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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.

How to format a research paper

Last updated

7 February 2023

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

Writing a research paper can be daunting if you’re not experienced with the process. Getting the proper format is one of the most challenging aspects of the task. Reviewers will immediately dismiss a paper that doesn't comply with standard formatting, regardless of the valuable content it contains. 

In this article, we'll delve into the essential characteristics of a research paper, including the proper formatting.

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• What is a research paper?

A research paper is a document that provides a thorough analysis of a topic , usually for an academic institution or professional organization. A research paper may be of any length, but they are typically 2,000–10,000 words. 

Unlike less formal papers, such as articles or essays, empirical evidence and data are key to research papers. In addition to students handing in papers, scientists, attorneys, medical researchers, and independent scholars may need to produce research papers.

People typically write research papers to prove a particular point or make an argument. This could support or disprove a theoretical point, legal case, scientific theory, or an existing piece of research on any topic. 

One of the distinguishing characteristics of research papers is that they contain citations to prior research. Citing sources using the correct format is essential for creating a legitimate research paper. 

• Top considerations for writing a research paper

To write a research paper, you must consider several factors. Fields such as the sciences, humanities, and technical professions have certain criteria for writing research papers. 

You’ll write a research paper using one of several types of formatting. These include APA, MLA, and CMOS styles, which we’ll cover in detail to guide you on citations and other formatting rules. 

Specific requirements of the assignment

If the paper is for a college, university, or any specific organization, they’ll give you certain requirements, such as the range of topics, length, and formatting requirements.

You should study the specifics of the assignment carefully, as these will override more general guidelines you may find elsewhere. If you're writing for a particular professor, they may ask for single or double spacing or a certain citation style. 

• Components of a research paper

Here are the basic steps to writing a quality research paper, assuming you've chosen your topic and considered the requirements of the paper. Depending on the specific conditions of the paper you're writing, you may need the following elements:

Thesis statement

The thesis statement provides a blueprint for the paper. It conveys the theme and purpose of the paper. It also informs you and readers what your paper will argue and the type of research it will contain. As you write the paper, you can refer to the thesis statement to help you decide whether or not to include certain items.

Most research papers require an abstract as well as a thesis. While the thesis is a short (usually a single sentence) summary of the work, an abstract contains more detail. Many papers use the IMRaD structure for the abstract, especially in scientific fields. This consists of four elements:

Introduction : Summarize the purpose of the paper

Methods : Describe the research methods (e.g., collecting data , interviews , field research)

Results: Summarize your conclusions.  

Discussion: Discuss the implications of your research. Mention any significant limitations to your approach and suggest areas for further research.

The thesis and abstract come at the beginning of a paper, but you should write them after completing the paper. This approach ensures a clear idea of your main topic and argument, which can evolve as you write the paper.

Table of contents

Like most nonfiction books, a research paper usually includes a table of contents. 

Tables, charts, and illustrations

If your paper contains multiple tables, charts, illustrations, or other graphics, you can create a list of these. 

Works cited or reference page

This page lists all the works you cited in your paper. For MLA and APA styles, you will use in-text citations in the body of the paper. For Chicago (CMOS) style, you'll use footnotes. 

Bibliography

While you use a reference page to note all cited papers, a bibliography lists all the works you consulted in your research, even if you don't specifically cite them. 

While references are essential, a bibliography is optional but usually advisable to demonstrate the breadth of your research.

Dedication and acknowledgments

You may include a dedication or acknowledgments at the beginning of the paper directly after the title page and before the abstract.

• Steps for writing a research paper

These are the most critical steps for researching, writing, and formatting a research paper:

Create an outline

The outline is not part of the published paper; it’s for your use. An outline makes it easier to structure the paper, ensuring you include all necessary points and research. 

Here you can list all topics and subtopics that will support your argument. When doing your research, you can refer to the outline to ensure you include everything. 

Gather research

Solid research is the hallmark of a research paper. In addition to accumulating research, you need to present it clearly. However, gathering research is one of the first tasks. If you compile each piece of research correctly, it will be easier to format the paper correctly. You want to avoid having to go back and look up information constantly.

Start by skimming potentially useful sources and putting them aside for later use. Reading each source thoroughly at this stage will be time-consuming and slow your progress. You can thoroughly review the sources to decide what to include and discard later. At this stage, note essential information such as names, dates, page numbers, and website links. Citing sources will be easier when you’ve written all the information down.

Be aware of the quality of your sources. A research paper should reference scholarly, academic, or scientific journals. It’s vital to understand the difference between primary and secondary sources. 

A primary source is an original, firsthand account of a topic. A secondary source is someone else covering the topic, as in a popular article or interview. While you may include secondary sources, your paper should also include primary research . Online research can be convenient, but you need to be extra careful when assessing the quality of your sources.

Write the first draft

Create a first draft where you put together all your research and address the topic described in your thesis and abstract. 

Edit and format the paper

Proofread, edit, and make any necessary adjustments and improvements to the first draft. List your citations as described below. Ensure your thesis and abstract describe your research accurately. 

• Formatting a research paper: MLA, APA, and CMOS styles

There are several popular formats for research papers: MLA (Modern Language Association) and APA (American Psychological Association). Certain academic papers use CMOS (Chicago Manual of Style). Other formats may apply to particular fields. 

For example, medical research may use AMA (American Medical Association) formatting and IEEE (Institute of Electrical and Electronics Engineers) for particular technical papers. The following are the guidelines and examples of the most popular formats:

The humanities typically use MLA format, including literature, history, and culture. Look over examples of papers created in MLA format . Here are the main rules to keep in mind:

Double-spaced lines.

Indent new paragraphs 1/2 inch.

Title case for headings, where all major words are capitalized, as in "How to Write a Research Paper." 

Use a popular font such as Times New Roman. This applies to all formatting styles.

Use one-inch margins on all sides. 

Number sections of the paper using Arabic numerals (1, 2, 3, etc.). 

Use a running head for each page on the upper right-hand corner, which consists of your last name and the page number.

Use an in-text citation within the text, using the author's last name followed by the page number: "Anything worth dying for is certainly worth living for" (Heller 155).  

On the citations page, list the full name, book or periodical, and other information. For MLA, you will not need footnotes, only in-text citations.

List citations in alphabetical order on a separate page at the end of the paper entitled “Works Cited.” 

Continuing with the above example from Heller, the listing would be: Heller, Joseph. Catch-22, Simon & Schuster, 1961.

For a periodical, the format is "Thompson, Hunter S. "The Kentucky Derby is Decadent and Depraved" Scanlon's, June 1970."

Use title case for source titles, as in "On the Origin of Species."

The sciences typically use APA format, including physical sciences such as physics and social sciences such as psychology. Simply Psychology provides examples of APA formatting . The following are the most important rules of the APA format.

Begin the paper with a title page, which is not required for MLA.

Use double-line spacing.

Use a running head for each page in the upper right-hand corner, which consists of the paper's title in capital letters followed by the page number.

The citations page at the end should be titled "References."

In-text citations should include the publication date: (Smith, 1999, p. 50). Note also that there's a "p" for "page," whereas in MLA, you write the page number without a "p."

As with MLA, use title case for headings, as in "Most Popular Treatments for Cognitive Disorders."

Use sentence case for titles of sources, as in "History of the decline and fall of the Roman empire." Note "Roman" starts with a capital because it's a proper noun.  

When citing in-text references, use the author's last name and the first and middle initials. 

Always use the Oxford comma. This comma goes before the words "or" and "and" in a list. For example, "At the store, I bought oranges, paper towels, and pasta."

CMOS formatting

Book publishers and many academic papers use CMOS formatting based on the Chicago Manual of Style. CMOS is also called Turabian, named after Kate L. Turabian, who wrote the first manual for this style. Here are examples of CMOS style formatting and citations.

Include an unnumbered title page.

Place page numbers on the upper right-hand corner of the page. Do not list your name or the paper's title as you would for MLA or APA styles.

Use title case for both headings and sources (same as MLA).

Unlike MLA and APA, the Chicago style uses footnotes for citations. Use a superscript for footnotes: "Smith argues against Jones' theory¹.” Footnotes may appear at the bottom of the page or the end of the document.  

CMOS supports both short notes and full notes. In most cases, you'll use the full note: "Michael Pollan, The Omnivore's Dilemma: A Natural History of Four Meals (New York: Penguin, 2006), 76." For further references to the same source, use a short note: " Pollan, Omnivore's Dilemma, 45." The requirements of some papers may specify using only short notes for all footnotes.

• General guidelines for writing and formatting research papers

Keep these guidelines in mind for all types of research papers:

Initial formatting

As you create your first draft, don't worry about formatting. If you try to format it perfectly as you write the paper, it will be difficult to progress and develop a flow of thought. With the first draft, you don't have to be concerned about ordering the sections. You can rearrange headings and sections later. 

Citation tools

Use automation tools for citations . Some useful tools make citations easier by automatically generating a citation list and bibliography. Many work with APA, MLA, and CMOS styles.

Check for plagiarism

Use a plagiarism detector to make sure your paper isn't unintentionally plagiarizing. There are many free and paid plagiarism checkers online, such as Grammarly. 

Proofread your work

Do several rounds of editing and proofreading. Editing is necessary for any type of writing, but you’ll need to revisit several distinct areas with a research paper:

Check for spelling and grammatical errors.

Read the paper to make sure it's well-argued and that you’ve organized it properly. 

Check that you’ve correctly formatted citations. It's easy to make errors, such as incorrect numbering of footnotes (e.g., Chicago style) or forgetting to include a source on your citations page.

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Structure of a Research Paper

Structure of a Research Paper: IMRaD Format

I. The Title Page

• Title: Tells the reader what to expect in the paper.
• Author(s): Most papers are written by one or two primary authors. The remaining authors have reviewed the work and/or aided in study design or data analysis (International Committee of Medical Editors, 1997). Check the Instructions to Authors for the target journal for specifics about authorship.
• Keywords [according to the journal]
• Corresponding Author: Full name and affiliation for the primary contact author for persons who have questions about the research.
• Financial & Equipment Support [if needed]: Specific information about organizations, agencies, or companies that supported the research.
• Conflicts of Interest [if needed]: List and explain any conflicts of interest.

II. Abstract: “Structured abstract” has become the standard for research papers (introduction, objective, methods, results and conclusions), while reviews, case reports and other articles have non-structured abstracts. The abstract should be a summary/synopsis of the paper.

III. Introduction: The “why did you do the study”; setting the scene or laying the foundation or background for the paper.

IV. Methods: The “how did you do the study.” Describe the --

• Context and setting of the study
• Specify the study design
• Population (patients, etc. if applicable)
• Sampling strategy
• Intervention (if applicable)
• Identify the main study variables
• Data collection instruments and procedures
• Outline analysis methods

V. Results: The “what did you find” --

• Report on data collection and/or recruitment
• Participants (demographic, clinical condition, etc.)
• Present key findings with respect to the central research question
• Secondary findings (secondary outcomes, subgroup analyses, etc.)

VI. Discussion: Place for interpreting the results

• Main findings of the study
• Discuss the main results with reference to previous research
• Policy and practice implications of the results
• Strengths and limitations of the study

VII. Conclusions: [occasionally optional or not required]. Do not reiterate the data or discussion. Can state hunches, inferences or speculations. Offer perspectives for future work.

VIII. Acknowledgements: Names people who contributed to the work, but did not contribute sufficiently to earn authorship. You must have permission from any individuals mentioned in the acknowledgements sections. 

IX. References:  Complete citations for any articles or other materials referenced in the text of the article.

• IMRD Cheatsheet (Carnegie Mellon) pdf.
• Adewasi, D. (2021 June 14).  What Is IMRaD? IMRaD Format in Simple Terms! . Scientific-editing.info. 
• Nair, P.K.R., Nair, V.D. (2014). Organization of a Research Paper: The IMRAD Format. In: Scientific Writing and Communication in Agriculture and Natural Resources. Springer, Cham. https://doi.org/10.1007/978-3-319-03101-9_2
• Sollaci, L. B., & Pereira, M. G. (2004). The introduction, methods, results, and discussion (IMRAD) structure: a fifty-year survey.   Journal of the Medical Library Association : JMLA ,  92 (3), 364–367.
• Cuschieri, S., Grech, V., & Savona-Ventura, C. (2019). WASP (Write a Scientific Paper): Structuring a scientific paper.   Early human development ,  128 , 114–117. https://doi.org/10.1016/j.earlhumdev.2018.09.011

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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

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Researcher, Academic Writer, Web developer

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

Need a helping hand?

Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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10 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

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Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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Article Contents

Introduction, contents of a research study protocol, conflict of interest statement, how to write a research study protocol.

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Julien Al Shakarchi, How to write a research study protocol, Journal of Surgical Protocols and Research Methodologies , Volume 2022, Issue 1, January 2022, snab008, https://doi.org/10.1093/jsprm/snab008

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A study protocol is an important document that specifies the research plan for a clinical study. Many funders such as the NHS Health Research Authority encourage researchers to publish their study protocols to create a record of the methodology and reduce duplication of research effort. In this paper, we will describe how to write a research study protocol.

A study protocol is an essential part of a research project. It describes the study in detail to allow all members of the team to know and adhere to the steps of the methodology. Most funders, such as the NHS Health Research Authority in the United Kingdom, encourage researchers to publish their study protocols to create a record of the methodology, help with publication of the study and reduce duplication of research effort. In this paper, we will explain how to write a research protocol by describing what should be included.

Introduction

The introduction is vital in setting the need for the planned research and the context of the current evidence. It should be supported by a background to the topic with appropriate references to the literature. A thorough review of the available evidence is expected to document the need for the planned research. This should be followed by a brief description of the study and the target population. A clear explanation for the rationale of the project is also expected to describe the research question and justify the need of the study.

Methods and analysis

A suitable study design and methodology should be chosen to reflect the aims of the research. This section should explain the study design: single centre or multicentre, retrospective or prospective, controlled or uncontrolled, randomised or not, and observational or experimental. Efforts should be made to explain why that particular design has been chosen. The studied population should be clearly defined with inclusion and exclusion criteria. These criteria will define the characteristics of the population the study is proposing to investigate and therefore outline the applicability to the reader. The size of the sample should be calculated with a power calculation if possible.

The protocol should describe the screening process about how, when and where patients will be recruited in the process. In the setting of a multicentre study, each participating unit should adhere to the same recruiting model or the differences should be described in the protocol. Informed consent must be obtained prior to any individual participating in the study. The protocol should fully describe the process of gaining informed consent that should include a patient information sheet and assessment of his or her capacity.

The intervention should be described in sufficient detail to allow an external individual or group to replicate the study. The differences in any changes of routine care should be explained. The primary and secondary outcomes should be clearly defined and an explanation of their clinical relevance is recommended. Data collection methods should be described in detail as well as where the data will be kept secured. Analysis of the data should be explained with clear statistical methods. There should also be plans on how any reported adverse events and other unintended effects of trial interventions or trial conduct will be reported, collected and managed.

Ethics and dissemination

A clear explanation of the risk and benefits to the participants should be included as well as addressing any specific ethical considerations. The protocol should clearly state the approvals the research has gained and the minimum expected would be ethical and local research approvals. For multicentre studies, the protocol should also include a statement of how the protocol is in line with requirements to gain approval to conduct the study at each proposed sites.

It is essential to comment on how personal information about potential and enrolled participants will be collected, shared and maintained in order to protect confidentiality. This part of the protocol should also state who owns the data arising from the study and for how long the data will be stored. It should explain that on completion of the study, the data will be analysed and a final study report will be written. We would advise to explain if there are any plans to notify the participants of the outcome of the study, either by provision of the publication or via another form of communication.

The authorship of any publication should have transparent and fair criteria, which should be described in this section of the protocol. By doing so, it will resolve any issues arising at the publication stage.

Funding statement

It is important to explain who are the sponsors and funders of the study. It should clarify the involvement and potential influence of any party. The sponsor is defined as the institution or organisation assuming overall responsibility for the study. Identification of the study sponsor provides transparency and accountability. The protocol should explicitly outline the roles and responsibilities of any funder(s) in study design, data analysis and interpretation, manuscript writing and dissemination of results. Any competing interests of the investigators should also be stated in this section.

A study protocol is an important document that specifies the research plan for a clinical study. It should be written in detail and researchers should aim to publish their study protocols as it is encouraged by many funders. The spirit 2013 statement provides a useful checklist on what should be included in a research protocol [ 1 ]. In this paper, we have explained a straightforward approach to writing a research study protocol.

None declared.

Chan   A-W , Tetzlaff   JM , Gøtzsche   PC , Altman   DG , Mann   H , Berlin   J , et al.    SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials . BMJ   2013 ; 346 : e7586 .

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What Is a Research Design? | Definition, Types & Guide

Introduction

Parts of a research design, types of research methodology in qualitative research, narrative research designs, phenomenological research designs, grounded theory research designs.

• Ethnographic research designs

Case study research design

Important reminders when designing a research study.

A research design in qualitative research is a critical framework that guides the methodological approach to studying complex social phenomena. Qualitative research designs determine how data is collected, analyzed, and interpreted, ensuring that the research captures participants' nuanced and subjective perspectives. Research designs also recognize ethical considerations and involve informed consent, ensuring confidentiality, and handling sensitive topics with the utmost respect and care. These considerations are crucial in qualitative research and other contexts where participants may share personal or sensitive information. A research design should convey coherence as it is essential for producing high-quality qualitative research, often following a recursive and evolving process.

Theoretical concepts and research question

The first step in creating a research design is identifying the main theoretical concepts. To identify these concepts, a researcher should ask which theoretical keywords are implicit in the investigation. The next step is to develop a research question using these theoretical concepts. This can be done by identifying the relationship of interest among the concepts that catch the focus of the investigation. The question should address aspects of the topic that need more knowledge, shed light on new information, and specify which aspects should be prioritized before others. This step is essential in identifying which participants to include or which data collection methods to use. Research questions also put into practice the conceptual framework and make the initial theoretical concepts more explicit. Once the research question has been established, the main objectives of the research can be specified. For example, these objectives may involve identifying shared experiences around a phenomenon or evaluating perceptions of a new treatment.

Methodology

After identifying the theoretical concepts, research question, and objectives, the next step is to determine the methodology that will be implemented. This is the lifeline of a research design and should be coherent with the objectives and questions of the study. The methodology will determine how data is collected, analyzed, and presented. Popular qualitative research methodologies include case studies, ethnography , grounded theory , phenomenology, and narrative research . Each methodology is tailored to specific research questions and facilitates the collection of rich, detailed data. For example, a narrative approach may focus on only one individual and their story, while phenomenology seeks to understand participants' lived common experiences. Qualitative research designs differ significantly from quantitative research, which often involves experimental research, correlational designs, or variance analysis to test hypotheses about relationships between two variables, a dependent variable and an independent variable while controlling for confounding variables.

Literature review

After the methodology is identified, conducting a thorough literature review is integral to the research design. This review identifies gaps in knowledge, positioning the new study within the larger academic dialogue and underlining its contribution and relevance. Meta-analysis, a form of secondary research, can be particularly useful in synthesizing findings from multiple studies to provide a clear picture of the research landscape.

