what is related studies in research example

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How to Make a Literature Review in Research (RRL Example)

What is an RRL in a research paper?

A relevant review of the literature (RRL) is an objective, concise, critical summary of published research literature relevant to a topic being researched in an article. In an RRL, you discuss knowledge and findings from existing literature relevant to your study topic. If there are conflicts or gaps in existing literature, you can also discuss these in your review, as well as how you will confront these missing elements or resolve these issues in your study.

To complete an RRL, you first need to collect relevant literature; this can include online and offline sources. Save all of your applicable resources as you will need to include them in your paper. When looking through these sources, take notes and identify concepts of each source to describe in the review of the literature.

A good RRL does NOT:

A literature review does not simply reference and list all of the material you have cited in your paper.

• Presenting material that is not directly relevant to your study will distract and frustrate the reader and make them lose sight of the purpose of your study.
• Starting a literature review with “A number of scholars have studied the relationship between X and Y” and simply listing who has studied the topic and what each scholar concluded is not going to strengthen your paper.

A good RRL DOES:

• Present a brief typology that orders articles and books into groups to help readers focus on unresolved debates, inconsistencies, tensions, and new questions about a research topic.
• Summarize the most relevant and important aspects of the scientific literature related to your area of research
• Synthesize what has been done in this area of research and by whom, highlight what previous research indicates about a topic, and identify potential gaps and areas of disagreement in the field
• Give the reader an understanding of the background of the field and show which studies are important—and highlight errors in previous studies

How long is a review of the literature for a research paper?

The length of a review of the literature depends on its purpose and target readership and can vary significantly in scope and depth. In a dissertation, thesis, or standalone review of literature, it is usually a full chapter of the text (at least 20 pages). Whereas, a standard research article or school assignment literature review section could only be a few paragraphs in the Introduction section .

Building Your Literature Review Bookshelf

One way to conceive of a literature review is to think about writing it as you would build a bookshelf. You don’t need to cut each piece by yourself from scratch. Rather, you can take the pieces that other researchers have cut out and put them together to build a framework on which to hang your own “books”—that is, your own study methods, results, and conclusions.

What Makes a Good Literature Review?

The contents of a literature review (RRL) are determined by many factors, including its precise purpose in the article, the degree of consensus with a given theory or tension between competing theories, the length of the article, the number of previous studies existing in the given field, etc. The following are some of the most important elements that a literature review provides.

Historical background for your research

Analyze what has been written about your field of research to highlight what is new and significant in your study—or how the analysis itself contributes to the understanding of this field, even in a small way. Providing a historical background also demonstrates to other researchers and journal editors your competency in discussing theoretical concepts. You should also make sure to understand how to paraphrase scientific literature to avoid plagiarism in your work.

The current context of your research

Discuss central (or peripheral) questions, issues, and debates in the field. Because a field is constantly being updated by new work, you can show where your research fits into this context and explain developments and trends in research.

A discussion of relevant theories and concepts

Theories and concepts should provide the foundation for your research. For example, if you are researching the relationship between ecological environments and human populations, provide models and theories that focus on specific aspects of this connection to contextualize your study. If your study asks a question concerning sustainability, mention a theory or model that underpins this concept. If it concerns invasive species, choose material that is focused in this direction.

Definitions of relevant terminology

In the natural sciences, the meaning of terms is relatively straightforward and consistent. But if you present a term that is obscure or context-specific, you should define the meaning of the term in the Introduction section (if you are introducing a study) or in the summary of the literature being reviewed.

Description of related relevant research

Include a description of related research that shows how your work expands or challenges earlier studies or fills in gaps in previous work. You can use your literature review as evidence of what works, what doesn’t, and what is missing in the field.

Supporting evidence for a practical problem or issue your research is addressing that demonstrates its importance: Referencing related research establishes your area of research as reputable and shows you are building upon previous work that other researchers have deemed significant.

Types of Literature Reviews

Literature reviews can differ in structure, length, amount, and breadth of content included. They can range from selective (a very narrow area of research or only a single work) to comprehensive (a larger amount or range of works). They can also be part of a larger work or stand on their own.

• A course assignment is an example of a selective, stand-alone work. It focuses on a small segment of the literature on a topic and makes up an entire work on its own.
• The literature review in a dissertation or thesis is both comprehensive and helps make up a larger work.
• A majority of journal articles start with a selective literature review to provide context for the research reported in the study; such a literature review is usually included in the Introduction section (but it can also follow the presentation of the results in the Discussion section ).
• Some literature reviews are both comprehensive and stand as a separate work—in this case, the entire article analyzes the literature on a given topic.

Literature Reviews Found in Academic Journals

The two types of literature reviews commonly found in journals are those introducing research articles (studies and surveys) and stand-alone literature analyses. They can differ in their scope, length, and specific purpose.

Literature reviews introducing research articles

The literature review found at the beginning of a journal article is used to introduce research related to the specific study and is found in the Introduction section, usually near the end. It is shorter than a stand-alone review because it must be limited to very specific studies and theories that are directly relevant to the current study. Its purpose is to set research precedence and provide support for the study’s theory, methods, results, and/or conclusions. Not all research articles contain an explicit review of the literature, but most do, whether it is a discrete section or indistinguishable from the rest of the Introduction.

