician,” you decide which titles will be coded with this category ( senator, governor, counselor, mayor ). With “trustworthy”, you decide which specific words or phrases related to trustworthiness (e.g. honest and reliable ) will be coded in this category.

4. Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti and Diction , which can help speed up the process of counting and categorizing words and phrases.

Following your coding rules, you examine each newspaper article in your sample. You record the characteristics of each politician mentioned, along with all words and phrases related to trustworthiness that are used to describe them.

5. Analyze the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context and audience of the texts.

Let’s say the results reveal that words and phrases related to trustworthiness appeared in the same sentence as an older politician more frequently than they did in the same sentence as a younger politician. From these results, you conclude that national newspapers present older politicians as more trustworthy than younger politicians, and infer that this might have an effect on readers’ perceptions of younger people in politics.

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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
• 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

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How Spammers, Scammers and Creators Leverage AI-Generated Images on Facebook for Audience Growth

• Renee DiResta ,
• Josh A. Goldstein

Text-to-image models such as DALL-E and Midjourney can produce impressive, at times even photorealistic, images. Past research by the Stanford Internet Observatory  has covered the very serious implications for child safety and nonconsensual intimate imagery. Researchers and policymakers have  expressed fears that they could be misused to inject false information into political discourse. 

In a new preprint paper , we discuss something else entirely: spam and scams. Behold, Shrimp Jesus.  

The magnificent surrealism of Shrimp Jesus—or, relatedly, Crab Jesus, Watermelon Jesus, Fanta Jesus, and Spaghetti Jesus—is captivating. What  is that? Why does that exist? You perhaps feel motivated to share it with your friends, so that they can share in your WTF moment. (We encourage you to share this post, of course.) 

But that capacity to produce captivating, novel, and immersive imagery, cheaply and instantly, and to immediately double down on wins that generate significant engagement, is also what makes the technology appealing to spammers and scammers. These innovative actors, seemingly motivated primarily by profit or clout (not ideology) have been using AI-generated images to gain viral traction on Facebook since AI image-generation tools became readily available. And Facebook, it appears, is actively recommending their content by pushing it into users’ Feeds. In 2016, the “fake news” stories  produced by Macedonian teenagers and designed for Facebook’s algorithms  pulled in tens of millions of page views; AI artisans tempt Facebook’s Feed ranking algorithms today. 

To understand how the technology is being used for page growth and incorporated into spam and scams, we examined more than 100 Facebook Pages that each posted 50+ AI-generated images. Some form coordinated clusters, which post large numbers of AI-generated images. Apparent motivations include driving people to off-platform websites, selling products, and building bigger followings. We focused on the spammers, which we defined as accounts that were pushing their audiences out to a content farm, and scammers, who were attempting either to sell products that do not appear to exist, had stolen the pages they operated, or were attempting to manipulate their audiences within the comments. These images in total account for hundreds of millions of interactions and are shown through Facebook’s Feed to some Facebook users who do not follow the Pages. While Shrimp Jesus is (perhaps) obviously an artistic fantasy—created by a page that previously shared clickbait links to a content farm—comments on many of the AI-generated images of more mundane things, like housewares, homes, or artwork purportedly created by children, suggest many users are unaware of the synthetic origin, although a subset of users post comments or infographics attempting to warn other users. Our research highlights routine but non-transparent uses of AI-generated images on Facebook and the need for better provenance and transparency methods.

In the words of the copypasta captions: thanks to everyone who appreciates this. 

Key takeaways:

• We studied 120 Facebook Pages that posted at least 50 AI-generated images each, classifying the Pages into spam, scam, and ‘other creator’ categories. Some were coordinated clusters of Pages run by the same administrators. 
• These images collectively received hundreds of millions of engagements. A post including an AI-generated image was one of the 20 most viewed pieces of content on Facebook in Q3 2023 (with 40 million views). 
• Spam Pages used clickbait tactics and attempted to direct users to off-platform content farms and low-quality domains. Scam Pages attempted to sell products that do not exist or to get users to divulge personal details; some were posting the AI-generated images on stolen Pages.
• AI-generated images are shown on the Facebook Feed to users who do not follow the Pages. We suspect that AI-generated images appear on users’ Feeds because the Facebook Feed ranking algorithm promotes content that is likely to generate engagement. Comments on the AI-generated images suggest many users are unaware of the synthetic origin of the images, though a subset of users post comments or infographics alerting others. The fact that viewers are deceived by these images highlights the importance of labeling and additional transparency measures moving forward.
• Some of the Facebook Pages we studied also used known deceptive practices, such as account theft or takeover, and exhibited suspicious follower growth.
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