Data collection

The sampling method in qualitative research is designed to delve deeply into specific phenomena rather than to generalize findings across a broader population. The data collection methods—whether interviews, focus groups, observations, or document analysis—should align with the chosen methodology, ethical considerations, and other factors such as sample size. In some cases, repeated measures may be collected to observe changes over time.

Data analysis

Analysis in qualitative research typically involves methods such as coding and thematic analysis to distill patterns from the collected data. This process delineates how the research results will be systematically derived from the data. It is recommended that the researcher ensures that the final interpretations are coherent with the observations and analyses, making clear connections between the data and the conclusions drawn. Reporting should be narrative-rich, offering a comprehensive view of the context and findings.

Overall, a coherent qualitative research design that incorporates these elements facilitates a study that not only adds theoretical and practical value to the field but also adheres to high quality. This methodological thoroughness is essential for achieving significant, insightful findings. Examples of well-executed research designs can be valuable references for other researchers conducting qualitative or quantitative investigations. An effective research design is critical for producing robust and impactful research outcomes.

Each qualitative research design is unique, diverse, and meticulously tailored to answer specific research questions, meet distinct objectives, and explore the unique nature of the phenomenon under investigation. The methodology is the wider framework that a research design follows. Each methodology in a research design consists of methods, tools, or techniques that compile data and analyze it following a specific approach.

The methods enable researchers to collect data effectively across individuals, different groups, or observations, ensuring they are aligned with the research design. The following list includes the most commonly used methodologies employed in qualitative research designs, highlighting how they serve different purposes and utilize distinct methods to gather and analyze data.

The narrative approach in research focuses on the collection and detailed examination of life stories, personal experiences, or narratives to gain insights into individuals' lives as told from their perspectives. It involves constructing a cohesive story out of the diverse experiences shared by participants, often using chronological accounts. It seeks to understand human experience and social phenomena through the form and content of the stories. These can include spontaneous narrations such as memoirs or diaries from participants or diaries solicited by the researcher. Narration helps construct the identity of an individual or a group and can rationalize, persuade, argue, entertain, confront, or make sense of an event or tragedy. To conduct a narrative investigation, it is recommended that researchers follow these steps:

Identify if the research question fits the narrative approach. Its methods are best employed when a researcher wants to learn about the lifestyle and life experience of a single participant or a small number of individuals.

Select the best-suited participants for the research design and spend time compiling their stories using different methods such as observations, diaries, interviewing their family members, or compiling related secondary sources.

Compile the information related to the stories. Narrative researchers collect data based on participants' stories concerning their personal experiences, for example about their workplace or homes, their racial or ethnic culture, and the historical context in which the stories occur.

Analyze the participant stories and "restore" them within a coherent framework. This involves collecting the stories, analyzing them based on key elements such as time, place, plot, and scene, and then rewriting them in a chronological sequence (Ollerenshaw & Creswell, 2000). The framework may also include elements such as a predicament, conflict, or struggle; a protagonist; and a sequence with implicit causality, where the predicament is somehow resolved (Carter, 1993).

Collaborate with participants by actively involving them in the research. Both the researcher and the participant negotiate the meaning of their stories, adding a credibility check to the analysis (Creswell & Miller, 2000).

A narrative investigation includes collecting a large amount of data from the participants and the researcher needs to understand the context of the individual's life. A keen eye is needed to collect particular stories that capture the individual experiences. Active collaboration with the participant is necessary, and researchers need to discuss and reflect on their own beliefs and backgrounds. Multiple questions could arise in the collection, analysis, and storytelling of individual stories that need to be addressed, such as: Whose story is it? Who can tell it? Who can change it? Which version is compelling? What happens when narratives compete? In a community, what do the stories do among them? (Pinnegar & Daynes, 2006).

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A research design based on phenomenology aims to understand the essence of the lived experiences of a group of people regarding a particular concept or phenomenon. Researchers gather deep insights from individuals who have experienced the phenomenon, striving to describe "what" they experienced and "how" they experienced it. This approach to a research design typically involves detailed interviews and aims to reach a deep existential understanding. The purpose is to reduce individual experiences to a description of the universal essence or understanding the phenomenon's nature (van Manen, 1990). In phenomenology, the following steps are usually followed:

Identify a phenomenon of interest . For example, the phenomenon might be anger, professionalism in the workplace, or what it means to be a fighter.

Recognize and specify the philosophical assumptions of phenomenology , for example, one could reflect on the nature of objective reality and individual experiences.

Collect data from individuals who have experienced the phenomenon . This typically involves conducting in-depth interviews, including multiple sessions with each participant. Additionally, other forms of data may be collected using several methods, such as observations, diaries, art, poetry, music, recorded conversations, written responses, or other secondary sources.

Ask participants two general questions that encompass the phenomenon and how the participant experienced it (Moustakas, 1994). For example, what have you experienced in this phenomenon? And what contexts or situations have typically influenced your experiences within the phenomenon? Other open-ended questions may also be asked, but these two questions particularly focus on collecting research data that will lead to a textural description and a structural description of the experiences, and ultimately provide an understanding of the common experiences of the participants.

Review data from the questions posed to participants . It is recommended that researchers review the answers and highlight "significant statements," phrases, or quotes that explain how participants experienced the phenomenon. The researcher can then develop meaningful clusters from these significant statements into patterns or key elements shared across participants.

Write a textual description of what the participants experienced based on the answers and themes of the two main questions. The answers are also used to write about the characteristics and describe the context that influenced the way the participants experienced the phenomenon, called imaginative variation or structural description. Researchers should also write about their own experiences and context or situations that influenced them.

Write a composite description from the structural and textural description that presents the "essence" of the phenomenon, called the essential and invariant structure.

A phenomenological approach to a research design includes the strict and careful selection of participants in the study where bracketing personal experiences can be difficult to implement. The researcher decides how and in which way their knowledge will be introduced. It also involves some understanding and identification of the broader philosophical assumptions.

Grounded theory is used in a research design when the goal is to inductively develop a theory "grounded" in data that has been systematically gathered and analyzed. Starting from the data collection, researchers identify characteristics, patterns, themes, and relationships, gradually forming a theoretical framework that explains relevant processes, actions, or interactions grounded in the observed reality. A grounded theory study goes beyond descriptions and its objective is to generate a theory, an abstract analytical scheme of a process. Developing a theory doesn't come "out of nothing" but it is constructed and based on clear data collection. We suggest the following steps to follow a grounded theory approach in a research design:

Determine if grounded theory is the best for your research problem . Grounded theory is a good design when a theory is not already available to explain a process.

Develop questions that aim to understand how individuals experienced or enacted the process (e.g., What was the process? How did it unfold?). Data collection and analysis occur in tandem, so that researchers can ask more detailed questions that shape further analysis, such as: What was the focal point of the process (central phenomenon)? What influenced or caused this phenomenon to occur (causal conditions)? What strategies were employed during the process? What effect did it have (consequences)?

Gather relevant data about the topic in question . Data gathering involves questions that are usually asked in interviews, although other forms of data can also be collected, such as observations, documents, and audio-visual materials from different groups.

Carry out the analysis in stages . Grounded theory analysis begins with open coding, where the researcher forms codes that inductively emerge from the data (rather than preconceived categories). Researchers can thus identify specific properties and dimensions relevant to their research question.

Assemble the data in new ways and proceed to axial coding . Axial coding involves using a coding paradigm or logic diagram, such as a visual model, to systematically analyze the data. Begin by identifying a central phenomenon, which is the main category or focus of the research problem. Next, explore the causal conditions, which are the categories of factors that influence the phenomenon. Specify the strategies, which are the actions or interactions associated with the phenomenon. Then, identify the context and intervening conditions—both narrow and broad factors that affect the strategies. Finally, delineate the consequences, which are the outcomes or results of employing the strategies.

Use selective coding to construct a "storyline" that links the categories together. Alternatively, the researcher may formulate propositions or theory-driven questions that specify predicted relationships among these categories.

Develop and visually present a matrix that clarifies the social, historical, and economic conditions influencing the central phenomenon. This optional step encourages viewing the model from the narrowest to the broadest perspective.

Write a substantive-level theory that is closely related to a specific problem or population. This step is optional but provides a focused theoretical framework that can later be tested with quantitative data to explore its generalizability to a broader sample.

Allow theory to emerge through the memo-writing process, where ideas about the theory evolve continuously throughout the stages of open, axial, and selective coding.

The researcher should initially set aside any preconceived theoretical ideas to allow for the emergence of analytical and substantive theories. This is a systematic research approach, particularly when following the methodological steps outlined by Strauss and Corbin (1990). For those seeking more flexibility in their research process, the approach suggested by Charmaz (2006) might be preferable.

One of the challenges when using this method in a research design is determining when categories are sufficiently saturated and when the theory is detailed enough. To achieve saturation, discriminant sampling may be employed, where additional information is gathered from individuals similar to those initially interviewed to verify the applicability of the theory to these new participants. Ultimately, its goal is to develop a theory that comprehensively describes the central phenomenon, causal conditions, strategies, context, and consequences.

Ethnographic research design

An ethnographic approach in research design involves the extended observation and data collection of a group or community. The researcher immerses themselves in the setting, often living within the community for long periods. During this time, they collect data by observing and recording behaviours, conversations, and rituals to understand the group's social dynamics and cultural norms. We suggest following these steps for ethnographic methods in a research design:

Assess whether ethnography is the best approach for the research design and questions. It's suitable if the goal is to describe how a cultural group functions and to delve into their beliefs, language, behaviours, and issues like power, resistance, and domination, particularly if there is limited literature due to the group’s marginal status or unfamiliarity to mainstream society.

Identify and select a cultural group for your research design. Choose one that has a long history together, forming distinct languages, behaviours, and attitudes. This group often might be marginalized within society.

Choose cultural themes or issues to examine within the group. Analyze interactions in everyday settings to identify pervasive patterns such as life cycles, events, and overarching cultural themes. Culture is inferred from the group members' words, actions, and the tension between their actual and expected behaviours, as well as the artifacts they use.

Conduct fieldwork to gather detailed information about the group’s living and working environments. Visit the site, respect the daily lives of the members, and collect a diverse range of materials, considering ethical aspects such as respect and reciprocity.

Compile and analyze cultural data to develop a set of descriptive and thematic insights. Begin with a detailed description of the group based on observations of specific events or activities over time. Then, conduct a thematic analysis to identify patterns or themes that illustrate how the group functions and lives. The final output should be a comprehensive cultural portrait that integrates both the participants (emic) and the researcher’s (etic) perspectives, potentially advocating for the group’s needs or suggesting societal changes to better accommodate them.

Researchers engaging in ethnography need a solid understanding of cultural anthropology and the dynamics of sociocultural systems, which are commonly explored in ethnographic research. The data collection phase is notably extensive, requiring prolonged periods in the field. Ethnographers often employ a literary, quasi-narrative style in their narratives, which can pose challenges for those accustomed to more conventional social science writing methods.

Another potential issue is the risk of researchers "going native," where they become overly assimilated into the community under study, potentially jeopardizing the objectivity and completion of their research. It's crucial for researchers to be aware of their impact on the communities and environments they are studying.

The case study approach in a research design focuses on a detailed examination of a single case or a small number of cases. Cases can be individuals, groups, organizations, or events. Case studies are particularly useful for research designs that aim to understand complex issues in real-life contexts. The aim is to provide a thorough description and contextual analysis of the cases under investigation. We suggest following these steps in a case study design:

Assess if a case study approach suits your research questions . This approach works well when you have distinct cases with defined boundaries and aim to deeply understand these cases or compare multiple cases.

Choose your case or cases. These could involve individuals, groups, programs, events, or activities. Decide whether an individual or collective, multi-site or single-site case study is most appropriate, focusing on specific cases or themes (Stake, 1995; Yin, 2003).

Gather data extensively from diverse sources . Collect information through archival records, interviews, direct and participant observations, and physical artifacts (Yin, 2003).

Analyze the data holistically or in focused segments . Provide a comprehensive overview of the entire case or concentrate on specific aspects. Start with a detailed description including the history of the case and its chronological events then narrow down to key themes. The aim is to delve into the case's complexity rather than generalize findings.

Interpret and report the significance of the case in the final phase . Explain what insights were gained, whether about the subject of the case in an instrumental study or an unusual situation in an intrinsic study (Lincoln & Guba, 1985).

The investigator must carefully select the case or cases to study, recognizing that multiple potential cases could illustrate a chosen topic or issue. This selection process involves deciding whether to focus on a single case for deeper analysis or multiple cases, which may provide broader insights but less depth per case. Each choice requires a well-justified rationale for the selected cases. Researchers face the challenge of defining the boundaries of a case, such as its temporal scope and the events and processes involved. This decision in a research design is crucial as it affects the depth and value of the information presented in the study, and therefore should be planned to ensure a comprehensive portrayal of the case.

Qualitative and quantitative research designs are distinct in their approach to data collection and data analysis. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research prioritizes understanding the depth and richness of human experiences, behaviours, and interactions.

Qualitative methods in a research design have to have internal coherence, meaning that all elements of the research project—research question, data collection, data analysis, findings, and theory—are well-aligned and consistent with each other. This coherence in the research study is especially crucial in inductive qualitative research, where the research process often follows a recursive and evolving path. Ensuring that each component of the research design fits seamlessly with the others enhances the clarity and impact of the study, making the research findings more robust and compelling. Whether it is a descriptive research design, explanatory research design, diagnostic research design, or correlational research design coherence is an important element in both qualitative and quantitative research.

Finally, a good research design ensures that the research is conducted ethically and considers the well-being and rights of participants when managing collected data. The research design guides researchers in providing a clear rationale for their methodologies, which is crucial for justifying the research objectives to the scientific community. A thorough research design also contributes to the body of knowledge, enabling researchers to build upon past research studies and explore new dimensions within their fields. At the core of the design, there is a clear articulation of the research objectives. These objectives should be aligned with the underlying concepts being investigated, offering a concise method to answer the research questions and guiding the direction of the study with proper qualitative methods.

Carter, K. (1993). The place of a story in the study of teaching and teacher education. Educational Researcher, 22(1), 5-12, 18.

Charmaz, K. (2006). Constructing grounded theory. London: Sage.

Creswell, J. W., & Miller, D. L. (2000). Determining validity in qualitative inquiry. Theory Into Practice, 39(3), 124-130.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Ollerenshaw, J. A., & Creswell, J. W. (2000, April). Data analysis in narrative research: A comparison of two “restoring” approaches. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.

Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA: Sage.

van Manen, M. (1990). Researching lived experience: Human science for an action sensitive pedagogy. Ontario, Canada: University of Western Ontario.

Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage

Whatever your research objectives, make it happen with ATLAS.ti!

Download a free trial today.

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11 Transforming Student Learning with Effective Study Techniques

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Effective study techniques can significantly enhance student learning and academic performance. In today’s fast-paced educational environment, students face numerous challenges, from managing multiple assignments and homework to balancing extracurricular activities. Developing strong study habits is essential for success in both school and college.

Understanding how to study efficiently can make a significant difference in a student’s academic journey. By implementing the right techniques, students can improve their comprehension, retention, and overall performance. This article explores various study methods and provides valuable tips for students looking to transform their learning experiences with paperwriter .

The Importance of Effective Study Techniques

Effective study techniques are crucial for students aiming to achieve their academic goals. These techniques help in better time management, reducing stress, and improving understanding of complex subjects. With the right approach, students can make their study sessions more productive and less overwhelming.

One of the biggest challenges students face is the sheer volume of information they need to learn and retain. Effective study techniques can help break down this information into manageable chunks, making it easier to digest and remember. Moreover, students who develop good study habits early on are more likely to succeed in their future academic and professional endeavors.

Creating a Conducive Study Environment

A conducive study environment is essential for effective learning. Students should choose a quiet, comfortable place with minimal distractions to focus on their studies. A well-organized study space can significantly enhance concentration and productivity.

Tips for Creating a Conducive Study Environment:

• Choose a quiet location: Find a place free from noise and interruptions.
• Ensure good lighting: Proper lighting reduces eye strain and improves focus.
• Organize your materials: Keep all necessary supplies within reach to avoid unnecessary distractions.
• Comfortable seating: Choose a chair and desk that provide good support to maintain good posture.

Time Management and Scheduling

Time management is a critical skill for students. Balancing school, college, assignments, and homework can be challenging. Effective scheduling ensures that students allocate sufficient time for each subject and activity.

Strategies for Better Time Management:

• Create a study schedule: Plan your study sessions and stick to the schedule.
• Set priorities: Focus on the most important tasks first.
• Break tasks into smaller steps: Divide large assignments into manageable parts.
• Use a planner: Keep track of deadlines, assignments, and exams.

Active Learning Techniques

Active learning involves engaging with the material actively rather than passively reading or listening. This approach enhances understanding and retention.

Effective Active Learning Techniques:

• Summarization: Summarize key points in your own words.
• Questioning: Ask questions about the material and seek answers.
• Discussion: Discuss topics with classmates to gain different perspectives.
• Application: Apply what you have learned to real-world scenarios.

The Role of Technology in Studying

Technology can be a powerful tool for enhancing learning. Various apps and online resources can help students manage their study time, organize notes, and access educational materials.

Useful Technological Tools for Students:

• Note-taking apps: Apps like Evernote and OneNote help organize and store notes efficiently.
• Study apps: Apps like Quizlet and Anki offer flashcards and quizzes for effective revision.
• Time management apps: Tools like Trello and Todoist help students plan and track their tasks.( or visit https://do-my-math.com/ )
• Online resources: Websites like Khan Academy and Coursera provide additional learning materials.

Enhancing Memory and Retention

Improving memory and retention is vital for academic success. Students can employ various techniques to boost their ability to remember and recall information.

Techniques to Enhance Memory and Retention:

• Mnemonics: Use mnemonic devices to remember complex information.
• Visualization: Create mental images to associate with the material.
• Repetition: Review material regularly to reinforce learning.
• Teaching others: Explaining concepts to others helps solidify understanding.

Staying Motivated and Managing Stress

Maintaining motivation and managing stress are essential components of effective studying. Students need to find ways to stay motivated and cope with academic pressures.

Tips for Staying Motivated and Managing Stress:

• Set realistic goals: Set achievable short-term and long-term goals.
• Take breaks: Regular breaks prevent burnout and improve focus.
• Stay positive: Maintain a positive attitude towards learning.
• Seek support: Reach out to teachers, peers, or counselors for help when needed.

Incorporating effective study techniques can transform the learning experience for students. By creating a conducive study environment, managing time efficiently, engaging in active learning, utilizing technology, enhancing memory, and staying motivated, students can achieve academic success. Remember, the key to effective studying lies in consistency and dedication. With the right approach, every student can improve their learning outcomes and reach their full potential.

Effective study habits not only help students excel in their current studies but also prepare them for future challenges. By implementing these strategies, students can turn studying into a more enjoyable and rewarding experience, ultimately leading to better academic performance and personal growth. Investing time in developing good study habits today will pay off in the long run, making the journey through school and college a successful one.

Education Copyright © by john44. All Rights Reserved.

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Participating in research: What can be learned from a real example?

When: Thursday, November 8th, 2018 Time: 10am PST / 6pm GMT

The webinar will be available on-demand after this date.