How to structure a literature review for an article

When writing a literature review as part of an introduction to a study, simply follow the structure of the Introduction and move from the general to the specific—presenting the broadest background information about a topic first and then moving to specific studies that support your rationale , finally leading to your hypothesis statement. Such a literature review is often indistinguishable from the Introduction itself—the literature is INTRODUCING the background and defining the gaps your study aims to fill.

The stand-alone literature review

The literature review published as a stand-alone article presents and analyzes as many of the important publications in an area of study as possible to provide background information and context for a current area of research or a study. Stand-alone reviews are an excellent resource for researchers when they are first searching for the most relevant information on an area of study.

Such literature reviews are generally a bit broader in scope and can extend further back in time. This means that sometimes a scientific literature review can be highly theoretical, in addition to focusing on specific methods and outcomes of previous studies. In addition, all sections of such a “review article” refer to existing literature rather than describing the results of the authors’ own study.

In addition, this type of literature review is usually much longer than the literature review introducing a study. At the end of the review follows a conclusion that once again explicitly ties all of the cited works together to show how this analysis is itself a contribution to the literature. While not absolutely necessary, such articles often include the terms “Literature Review” or “Review of the Literature” in the title. Whether or not that is necessary or appropriate can also depend on the specific author instructions of the target journal. Have a look at this article for more input on how to compile a stand-alone review article that is insightful and helpful for other researchers in your field.

How to Write a Literature Review in 6 Steps

So how do authors turn a network of articles into a coherent review of relevant literature?

Writing a literature review is not usually a linear process—authors often go back and check the literature while reformulating their ideas or making adjustments to their study. Sometimes new findings are published before a study is completed and need to be incorporated into the current work. This also means you will not be writing the literature review at any one time, but constantly working on it before, during, and after your study is complete.

Here are some steps that will help you begin and follow through on your literature review.

Step 1: Choose a topic to write about—focus on and explore this topic.

Choose a topic that you are familiar with and highly interested in analyzing; a topic your intended readers and researchers will find interesting and useful; and a topic that is current, well-established in the field, and about which there has been sufficient research conducted for a review. This will help you find the “sweet spot” for what to focus on.

Step 2: Research and collect all the scholarly information on the topic that might be pertinent to your study.

This includes scholarly articles, books, conventions, conferences, dissertations, and theses—these and any other academic work related to your area of study is called “the literature.”

Step 3: Analyze the network of information that extends or responds to the major works in your area; select the material that is most useful.

Use thought maps and charts to identify intersections in the research and to outline important categories; select the material that will be most useful to your review.

Step 4: Describe and summarize each article—provide the essential information of the article that pertains to your study.

Determine 2-3 important concepts (depending on the length of your article) that are discussed in the literature; take notes about all of the important aspects of this study relevant to the topic being reviewed.

For example, in a given study, perhaps some of the main concepts are X, Y, and Z. Note these concepts and then write a brief summary about how the article incorporates them. In reviews that introduce a study, these can be relatively short. In stand-alone reviews, there may be significantly more texts and more concepts.

Step 5: Demonstrate how these concepts in the literature relate to what you discovered in your study or how the literature connects the concepts or topics being discussed.

In a literature review intro for an article, this information might include a summary of the results or methods of previous studies that correspond to and/or confirm those sections in your own study. For a stand-alone literature review, this may mean highlighting the concepts in each article and showing how they strengthen a hypothesis or show a pattern.

Discuss unaddressed issues in previous studies. These studies that are missing something you address are important to include in your literature review. In addition, those works whose theories and conclusions directly support your findings will be valuable to review here.

Step 6: Identify relationships in the literature and develop and connect your own ideas to them.

This is essentially the same as step 5 but focused on the connections between the literature and the current study or guiding concepts or arguments of the paper, not only on the connections between the works themselves.

Your hypothesis, argument, or guiding concept is the “golden thread” that will ultimately tie the works together and provide readers with specific insights they didn’t have before reading your literature review. Make sure you know where to put the research question , hypothesis, or statement of the problem in your research paper so that you guide your readers logically and naturally from your introduction of earlier work and evidence to the conclusions you want them to draw from the bigger picture.

Your literature review will not only cover publications on your topics but will include your own ideas and contributions. By following these steps you will be telling the specific story that sets the background and shows the significance of your research and you can turn a network of related works into a focused review of the literature.