Ever wondered what it’s like to volunteer in a scientific study? Donating blood, saliva or even simply answering a questionnaire can go a long way in assisting crucial research, and is a great way to influence the future of healthcare from outside the lab.

For this feature, we spoke to a recent study participant, Dave, who took part in Our Future Health – a UK initiative aiming to recruit millions of people, whose data will inform research into preventative medicine and new treatments. We explore Dave’s experience, the impact it’s had on him, and what researchers can learn from a volunteer’s point of view.

What is Our Future Health?

Before going any further, we’d like to give an overview of what the Our Future Health initiative is. Our Future Health is the UK’s largest ever health research programme, aiming to ‘revolutionise the way we fight disease’. The main way in which the initiative aims to achieve this goal is through finding ways prevent disease, and detect and treat illnesses earlier.

In order to meet this goal, Our Future Health are aiming to recruit 5 million volunteers, who will answer questionnaires about their health and provide blood samples so that their genetic data can be analysed. Recruiting a large and diverse community of volunteers is vital to ensure that the data is representative of the population and can provide the most value for the most people.

How to find a volunteering opportunity

Volunteering in a study such as this is one of the best ways that the general public can work to improve healthcare and scientific research. But getting involved in a large-scale study, as one of millions of participants, can be daunting for some. We recently spoke to Dave, who told us about his experience participating in the Our Future Health initiative, and his views on volunteering for further studies.

The first question many of you may have: how do I even find initiatives like this? We asked Dave how he came across the study, as a member of the public who is typically not involved in science.

“I learned about it via a letter that I got. I checked it out and it said ‘please join Our Future Health.  You’ll be a big help. We’re doing big things, big science.’ So, of course I thought it was a good idea.”

Our Future Health are sending letters to various cities and towns, detailing the role you can play in the future of healthcare. But many studies don’t reach the general public in such a targeted way – Dave himself admits he likely wouldn’t have taken part if the information hadn’t been sent straight to his front door. This begs the question, how can researchers ensure that volunteering opportunities are made public, so that a more diverse range of people can participate? This is something that is especially tricky if a project doesn’t have a large budget, but this has clearly been effective for Our Future Health.

What is it like to participate?

The next question you may be asking about participating in a study like this is, ‘what is it actually like?’ Whilst we can’t make a case for every study – some will ask for blood, salvia or simple questionnaire answers, while others will have more serious or time-consuming requests – Our Future Health asks participants to complete a health questionnaire and provide a small blood sample at a local clinic.

“Booking the appointment was no problem,” said Dave. “It was just in the local Boots. I actually hate needles and felt really faint, but the staff were really understanding and helpful. The questionnaire was also fairly standard and didn’t take too long.”

Why take part in a scientific study?

It may seem obvious to researchers and those working in the field, but many members of the public may not know why they should participate in research. Having a large and diverse cohort is vital in genomics research, in particular, but without a background in science, many may not appreciate this. As someone without a science background, Dave stated that he understood that he was taking part in medical research, but ultimately wasn’t sure what the project would entail. That said, he told us that he was glad he took part, and that the only thing that was needed to convince him was the idea that it would help a lot of people.

“It’s something that I’m interested in, but don’t actively seek out, but I do like the idea of helping people.”

This highlights an important part of the scientific process that is often overlooked – altruism. Without volunteers coming forward with a desire to help, research would stall. Therefore, many studies rely on their volunteers having a degree of selflessness, as most studies don’t have the budget to reimburse participants. However, Our Future Health are now offering a £10 high street voucher (that can also be donated to charity) for completing the process, and volunteers may be given an update on things like their cholesterol levels after donating their sample.

“Honestly, that was a big part of my motivation. I hadn’t had a health check in so long, so I wanted to know my blood pressure and cholesterol levels!”

What can researchers learn from this?

Recruiting the public for studies, particularly large-scale projects, can be difficult, but is necessary for ensuring the quality and diversity of the data. The main messages that we took away from Dave’s interview were that often the public are not seeking studies out, and some individuals may need a nudge in the form of some kind of targeted advertisement (like the letters sent by Our Future Health) in order to become interested. Clear messaging around the purpose and aims of the research, including how sensitive data is handled, in all materials is also necessary, as well as ensuring a smooth volunteering process.

Is it necessary to entice volunteers with some kind of reimbursement? Dave said no. Although he valued the information he received after his appointment regarding cholesterol levels, the idea of making a difference was enough to him, and he would happily volunteer in future studies, too. He says, “it was easy. It only took 20 minutes out of my day. So, I’d like the idea of doing something like it again.”

Looking to the future

Large-scale projects like Our Future Health are different to the typical research that most scientists are carrying out, but the principles around recruiting volunteers are similar across all fields and projects of all sizes.

Our Future Health recently updated their website to say that over 1.5 million individuals have now participated in the initiative – a significant number in comparison to the widely used UK Biobank, which has been integral in genomics research over the years, yet only follows the health of 500,000 patients. Our Future Health aim to more than triple the current number of volunteers, making it one of the largest projects of its kind. To do so, they require an effective recruitment method, and they’re clearly doing something right.

So, what’s next? Dave says that he’ll be watching for results coming from the data, despite having had little interest in the topic before taking part, and will watch out for future opportunities. This highlights the benefits of involving the public in research in some capacity, with the knock-on effect being an enhanced understanding and interest in the subject.

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Gene variants foretell the biology of future breast cancers in Stanford Medicine study

In a finding that vastly expands the understanding of tumor evolution, researchers discover genetic biomarkers that can predict the breast cancer subtype a patient is likely to develop.

May 30, 2024 - By Krista Conger

Stanford Medicine researchers found that inherited gene sequences can predict what type of breast cancer a patient is likely to develop, along with how aggressive that cancer may be.   Emily Moskal

A Stanford Medicine study of thousands of breast cancers has found that the gene sequences we inherit at conception are powerful predictors of the breast cancer type we might develop decades later and how deadly it might be.

The study challenges the dogma that most cancers arise as the result of random mutations that accumulate during our lifetimes. Instead, it points to the active involvement of gene sequences we inherit from our parents — what’s known as your germline genome — in determining whether cells bearing potential cancer-causing mutations are recognized and eliminated by the immune system or skitter under the radar to become nascent cancers. 

“Apart from a few highly penetrant genes that confer significant cancer risk, the role of hereditary factors remains poorly understood, and most malignancies are assumed to result from random errors during cell division or bad luck,” said Christina Curtis , PhD, the RZ Cao Professor of Medicine and a professor of genetics and of biomedical data science. “This would imply that tumor initiation is random, but that is not what we observe. Rather, we find that the path to tumor development is constrained by hereditary factors and immunity. This new result unearths a new class of biomarkers to forecast tumor progression and an entirely new way of understanding breast cancer origins.”

Curtis is the senior author of the study, which will be published May 31 in Science . Postdoctoral scholar Kathleen Houlahan , PhD, is the lead author of the research.

“Back in 2015, we had posited that some tumors are ‘born to be bad’ — meaning that their malignant and even metastatic potential is determined early in the disease course,” Curtis said. “We and others have since corroborated this finding across multiple tumors, but these findings cast a whole new light on just how early this happens.”

A new take on cancer’s origin

The study, which gives a nuanced and powerful new understanding of the interplay between newly arisen cancer cells and the immune system, is likely to help researchers and clinicians better predict and combat breast tumors.

Currently, only a few high-profile cancer-associated mutations in genes are regularly used to predict cancers, but these account for a small minority of cases. Those include BRCA1 and BRCA2, which occur in about one of every 500 women and confer an increased risk of breast or ovarian cancer, and rarer mutations in a gene called TP53 that causes a disease called Li Fraumeni syndrome, which predisposes to childhood and adult-onset tumors.

Christina Curtis

The findings suggest there are tens or hundreds of additional gene variants — identifiable in healthy people — that through interactions with the immune system pull the strings that determine why some people remain cancer-free throughout their lives.

“Our findings not only explain which subtype of breast cancer an individual is likely to develop,” Houlahan said, “but they also hint at how aggressive and prone to metastasizing that subtype will be. Beyond that, we speculate that these inherited variants may influence a person’s risk of developing breast cancer. However, future studies will be needed to examine this.”  

The genes we inherit from our parents are known as our germline genome. They’re mirrors of our parents’ genetic makeup, and they can vary among people in small ways that give some of us blue eyes, brown hair or type O blood. Some inherited genes include mutations that confer increased cancer risk from the get-go, such as BRCA1, BRCA2 and TP53.

In contrast, most cancer-associated genes are part of what’s known as our somatic genome. As we live our lives, our cells divide and die in the tens of millions. Each time the DNA in a cell is copied, mistakes happen and mutations can accumulate. DNA in tumors is often compared with the germline genomes in blood or normal tissues in an individual to pinpoint which changes likely led to the cell’s cancerous transformation.

Classifying breast cancers

In 2012, Curtis began a deep dive — assisted by machine learning — into the types of somatic mutations that occur in thousands of breast cancers. She was eventually able to categorize the disease into 11 subtypes with varying prognoses and risk of recurrence, finding that four of the 11 groups were significantly more likely to recur even 10 or 20 years after diagnosis — critical information for clinicians making treatment decisions and discussing long-term prognoses with their patients.

Prior studies had shown that people with inherited BRCA1 mutations tend to develop a subtype of breast cancer known as triple negative breast cancer. This correlation implies some behind-the-scenes shenanigans by the germline genome that affects what subtype of breast cancer someone might develop.

“We wanted to understand how inherited DNA might sculpt how a tumor evolves,” Houlahan said. To do so, they took a close look at the immune system.

It’s a quirk of biology that even healthy cells routinely decorate their outer membranes with small chunks of the proteins they have bobbing in their cytoplasm — an outward display that reflects their inner style.

Kathleen Houlahan

The foundations for this display are what’s known as HLA proteins, and they are highly variable among individuals. Like fashion police, immune cells called T cells prowl the body looking for any suspicious or overly flashy bling (called epitopes) that might signal something is amiss inside the cell. A cell infected with a virus will display bits of viral proteins; a sick or cancerous cell will adorn itself with abnormal proteins. These faux pas trigger the T cells to destroy the offenders.

Houlahan and Curtis decided to focus on oncogenes, normal genes that, when mutated, can free a cell from regulatory pathways meant to keep it on the straight and narrow. Often, these mutations take the form of multiple copies of the normal gene, arranged nose to tail along the DNA — the result of a kind of genomic stutter called amplification. Amplifications in specific oncogenes drive different cancer pathways and were used to differentiate one breast cancer subtype from another in Curtis’ original studies.

The importance of bling

The researchers wondered whether highly recognizable epitopes would be more likely to attract T cells’ attention than other, more modest displays (think golf-ball-sized, dangly turquoise earrings versus a simple silver stud). If so, a cell that had inherited a flashy version of an oncogene might be less able to pull off its amplification without alerting the immune system than a cell with a more modest version of the same gene. (One pair of overly gaudy turquoise earrings can be excused; five pairs might cause a patrolling fashionista T cell to switch from tutting to terminating.)

The researchers studied nearly 6,000 breast tumors spanning various stages of disease to learn whether the subtype of each tumor correlated with the patients’ germline oncogene sequences. They found that people who had inherited an oncogene with a high germline epitope burden (read: lots of bling) — and an HLA type that can display that epitope prominently — were significantly less likely to develop breast cancer subtypes in which that oncogene is amplified.

There was a surprise, though. The researchers found that cancers with a large germline epitope burden that manage to escape the roving immune cells early in their development tended to be more aggressive and have a poorer prognosis than their more subdued peers.

“At the early, pre-invasive stage, a high germline epitope burden is protective against cancer,” Houlahan said. “But once it’s been forced to wrestle with the immune system and come up with mechanisms to overcome it, tumors with high germline epitope burden are more aggressive and prone to metastasis. The pattern flips during tumor progression.”

“Basically, there is a tug of war between tumor and immune cells,” Curtis said. “In the preinvasive setting, the nascent tumor may initially be more susceptible to immune surveillance and destruction. Indeed, many tumors are likely eliminated in this manner and go unnoticed. However, the immune system does not always win. Some tumor cells may not be eliminated and those that persist develop ways to evade immune recognition and destruction. Our findings shed light on this opaque process and may inform the optimal timing of therapeutic intervention, as well as how to make an immunologically cold tumor become hot, rendering it more sensitive to therapy.”

The researchers envision a future when the germline genome is used to further stratify the 11 breast cancer subtypes identified by Curtis to guide treatment decisions and improve prognoses and monitoring for recurrence. The study’s findings may also give additional clues in the hunt for personalized cancer immunotherapies and may enable clinicians to one day predict a healthy person’s risk of developing an invasive breast cancer from a simple blood sample.

“We started with a bold hypothesis,” Curtis said. “The field had not thought about tumor origins and evolution in this way. We’re examining other cancers through this new lens of hereditary and acquired factors and tumor-immune co-evolution.”

The study was funded by the National Institutes of Health (grants DP1-CA238296 and U54CA261719), the Canadian Institutes of Health Research and the Chan Zuckerberg Biohub.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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Psychiatry’s new frontiers

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• Open access
• Published: 17 October 2023

The impact of founder personalities on startup success

• Paul X. McCarthy 1 , 2 ,
• Xian Gong 3 ,
• Fabian Braesemann 4 , 5 ,
• Fabian Stephany 4 , 5 ,
• Marian-Andrei Rizoiu 3 &
• Margaret L. Kern 6  

Scientific Reports volume  13 , Article number:  17200 ( 2023 ) Cite this article

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An Author Correction to this article was published on 07 May 2024

This article has been updated

Startup companies solve many of today’s most challenging problems, such as the decarbonisation of the economy or the development of novel life-saving vaccines. Startups are a vital source of innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors, as well as the team’s size. The effects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We find that the Big Five personality traits of startup founders across 30 dimensions significantly differ from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). We do not find one ’Founder-type’ personality; instead, six different personality types appear. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which show an increased likelihood of success. The findings emphasise the role of the diversity of personality types as a novel dimension of team diversity that influences performance and success.

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The nexus of top executives’ attributes, firm strategies, and outcomes: Large firms versus SMEs

Introduction.

The success of startups is vital to economic growth and renewal, with a small number of young, high-growth firms creating a disproportionately large share of all new jobs 1 , 2 . Startups create jobs and drive economic growth, and they are also an essential vehicle for solving some of society’s most pressing challenges.

As a poignant example, six centuries ago, the German city of Mainz was abuzz as the birthplace of the world’s first moveable-type press created by Johannes Gutenberg. However, in the early part of this century, it faced several economic challenges, including rising unemployment and a significant and growing municipal debt. Then in 2008, two Turkish immigrants formed the company BioNTech in Mainz with another university research colleague. Together they pioneered new mRNA-based technologies. In 2020, BioNTech partnered with US pharmaceutical giant Pfizer to create one of only a handful of vaccines worldwide for Covid-19, saving an estimated six million lives 3 . The economic benefit to Europe and, in particular, the German city where the vaccine was developed has been significant, with windfall tax receipts to the government clearing Mainz’s €1.3bn debt and enabling tax rates to be reduced, attracting other businesses to the region as well as inspiring a whole new generation of startups 4 .

While stories such as the success of BioNTech are often retold and remembered, their success is the exception rather than the rule. The overwhelming majority of startups ultimately fail. One study of 775 startups in Canada that successfully attracted external investment found only 35% were still operating seven years later 5 .

But what determines the success of these ‘lucky few’? When assessing the success factors of startups, especially in the early-stage unproven phase, venture capitalists and other investors offer valuable insights. Three different schools of thought characterise their perspectives: first, supply-side or product investors : those who prioritise investing in firms they consider to have novel and superior products and services, investing in companies with intellectual property such as patents and trademarks. Secondly, demand-side or market-based investors : those who prioritise investing in areas of highest market interest, such as in hot areas of technology like quantum computing or recurrent or emerging large-scale social and economic challenges such as the decarbonisation of the economy. Thirdly, talent investors : those who prioritise the foundation team above the startup’s initial products or what industry or problem it is looking to address.

Investors who adopt the third perspective and prioritise talent often recognise that a good team can overcome many challenges in the lead-up to product-market fit. And while the initial products of a startup may or may not work a successful and well-functioning team has the potential to pivot to new markets and new products, even if the initial ones prove untenable. Not surprisingly, an industry ‘autopsy’ into 101 tech startup failures found 23% were due to not having the right team—the number three cause of failure ahead of running out of cash or not having a product that meets the market need 6 .

Accordingly, early entrepreneurship research was focused on the personality of founders, but the focus shifted away in the mid-1980s onwards towards more environmental factors such as venture capital financing 7 , 8 , 9 , networks 10 , location 11 and due to a range of issues and challenges identified with the early entrepreneurship personality research 12 , 13 . At the turn of the 21st century, some scholars began exploring ways to combine context and personality and reconcile entrepreneurs’ individual traits with features of their environment. In her influential work ’The Sociology of Entrepreneurship’, Patricia H. Thornton 14 discusses two perspectives on entrepreneurship: the supply-side perspective (personality theory) and the demand-side perspective (environmental approach). The supply-side perspective focuses on the individual traits of entrepreneurs. In contrast, the demand-side perspective focuses on the context in which entrepreneurship occurs, with factors such as finance, industry and geography each playing their part. In the past two decades, there has been a revival of interest and research that explores how entrepreneurs’ personality relates to the success of their ventures. This new and growing body of research includes several reviews and meta-studies, which show that personality traits play an important role in both career success and entrepreneurship 15 , 16 , 17 , 18 , 19 , that there is heterogeneity in definitions and samples used in research on entrepreneurship 16 , 18 , and that founder personality plays an important role in overall startup outcomes 17 , 19 .

Motivated by the pivotal role of the personality of founders on startup success outlined in these recent contributions, we investigate two main research questions:

Which personality features characterise founders?

Do their personalities, particularly the diversity of personality types in founder teams, play a role in startup success?

We aim to understand whether certain founder personalities and their combinations relate to startup success, defined as whether their company has been acquired, acquired another company or listed on a public stock exchange. For the quantitative analysis, we draw on a previously published methodology 20 , which matches people to their ‘ideal’ jobs based on social media-inferred personality traits.

We find that personality traits matter for startup success. In addition to firm-level factors of location, industry and company age, we show that founders’ specific Big Five personality traits, such as adventurousness and openness, are significantly more widespread among successful startups. As we find that companies with multi-founder teams are more likely to succeed, we cluster founders in six different and distinct personality groups to underline the relevance of the complementarity in personality traits among founder teams. Startups with diverse and specific combinations of founder types (e. g., an adventurous ‘Leader’, a conscientious ‘Accomplisher’, and an extroverted ‘Developer’) have significantly higher odds of success.

We organise the rest of this paper as follows. In the Section " Results ", we introduce the data used and the methods applied to relate founders’ psychological traits with their startups’ success. We introduce the natural language processing method to derive individual and team personality characteristics and the clustering technique to identify personality groups. Then, we present the result for multi-variate regression analysis that allows us to relate firm success with external and personality features. Subsequently, the Section " Discussion " mentions limitations and opportunities for future research in this domain. In the Section " Methods ", we describe the data, the variables in use, and the clustering in greater detail. Robustness checks and additional analyses can be found in the Supplementary Information.

Our analysis relies on two datasets. We infer individual personality facets via a previously published methodology 20 from Twitter user profiles. Here, we restrict our analysis to founders with a Crunchbase profile. Crunchbase is the world’s largest directory on startups. It provides information about more than one million companies, primarily focused on funding and investors. A company’s public Crunchbase profile can be considered a digital business card of an early-stage venture. As such, the founding teams tend to provide information about themselves, including their educational background or a link to their Twitter account.

We infer the personality profiles of the founding teams of early-stage ventures from their publicly available Twitter profiles, using the methodology described by Kern et al. 20 . Then, we correlate this information to data from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures. The final dataset used in the success prediction model contains n = 21,187 startup companies (for more details on the data see the Methods section and SI section  A.5 ).

Revisions of Crunchbase as a data source for investigations on a firm and industry level confirm the platform to be a useful and valuable source of data for startups research, as comparisons with other sources at micro-level, e.g., VentureXpert or PwC, also suggest that the platform’s coverage is very comprehensive, especially for start-ups located in the United States 21 . Moreover, aggregate statistics on funding rounds by country and year are quite similar to those produced with other established sources, going to validate the use of Crunchbase as a reliable source in terms of coverage of funded ventures. For instance, Crunchbase covers about the same number of investment rounds in the analogous sectors as collected by the National Venture Capital Association 22 . However, we acknowledge that the data source might suffer from registration latency (a certain delay between the foundation of the company and its actual registration on Crunchbase) and success bias in company status (the likeliness that failed companies decide to delete their profile from the database).

The definition of startup success

The success of startups is uncertain, dependent on many factors and can be measured in various ways. Due to the likelihood of failure in startups, some large-scale studies have looked at which features predict startup survival rates 23 , and others focus on fundraising from external investors at various stages 24 . Success for startups can be measured in multiple ways, such as the amount of external investment attracted, the number of new products shipped or the annual growth in revenue. But sometimes external investments are misguided, revenue growth can be short-lived, and new products may fail to find traction.

Success in a startup is typically staged and can appear in different forms and times. For example, a startup may be seen to be successful when it finds a clear solution to a widely recognised problem, such as developing a successful vaccine. On the other hand, it could be achieving some measure of commercial success, such as rapidly accelerating sales or becoming profitable or at least cash positive. Or it could be reaching an exit for foundation investors via a trade sale, acquisition or listing of its shares for sale on a public stock exchange via an Initial Public Offering (IPO).

For our study, we focused on the startup’s extrinsic success rather than the founders’ intrinsic success per se, as its more visible, objective and measurable. A frequently considered measure of success is the attraction of external investment by venture capitalists 25 . However, this is not in and of itself a good measure of clear, incontrovertible success, particularly for early-stage ventures. This is because it reflects investors’ expectations of a startup’s success potential rather than actual business success. Similarly, we considered other measures like revenue growth 26 , liquidity events 27 , 28 , 29 , profitability 30 and social impact 31 , all of which have benefits as they capture incremental success, but each also comes with operational measurement challenges.

Therefore, we apply the success definition initially introduced by Bonaventura et al. 32 , namely that a startup is acquired, acquires another company or has an initial public offering (IPO). We consider any of these major capital liquidation events as a clear threshold signal that the company has matured from an early-stage venture to becoming or is on its way to becoming a mature company with clear and often significant business growth prospects. Together these three major liquidity events capture the primary forms of exit for external investors (an acquisition or trade sale and an IPO). For companies with a longer autonomous growth runway, acquiring another company marks a similar milestone of scale, maturity and capability.

Using multifactor analysis and a binary classification prediction model of startup success, we looked at many variables together and their relative influence on the probability of the success of startups. We looked at seven categories of factors through three lenses of firm-level factors: (1) location, (2) industry, (3) age of the startup; founder-level factors: (4) number of founders, (5) gender of founders, (6) personality characteristics of founders and; lastly team-level factors: (7) founder-team personality combinations. The model performance and relative impacts on the probability of startup success of each of these categories of founders are illustrated in more detail in section  A.6 of the Supplementary Information (in particular Extended Data Fig.  19 and Extended Data Fig.  20 ). In total, we considered over three hundred variables (n = 323) and their relative significant associations with success.

The personality of founders

Besides product-market, industry, and firm-level factors (see SI section  A.1 ), research suggests that the personalities of founders play a crucial role in startup success 19 . Therefore, we examine the personality characteristics of individual startup founders and teams of founders in relationship to their firm’s success by applying the success definition used by Bonaventura et al. 32 .

Employing established methods 33 , 34 , 35 , we inferred the personality traits across 30 dimensions (Big Five facets) of a large global sample of startup founders. The startup founders cohort was created from a subset of founders from the global startup industry directory Crunchbase, who are also active on the social media platform Twitter.

To measure the personality of the founders, we used the Big Five, a popular model of personality which includes five core traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional stability. Each of these traits can be further broken down into thirty distinct facets. Studies have found that the Big Five predict meaningful life outcomes, such as physical and mental health, longevity, social relationships, health-related behaviours, antisocial behaviour, and social contribution, at levels on par with intelligence and socioeconomic status 36 Using machine learning to infer personality traits by analysing the use of language and activity on social media has been shown to be more accurate than predictions of coworkers, friends and family and similar in accuracy to the judgement of spouses 37 . Further, as other research has shown, we assume that personality traits remain stable in adulthood even through significant life events 38 , 39 , 40 . Personality traits have been shown to emerge continuously from those already evident in adolescence 41 and are not significantly influenced by external life events such as becoming divorced or unemployed 42 . This suggests that the direction of any measurable effect goes from founder personalities to startup success and not vice versa.

As a first investigation to what extent personality traits might relate to entrepreneurship, we use the personality characteristics of individuals to predict whether they were an entrepreneur or an employee. We trained and tested a machine-learning random forest classifier to distinguish and classify entrepreneurs from employees and vice-versa using inferred personality vectors alone. As a result, we found we could correctly predict entrepreneurs with 77% accuracy and employees with 88% accuracy (Fig.  1 A). Thus, based on personality information alone, we correctly predict all unseen new samples with 82.5% accuracy (See SI section  A.2 for more details on this analysis, the classification modelling and prediction accuracy).

We explored in greater detail which personality features are most prominent among entrepreneurs. We found that the subdomain or facet of Adventurousness within the Big Five Domain of Openness was significant and had the largest effect size. The facet of Modesty within the Big Five Domain of Agreeableness and Activity Level within the Big Five Domain of Extraversion was the subsequent most considerable effect (Fig.  1 B). Adventurousness in the Big Five framework is defined as the preference for variety, novelty and starting new things—which are consistent with the role of a startup founder whose role, especially in the early life of the company, is to explore things that do not scale easily 43 and is about developing and testing new products, services and business models with the market.

Once we derived and tested the Big Five personality features for each entrepreneur in our data set, we examined whether there is evidence indicating that startup founders naturally cluster according to their personality features using a Hopkins test (see Extended Data Figure  6 ). We discovered clear clustering tendencies in the data compared with other renowned reference data sets known to have clusters. Then, once we established the founder data clusters, we used agglomerative hierarchical clustering. This ‘bottom-up’ clustering technique initially treats each observation as an individual cluster. Then it merges them to create a hierarchy of possible cluster schemes with differing numbers of groups (See Extended Data Fig.  7 ). And lastly, we identified the optimum number of clusters based on the outcome of four different clustering performance measurements: Davies-Bouldin Index, Silhouette coefficients, Calinski-Harabas Index and Dunn Index (see Extended Data Figure  8 ). We find that the optimum number of clusters of startup founders based on their personality features is six (labelled #0 through to #5), as shown in Fig.  1 C.

To better understand the context of different founder types, we positioned each of the six types of founders within an occupation-personality matrix established from previous research 44 . This research showed that ‘each job has its own personality’ using a substantial sample of employees across various jobs. Utilising the methodology employed in this study, we assigned labels to the cluster names #0 to #5, which correspond to the identified occupation tribes that best describe the personality facets represented by the clusters (see Extended Data Fig.  9 for an overview of these tribes, as identified by McCarthy et al. 44 ).

Utilising this approach, we identify three ’purebred’ clusters: #0, #2 and #5, whose members are dominated by a single tribe (larger than 60% of all individuals in each cluster are characterised by one tribe). Thus, these clusters represent and share personality attributes of these previously identified occupation-personality tribes 44 , which have the following known distinctive personality attributes (see also Table  1 ):

Accomplishers (#0) —Organised & outgoing. confident, down-to-earth, content, accommodating, mild-tempered & self-assured.

Leaders (#2) —Adventurous, persistent, dispassionate, assertive, self-controlled, calm under pressure, philosophical, excitement-seeking & confident.

Fighters (#5) —Spontaneous and impulsive, tough, sceptical, and uncompromising.

We labelled these clusters with the tribe names, acknowledging that labels are somewhat arbitrary, based on our best interpretation of the data (See SI section  A.3 for more details).

For the remaining three clusters #1, #3 and #4, we can see they are ‘hybrids’, meaning that the founders within them come from a mix of different tribes, with no one tribe representing more than 50% of the members of that cluster. However, the tribes with the largest share were noted as #1 Experts/Engineers, #3 Fighters, and #4 Operators.

To label these three hybrid clusters, we examined the closest occupations to the median personality features of each cluster. We selected a name that reflected the common themes of these occupations, namely:

Experts/Engineers (#1) as the closest roles included Materials Engineers and Chemical Engineers. This is consistent with this cluster’s personality footprint, which is highest in openness in the facets of imagination and intellect.

Developers (#3) as the closest roles include Application Developers and related technology roles such as Business Systems Analysts and Product Managers.

Operators (#4) as the closest roles include service, maintenance and operations functions, including Bicycle Mechanic, Mechanic and Service Manager. This is also consistent with one of the key personality traits of high conscientiousness in the facet of orderliness and high agreeableness in the facet of humility for founders in this cluster.

Founder-Level Factors of Startup Success. ( A ), Successful entrepreneurs differ from successful employees. They can be accurately distinguished using a classifier with personality information alone. ( B ), Successful entrepreneurs have different Big Five facet distributions, especially on adventurousness, modesty and activity level. ( C ), Founders come in six different types: Fighters, Operators, Accomplishers, Leaders, Engineers and Developers (FOALED) ( D ), Each founder Personality-Type has its distinct facet.

Together, these six different types of startup founders (Fig.  1 C) represent a framework we call the FOALED model of founder types—an acronym of Fighters, Operators, Accomplishers, Leaders, Engineers and D evelopers.

Each founder’s personality type has its distinct facet footprint (for more details, see Extended Data Figure  10 in SI section  A.3 ). Also, we observe a central core of correlated features that are high for all types of entrepreneurs, including intellect, adventurousness and activity level (Fig.  1 D).To test the robustness of the clustering of the personality facets, we compare the mean scores of the individual facets per cluster with a 20-fold resampling of the data and find that the clusters are, overall, largely robust against resampling (see Extended Data Figure  11 in SI section  A.3 for more details).

We also find that the clusters accord with the distribution of founders’ roles in their startups. For example, Accomplishers are often Chief Executive Officers, Chief Financial Officers, or Chief Operating Officers, while Fighters tend to be Chief Technical Officers, Chief Product Officers, or Chief Commercial Officers (see Extended Data Fig.  12 in SI section  A.4 for more details).

The ensemble theory of success

While founders’ individual personality traits, such as Adventurousness or Openness, show to be related to their firms’ success, we also hypothesise that the combination, or ensemble, of personality characteristics of a founding team impacts the chances of success. The logic behind this reasoning is complementarity, which is proposed by contemporary research on the functional roles of founder teams. Examples of these clear functional roles have evolved in established industries such as film and television, construction, and advertising 45 . When we subsequently explored the combinations of personality types among founders and their relationship to the probability of startup success, adjusted for a range of other factors in a multi-factorial analysis, we found significantly increased chances of success for mixed foundation teams:

Initially, we find that firms with multiple founders are more likely to succeed, as illustrated in Fig.  2 A, which shows firms with three or more founders are more than twice as likely to succeed than solo-founded startups. This finding is consistent with investors’ advice to founders and previous studies 46 . We also noted that some personality types of founders increase the probability of success more than others, as shown in SI section  A.6 (Extended Data Figures  16 and 17 ). Also, we note that gender differences play out in the distribution of personality facets: successful female founders and successful male founders show facet scores that are more similar to each other than are non-successful female founders to non-successful male founders (see Extended Data Figure  18 ).

The Ensemble Theory of Team-Level Factors of Startup Success. ( A ) Having a larger founder team elevates the chances of success. This can be due to multiple reasons, e.g., a more extensive network or knowledge base but also personality diversity. ( B ) We show that joint personality combinations of founders are significantly related to higher chances of success. This is because it takes more than one founder to cover all beneficial personality traits that ‘breed’ success. ( C ) In our multifactor model, we show that firms with diverse and specific combinations of types of founders have significantly higher odds of success.

Access to more extensive networks and capital could explain the benefits of having more founders. Still, as we find here, it also offers a greater diversity of combined personalities, naturally providing a broader range of maximum traits. So, for example, one founder may be more open and adventurous, and another could be highly agreeable and trustworthy, thus, potentially complementing each other’s particular strengths associated with startup success.

The benefits of larger and more personality-diverse foundation teams can be seen in the apparent differences between successful and unsuccessful firms based on their combined Big Five personality team footprints, as illustrated in Fig.  2 B. Here, maximum values for each Big Five trait of a startup’s co-founders are mapped; stratified by successful and non-successful companies. Founder teams of successful startups tend to score higher on Openness, Conscientiousness, Extraversion, and Agreeableness.

When examining the combinations of founders with different personality types, we find that some ensembles of personalities were significantly correlated with greater chances of startup success—while controlling for other variables in the model—as shown in Fig.  2 C (for more details on the modelling, the predictive performance and the coefficient estimates of the final model, see Extended Data Figures  19 , 20 , and 21 in SI section  A.6 ).

Three combinations of trio-founder companies were more than twice as likely to succeed than other combinations, namely teams with (1) a Leader and two Developers , (2) an Operator and two Developers , and (3) an Expert/Engineer , Leader and Developer . To illustrate the potential mechanisms on how personality traits might influence the success of startups, we provide some examples of well-known, successful startup founders and their characteristic personality traits in Extended Data Figure  22 .

Startups are one of the key mechanisms for brilliant ideas to become solutions to some of the world’s most challenging economic and social problems. Examples include the Google search algorithm, disability technology startup Fingerwork’s touchscreen technology that became the basis of the Apple iPhone, or the Biontech mRNA technology that powered Pfizer’s COVID-19 vaccine.

We have shown that founders’ personalities and the combination of personalities in the founding team of a startup have a material and significant impact on its likelihood of success. We have also shown that successful startup founders’ personality traits are significantly different from those of successful employees—so much so that a simple predictor can be trained to distinguish between employees and entrepreneurs with more than 80% accuracy using personality trait data alone.

Just as occupation-personality maps derived from data can provide career guidance tools, so too can data on successful entrepreneurs’ personality traits help people decide whether becoming a founder may be a good choice for them.

We have learnt through this research that there is not one type of ideal ’entrepreneurial’ personality but six different types. Many successful startups have multiple co-founders with a combination of these different personality types.

To a large extent, founding a startup is a team sport; therefore, diversity and complementarity of personalities matter in the foundation team. It has an outsized impact on the company’s likelihood of success. While all startups are high risk, the risk becomes lower with more founders, particularly if they have distinct personality traits.

Our work demonstrates the benefits of personality diversity among the founding team of startups. Greater awareness of this novel form of diversity may help create more resilient startups capable of more significant innovation and impact.

The data-driven research approach presented here comes with certain methodological limitations. The principal data sources of this study—Crunchbase and Twitter—are extensive and comprehensive, but there are characterised by some known and likely sample biases.

Crunchbase is the principal public chronicle of venture capital funding. So, there is some likely sample bias toward: (1) Startup companies that are funded externally: self-funded or bootstrapped companies are less likely to be represented in Crunchbase; (2) technology companies, as that is Crunchbase’s roots; (3) multi-founder companies; (4) male founders: while the representation of female founders is now double that of the mid-2000s, women still represent less than 25% of the sample; (5) companies that succeed: companies that fail, especially those that fail early, are likely to be less represented in the data.

Samples were also limited to those founders who are active on Twitter, which adds additional selection biases. For example, Twitter users typically are younger, more educated and have a higher median income 47 . Another limitation of our approach is the potentially biased presentation of a person’s digital identity on social media, which is the basis for identifying personality traits. For example, recent research suggests that the language and emotional tone used by entrepreneurs in social media can be affected by events such as business failure 48 , which might complicate the personality trait inference.

In addition to sampling biases within the data, there are also significant historical biases in startup culture. For many aspects of the entrepreneurship ecosystem, women, for example, are at a disadvantage 49 . Male-founded companies have historically dominated most startup ecosystems worldwide, representing the majority of founders and the overwhelming majority of venture capital investors. As a result, startups with women have historically attracted significantly fewer funds 50 , in part due to the male bias among venture investors, although this is now changing, albeit slowly 51 .

The research presented here provides quantitative evidence for the relevance of personality types and the diversity of personalities in startups. At the same time, it brings up other questions on how personality traits are related to other factors associated with success, such as:

Will the recent growing focus on promoting and investing in female founders change the nature, composition and dynamics of startups and their personalities leading to a more diverse personality landscape in startups?

Will the growth of startups outside of the United States change what success looks like to investors and hence the role of different personality traits and their association to diverse success metrics?

Many of today’s most renowned entrepreneurs are either Baby Boomers (such as Gates, Branson, Bloomberg) or Generation Xers (such as Benioff, Cannon-Brookes, Musk). However, as we can see, personality is both a predictor and driver of success in entrepreneurship. Will generation-wide differences in personality and outlook affect startups and their success?

Moreover, the findings shown here have natural extensions and applications beyond startups, such as for new projects within large established companies. While not technically startups, many large enterprises and industries such as construction, engineering and the film industry rely on forming new project-based, cross-functional teams that are often new ventures and share many characteristics of startups.

There is also potential for extending this research in other settings in government, NGOs, and within the research community. In scientific research, for example, team diversity in terms of age, ethnicity and gender has been shown to be predictive of impact, and personality diversity may be another critical dimension 52 .

Another extension of the study could investigate the development of the language used by startup founders on social media over time. Such an extension could investigate whether the language (and inferred psychological characteristics) change as the entrepreneurs’ ventures go through major business events such as foundation, funding, or exit.

Overall, this study demonstrates, first, that startup founders have significantly different personalities than employees. Secondly, besides firm-level factors, which are known to influence firm success, we show that a range of founder-level factors, notably the character traits of its founders, significantly impact a startup’s likelihood of success. Lastly, we looked at team-level factors. We discovered in a multifactor analysis that personality-diverse teams have the most considerable impact on the probability of a startup’s success, underlining the importance of personality diversity as a relevant factor of team performance and success.

Data sources

Entrepreneurs dataset.