Literature Review (RRL) Examples

Because creating sample literature reviews would take too long and not properly capture the nuances and detailed information needed for a good review, we have included some links to different types of literature reviews below. You can find links to more literature reviews in these categories by visiting the TUS Library’s website . Sample literature reviews as part of an article, dissertation, or thesis:

• Critical Thinking and Transferability: A Review of the Literature (Gwendolyn Reece)
• Building Customer Loyalty: A Customer Experience Based Approach in a Tourism Context (Martina Donnelly)

Sample stand-alone literature reviews

• Literature Review on Attitudes towards Disability (National Disability Authority)
• The Effects of Communication Styles on Marital Satisfaction (Hannah Yager)

Additional Literature Review Format Guidelines

In addition to the content guidelines above, authors also need to check which style guidelines to use ( APA , Chicago, MLA, etc.) and what specific rules the target journal might have for how to structure such articles or how many studies to include—such information can usually be found on the journals’ “Guide for Authors” pages. Additionally, use one of the four Wordvice citation generators below, choosing the citation style needed for your paper:

Wordvice Writing and Academic Editing Resources

Finally, after you have finished drafting your literature review, be sure to receive professional proofreading services , including paper editing for your academic work. A competent proofreader who understands academic writing conventions and the specific style guides used by academic journals will ensure that your paper is ready for publication in your target journal.

See our academic resources for further advice on references in your paper , how to write an abstract , how to write a research paper title, how to impress the editor of your target journal with a perfect cover letter , and dozens of other research writing and publication topics.

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A literature review surveys prior research published in books, scholarly articles, and any other sources relevant to a particular issue, area of research, or theory, and by so doing, provides a description, summary, and critical evaluation of these works in relation to the research problem being investigated. Literature reviews are designed to provide an overview of sources you have used in researching a particular topic and to demonstrate to your readers how your research fits within existing scholarship about the topic.

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . Fourth edition. Thousand Oaks, CA: SAGE, 2014.

Importance of a Good Literature Review

A literature review may consist of simply a summary of key sources, but in the social sciences, a literature review usually has an organizational pattern and combines both summary and synthesis, often within specific conceptual categories . A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information in a way that informs how you are planning to investigate a research problem. The analytical features of a literature review might:

• Give a new interpretation of old material or combine new with old interpretations,
• Trace the intellectual progression of the field, including major debates,
• Depending on the situation, evaluate the sources and advise the reader on the most pertinent or relevant research, or
• Usually in the conclusion of a literature review, identify where gaps exist in how a problem has been researched to date.

Given this, the purpose of a literature review is to:

• Place each work in the context of its contribution to understanding the research problem being studied.
• Describe the relationship of each work to the others under consideration.
• Identify new ways to interpret prior research.
• Reveal any gaps that exist in the literature.
• Resolve conflicts amongst seemingly contradictory previous studies.
• Identify areas of prior scholarship to prevent duplication of effort.
• Point the way in fulfilling a need for additional research.
• Locate your own research within the context of existing literature [very important].

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper. 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . Los Angeles, CA: SAGE, 2011; Knopf, Jeffrey W. "Doing a Literature Review." PS: Political Science and Politics 39 (January 2006): 127-132; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012.

Types of Literature Reviews

It is important to think of knowledge in a given field as consisting of three layers. First, there are the primary studies that researchers conduct and publish. Second are the reviews of those studies that summarize and offer new interpretations built from and often extending beyond the primary studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally among scholars that become part of the body of epistemological traditions within the field.

In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews. Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are a number of approaches you could adopt depending upon the type of analysis underpinning your study.

Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply embedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to make summary claims of the sort found in systematic reviews [see below].

Integrative Review Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses or research problems. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication. This is the most common form of review in the social sciences.

Historical Review Few things rest in isolation from historical precedent. Historical literature reviews focus on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research.

Methodological Review A review does not always focus on what someone said [findings], but how they came about saying what they say [method of analysis]. Reviewing methods of analysis provides a framework of understanding at different levels [i.e. those of theory, substantive fields, research approaches, and data collection and analysis techniques], how researchers draw upon a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection, and data analysis. This approach helps highlight ethical issues which you should be aware of and consider as you go through your own study.

Systematic Review This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyze data from the studies that are included in the review. The goal is to deliberately document, critically evaluate, and summarize scientifically all of the research about a clearly defined research problem . Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?" This type of literature review is primarily applied to examining prior research studies in clinical medicine and allied health fields, but it is increasingly being used in the social sciences.

Theoretical Review The purpose of this form is to examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review helps to establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework.

NOTE : Most often the literature review will incorporate some combination of types. For example, a review that examines literature supporting or refuting an argument, assumption, or philosophical problem related to the research problem will also need to include writing supported by sources that establish the history of these arguments in the literature.

Baumeister, Roy F. and Mark R. Leary. "Writing Narrative Literature Reviews."  Review of General Psychology 1 (September 1997): 311-320; Mark R. Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147; Petticrew, Mark and Helen Roberts. Systematic Reviews in the Social Sciences: A Practical Guide . Malden, MA: Blackwell Publishers, 2006; Torracro, Richard. "Writing Integrative Literature Reviews: Guidelines and Examples." Human Resource Development Review 4 (September 2005): 356-367; Rocco, Tonette S. and Maria S. Plakhotnik. "Literature Reviews, Conceptual Frameworks, and Theoretical Frameworks: Terms, Functions, and Distinctions." Human Ressource Development Review 8 (March 2008): 120-130; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

Structure and Writing Style

I.  Thinking About Your Literature Review

The structure of a literature review should include the following in support of understanding the research problem :

• An overview of the subject, issue, or theory under consideration, along with the objectives of the literature review,
• Division of works under review into themes or categories [e.g. works that support a particular position, those against, and those offering alternative approaches entirely],
• An explanation of how each work is similar to and how it varies from the others,
• Conclusions as to which pieces are best considered in their argument, are most convincing of their opinions, and make the greatest contribution to the understanding and development of their area of research.

The critical evaluation of each work should consider :

• Provenance -- what are the author's credentials? Are the author's arguments supported by evidence [e.g. primary historical material, case studies, narratives, statistics, recent scientific findings]?
• Methodology -- were the techniques used to identify, gather, and analyze the data appropriate to addressing the research problem? Was the sample size appropriate? Were the results effectively interpreted and reported?
• Objectivity -- is the author's perspective even-handed or prejudicial? Is contrary data considered or is certain pertinent information ignored to prove the author's point?
• Persuasiveness -- which of the author's theses are most convincing or least convincing?
• Validity -- are the author's arguments and conclusions convincing? Does the work ultimately contribute in any significant way to an understanding of the subject?

II.  Development of the Literature Review

Four Basic Stages of Writing 1.  Problem formulation -- which topic or field is being examined and what are its component issues? 2.  Literature search -- finding materials relevant to the subject being explored. 3.  Data evaluation -- determining which literature makes a significant contribution to the understanding of the topic. 4.  Analysis and interpretation -- discussing the findings and conclusions of pertinent literature.

Consider the following issues before writing the literature review: Clarify If your assignment is not specific about what form your literature review should take, seek clarification from your professor by asking these questions: 1.  Roughly how many sources would be appropriate to include? 2.  What types of sources should I review (books, journal articles, websites; scholarly versus popular sources)? 3.  Should I summarize, synthesize, or critique sources by discussing a common theme or issue? 4.  Should I evaluate the sources in any way beyond evaluating how they relate to understanding the research problem? 5.  Should I provide subheadings and other background information, such as definitions and/or a history? Find Models Use the exercise of reviewing the literature to examine how authors in your discipline or area of interest have composed their literature review sections. Read them to get a sense of the types of themes you might want to look for in your own research or to identify ways to organize your final review. The bibliography or reference section of sources you've already read, such as required readings in the course syllabus, are also excellent entry points into your own research. Narrow the Topic The narrower your topic, the easier it will be to limit the number of sources you need to read in order to obtain a good survey of relevant resources. Your professor will probably not expect you to read everything that's available about the topic, but you'll make the act of reviewing easier if you first limit scope of the research problem. A good strategy is to begin by searching the USC Libraries Catalog for recent books about the topic and review the table of contents for chapters that focuses on specific issues. You can also review the indexes of books to find references to specific issues that can serve as the focus of your research. For example, a book surveying the history of the Israeli-Palestinian conflict may include a chapter on the role Egypt has played in mediating the conflict, or look in the index for the pages where Egypt is mentioned in the text. Consider Whether Your Sources are Current Some disciplines require that you use information that is as current as possible. This is particularly true in disciplines in medicine and the sciences where research conducted becomes obsolete very quickly as new discoveries are made. However, when writing a review in the social sciences, a survey of the history of the literature may be required. In other words, a complete understanding the research problem requires you to deliberately examine how knowledge and perspectives have changed over time. Sort through other current bibliographies or literature reviews in the field to get a sense of what your discipline expects. You can also use this method to explore what is considered by scholars to be a "hot topic" and what is not.

III.  Ways to Organize Your Literature Review

Chronology of Events If your review follows the chronological method, you could write about the materials according to when they were published. This approach should only be followed if a clear path of research building on previous research can be identified and that these trends follow a clear chronological order of development. For example, a literature review that focuses on continuing research about the emergence of German economic power after the fall of the Soviet Union. By Publication Order your sources by publication chronology, then, only if the order demonstrates a more important trend. For instance, you could order a review of literature on environmental studies of brown fields if the progression revealed, for example, a change in the soil collection practices of the researchers who wrote and/or conducted the studies. Thematic [“conceptual categories”] A thematic literature review is the most common approach to summarizing prior research in the social and behavioral sciences. Thematic reviews are organized around a topic or issue, rather than the progression of time, although the progression of time may still be incorporated into a thematic review. For example, a review of the Internet’s impact on American presidential politics could focus on the development of online political satire. While the study focuses on one topic, the Internet’s impact on American presidential politics, it would still be organized chronologically reflecting technological developments in media. The difference in this example between a "chronological" and a "thematic" approach is what is emphasized the most: themes related to the role of the Internet in presidential politics. Note that more authentic thematic reviews tend to break away from chronological order. A review organized in this manner would shift between time periods within each section according to the point being made. Methodological A methodological approach focuses on the methods utilized by the researcher. For the Internet in American presidential politics project, one methodological approach would be to look at cultural differences between the portrayal of American presidents on American, British, and French websites. Or the review might focus on the fundraising impact of the Internet on a particular political party. A methodological scope will influence either the types of documents in the review or the way in which these documents are discussed.