Data about the founders of startups were collected from Crunchbase (Table  2 ), an open reference platform for business information about private and public companies, primarily early-stage startups. It is one of the largest and most comprehensive data sets of its kind and has been used in over 100 peer-reviewed research articles about economic and managerial research.

Crunchbase contains data on over two million companies - mainly startup companies and the companies who partner with them, acquire them and invest in them, as well as profiles on well over one million individuals active in the entrepreneurial ecosystem worldwide from over 200 countries and spans. Crunchbase started in the technology startup space, and it now covers all sectors, specifically focusing on entrepreneurship, investment and high-growth companies.

While Crunchbase contains data on over one million individuals in the entrepreneurial ecosystem, some are not entrepreneurs or startup founders but play other roles, such as investors, lawyers or executives at companies that acquire startups. To create a subset of only entrepreneurs, we selected a subset of 32,732 who self-identify as founders and co-founders (by job title) and who are also publicly active on the social media platform Twitter. We also removed those who also are venture capitalists to distinguish between investors and founders.

We selected founders active on Twitter to be able to use natural language processing to infer their Big Five personality features using an open-vocabulary approach shown to be accurate in the previous research by analysing users’ unstructured text, such as Twitter posts in our case. For this project, as with previous research 20 , we employed a commercial service, IBM Watson Personality Insight, to infer personality facets. This service provides raw scores and percentile scores of Big Five Domains (Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability) and the corresponding 30 subdomains or facets. In addition, the public content of Twitter posts was collected, and there are 32,732 profiles that each had enough Twitter posts (more than 150 words) to get relatively accurate personality scores (less than 12.7% Average Mean Absolute Error).

The entrepreneurs’ dataset is analysed in combination with other data about the companies they founded to explore questions about the nature and patterns of personality traits of entrepreneurs and the relationships between these patterns and company success.

For the multifactor analysis, we further filtered the data in several preparatory steps for the success prediction modelling (for more details, see SI section  A.5 ). In particular, we removed data points with missing values (Extended Data Fig.  13 ) and kept only companies in the data that were founded from 1990 onward to ensure consistency with previous research 32 (see Extended Data Fig.  14 ). After cleaning, filtering and pre-processing the data, we ended up with data from 25,214 founders who founded 21,187 startup companies to be used in the multifactor analysis. Of those, 3442 startups in the data were successful, 2362 in the first seven years after they were founded (see Extended Data Figure  15 for more details).

Entrepreneurs and employees dataset

To investigate whether startup founders show personality traits that are similar or different from the population at large (i. e. the entrepreneurs vs employees sub-analysis shown in Fig.  1 A and B), we filtered the entrepreneurs’ data further: we reduced the sample to those founders of companies, which attracted more than US$100k in investment to create a reference set of successful entrepreneurs (n \(=\) 4400).

To create a control group of employees who are not also entrepreneurs or very unlikely to be of have been entrepreneurs, we leveraged the fact that while some occupational titles like CEO, CTO and Public Speaker are commonly shared by founders and co-founders, some others such as Cashier , Zoologist and Detective very rarely co-occur seem to be founders or co-founders. To illustrate, many company founders also adopt regular occupation titles such as CEO or CTO. Many founders will be Founder and CEO or Co-founder and CTO. While founders are often CEOs or CTOs, the reverse is not necessarily true, as many CEOs are professional executives that were not involved in the establishment or ownership of the firm.

Using data from LinkedIn, we created an Entrepreneurial Occupation Index (EOI) based on the ratio of entrepreneurs for each of the 624 occupations used in a previous study of occupation-personality fit 44 . It was calculated based on the percentage of all people working in the occupation from LinkedIn compared to those who shared the title Founder or Co-founder (See SI section  A.2 for more details). A reference set of employees (n=6685) was then selected across the 112 different occupations with the lowest propensity for entrepreneurship (less than 0.5% EOI) from a large corpus of Twitter users with known occupations, which is also drawn from the previous occupational-personality fit study 44 .

These two data sets were used to test whether it may be possible to distinguish successful entrepreneurs from successful employees based on the different patterns of personality traits alone.

Hierarchical clustering

We applied several clustering techniques and tests to the personality vectors of the entrepreneurs’ data set to determine if there are natural clusters and, if so, how many are the optimum number.

Firstly, to determine if there is a natural typology to founder personalities, we applied the Hopkins statistic—a statistical test we used to answer whether the entrepreneurs’ dataset contains inherent clusters. It measures the clustering tendency based on the ratio of the sum of distances of real points within a sample of the entrepreneurs’ dataset to their nearest neighbours and the sum of distances of randomly selected artificial points from a simulated uniform distribution to their nearest neighbours in the real entrepreneurs’ dataset. The ratio measures the difference between the entrepreneurs’ data distribution and the simulated uniform distribution, which tests the randomness of the data. The range of Hopkins statistics is from 0 to 1. The scores are close to 0, 0.5 and 1, respectively, indicating whether the dataset is uniformly distributed, randomly distributed or highly clustered.

To cluster the founders by personality facets, we used Agglomerative Hierarchical Clustering (AHC)—a bottom-up approach that treats an individual data point as a singleton cluster and then iteratively merges pairs of clusters until all data points are included in the single big collection. Ward’s linkage method is used to choose the pair of groups for minimising the increase in the within-cluster variance after combining. AHC was widely applied to clustering analysis since a tree hierarchy output is more informative and interpretable than K-means. Dendrograms were used to visualise the hierarchy to provide the perspective of the optimal number of clusters. The heights of the dendrogram represent the distance between groups, with lower heights representing more similar groups of observations. A horizontal line through the dendrogram was drawn to distinguish the number of significantly different clusters with higher heights. However, as it is not possible to determine the optimum number of clusters from the dendrogram, we applied other clustering performance metrics to analyse the optimal number of groups.

A range of Clustering performance metrics were used to help determine the optimal number of clusters in the dataset after an apparent clustering tendency was confirmed. The following metrics were implemented to evaluate the differences between within-cluster and between-cluster distances comprehensively: Dunn Index, Calinski-Harabasz Index, Davies-Bouldin Index and Silhouette Index. The Dunn Index measures the ratio of the minimum inter-cluster separation and the maximum intra-cluster diameter. At the same time, the Calinski-Harabasz Index improves the measurement of the Dunn Index by calculating the ratio of the average sum of squared dispersion of inter-cluster and intra-cluster. The Davies-Bouldin Index simplifies the process by treating each cluster individually. It compares the sum of the average distance among intra-cluster data points to the cluster centre of two separate groups with the distance between their centre points. Finally, the Silhouette Index is the overall average of the silhouette coefficients for each sample. The coefficient measures the similarity of the data point to its cluster compared with the other groups. Higher scores of the Dunn, Calinski-Harabasz and Silhouette Index and a lower score of the Davies-Bouldin Index indicate better clustering configuration.

Classification modelling

Classification algorithms.

To obtain a comprehensive and robust conclusion in the analysis predicting whether a given set of personality traits corresponds to an entrepreneur or an employee, we explored the following classifiers: Naïve Bayes, Elastic Net regularisation, Support Vector Machine, Random Forest, Gradient Boosting and Stacked Ensemble. The Naïve Bayes classifier is a probabilistic algorithm based on Bayes’ theorem with assumptions of independent features and equiprobable classes. Compared with other more complex classifiers, it saves computing time for large datasets and performs better if the assumptions hold. However, in the real world, those assumptions are generally violated. Elastic Net regularisation combines the penalties of Lasso and Ridge to regularise the Logistic classifier. It eliminates the limitation of multicollinearity in the Lasso method and improves the limitation of feature selection in the Ridge method. Even though Elastic Net is as simple as the Naïve Bayes classifier, it is more time-consuming. The Support Vector Machine (SVM) aims to find the ideal line or hyperplane to separate successful entrepreneurs and employees in this study. The dividing line can be non-linear based on a non-linear kernel, such as the Radial Basis Function Kernel. Therefore, it performs well on high-dimensional data while the ’right’ kernel selection needs to be tuned. Random Forest (RF) and Gradient Boosting Trees (GBT) are ensembles of decision trees. All trees are trained independently and simultaneously in RF, while a new tree is trained each time and corrected by previously trained trees in GBT. RF is a more robust and straightforward model since it does not have many hyperparameters to tune. GBT optimises the objective function and learns a more accurate model since there is a successive learning and correction process. Stacked Ensemble combines all existing classifiers through a Logistic Regression. Better than bagging with only variance reduction and boosting with only bias reduction, the ensemble leverages the benefit of model diversity with both lower variance and bias. All the above classification algorithms distinguish successful entrepreneurs and employees based on the personality matrix.

Evaluation metrics

A range of evaluation metrics comprehensively explains the performance of a classification prediction. The most straightforward metric is accuracy, which measures the overall portion of correct predictions. It will mislead the performance of an imbalanced dataset. The F1 score is better than accuracy by combining precision and recall and considering the False Negatives and False Positives. Specificity measures the proportion of detecting the true negative rate that correctly identifies employees, while Positive Predictive Value (PPV) calculates the probability of accurately predicting successful entrepreneurs. Area Under the Receiver Operating Characteristic Curve (AUROC) determines the capability of the algorithm to distinguish between successful entrepreneurs and employees. A higher value means the classifier performs better on separating the classes.

Feature importance

To further understand and interpret the classifier, it is critical to identify variables with significant predictive power on the target. Feature importance of tree-based models measures Gini importance scores for all predictors, which evaluate the overall impact of the model after cutting off the specific feature. The measurements consider all interactions among features. However, it does not provide insights into the directions of impacts since the importance only indicates the ability to distinguish different classes.

Statistical analysis

T-test, Cohen’s D and two-sample Kolmogorov-Smirnov test are introduced to explore how the mean values and distributions of personality facets between entrepreneurs and employees differ. The T-test is applied to determine whether the mean of personality facets of two group samples are significantly different from one another or not. The facets with significant differences detected by the hypothesis testing are critical to separate the two groups. Cohen’s d is to measure the effect size of the results of the previous t-test, which is the ratio of the mean difference to the pooled standard deviation. A larger Cohen’s d score indicates that the mean difference is greater than the variability of the whole sample. Moreover, it is interesting to check whether the two groups’ personality facets’ probability distributions are from the same distribution through the two-sample Kolmogorov-Smirnov test. There is no assumption about the distributions, but the test is sensitive to deviations near the centre rather than the tail.

Privacy and ethics

The focus of this research is to provide high-level insights about groups of startups, founders and types of founder teams rather than on specific individuals or companies. While we used unit record data from the publicly available data of company profiles from Crunchbase , we removed all identifiers from the underlying data on individual companies and founders and generated aggregate results, which formed the basis for our analysis and conclusions.

Data availability

A dataset which includes only aggregated statistics about the success of startups and the factors that influence is released as part of this research. Underlying data for all figures and the code to reproduce them are available on GitHub: https://github.com/Braesemann/FounderPersonalities . Please contact Fabian Braesemann ( [email protected] ) in case you have any further questions.

Change history

07 may 2024.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-024-61082-7

Henrekson, M. & Johansson, D. Gazelles as job creators: A survey and interpretation of the evidence. Small Bus. Econ. 35 , 227–244 (2010).

Article   Google Scholar  

Davila, A., Foster, G., He, X. & Shimizu, C. The rise and fall of startups: Creation and destruction of revenue and jobs by young companies. Aust. J. Manag. 40 , 6–35 (2015).

Which vaccine saved the most lives in 2021?: Covid-19. The Economist (Online) (2022). noteName - AstraZeneca; Pfizer Inc; BioNTech SE; Copyright - Copyright The Economist Newspaper NA, Inc. Jul 14, 2022; Last updated - 2022-11-29.

Oltermann, P. Pfizer/biontech tax windfall brings mainz an early christmas present (2021). noteName - Pfizer Inc; BioNTech SE; Copyright - Copyright Guardian News & Media Limited Dec 27, 2021; Last updated - 2021-12-28.

Grant, K. A., Croteau, M. & Aziz, O. The survival rate of startups funded by angel investors. I-INC WHITE PAPER SER.: MAR 2019 , 1–21 (2019).

Google Scholar  

Top 20 reasons start-ups fail - cb insights version (2019). noteCopyright - Copyright Newstex Oct 21, 2019; Last updated - 2022-10-25.

Hochberg, Y. V., Ljungqvist, A. & Lu, Y. Whom you know matters: Venture capital networks and investment performance. J. Financ. 62 , 251–301 (2007).

Fracassi, C., Garmaise, M. J., Kogan, S. & Natividad, G. Business microloans for us subprime borrowers. J. Financ. Quantitative Ana. 51 , 55–83 (2016).

Davila, A., Foster, G. & Gupta, M. Venture capital financing and the growth of startup firms. J. Bus. Ventur. 18 , 689–708 (2003).

Nann, S. et al. Comparing the structure of virtual entrepreneur networks with business effectiveness. Proc. Soc. Behav. Sci. 2 , 6483–6496 (2010).

Guzman, J. & Stern, S. Where is silicon valley?. Science 347 , 606–609 (2015).

Article   ADS   CAS   PubMed   Google Scholar  

Aldrich, H. E. & Wiedenmayer, G. From traits to rates: An ecological perspective on organizational foundings. 61–97 (2019).

Gartner, W. B. Who is an entrepreneur? is the wrong question. Am. J. Small Bus. 12 , 11–32 (1988).

Thornton, P. H. The sociology of entrepreneurship. Ann. Rev. Sociol. 25 , 19–46 (1999).

Eikelboom, M. E., Gelderman, C. & Semeijn, J. Sustainable innovation in public procurement: The decisive role of the individual. J. Public Procure. 18 , 190–201 (2018).

Kerr, S. P. et al. Personality traits of entrepreneurs: A review of recent literature. Found. Trends Entrep. 14 , 279–356 (2018).

Hamilton, B. H., Papageorge, N. W. & Pande, N. The right stuff? Personality and entrepreneurship. Quant. Econ. 10 , 643–691 (2019).

Salmony, F. U. & Kanbach, D. K. Personality trait differences across types of entrepreneurs: A systematic literature review. RMS 16 , 713–749 (2022).

Freiberg, B. & Matz, S. C. Founder personality and entrepreneurial outcomes: A large-scale field study of technology startups. Proc. Natl. Acad. Sci. 120 , e2215829120 (2023).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kern, M. L., McCarthy, P. X., Chakrabarty, D. & Rizoiu, M.-A. Social media-predicted personality traits and values can help match people to their ideal jobs. Proc. Natl. Acad. Sci. 116 , 26459–26464 (2019).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Dalle, J.-M., Den Besten, M. & Menon, C. Using crunchbase for economic and managerial research. (2017).

Block, J. & Sandner, P. What is the effect of the financial crisis on venture capital financing? Empirical evidence from us internet start-ups. Ventur. Cap. 11 , 295–309 (2009).

Antretter, T., Blohm, I. & Grichnik, D. Predicting startup survival from digital traces: Towards a procedure for early stage investors (2018).

Dworak, D. Analysis of founder background as a predictor for start-up success in achieving successive fundraising rounds. (2022).

Hsu, D. H. Venture capitalists and cooperative start-up commercialization strategy. Manage. Sci. 52 , 204–219 (2006).

Blank, S. Why the lean start-up changes everything (2018).

Kaplan, S. N. & Lerner, J. It ain’t broke: The past, present, and future of venture capital. J. Appl. Corp. Financ. 22 , 36–47 (2010).

Hallen, B. L. & Eisenhardt, K. M. Catalyzing strategies and efficient tie formation: How entrepreneurial firms obtain investment ties. Acad. Manag. J. 55 , 35–70 (2012).

Gompers, P. A. & Lerner, J. The Venture Capital Cycle (MIT Press, 2004).

Shane, S. & Venkataraman, S. The promise of entrepreneurship as a field of research. Acad. Manag. Rev. 25 , 217–226 (2000).

Zahra, S. A. & Wright, M. Understanding the social role of entrepreneurship. J. Manage. Stud. 53 , 610–629 (2016).

Bonaventura, M. et al. Predicting success in the worldwide start-up network. Sci. Rep. 10 , 1–6 (2020).

Schwartz, H. A. et al. Personality, gender, and age in the language of social media: The open-vocabulary approach. PLoS ONE 8 , e73791 (2013).

Plank, B. & Hovy, D. Personality traits on twitter-or-how to get 1,500 personality tests in a week. In Proceedings of the 6th workshop on computational approaches to subjectivity, sentiment and social media analysis , pp 92–98 (2015).

Arnoux, P.-H. et al. 25 tweets to know you: A new model to predict personality with social media. In booktitleEleventh international AAAI conference on web and social media (2017).

Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A. & Goldberg, L. R. The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspect. Psychol. Sci. 2 , 313–345 (2007).

Article   PubMed   PubMed Central   Google Scholar  

Youyou, W., Kosinski, M. & Stillwell, D. Computer-based personality judgments are more accurate than those made by humans. Proc. Natl. Acad. Sci. 112 , 1036–1040 (2015).

Soldz, S. & Vaillant, G. E. The big five personality traits and the life course: A 45-year longitudinal study. J. Res. Pers. 33 , 208–232 (1999).

Damian, R. I., Spengler, M., Sutu, A. & Roberts, B. W. Sixteen going on sixty-six: A longitudinal study of personality stability and change across 50 years. J. Pers. Soc. Psychol. 117 , 674 (2019).

Article   PubMed   Google Scholar  

Rantanen, J., Metsäpelto, R.-L., Feldt, T., Pulkkinen, L. & Kokko, K. Long-term stability in the big five personality traits in adulthood. Scand. J. Psychol. 48 , 511–518 (2007).

Roberts, B. W., Caspi, A. & Moffitt, T. E. The kids are alright: Growth and stability in personality development from adolescence to adulthood. J. Pers. Soc. Psychol. 81 , 670 (2001).

Article   CAS   PubMed   Google Scholar  

Cobb-Clark, D. A. & Schurer, S. The stability of big-five personality traits. Econ. Lett. 115 , 11–15 (2012).

Graham, P. Do Things that Don’t Scale (Paul Graham, 2013).

McCarthy, P. X., Kern, M. L., Gong, X., Parker, M. & Rizoiu, M.-A. Occupation-personality fit is associated with higher employee engagement and happiness. (2022).

Pratt, A. C. Advertising and creativity, a governance approach: A case study of creative agencies in London. Environ. Plan A 38 , 1883–1899 (2006).

Klotz, A. C., Hmieleski, K. M., Bradley, B. H. & Busenitz, L. W. New venture teams: A review of the literature and roadmap for future research. J. Manag. 40 , 226–255 (2014).

Duggan, M., Ellison, N. B., Lampe, C., Lenhart, A. & Madden, M. Demographics of key social networking platforms. Pew Res. Center 9 (2015).

Fisch, C. & Block, J. H. How does entrepreneurial failure change an entrepreneur’s digital identity? Evidence from twitter data. J. Bus. Ventur. 36 , 106015 (2021).

Brush, C., Edelman, L. F., Manolova, T. & Welter, F. A gendered look at entrepreneurship ecosystems. Small Bus. Econ. 53 , 393–408 (2019).

Kanze, D., Huang, L., Conley, M. A. & Higgins, E. T. We ask men to win and women not to lose: Closing the gender gap in startup funding. Acad. Manag. J. 61 , 586–614 (2018).