Other Sections of Your Literature Review Once you've decided on the organizational method for your literature review, the sections you need to include in the paper should be easy to figure out because they arise from your organizational strategy. In other words, a chronological review would have subsections for each vital time period; a thematic review would have subtopics based upon factors that relate to the theme or issue. However, sometimes you may need to add additional sections that are necessary for your study, but do not fit in the organizational strategy of the body. What other sections you include in the body is up to you. However, only include what is necessary for the reader to locate your study within the larger scholarship about the research problem.

Here are examples of other sections, usually in the form of a single paragraph, you may need to include depending on the type of review you write:

• Current Situation : Information necessary to understand the current topic or focus of the literature review.
• Sources Used : Describes the methods and resources [e.g., databases] you used to identify the literature you reviewed.
• History : The chronological progression of the field, the research literature, or an idea that is necessary to understand the literature review, if the body of the literature review is not already a chronology.
• Selection Methods : Criteria you used to select (and perhaps exclude) sources in your literature review. For instance, you might explain that your review includes only peer-reviewed [i.e., scholarly] sources.
• Standards : Description of the way in which you present your information.
• Questions for Further Research : What questions about the field has the review sparked? How will you further your research as a result of the review?

IV.  Writing Your Literature Review

Once you've settled on how to organize your literature review, you're ready to write each section. When writing your review, keep in mind these issues.

Use Evidence A literature review section is, in this sense, just like any other academic research paper. Your interpretation of the available sources must be backed up with evidence [citations] that demonstrates that what you are saying is valid. Be Selective Select only the most important points in each source to highlight in the review. The type of information you choose to mention should relate directly to the research problem, whether it is thematic, methodological, or chronological. Related items that provide additional information, but that are not key to understanding the research problem, can be included in a list of further readings . Use Quotes Sparingly Some short quotes are appropriate if you want to emphasize a point, or if what an author stated cannot be easily paraphrased. Sometimes you may need to quote certain terminology that was coined by the author, is not common knowledge, or taken directly from the study. Do not use extensive quotes as a substitute for using your own words in reviewing the literature. Summarize and Synthesize Remember to summarize and synthesize your sources within each thematic paragraph as well as throughout the review. Recapitulate important features of a research study, but then synthesize it by rephrasing the study's significance and relating it to your own work and the work of others. Keep Your Own Voice While the literature review presents others' ideas, your voice [the writer's] should remain front and center. For example, weave references to other sources into what you are writing but maintain your own voice by starting and ending the paragraph with your own ideas and wording. Use Caution When Paraphrasing When paraphrasing a source that is not your own, be sure to represent the author's information or opinions accurately and in your own words. Even when paraphrasing an author’s work, you still must provide a citation to that work.

V.  Common Mistakes to Avoid

These are the most common mistakes made in reviewing social science research literature.

• Sources in your literature review do not clearly relate to the research problem;
• You do not take sufficient time to define and identify the most relevant sources to use in the literature review related to the research problem;
• Relies exclusively on secondary analytical sources rather than including relevant primary research studies or data;
• Uncritically accepts another researcher's findings and interpretations as valid, rather than examining critically all aspects of the research design and analysis;
• Does not describe the search procedures that were used in identifying the literature to review;
• Reports isolated statistical results rather than synthesizing them in chi-squared or meta-analytic methods; and,
• Only includes research that validates assumptions and does not consider contrary findings and alternative interpretations found in the literature.

Cook, Kathleen E. and Elise Murowchick. “Do Literature Review Skills Transfer from One Course to Another?” Psychology Learning and Teaching 13 (March 2014): 3-11; Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . London: SAGE, 2011; Literature Review Handout. Online Writing Center. Liberty University; Literature Reviews. The Writing Center. University of North Carolina; Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: SAGE, 2016; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012; Randolph, Justus J. “A Guide to Writing the Dissertation Literature Review." Practical Assessment, Research, and Evaluation. vol. 14, June 2009; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016; Taylor, Dena. The Literature Review: A Few Tips On Conducting It. University College Writing Centre. University of Toronto; Writing a Literature Review. Academic Skills Centre. University of Canberra.

Writing Tip

Break Out of Your Disciplinary Box!

Thinking interdisciplinarily about a research problem can be a rewarding exercise in applying new ideas, theories, or concepts to an old problem. For example, what might cultural anthropologists say about the continuing conflict in the Middle East? In what ways might geographers view the need for better distribution of social service agencies in large cities than how social workers might study the issue? You don’t want to substitute a thorough review of core research literature in your discipline for studies conducted in other fields of study. However, particularly in the social sciences, thinking about research problems from multiple vectors is a key strategy for finding new solutions to a problem or gaining a new perspective. Consult with a librarian about identifying research databases in other disciplines; almost every field of study has at least one comprehensive database devoted to indexing its research literature.

Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Just Review for Content!

While conducting a review of the literature, maximize the time you devote to writing this part of your paper by thinking broadly about what you should be looking for and evaluating. Review not just what scholars are saying, but how are they saying it. Some questions to ask:

• How are they organizing their ideas?
• What methods have they used to study the problem?
• What theories have been used to explain, predict, or understand their research problem?
• What sources have they cited to support their conclusions?
• How have they used non-textual elements [e.g., charts, graphs, figures, etc.] to illustrate key points?

When you begin to write your literature review section, you'll be glad you dug deeper into how the research was designed and constructed because it establishes a means for developing more substantial analysis and interpretation of the research problem.

Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1 998.

Yet Another Writing Tip

When Do I Know I Can Stop Looking and Move On?

Here are several strategies you can utilize to assess whether you've thoroughly reviewed the literature:

• Look for repeating patterns in the research findings . If the same thing is being said, just by different people, then this likely demonstrates that the research problem has hit a conceptual dead end. At this point consider: Does your study extend current research?  Does it forge a new path? Or, does is merely add more of the same thing being said?
• Look at sources the authors cite to in their work . If you begin to see the same researchers cited again and again, then this is often an indication that no new ideas have been generated to address the research problem.
• Search Google Scholar to identify who has subsequently cited leading scholars already identified in your literature review [see next sub-tab]. This is called citation tracking and there are a number of sources that can help you identify who has cited whom, particularly scholars from outside of your discipline. Here again, if the same authors are being cited again and again, this may indicate no new literature has been written on the topic.

Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: Sage, 2016; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

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Student Academic Success Center

Related work / literature review / research review, download pdf handout:   literature reviews, watch video:   literature reviews.

A  literature review, research review,  or  related work   section compares, contrasts, synthesizes, and provides introspection about the available knowledge for a given topic or field. The two terms are sometimes used interchangeably (as they are here), but while both can refer to a section of a longer work, “literature review” can also describe a stand-alone paper.

When you start writing a literature review, the most straightforward course may be to compile all relevant sources and compare them, perhaps evaluating their strengths and weaknesses. While this is a good place to start, your literature review is incomplete unless it creates something new through these comparisons. Luckily, our resources can help you do this!

With these resources, you’ll learn:

• How to write a literature review that  contributes  rather than  summarizes
• Common mistakes to avoid
• Useful phrases to show agreement and disagreement between sources

Need one-on-one help with your literature review or research article? Schedule an appointment with one of our consultants now!

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Review of Related Literature: Format, Example, & How to Make RRL

A review of related literature is a separate paper or a part of an article that collects and synthesizes discussion on a topic. Its purpose is to show the current state of research on the issue and highlight gaps in existing knowledge. A literature review can be included in a research paper or scholarly article, typically following the introduction and before the research methods section.

This article will clarify the definition, significance, and structure of a review of related literature. You’ll also learn how to organize your literature review and discover ideas for an RRL in different subjects.

🔤 What Is RRL?

• ❗ Significance of Literature Review
• 🔎 How to Search for Literature
• 🧩 Literature Review Structure
• 📋 Format of RRL — APA, MLA, & Others
• ✍️ How to Write an RRL
• 📚 Examples of RRL

🔗 References

A review of related literature (RRL) is a part of the research report that examines significant studies, theories, and concepts published in scholarly sources on a particular topic. An RRL includes 3 main components:

• A short overview and critique of the previous research.
• Similarities and differences between past studies and the current one.
• An explanation of the theoretical frameworks underpinning the research.

❗ Significance of Review of Related Literature

Although the goal of a review of related literature differs depending on the discipline and its intended use, its significance cannot be overstated. Here are some examples of how a review might be beneficial:

• It helps determine knowledge gaps .
• It saves from duplicating research that has already been conducted.
• It provides an overview of various research areas within the discipline.
• It demonstrates the researcher’s familiarity with the topic.

🔎 How to Perform a Literature Search

Including a description of your search strategy in the literature review section can significantly increase your grade. You can search sources with the following steps:

🧩 Literature Review Structure Example

The majority of literature reviews follow a standard introduction-body-conclusion structure. Let’s look at the RRL structure in detail.

Introduction of Review of Related Literature: Sample

An introduction should clarify the study topic and the depth of the information to be delivered. It should also explain the types of sources used. If your lit. review is part of a larger research proposal or project, you can combine its introductory paragraph with the introduction of your paper.

Here is a sample introduction to an RRL about cyberbullying:

Bullying has troubled people since the beginning of time. However, with modern technological advancements, especially social media, bullying has evolved into cyberbullying. As a result, nowadays, teenagers and adults cannot flee their bullies, which makes them feel lonely and helpless. This literature review will examine recent studies on cyberbullying.

Sample Review of Related Literature Thesis

A thesis statement should include the central idea of your literature review and the primary supporting elements you discovered in the literature. Thesis statements are typically put at the end of the introductory paragraph.

Look at a sample thesis of a review of related literature:

This literature review shows that scholars have recently covered the issues of bullies’ motivation, the impact of bullying on victims and aggressors, common cyberbullying techniques, and victims’ coping strategies. However, there is still no agreement on the best practices to address cyberbullying.