Fan, J. S. Startup biases. UC Davis Law Review (2022).

AlShebli, B. K., Rahwan, T. & Woon, W. L. The preeminence of ethnic diversity in scientific collaboration. Nat. Commun. 9 , 1–10 (2018).

Article   CAS   Google Scholar  

Żbikowski, K. & Antosiuk, P. A machine learning, bias-free approach for predicting business success using crunchbase data. Inf. Process. Manag. 58 , 102555 (2021).

Corea, F., Bertinetti, G. & Cervellati, E. M. Hacking the venture industry: An early-stage startups investment framework for data-driven investors. Mach. Learn. Appl. 5 , 100062 (2021).

Chapman, G. & Hottenrott, H. Founder personality and start-up subsidies. Founder Personality and Start-up Subsidies (2021).

Antoncic, B., Bratkovicregar, T., Singh, G. & DeNoble, A. F. The big five personality-entrepreneurship relationship: Evidence from slovenia. J. Small Bus. Manage. 53 , 819–841 (2015).

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Acknowledgements

We thank Gary Brewer from BuiltWith ; Leni Mayo from Influx , Rachel Slattery from TeamSlatts and Daniel Petre from AirTree Ventures for their ongoing generosity and insights about startups, founders and venture investments. We also thank Tim Li from Crunchbase for advice and liaison regarding data on startups and Richard Slatter for advice and referrals in Twitter .

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All authors designed research; All authors analysed data and undertook investigation; F.B. and F.S. led multi-factor analysis; P.M., X.G. and M.A.R. led the founder/employee prediction; M.L.K. led personality insights; X.G. collected and tabulated the data; X.G., F.B., and F.S. created figures; X.G. created final art, and all authors wrote the paper.

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Cancer patients often do better with less intensive treatment, research shows

Scaling back treatment for three kinds of cancer can make life easier for patients without compromising outcomes, doctors reported at the world’s largest cancer conference .

It’s part of a long-term trend toward studying whether doing less — less surgery, less chemotherapy or less radiation — can help patients live longer and feel better. The latest studies involved ovarian and esophageal cancer and Hodgkin lymphoma.

Thirty years ago, cancer research was about doing more, not less. In one sobering example, women with advanced breast cancer were pushed to the brink of death with massive doses of chemotherapy and bone marrow transplants. The  approach didn’t work  any better than chemotherapy and patients suffered.

Now, in a quest to optimize cancer care, researchers are asking: “Do we need all that treatment that we have used in the past?”

It’s a question, “that should be asked over and over again,” said Dr. Tatjana Kolevska, medical director for the Kaiser Permanente National Cancer Excellence Program, who was not involved in the new research.

Often, doing less works because of improved drugs.

“The good news is that cancer treatment is not only becoming more effective, it’s becoming easier to tolerate and associated with less short-term and long-term complications,” said Dr. William G. Nelson of Johns Hopkins School of Medicine, who was also not involved in the new research.

Latest news on cancer treatment

• Cancer-fighting antibodies inject chemo directly into tumor cells, upping effectiveness.
• Long-term study shows 'remarkable' treatment helps patients with deadly nonsmoking-related lung cancer.
• FDA approves groundbreaking treatment for advanced melanoma.

Studies demonstrating the trend were discussed over the weekend at an American Society of Clinical Oncology conference in Chicago. Here are the highlights:

Ovarian cancer

French researchers found that it’s safe to avoid removing lymph nodes that appear healthy during surgery for advanced ovarian cancer. The study compared the results for 379 patients — half had their lymph nodes removed and half did not. After nine years, there was no difference in how long the patients lived and those with less-extreme surgery had fewer complications, such as the need for blood transfusions. The research was funded by the National Institute of Cancer in France.

Esophageal cancer

This German study looked at 438 people with a type of cancer of the esophagus that can be treated with surgery. Half received a common treatment plan that included chemotherapy and surgery on the esophagus, the tube that carries food from the throat to the stomach. Half got another approach that includes radiation too. Both techniques are considered standard. Which one patients get can depend on where they get treatment.

After three years, 57% of those who got chemo and surgery were alive, compared to 51% of those who got chemo, surgery and radiation. The German Research Foundation funded the study.

Hodgkin lymphoma

A comparison of two chemotherapy regimens for advanced Hodgkin lymphoma found the less intensive treatment was more effective for the blood cancer and caused fewer side effects.

After four years, the less harsh chemo kept the disease in check in 94% of people, compared to 91% of those who had the more intense treatment. The trial included 1,482 people in nine countries — Germany, Austria, Switzerland, the Netherlands, Denmark, Sweden, Norway, Australia and New Zealand — and was funded by Takeda Oncology, the maker of one of the drugs used in the gentler chemo that was studied.

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Published on 3.6.2024 in Vol 26 (2024)

Electronic Health Literacy Scale-Web3.0 for Older Adults with Noncommunicable Diseases: Validation Study

Authors of this article:

Original Paper

• Wenfei Cai 1 , MEd   ; 
• Wei Liang 1 , PhD   ; 
• Huaxuan Liu 2 , PhD   ; 
• Rundong Zhou 1 , MEd   ; 
• Jie Zhang 1 , PhD   ; 
• Lin Zhou 3 , PhD   ; 
• Ning Su 1 , PhD   ; 
• Hanxiao Zhu 1 , PhD   ; 
• Yide Yang 4 , PhD  

1 School of Physical Education, Shenzhen University, Shenzhen, China

2 School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, China

3 School of Physical Education, Hebei Normal University, Shijiazhuang, China

4 School of Medicine, Hunan Normal University, Changsha, China

Corresponding Author:

Wei Liang, PhD

School of Physical Education

Shenzhen University

3688 Nanhai Road, Nanshan District

Shenzhen, 518060

Phone: 86 15217940540

Email: [email protected]

Background: In the current digital era, eHealth literacy plays an indispensable role in health care and self-management among older adults with noncommunicable diseases (NCDs). Measuring eHealth literacy appropriately and accurately ensures the successful implementation and evaluation of pertinent research and interventions. However, existing eHealth literacy measures focus mainly on individuals’ abilities of accessing and comprehending eHealth information (Web1.0), whereas the capabilities for web-based interaction (Web2.0) and using eHealth information (Web3.0) have not been adequately evaluated.

Objective: This study aimed to examine the reliability, validity, and measurement invariance of the eHealth Literacy Scale-Web3.0 (eHLS-Web3.0) among older adults with NCDs.

Methods: A total of 642 Chinese older adults with NCDs (mean age 65.78, SD 3.91 years; 55.8% female) were recruited in the baseline assessment, of whom 134 (mean age 65.63, SD 3.99 years; 58.2% female) completed the 1-month follow-up assessment. Baseline measures included the Chinese version of the 24-item 3D eHLS-Web3.0, the Chinese version of the 8-item unidimensional eHealth Literacy Scale (eHEALS), and demographic information. Follow-up measures included the 24-item eHLS-Web3.0 and accelerometer-measured physical activity and sedentary behavior. A series of statistical analyses, for example, Cronbach α, composite reliability coefficient (CR), confirmatory factor analysis (CFA), and multigroup CFA, were performed to examine the internal consistency and test-retest reliabilities, as well as the construct, concurrent, convergent, discriminant, and predictive validities, and the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence.

Results: Cronbach α and CR were within acceptable ranges of 0.89-0.94 and 0.90-0.97, respectively, indicating adequate internal consistency of the eHLS-Web3.0 and its subscales. The eHLS-Web3.0 also demonstrated cross-time stability, with baseline and follow-up measures showing a significant intraclass correlation of 0.81-0.91. The construct validity of the 3D structure model of the eHLS-Web3.0 was supported by confirmatory factor analyses. The eHLS-Web3.0 exhibited convergent validity with an average variance extracted value of 0.58 and a CR value of 0.97. Discriminant validity was supported by CFA results for a proposed 4-factor model integrating the 3 eHLS-Web3.0 subscales and eHEALS. The predictive validity of the eHLS-Web3.0 for health behaviors was supported by significant associations of the eHLS-Web3.0 with light physical activity (β=.36, P =.004), moderate to vigorous physical activity ( β =.49, P <.001), and sedentary behavior ( β =–.26, P =.002). Finally, the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence was supported by the establishment of configural, metric, strong, and strict invariances.

Conclusions: The present study provides timely empirical evidence on the reliability, validity, and measurement invariance of the eHLS-Web3.0, suggesting that the 24-item 3D eHLS-Web3.0 is an appropriate and valid tool for measuring eHealth literacy among older adults with NCDs within the Web3.0 sphere.

Introduction

Noncommunicable diseases (NCDs), known as chronic diseases, result in the mortality of 41 million people annually, equivalent to approximately 74% of all global deaths [ 1 ]. Characterized by high morbidity, high mortality, low control rates, and limited awareness, NCDs impose a considerable financial burden on individuals, their families, and society as a whole, particularly among older patients [ 2 ]. In China, the prevalence rate of NCDs among older adults aged 60 years and older was 50%-75%, as reported in recent epidemiological studies [ 3 - 5 ]. Therefore, NCDs in older adults are a vital public health concern, and their management is a global challenge.

Previous evidence has demonstrated that empowering and educating patients with NCDs to focus on self-management and health promotion is essential [ 2 , 6 ]. Enabling patients to inquire about their medical status, comply with medication instructions, enhance their engagement and compliance in the health care process, adopt healthier lifestyles, and ultimately reduce reliance on constant supervision from health care professionals is a challenging task [ 6 , 7 ]. Nevertheless, facilitating patient self-care is a critical step toward improving the overall health status and alleviating the burden on health care facilities, especially within low- and middle-income countries [ 7 , 8 ].

With the rapid advancement of technology, the internet has become the quickest and most easily accessible resource for obtaining and delivering health information, offering ample opportunities for self-management and health promotion [ 6 , 9 ]. Recent review studies have consistently shown that internet-based health interventions for individuals with NCDs can have a substantial impact on enhancing self-management and patient engagement and compliance with their health care [ 10 , 11 ]. Despite the potential of the internet to improve health care services for NCDs, older adults encounter significant challenges in using digital health technologies [ 12 ]. In particular, the information found on the internet originates from numerous providers and sources that are difficult to regulate, thereby leading to potential problems in terms of accuracy and the potential dissemination of prejudiced content that aligns with the interests and objectives of certain parties involved [ 13 ]. Previous research has highlighted the considerable difficulties faced by older adults in accessing reliable and high-quality health information that addresses their specific health needs [ 14 , 15 ]. Furthermore, studies have revealed that a noteworthy proportion of older internet users lack confidence in their capability to execute basic tasks on the internet [ 15 ]. The challenges mentioned above not only impede older adults from harnessing the internet’s full potential for health care purposes but also exacerbate the digital divide and health disparities [ 12 ]. In such a scenario, eHealth literacy is emphasized in numerous studies as a critical skill that older adults with NCDs must acquire in the digital era of disease management and health care [ 2 , 6 , 16 ].

eHealth literacy, first proposed by Norman and Skinner in 2006 [ 17 ], refers to “individual’s abilities to seek, find, understand, and appraise health information from electronic resources and apply that knowledge to solve a health problem or make a health-related decision.” The concept of eHealth literacy is founded on social cognitive theory, consisting of 6 essential skills or literacies: traditional literacy, health literacy, information literacy, scientific literacy, media literacy, and computer literacy [ 17 , 18 ]. To provide a general assessment of this concept that can assist in clinical decision-making and health promotion planning for individuals or specific samples, Norman and Skinner [ 17 ] developed an 8-item unidimensional eHealth Literacy Scale (eHEALS). The eHEALS is the most well-known and extensively used instrument for assessing eHealth literacy to date [ 19 ]. The reliability and validity of the eHEALS have been extensively examined in diverse cultural contexts, including English [ 17 , 20 ], German [ 21 ], Spanish [ 22 ], Dutch [ 23 ], Italian [ 24 ], Portuguese [ 25 ], Japanese [ 26 ], and Chinese [ 27 ], providing compelling evidence of its efficacy across multiple languages and cultures.

However, as technology continues to advance, there has been an increasing acknowledgment of the necessity to update the content of eHealth literacy to ensure optimal synchronization with the evolving internet landscape [ 23 , 28 , 29 ]. Several studies have raised concerns regarding the unidimensional nature of the eHEALS, as well as its inadequate performance in psychometric evaluations, particularly when using it to measure the usage of novel technologies in seeking and assessing health information [ 23 , 27 - 30 ]. For example, previous research has shown a weak association between eHEALS and eHealth behaviors beyond web-based information-researching skills, indicating the requirement to update the tool [ 23 ]. Furthermore, a recent systematic review indicated that the structure of the eHEALS varied across multiple studies, where a 2-factor or 3-factor structure was also identified in certain studies [ 19 ]. There has also been some questioning of the variability of the items, even though the eHEALS had the same factor construct [ 19 ]. Alongside the influence of cultural contexts, a primary reason for the inconsistencies of the factor structures and corresponding items may be that the eHEALS is outdated for use in evaluating eHealth literacy in the new digital age [ 19 ].

Indeed, the arguments outlined above are reasonable. As per the widely acknowledged generation divisions of internet evolution, the present internet landscape has progressed through 3 distinct phases, starting with Web1.0 (a read-only web) to Web2.0 (ie, a read-write mode that provides a participatory social web with increased collaboration and interaction among consumers, programmers, service providers, and organization) and to current Web3.0 (ie, a read-write-execute mode that provides digital, personalized, and intelligent services; also known as semantic web) [ 23 , 30 , 31 ]. The eHEALS was developed 15 years ago for measuring individuals’ capability related to reading and viewing within a Web1.0 context, and therefore, it is necessary to update it to effectively scale current eHealth usage.

To fill this gap, numerous new measurement tools of eHealth literacy have been developed. One example is the 20-item extended version of the eHEALS (eHEALS-E) created by Petrič et al [ 32 ], which is designed to better encompass the complicated factors contributing to eHealth literacy. However, the eHEALS-E is based on the same definition as the original eHEALS, and therefore, it may also have limitations in measuring only a narrow aspect of eHealth literacy [ 19 ]. Furthermore, second-generation instruments of eHealth literacy have been developed (eg, eHealth Literacy Scale [eHLS], Digital Health Literacy Instrument [DHLI], Transactional eHealth Literacy Instrument, eHealth Literacy Assessment Toolkit, and Chinese version of the electronic eHealth Literacy Scale [C-eHEALS]) to assess a broader spectrum of eHealth literacy concepts, ensuring their relevance in the age of social media and eHealth [ 19 , 28 ]. These measures have provided novel approaches for evaluating eHealth literacy, with some of them specifically designed to measure web communication capabilities. However, their coverage is limited to Web2.0 skills, and evaluation of eHealth literacy content relevant to Web3.0 technologies remains absent [ 33 ]. Recently, Liu et al [ 33 ] developed a 24-item eHealth Literacy Scale-Web3.0 (eHLS-Web3.0) to measure eHealth skills in the context of Web3.0. Compared with previous eHEALS and second-generation eHealth literacy scales, the eHLS-Web3.0 is an improvement consisting of 3 distinct dimensions (ie, acquisition, verification, and application) that evaluates the abilities covering the entire spectrum of Web1.0 (eg, searching, understanding, and identifying), 2.0 (eg, sharing and interactive communication), and 3.0 (eg, recording, self-managing, applying, and adjusting) [ 33 ]. The reliabilities, validities, and measurement invariance of the eHLS-Web3.0 across gender and region have been supported in a previous study with young adults [ 33 ], whereas its psychometric properties remain unexplored in older adults, especially those living with NCDs. Therefore, further research is needed to investigate the applicability and psychometric properties of the eHLS-Web3.0 in older adults with NCDs, which may inform the development of effective interventions to improve eHealth literacy and promote better health care outcomes in this population.

Given the above, the purpose of this study was to examine the reliability, validity, and measure invariance of the eHLS-Web3.0 in a sample of older adults with NCDs. In particular, this study has 3 main objectives. First, the internal consistency and test-retest reliabilities of the eHLS-Web3.0 would be examined. Based on previous studies [ 12 , 17 , 20 , 34 - 36 ], a conventional 1-month time frame for evaluating the test-retest reliability was used in this study. Second, the construct, convergent, concurrent, discriminant, and predictive validities of the 24-item 3D eHLS-Web3.0 would be ascertained. Because the eHEALS has been proven to be a reliable tool for measuring eHealth literacy among older adults with NCDs in previous studies [ 2 , 17 ], this study would use the eHEALS as the criteria scale for the examination of the concurrent validity of the eHLS-Web3.0. Furthermore, considering the content distinction between the eHLS-Web3.0 and the eHEALS, the discriminant validity of the eHLS-Web3.0 would be examined by comparing the eHLS-Web3.0 subscales and the eHEALS. Additionally, previous studies have established a strong association between eHealth literacy and various health behaviors [ 13 , 37 ]. Specifically, eHealth literacy has been shown to positively correlate with health-promoting behaviors (eg, physical activity) and negatively correlate with risk behaviors (eg, sedentary behavior) among young and older adults [ 38 , 39 ]. Therefore, this study would investigate the predictive validity of the eHLS-Web3.0 for 2 specific health behaviors (ie, physical activity and sedentary behavior), given their crucial impact on the physical and mental well-being of older adults with NCDs [ 40 ]. By identifying the predictive validity of the eHLS-Web3.0, this study is expected to make a noteworthy contribution to future research in this field. Finally, considering that gender, education level, and residence are potential correlates of eHealth literacy [ 6 , 33 , 41 ], the measurement invariance of the eHEALS would be examined at the configural, metric, strong, and strict levels across gender, education level, and residence.

Design, Participants, and Procedure

This study applied a 2-wave prospective design. Considering an item-to-response ratio of 1:10 and the recommendation for a minimum sample size of 200 in confirmatory factor analysis (CFA) [ 42 ], 240 participants were required to ensure a robust statistical estimation. With an approximate response rate of 85% and a prior estimated prevalence rate of NCDs of 50% in older adults [ 4 ], a minimum of 564 participants were required to be contacted at the initial recruitment stage. Eligible participants for the study were older adults who met the following inclusion criteria: (1) aged 60 years or older, (2) experiencing at least 1 type of NCD (eg, cardiovascular diseases, cancer, type 2 diabetes, and obesity), (3) no physical mobility restrictions, (4) no cognitive disorders, (5) proficient in reading and understanding Chinese, and (6) having access to a smartphone or laptop.

Participants were recruited from the outpatient departments of 6 hospitals from 3 cities (Shiyan, Wuhan, and Suizhou) of Hubei Province (Central China) using a convenience sampling approach. The survey was implemented using the SOJUMP web-based survey platform (Changsha Ranxing Information Technology Co, Ltd). Two health care professionals undertook an initial review of the scale items to ensure that the wording was appropriate for older adults with NCDs. Subsequently, 6 older adults with NCDs (3 female and 3 male) were invited to complete a pilot assessment aimed at (1) optimizing the design of the electronic questionnaires (eg, using the large font and highlighting the key information) and (2) refining the language and eliminating any errors to ensure that the scale items were easily comprehensible for the target population.