Literature Review Body Paragraph Example

The main body of a literature review should provide an overview of the existing research on the issue. Body paragraphs should not just summarize each source but analyze them. You can organize your paragraphs with these 3 elements:

• Claim . Start with a topic sentence linked to your literature review purpose.
• Evidence . Cite relevant information from your chosen sources.
• Discussion . Explain how the cited data supports your claim.

Here’s a literature review body paragraph example:

Scholars have examined the link between the aggressor and the victim. Beran et al. (2007) state that students bullied online often become cyberbullies themselves. Faucher et al. (2014) confirm this with their findings: they discovered that male and female students began engaging in cyberbullying after being subject to bullying. Hence, one can conclude that being a victim of bullying increases one’s likelihood of becoming a cyberbully.

Review of Related Literature: Conclusion

A conclusion presents a general consensus on the topic. Depending on your literature review purpose, it might include the following:

• Introduction to further research . If you write a literature review as part of a larger research project, you can present your research question in your conclusion .
• Overview of theories . You can summarize critical theories and concepts to help your reader understand the topic better.
• Discussion of the gap . If you identified a research gap in the reviewed literature, your conclusion could explain why that gap is significant.

Check out a conclusion example that discusses a research gap:

There is extensive research into bullies’ motivation, the consequences of bullying for victims and aggressors, strategies for bullying, and coping with it. Yet, scholars still have not reached a consensus on what to consider the best practices to combat cyberbullying. This question is of great importance because of the significant adverse effects of cyberbullying on victims and bullies.

📋 Format of RRL — APA, MLA, & Others

In this section, we will discuss how to format an RRL according to the most common citation styles: APA, Chicago, MLA, and Harvard.

Writing a literature review using the APA7 style requires the following text formatting:

• When using APA in-text citations , include the author’s last name and the year of publication in parentheses.
• For direct quotations , you must also add the page number. If you use sources without page numbers, such as websites or e-books, include a paragraph number instead.
• When referring to the author’s name in a sentence , you do not need to repeat it at the end of the sentence. Instead, include the year of publication inside the parentheses after their name.
• The reference list should be included at the end of your literature review. It is always alphabetized by the last name of the author (from A to Z), and the lines are indented one-half inch from the left margin of your paper. Do not forget to invert authors’ names (the last name should come first) and include the full titles of journals instead of their abbreviations. If you use an online source, add its URL.

The RRL format in the Chicago style is as follows:

• Author-date . You place your citations in brackets within the text, indicating the name of the author and the year of publication.
• Notes and bibliography . You place your citations in numbered footnotes or endnotes to connect the citation back to the source in the bibliography.
• The reference list, or bibliography , in Chicago style, is at the end of a literature review. The sources are arranged alphabetically and single-spaced. Each bibliography entry begins with the author’s name and the source’s title, followed by publication information, such as the city of publication, the publisher, and the year of publication.

Writing a literature review using the MLA style requires the following text formatting:

• In the MLA format, you can cite a source in the text by indicating the author’s last name and the page number in parentheses at the end of the citation. If the cited information takes several pages, you need to include all the page numbers.
• The reference list in MLA style is titled “ Works Cited .” In this section, all sources used in the paper should be listed in alphabetical order. Each entry should contain the author, title of the source, title of the journal or a larger volume, other contributors, version, number, publisher, and publication date.

The Harvard style requires you to use the following text formatting for your RRL:

• In-text citations in the Harvard style include the author’s last name and the year of publication. If you are using a direct quote in your literature review, you need to add the page number as well.
• Arrange your list of references alphabetically. Each entry should contain the author’s last name, their initials, the year of publication, the title of the source, and other publication information, like the journal title and issue number or the publisher.

✍️ How to Write Review of Related Literature – Sample

Literature reviews can be organized in many ways depending on what you want to achieve with them. In this section, we will look at 3 examples of how you can write your RRL.

Thematic Literature Review

A thematic literature review is arranged around central themes or issues discussed in the sources. If you have identified some recurring themes in the literature, you can divide your RRL into sections that address various aspects of the topic. For example, if you examine studies on e-learning, you can distinguish such themes as the cost-effectiveness of online learning, the technologies used, and its effectiveness compared to traditional education.

Chronological Literature Review

A chronological literature review is a way to track the development of the topic over time. If you use this method, avoid merely listing and summarizing sources in chronological order. Instead, try to analyze the trends, turning moments, and critical debates that have shaped the field’s path. Also, you can give your interpretation of how and why specific advances occurred.

Methodological Literature Review

A methodological literature review differs from the preceding ones in that it usually doesn’t focus on the sources’ content. Instead, it is concerned with the research methods . So, if your references come from several disciplines or fields employing various research techniques, you can compare the findings and conclusions of different methodologies, for instance:

• empirical vs. theoretical studies;
• qualitative vs. quantitative research.

📚 Examples of Review of Related Literature and Studies

We have prepared a short example of RRL on climate change for you to see how everything works in practice!