In the main study, participants were provided with a QR code through nurses to gain access to the web-based survey. Before answering the questionnaires, participants were required to sign an informed consent form on the first page of the survey. The web-based survey lasted approximately 20 minutes. To ensure a robust evaluation for the scale test-retest reliability and predictive validity, a minimum of 100 participants were needed [ 43 ]. Accounting for a potential 30% attrition rate (eg, no response and invalid or missing data) [ 4 ], a total of 142 participants were required for the second-wave data collection. Invitations were sent out randomly via SMS text messages to those who had completed the first round of data collection until enough participants agreed to participate in the follow-up survey, scheduled for 1 month later. Participants who agreed to participate in the second round of investigation were requested to revisit the hospital, where 2 qualified assistants guided them to complete the follow-up web-based survey and provided detailed instructions on the use of the accelerometer for data collection.

Ethical Considerations

This study adhered to the principles outlined in the Declaration of Helsinki by the World Medical Association. The Medical Ethics Committee of the Faculty of Medicine at Shenzhen University reviewed and approved this study (PN-202300066). All participants provided signed informed consent for both the primary study and the sensitivity analyses. The data were anonymized to protect participant privacy, and participation in the study was entirely voluntary. As a token of appreciation, participants received a participation fee of 5 RMB (US $0.7) on completing the data collection.

eHealth Literacy Scale-Web3.0

The 24-item eHLS-Web3.0 was originally developed by Liu et al [ 33 ] for the Chinese adult population. This scale comprises 3 dimensions: acquisition (items 1-4 and 11-14), verification (items 5-10), and application (items 15-24). Responses were indicated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The total score of the scale ranges from 24 to 120, with a higher score indicating a greater level of eHealth literacy. The reliability and validity of the eHLS-Web3.0 have been fully supported by previous research with Chinese young adults (Cronbach α=0.91-0.97).

The 8-item eHEALS was developed by Norman and Skinner [ 17 ] for use among Canadian adolescents. The original scale is unidimensional and has been validated in various countries across diverse populations. The Chinese version of the 8-item eHEALS has been examined in previous studies among older adults with NCDs, where the reliability and validity of the scale have been fully supported (Cronbach α=0.95-0.98).

Health Behaviors

Physical activity and sedentary behavior were measured using the ActiGraph GT3X+ (ActiGraph) on the right side of the waist for 7 consecutive days, with the exception of swimming, bathing, and sleeping time. The accelerometer sampling interval was set at 60-second epochs with a sampling frequency of 30 Hz. Nonwear time was defined by an interval of 60 consecutive minutes of 0 counts per minute, allowing for 2 minutes of nonzero count interruptions. Participants with at least 3 valid days of accelerometer use (2 weekdays and 1 weekend day) and a minimum wear time of 10 hours per day were eligible for inclusion in the data analysis. The Freedson cutoff point was used for categorizing light physical activity (100-1951 counts/minute), moderate to vigorous physical activity (>1951 counts/minute), and sedentary behavior (<100 counts/minute) [ 44 ].

Demographics

The demographic information included age, sex, marital status, education level, residence, monthly income, living situation, and BMI.

Statistical Analyses

The data analyses were performed using IBM SPSS Statistics (version 28.0; IBM Corp) and Mplus 8 (Muthén & Muthén). Data screening and diagnosis tests of data distribution (eg, mean, SD, skewness, and kurtosis) and missing patterns were performed before the descriptive analysis and scale validation. To ensure a reliable estimation for the multidimensional scale, both Cronbach α and composite reliability (CR) coefficients were calculated to evaluate the internal consistency reliability of the eHLS-Web3.0. Additionally, the test-retest reliability was estimated using the intraclass correlation coefficient of pre- and 1-month follow-up data.

The construct validity of the eHLS-Web3.0 was evaluated using CFAs with maximum likelihood estimation. Several goodness-of-fit indices were computed, including robust chi-square ( χ 2 R ), robust chi-square to degrees of freedom ratio ( χ 2 R / df ), comparative-fit index (CFI), Tucker-Lewis index (TLI), root-mean-square error of approximation (RMSEA) and its 90% CI, and standardized root-mean-square residual (SRMR). The following criteria were considered for a satisfactory model goodness of fit: ≤3 for χ 2 R / df , ≥0.9 for CFI and TLI, and ≤0.08 for RMSEA and SRMR [ 45 ].

Convergent validity was assessed by examining the average variance extracted (AVE) and CR for each subscale, with AVE >0.5 and CR >0.7 indicating satisfactory convergent validity for the scale. Concurrent validity was assessed by calculating the zero-order correlations of the eHLS-Web3.0 and its subscales with the eHEALS, adjusted for all demographic confounders. For the discriminant validity, a presumptive 4-factor model incorporating 3 eHLS-Web3.0 subscales and unidimensional eHEALS was estimated in the CFA. The discriminant validity of the subscales was confirmed if the 95% CI of the association between these subscales did encompass the value of 0 and if the Wald chi-square test demonstrated a significant change in model fit after removing a constraint that fixed the factor correlation to zero [ 45 ]. Additionally, structural equation models were performed to assess the predictive validity of the scale by estimating its association with health behaviors, including physical activity and sedentary behavior.

With a sequential model testing approach, multigroup CFA was used to examine the measurement invariance of the eHLS-Web3.0 across gender, education, and residence. Four distinctive levels of measurement invariance were examined by progressively constraining the parameter estimates of the models to be equivalent across the samples: (1) configural invariance, where no parameter estimates were restricted to equality; (2) metric invariance, where factor loadings were constrained to equality; (3) strong invariance, where both factor loadings and item intercepts were constrained to equality; and (4) structural and strict invariance, where all factor loadings, item intercepts, and factor variance and covariance were restricted to equality. The measure invariance was supported if the change in the value of CFI and RMSEA was ≤0.01 and ≤0.015, respectively [ 45 , 46 ].

Descriptive Information of the Study Sample

As outlined in Figure 1 , a total of 642 eligible participants (mean 65.78, SD 3.91 years; 55.8% female) were included in the data analysis, of whom 134 (mean 65.63, SD 3.99 years; 58.2% female) provided valid data at the follow-up assessment. From the diagnostic evaluation, there were no missing data for eHLS-Web3.0 and eHEALS items in the study sample. All the scale items adhered to the normality distribution with absolute values of skewness and kurtosis <1. Descriptive information of the study sample is shown in Table 1 .

a NCD: noncommunicable disease.

b LPA: light physical activity.

c N/A: not applicable.

d MVPA: moderate to vigorous physical activity.

Reliabilities of the eHLS-Web3.0 in the Study Sample

Table 2 shows the mean value, SD, score range, and internal consistency and test-retest reliabilities of the eHLS-Web3.0 in the study sample. Regarding the internal consistency reliability, the eHLS-Web3.0 and its 3 subscales exhibited adequate Cronbach α values (range=0.89-0.94) and CR coefficients (range=0.90-0.97). Regarding the test-retest reliability, 2 time-point measures showed a strong intraclass correlation for the eHLS-Web3.0 and 3 subscales of the eHLS-Web3.0 ( r =0.81-0.91).

Validities of the eHLS-Web3.0 in the Study Sample

For the constructive validity, the results of the CFA showed that the 24-item 3D eHLS-Web3.0 achieved the criteria for good model fit indices in the study sample, with χ 2 R =674.4, df =248, χ 2 R / df =2.72 (<3), CFI=0.952 (>0.9), TLI=0.946 (>0.9), RMSEA=0.052 (90% CI 0.047-0.056; <0.08), and SRMR=0.034 (<0.08). The standardized factor loadings of the eHLS-Web3.0 items ranged from 0.658 to 0.819 (see Table S1 in Multimedia Appendix 1 ).

For concurrent validity, the zero-order correlations between the eHLS-Web3.0 subscales and eHEALS were significant ( r= 0.47-0.76), indicating a satisfactory result, as outlined in Table 3 . The AVE and CR were calculated as 0.58 and 0.97, respectively, using the standardized factor loadings of the items, showing a satisfactory convergent validity of the eHLS-Web3.0.

The latent factor correlations in a proposed 4-factor CFA model (ie, 3 eHLS-Web3.0 subscales and eHEALS) were calculated to evaluate the discriminant validity of the eHLS-Web3.0 subscales and eHEALS in the study sample. The goodness-of-fit indices of the 4-factor model were inferior to those of the original 3-factor eHLS-Web3.0 model, with χ 2 R =2079.8, df =461, χ 2 R / df =4.51, CFI=0.874, TLI=0.865, RMSEA=0.074 (90% CI 0.071-0.077), and SRMR=0.078. Table 4 presents the statistical results of the discriminant validity analysis. Wald chi-square findings were statistically significant in the study sample (all P <.001), and the CIs for each correlation did not contain the value of 0, demonstrating a satisfactory discriminant validity of the eHLS-Web3.0.

In addition, the eHLS-Web3.0 significantly predicted light physical activity ( β =.36, 95% CI 0.19-0.53; P =.004), moderate to vigorous physical activity ( β =.49, 95% CI 0.35-0.62; P <.001), and sedentary behavior ( β =–.26, 95% CI–0.40 to –0.12; P =.002), supporting the predictive validity of the scale for health behaviors. The goodness-of-fit indices indicated a satisfactory result for the 3 models, with χ 2 R =398.3-403.8, df =271, χ 2 R / df =1.47-1.49, CFI=0.949-0.951, TLI=0.944-0.946, RMSEA=0.059-0.060, and SRMR=0.043-0.044.

a P <.001.

a Wald chi-square test: Wald chi-square test constraining the values of the latent interfactor correlations to zero.

b P <.001.

c eHEALS: 8-item eHealth Literacy Scale.

Measurement Invariance of the eHLS-Web3.0 in the Study Sample

Table 5 presents the results of the examination regarding the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence. The configural, metric, strong, and strict models were all shown to have a satisfactory fit to the data for all 3 pairs of subsamples, with ΔCFI <0.01 and ΔRMSEA <0.015. These indices provide support for the invariance of the factorial construct, factor loadings, intercepts, and residual variance of the eHLS-Web3.0 across gender, education level, and residence.

a Chi-square: robust chi-square.

b CFI: comparative fit index.

c ΔCFI: change in the CFI.

d RMSEA: root-mean-square error of approximation.

e ΔRMSEA: change in the RMSEA.

f M0: baseline configural invariance model.

g N/A: not applicable.

h M1: metric invariance model.

i M2: strong invariance model.

j M3: strict invariance model.

Principal Findings

This study aimed to evaluate the reliability, validity, and measurement invariance of the eHLS-Web3.0 for use with older adults who are living with NCDs. In particular, this study examined the internal consistency and test-retest reliabilities, as well as the construct, concurrent, convergent, discriminant, and predictive validities, and the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence. Overall, the results from this study suggest that the eHLS-Web3.0 is a reliable and valid tool for measuring eHealth literacy in Chinese older adults with NCDs.

Regarding the reliabilities, analyses of the Cronbach α and CR coefficients indicated adequate internal consistency reliability for both the eHLS-Web3.0 and its 3 subscales. These findings are consistent with previous research on the use of the eHLS-Web3.0 among Chinese young adults [ 33 ]. It is worth noting that while previous studies have generally supported the reliability of the eHEALS and other eHealth literacy assessments (eg, DHLI and C-eHEALS) among older adults or those with NCDs [ 2 , 27 , 28 ], the novel eHEALS-Web3.0 tool has not yet been evaluated for reliability in older populations. This study is the first to investigate the reliability of the eHEALS-Web3.0 among older adults with NCDs. Additionally, previous psychometric analyses of eHealth literacy measures have primarily focused on internal consistency reliability, with test-retest reliability often overlooked [ 19 , 27 , 28 ]. In contrast, this study further examined the test-retest reliability of the eHLS-Web3.0 and its subscales, and the findings demonstrated a strong cross-time stability for the scale, as evidenced by a significant correlation between baseline and 1-month follow-up measures.

Regarding the construct validity, the CFA results provided support for the 3D model structure of the eHLS-Web3.0 among Chinese older adults with NCDs. The acquisition and verification subscales of the eHEALS-Web3.0 assess individuals’ eHealth abilities in Web1.0 and Web2.0 contexts, similar to the eHEALS and second-general eHealth literacy measures [ 12 , 19 , 27 ]. However, the eHEALS-Web3.0 stands out by also evaluating individuals’ proficiency in applying eHealth information to evolving health needs in the Web3.0 era (ie, the application subscale). As the digital landscape advances, individuals have more opportunities and options to use eHealth information. For instance, they can use eHealth information to make informed health decisions or resolve health-related problems, create their own health data, monitor their health status, interact with others, exchange information, and provide health advice to other health information seekers [ 31 , 33 ]. The 3D eHLS-Web3.0 provides a comprehensive assessment of eHealth literacy, catering to the present digital circumstances.

For other validities of the eHLS-Web3.0, the concurrent validity was confirmed by a significant correlation between the eHLS-Web3.0 and its subscales with the eHEALS, while the AVE and CR supported the convergent validity of the scale. In addition, as the 3 eHLS-Web3.0 subscales and eHEALS differ in conceptual content, a 4-factor model integrating the acquisition, verification, and application subscales and unidimensional eHEALS was established to confirm the discriminant validity of the scale. Although the Wald chi-square test results supported the discriminant validity of the eHLS-Web3.0, a high correlation was observed among the latent factors in the 4-factor model, possibly due to measurement errors [ 47 ]. To validate the earlier findings, zero-order correlations were calculated using composite (averaged) scales. Fortunately, the overall results confirmed the discriminant validity of the scale. Finally, the predictive validity of the eHLS-Web3.0 was supported by a significant positive association between the eHLS-Web3.0 and physical activity, as well as a negative association with sedentary behavior. Previous studies have demonstrated a positive association between eHealth literacy and health-promoting behaviors (eg, physical activity) among diverse populations [ 13 , 37 , 48 ]. However, there is a lack of evidence on the relationship between eHealth literacy and risk behaviors (eg, sedentary behavior). Our findings underline the potential of including eHealth literacy as a modifiable factor in future eHealth interventions to facilitate health behaviors and improve health outcomes among older adults with NCDs.

For measurement invariance, the establishment of configural, metric, strong, and strict invariances demonstrated that the eHLS-Web3.0 is a psychometrically sound instrument for measuring eHealth literacy among Chinese older adults with NCDs, regardless of their gender, education level, and residence. These invariances provide a solid foundation for making appropriate and meaningful transgroup comparisons in future studies.

Limitations and Implications

Some limitations should be noted. First, the nonrandom sampling used in this study may have limited the representativeness of the study findings. Therefore, a stratified random sampling approach is warranted in future studies. Second, given that the study findings are based only on the sample of Chinese older adults with NCDs, one should be cautious when generalizing these results to other samples. Future studies should examine the psychometric properties of the scale across different populations and diverse cultural contexts. Moreover, self-reported measures may result in some response biases (eg, recall bias and social desirability); therefore, the inclusion of objective means for assessing eHealth literacy should be considered in the future. Additionally, it is worth exploring the prediction of eHealth literacy on other health outcomes and examining its underlying mechanisms. Finally, from a pragmatic perspective, it may be beneficial to develop and validate a brief version of the eHLS-Web3.0, particularly with regard to older populations who may struggle with completing lengthy self-reported scales.

Despite the aforementioned limitations, this study addresses a significant gap in the literature by validating and applying the eHLS-Web3.0, a specific measure of eHealth literacy used for Chinese older adults with NCDs in the Web3.0 landscape. Previous reviews have revealed a wide range of influential factors of eHealth literacy as well as a positive correlation between higher eHealth literacy and better health behaviors, knowledge, and attitudes in older adults [ 38 , 49 - 51 ]. These findings indicate the potential for developing eHealth literacy interventions to promote positive health behaviors in the future while considering various socioeconomic and cultural variables. However, previous studies have yielded conflicting results regarding certain physical and psychosocial outcomes [ 38 ], underlining the need for more high-quality research. It is important to note that the success of these efforts largely depends on a reliable and accurate assessment of eHealth literacy [ 52 ].

The findings of this study provide robust support for the reliability, validity, and measurement invariance of the eHLS-Web3.0, indicating that this up-to-date tool can be widely used in future research endeavors to appropriately and accurately assess older adults’ abilities to search for, retrieve, evaluate, and use web-based health resources. This advancement has the potential to significantly contribute to both the field of eHealth literacy research and the development of targeted health promotion programs in the future. As digital technology increasingly infiltrates the health care sector, promoting eHealth literacy among older adults is more critical than ever [ 52 , 53 ]. The development and validation of the eHLS-Web3.0 marks a significant milestone in the field of eHealth literacy research, serving as a necessary foundation for future empirical investigations and targeted interventions aimed at improving eHealth literacy among older adults.

Conclusions

To the best of our knowledge, this is the first study to examine the psychometric properties and measurement invariance of the eHLS-Web3.0 among Chinese older adults with NCDs. This study provides evidence for internal consistency and test-retest reliabilities, construct, concurrent, convergent, discriminant, and predictive validities, and the measurement invariance of the 24-item 3D eHLS-Web3.0 for use with Chinese older adults with NCDs. The eHLS-Web3.0 can serve as a psychometrically sound instrument for assessing eHealth literacy in the Chinese context.

Acknowledgments

This research was funded by the Humanities and Social Science Fund of Ministry of Education of China (23YJCZH121), as well as the Humanities and Social Sciences Revitalization Grant of Shenzhen University (WKZX0312). The funding organizations had no role in the study design, study implementation, manuscript preparation, or publication decision. This work is the responsibility of the authors.

Authors' Contributions

WL, WC, and HL conceived and designed the study. HZ, JZ, RZ, and LZ contributed to the preparation of the study materials. WL, WC, NS, HZ, YY, and LZ collected the data. WL screened and analyzed the data. WL and WC drafted and revised the manuscript. All authors have reviewed and approved the final version of the manuscript.

Conflicts of Interest

None declared.

Standardized factor loadings of the 24-item eHealth Literacy Scale-Web3.0.

• Noncommunicable diseases. World Health Organization. 2022. URL: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases [accessed 2023-08-10]
• Wu Y, Wen J, Wang X, Wang Q, Wang W, Wang X, et al. Associations between e-health literacy and chronic disease self-management in older Chinese patients with chronic non-communicable diseases: a mediation analysis. BMC Public Health. 2022;22(1):2226. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Liu J, Yu W, Zhou J, Yang Y, Chen S, Wu S. Relationship between the number of noncommunicable diseases and health-related quality of life in Chinese older adults: a cross-sectional survey. Int J Environ Res Public Health. 2020;17(14):5150. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Duan Y, Shang B, Liang W, Lin Z, Hu C, Baker JS, et al. Predicting hand washing, mask wearing and social distancing behaviors among older adults during the covid-19 pandemic: an integrated social cognition model. BMC Geriatr. 2022;22(1):91. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gong JB, Yu XW, Yi XR, Wang CH, Tuo XP. Epidemiology of chronic noncommunicable diseases and evaluation of life quality in elderly. Aging Med (Milton). 2018;1(1):64-66. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Shiferaw KB, Tilahun BC, Endehabtu BF, Gullslett MK, Mengiste SA. E-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional survey. BMC Med Inform Decis Mak. 2020;20(1):181. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Powers MA, Bardsley J, Cypress M, Duker P, Funnell MM, Fischl AH, et al. Diabetes self-management education and support in type 2 diabetes. Diabetes Educ. 2017;43(1):40-53. [ CrossRef ] [ Medline ]
• Dwarswaard J, Bakker EJM, van Staa AL, Boeije HR. Self-management support from the perspective of patients with a chronic condition: a thematic synthesis of qualitative studies. Health Expect. 2016;19(2):194-208. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bujnowska-Fedak M, Waligóra J, Mastalerz-Migas A. The internet as a source of health information and services. Adv Exp Med Biol. 2019;1211:1-16. [ CrossRef ] [ Medline ]
• Duff OM, Walsh DM, Furlong BA, O'Connor NE, Moran KA, Woods CB. Behavior change techniques in physical activity eHealth interventions for people with cardiovascular disease: systematic review. J Med Internet Res. 2017;19(8):e281. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Duan Y, Shang B, Liang W, Du G, Yang M, Rhodes RE. Effects of eHealth-based multiple health behavior change interventions on physical activity, healthy diet, and weight in people with noncommunicable diseases: systematic review and meta-analysis. J Med Internet Res. 2021;23(2):e23786. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Xie L, Mo PKH. Comparison of eHealth Literacy Scale (eHEALS) and Digital Health Literacy Instrument (DHLI) in assessing electronic health literacy in Chinese older adults: a mixed-methods approach. Int J Environ Res Public Health. 2023;20(4):3293. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kim K, Shin S, Kim S, Lee E. The relation between eHealth literacy and health-related behaviors: systematic review and meta-analysis. J Med Internet Res. 2023;25:e40778. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Stellefson M, Chaney B, Barry AE, Chavarria E, Tennant B, Walsh-Childers K, et al. Web 2.0 chronic disease self-management for older adults: a systematic review. J Med Internet Res. 2013;15(2):e35. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Stellefson M, Paige SR, Tennant B, Alber JM, Chaney BH, Chaney D, et al. Reliability and validity of the telephone-based eHealth Literacy Scale among older adults: cross-sectional survey. J Med Internet Res. 2017;19(10):e362. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Richtering SS, Hyun K, Neubeck L, Coorey G, Chalmers J, Usherwood T, et al. eHealth literacy: predictors in a population with moderate-to-high cardiovascular risk. JMIR Hum Factors. 2017;4(1):e4. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Norman CD, Skinner HA. eHEALS: the eHealth Literacy Scale. J Med Internet Res. 2006;8(4):e27. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bandura A. Self-Efficacy: the Exercise of Control. New York. W. H. Freeman; 1997.
• Lee J, Lee EH, Chae D. eHealth literacy instruments: systematic review of measurement properties. J Med Internet Res. 2021;23(11):e30644. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chung SY, Nahm ES. Testing reliability and validity of the eHealth Literacy Scale (eHEALS) for older adults recruited online. Comput Inform Nurs. 2015;33(4):150-156. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Marsall M, Engelmann G, Skoda EM, Teufel M, Bäuerle A. Measuring electronic health literacy: development, validation, and test of measurement invariance of a revised German version of the eHealth Literacy Scale. J Med Internet Res. 2022;24(2):e28252. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pérez GP, Almagro BJ, Gómez ÁH, Gómez JIA. Validación de la escala eHealth Literacy (eHEALS) en población universitaria española [Validation of the eHealth Literacy Scale (eHEALS) in Spanish University Students]. Rev Esp Salud Publica. 2015;89(3):329-338. [ FREE Full text ] [ CrossRef ] [ Medline ]
• van der Vaart R, van Deursen AJ, Drossaert CH, Taal E, van Dijk JA, van de Laar MA. Does the eHealth Literacy Scale (eHEALS) measure what it intends to measure? Validation of a Dutch version of the eHEALS in two adult populations. J Med Internet Res. 2011;13(4):e86. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Diviani N, Dima AL, Schulz PJ. A psychometric analysis of the Italian version of the eHealth Literacy Scale using item response and classical test theory methods. J Med Internet Res. 2017;19(4):e114. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mialhe FL, Moraes KL, de Carvalho Sampaio HA, Brasil VV, da Silva Carvalho Vila V, Soares GH, et al. Evaluating the psychometric properties of the eHealth Literacy Scale in Brazilian adults. Rev Bras Enferm. 2021;75(1):e20201320. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mitsutake S, Shibata A, Ishii K, Okazaki K, Oka K. [Developing Japanese version of the eHealth Literacy Scale (eHEALS)]. Nihon Koshu Eisei Zasshi. 2011;58(5):361-371. [ Medline ]
• Xu RH, Zhou L, Lu SY, Wong EL, Chang J, Wang D. Psychometric validation and cultural adaptation of the simplified Chinese eHealth Literacy Scale: cross-sectional study. J Med Internet Res. 2020;22(12):e18613. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chang A, Schulz PJ. The measurements and an elaborated understanding of Chinese eHealth Literacy (C-eHEALS) in chronic patients in China. Int J Environ Res Public Health. 2018;15(7):1553. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hyde LL, Boyes AW, Evans TJ, Mackenzie LJ, Sanson-Fisher R. Three-factor structure of the eHealth Literacy Scale among magnetic resonance imaging and computed tomography outpatients: a confirmatory factor analysis. JMIR Hum Factors. 2018;5(1):e6. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tennant B, Stellefson M, Dodd V, Chaney B, Chaney D, Paige S, et al. eHealth literacy and Web 2.0 health information seeking behaviors among baby boomers and older adults. J Med Internet Res. 2015;17(3):e70. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Rudman R, Bruwer R. Defining Web 3.0: opportunities and challenges. Electron Libr. 2016;34(1):132-154. [ CrossRef ]
• Petrič G, Atanasova S, Kamin T. Ill literates or illiterates? Investigating the eHealth literacy of users of online health communities. J Med Internet Res. 2017;19(10):e331. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Liu HX, Chow BC, Liang W, Hassel H, Huang YW. Measuring a broad spectrum of eHealth skills in the Web 3.0 context using an eHealth Literacy Scale: development and validation study. J Med Internet Res. 2021;23(9):e31627. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zrubka Z, Hajdu O, Rencz F, Baji P, Gulácsi L, Péntek M. Psychometric properties of the Hungarian version of the eHealth Literacy Scale. Eur J Health Econ. 2019;20(Suppl 1):57-69. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kim H, Yang E, Ryu H, Kim HJ, Jang SJ, Chang SJ. Psychometric comparisons of measures of eHealth literacy using a sample of Korean older adults. Int J Older People Nurs. 2021;16(3):e12369. [ CrossRef ] [ Medline ]
• Collins LM. Research design and methods. In: Birren JE, editor. Encyclopedia of Gerontology (Second Edition). Amsterdam. Elsevier/Academic Press; 2007:433-442.
• Cui GH, Li SJ, Yin YT, Chen LJ, Li JQ, Liang FY, et al. The relationship among social capital, eHealth literacy and health behaviours in Chinese elderly people: a cross-sectional study. BMC Public Health. 2021;21(1):45. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Xie L, Zhang S, Xin M, Zhu M, Lu W, Mo PKH. Electronic health literacy and health-related outcomes among older adults: a systematic review. Prev Med. 2022;157:106997. [ CrossRef ] [ Medline ]
• Hutchesson MJ, Gough C, Müller AM, Short CE, Whatnall MC, Ahmed M, et al. eHealth interventions targeting nutrition, physical activity, sedentary behavior, or obesity in adults: a scoping review of systematic reviews. Obes Rev. 2021;22(10):e13295. [ CrossRef ] [ Medline ]
• Li Y, Fan X, Wei L, Yang K, Jiao M. The impact of high-risk lifestyle factors on all-cause mortality in the US non-communicable disease population. BMC Public Health. 2023;23(1):422. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lee J, Tak SH. Factors associated with eHealth literacy focusing on digital literacy components: a cross-sectional study of middle-aged adults in South Korea. Digit Health. 2022;8:20552076221102765. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Byrne BM. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, Second Edition. New York. Routledge, Taylor & Francis Group; 2013.
• Walter SD, Eliasziw M, Donner A. Sample size and optimal designs for reliability studies. Stat Med. 1998;17(1):101-110. [ CrossRef ] [ Medline ]
• Peters TM, Moore SC, Xiang YB, Yang G, Shu XO, Ekelund U, et al. Accelerometer-measured physical activity in Chinese adults. Am J Prev Med. 2010;38(6):583-591. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Liang W, Wang DD, Shang BR, Zhang CQ, Duan YP, Si GY. Further examination of the psychometric properties of the Brief Self-Control Scale: evidence from Chinese athletes and students. Int J Sport Exerc Psychol. 2020;20(1):16-35. [ CrossRef ]
• Cheung GW, Rensvold RB. Evaluating goodness-of-fit indexes for testing measurement invariance. Struct Equ Model Multidiscip J. 2002;9(2):233-255. [ CrossRef ]
• Hagger MS, Hankonen N, Kangro EM, Lintunen T, Pagaduan J, Polet J, et al. Trait self-control, social cognition constructs, and intentions: correlational evidence for mediation and moderation effects in diverse health behaviours. Appl Psychol Health Well Being. 2019;11(3):407-437. [ CrossRef ] [ Medline ]
• Mitsutake S, Shibata A, Ishii K, Oka K. Associations of eHealth literacy with health behavior among adult internet users. J Med Internet Res. 2016;18(7):e192. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Watkins I, Xie B. eHealth literacy interventions for older adults: a systematic review of the literature. J Med Internet Res. 2014;16(11):e225. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Shi Y, Ma D, Zhang J, Chen B. In the digital age: a systematic literature review of the e-health literacy and influencing factors among Chinese older adults. Z Gesundh Wiss. 2023;31(5):679-687. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chang SJ, Jang SJ, Lee H, Kim H. Building on evidence to improve eHealth literacy in older adults: a systematic review. Comput Inform Nurs. 2021;39(5):241-247. [ CrossRef ] [ Medline ]
• Huang YQ, Liu L, Goodarzi Z, Watt JA. Diagnostic accuracy of eHealth literacy measurement tools in older adults: a systematic review. BMC Geriatr. 2023;23(1):181. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Arcury TA, Sandberg JC, Melius KP, Quandt SA, Leng X, Latulipe C, et al. Older adult internet use and eHealth literacy. J Appl Gerontol. 2020;39(2):141-150. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by T de Azevedo Cardoso; submitted 04.09.23; peer-reviewed by W Yu, S Jiang, B Shang; comments to author 21.11.23; revised version received 11.12.23; accepted 19.04.24; published 03.06.24.

©Wenfei Cai, Wei Liang, Huaxuan Liu, Rundong Zhou, Jie Zhang, Lin Zhou, Ning Su, Hanxiao Zhu, Yide Yang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.06.2024.

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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Harvard-led study IDs statin that may block pathway to some cancers

Cholesterol-lowering drug suppresses chronic inflammation that creates dangerous cascade

Tracy Hampton

MGH Communications

Statins, commonly used cholesterol-lowering drugs, may block a pathway that leads to the development of cancer from chronic inflammation, according to a new study led by investigators from Harvard-affiliated  Mass General Cancer Center .

The team’s experiments showed that environmental toxins, such as those caused by exposure to allergens and chemical irritants, create a cascade effect that stimulates inflammation in the skin and pancreas that, when chronic, can result in cancer. Their findings suggest that using statins to suppress this pathway may have a protective effect.

The findings are published in Nature Communications.

In mice, pitavastatin suppressed environmentally induced inflammation in the skin and the pancreas and prevented the development of inflammation-related pancreatic cancers. 

“Chronic inflammation is a major cause of cancer worldwide,” said senior author Shawn Demehri, a principal investigator at the Center for Cancer Immunology and Cutaneous Biology Research Center of Massachusetts General Hospital and an associate professor of dermatology at Harvard Medical School and the Bob and Rita Davis Family MGH Research Scholar 2023-2028 .

“We investigated the mechanism by which environmental toxins drive the initiation of cancer-prone chronic inflammation in the skin and pancreas. Furthermore, we examined safe and effective therapies to block this pathway in order to suppress chronic inflammation and its cancer aftermath,” Demehri said.

The study relied on cell lines, animal models, human tissue samples, and epidemiological data. The group’s cell-based experiments demonstrated that environmental toxins (such as exposure to allergens and chemical irritants) activate two connected signaling pathways called the TLR3/4 and TBK1-IRF3 pathways. This activation leads to the production of the interleukin-33 (IL-33) protein, which stimulates inflammation in the skin and pancreas that can contribute to the development of cancer.

When they screened a library of U.S. Food and Drug Administration-approved drugs, the researchers found that the statin pitavastatin effectively suppresses IL-33 expression by blocking the activation of the TBK1-IRF3 signaling pathway. In mice, pitavastatin suppressed environmentally induced inflammation in the skin and pancreas and prevented development of inflammation-related pancreatic cancers. 

In human pancreas tissue samples, IL-33 was overexpressed in samples from patients with chronic pancreatitis (inflammation) and pancreatic cancer compared with normal pancreatic tissue. Also, in analyses of electronic health records data on more than 200 million people across North America and Europe, use of pitavastatin was linked to a significantly reduced risk of chronic pancreatitis and pancreatic cancer.

The findings demonstrate that blocking IL-33 production with pitavastatin may be a safe and effective preventive strategy to suppress chronic inflammation and the subsequent development of certain cancers.

“Next, we aim to further examine the impact of statins in preventing cancer development in chronic inflammation in liver and gastrointestinal tract and to identify other novel, therapeutic approaches to suppress cancer-prone chronic inflammation” said Demehri.

Research support was provided by the Burroughs Wellcome Fund, the LEO Foundation, the Sidney Kimmel Foundation, and the National Institutes of Health.

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New study reveals key protein that could help prevent excessive bone loss in osteoporosis

Researchers discover novel suppressive role of ctdnep1 in osteoclast differentiation and bone resorption.

Osteoporosis, a condition characterized by porous and fragile bones, poses a significant threat to skeletal health. As the very framework of the human body, bones provide crucial structural support. When bone mass diminishes, it not only compromises this support but also impairs overall function, leading to a diminished quality of life. With the aging population experiencing a surge in osteoporosis cases, the strain on healthcare resources for long-term care is evident. Hence, there is a need to understand the mechanisms that contribute to osteoporosis and develop effective targeted therapies to minimize its long-term impact.

Osteoblasts and osteoclasts are two types of cells crucial for the maintenance and remodeling of bone tissue. While osteoblasts are the bone-forming cells responsible for synthesizing and depositing new bone tissue, osteoclasts are the bone-resorbing cells involved in the breakdown and removal of old or damaged bone tissue. An increase in the proportion of osteoclasts leads to loss of bone mass in conditions like osteoporosis, rheumatoid arthritis (joint inflammation), and bone metastases (cancer that has spread to the bones). Osteoclasts arise from the differentiation of macrophages or monocytes, which are types of immune cells. Suppressing osteoclast differentiation can, therefore, serve as a therapeutic strategy to prevent bone loss. However, the precise molecular mechanisms governing the complex process of bone remodeling remain unclear.

In a new groundbreaking study, Professor Tadayoshi Hayata, Mr. Takuto Konno, and Ms. Hitomi Murachi from Tokyo University of Science, along with their co-workers, delved deeper into the molecular regulation of osteoclast differentiation. Receptor activator of nuclear factor kappa B ligand (RANKL) stimulation induces the differentiation of macrophages into osteoclasts. Further, bone morphogenetic protein (BMP) and transforming growth factor (TGF)-β signaling pathways have been implicated in the regulation of RANKL-mediated osteoclast differentiation. In the current study, the researchers sought to investigate the role of Ctdnep1 -- a phosphatase (an enzyme that removes phosphate groups) that has been reported to suppress BMP and TGF-β signaling.

Giving further insight into their work set to be published on July 30, 2024, in Volume 719 of Biochemical and Biophysical Research Communications , Prof. Hayata states, " RANKL functions as an 'accelerator' for osteoclast cell differentiation. Driving a car requires not only the accelerator but also the brakes. Here, we find that Ctdnep1 functions as a 'brake' on osteoclast cell differentiation ."

First, the researchers examined the expression of Ctdnep1 in mouse-derived macrophages treated with RANKL and untreated control cells. They noted that Ctdnep1 expressionremained unchanged in response to RANKL stimulation. However, it localized in the cytoplasm in granular form in the macrophages and differentiated into osteoclasts, distinct from its normal peri-nuclear localization in other cell types, indicating its cytoplasmic function in osteoclast differentiation.

Further, Ctdnep1 knockdown (downregulation of gene expression) resulted in an increase in tartrate-resistant acid phosphatase-positive (TRAP) osteoclasts; wherein TRAP is a marker for differentiated osteoclasts. Additionally, Ctdnep1 knockdown led to an increase in the expression of crucial differentiation markers including 'Nfatc1', a RANKL-induced master transcription factor for osteoclast differentiation. These results support the 'brake function' of Ctdnep1, whereby, it negatively regulates osteoclast differentiation.

Moreover, Ctdnep1 knockdown also led to increased absorption of calcium phosphate, suggestive of the suppressive role of Ctdnep1 in bone resorption. Lastly, while, Ctdnep1 knockdown did not alter BMP and TGF-β signaling, cells deficient in Ctdnep1 showed elevated levels of phosphorylated (activated) proteins downstream of the RANKL signaling pathway. These findings suggest that the suppressive effect of Ctdnep1 in osteoclast differentiation may not be mediated by BMP and TGF-β signaling, but, through the negative regulation of RANKL signaling and Nfatc1 protein levels.

Overall, these findings provide novel insights into the process of osteoclast differentiation and reveal potential therapeutic targets which can be pursued to develop treatments that address bone loss due to excessive osteoclast activity. In addition to diseases characterized by bone loss, Ctdnep1 has also been reported as a causative factor in medulloblastoma -- a childhood brain tumor. The authors are, therefore, optimistic that their research can be extended to other human diseases beyond bone metabolism.

Prof. Hayata concludes by saying, "Our findings suggest that Ctdnep1 is necessary to prevent excessive osteoclastogenesis. These results can further expand the knowledge on how the phosphorylation-dephosphorylation network controls osteoclast differentiation, and may provide new therapeutic strategies for bone diseases related to excessive osteoclast cell activity."

• Osteoporosis
• Bone and Spine
• Women's Health
• Medical Topics
• Diseases and Conditions
• Immune System
• Bone marrow
• Bone fracture
• Bone marrow transplant
• Adult stem cell

Story Source:

Materials provided by Tokyo University of Science . Note: Content may be edited for style and length.

Journal Reference :

• Takuto Konno, Hitomi Murachi, Kanon Otsuka, Yuta Kimura, Chisato Sampei, Yasuhiro Arasaki, Yukihiro Kohara, Tadayoshi Hayata. Ctdnep1 phosphatase is required for negative regulation of RANKL-induced osteoclast differentiation in RAW264.7 cells . Biochemical and Biophysical Research Communications , 2024; 719: 150063 DOI: 10.1016/j.bbrc.2024.150063

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