Climate change is one of the most important issues nowadays. Based on a variety of facts, it is now clearer than ever that humans are altering the Earth's climate. The atmosphere and oceans have warmed, causing sea level rise, a significant loss of Arctic ice, and other climate-related changes. This literature review provides a thorough summary of research on climate change, focusing on climate change fingerprints and evidence of human influence on the Earth's climate system.

Physical Mechanisms and Evidence of Human Influence

Scientists are convinced that climate change is directly influenced by the emission of greenhouse gases. They have carefully analyzed various climate data and evidence, concluding that the majority of the observed global warming over the past 50 years cannot be explained by natural factors alone. Instead, there is compelling evidence pointing to a significant contribution of human activities, primarily the emission of greenhouse gases (Walker, 2014). For example, based on simple physics calculations, doubled carbon dioxide concentration in the atmosphere can lead to a global temperature increase of approximately 1 degree Celsius. (Elderfield, 2022). In order to determine the human influence on climate, scientists still have to analyze a lot of natural changes that affect temperature, precipitation, and other components of climate on timeframes ranging from days to decades and beyond.

Fingerprinting Climate Change

Fingerprinting climate change is a useful tool to identify the causes of global warming because different factors leave unique marks on climate records. This is evident when scientists look beyond overall temperature changes and examine how warming is distributed geographically and over time (Watson, 2022). By investigating these climate patterns, scientists can obtain a more complex understanding of the connections between natural climate variability and climate variability caused by human activity.

Modeling Climate Change and Feedback

To accurately predict the consequences of feedback mechanisms, the rate of warming, and regional climate change, scientists can employ sophisticated mathematical models of the atmosphere, ocean, land, and ice (the cryosphere). These models are grounded in well-established physical laws and incorporate the latest scientific understanding of climate-related processes (Shuckburgh, 2013). Although different climate models produce slightly varying projections for future warming, they all will agree that feedback mechanisms play a significant role in amplifying the initial warming caused by greenhouse gas emissions. (Meehl, 2019).

In conclusion, the literature on global warming indicates that there are well-understood physical processes that link variations in greenhouse gas concentrations to climate change. In addition, it covers the scientific proof that the rates of these gases in the atmosphere have increased and continue to rise fast. According to the sources, the majority of this recent change is almost definitely caused by greenhouse gas emissions produced by human activities. Citizens and governments can alter their energy production methods and consumption patterns to reduce greenhouse gas emissions and, thus, the magnitude of climate change. By acting now, society can prevent the worst consequences of climate change and build a more resilient and sustainable future for generations to come.

Have you ever struggled with finding the topic for an RRL in different subjects? Read the following paragraphs to get some ideas!

Nursing Literature Review Example

Many topics in the nursing field require research. For example, you can write a review of literature related to dengue fever . Give a general overview of dengue virus infections, including its clinical symptoms, diagnosis, prevention, and therapy.

Another good idea is to review related literature and studies about teenage pregnancy . This review can describe the effectiveness of specific programs for adolescent mothers and their children and summarize recommendations for preventing early pregnancy.

📝 Check out some more valuable examples below:

• Hospital Readmissions: Literature Review .
• Literature Review: Lower Sepsis Mortality Rates .
• Breast Cancer: Literature Review .
• Sexually Transmitted Diseases: Literature Review .
• PICO for Pressure Ulcers: Literature Review .
• COVID-19 Spread Prevention: Literature Review .
• Chronic Obstructive Pulmonary Disease: Literature Review .
• Hypertension Treatment Adherence: Literature Review .
• Neonatal Sepsis Prevention: Literature Review .
• Healthcare-Associated Infections: Literature Review .
• Understaffing in Nursing: Literature Review .

Psychology Literature Review Example

If you look for an RRL topic in psychology , you can write a review of related literature about stress . Summarize scientific evidence about stress stages, side effects, types, or reduction strategies. Or you can write a review of related literature about computer game addiction . In this case, you may concentrate on the neural mechanisms underlying the internet gaming disorder, compare it to other addictions, or evaluate treatment strategies.

A review of related literature about cyberbullying is another interesting option. You can highlight the impact of cyberbullying on undergraduate students’ academic, social, and emotional development.

📝 Look at the examples that we have prepared for you to come up with some more ideas:

• Mindfulness in Counseling: A Literature Review .
• Team-Building Across Cultures: Literature Review .
• Anxiety and Decision Making: Literature Review .
• Literature Review on Depression .
• Literature Review on Narcissism .
• Effects of Depression Among Adolescents .
• Causes and Effects of Anxiety in Children .

Literature Review — Sociology Example

Sociological research poses critical questions about social structures and phenomena. For example, you can write a review of related literature about child labor , exploring cultural beliefs and social norms that normalize the exploitation of children. Or you can create a review of related literature about social media . It can investigate the impact of social media on relationships between adolescents or the role of social networks on immigrants’ acculturation .

📝 You can find some more ideas below!

• Single Mothers’ Experiences of Relationships with Their Adolescent Sons .
• Teachers and Students’ Gender-Based Interactions .
• Gender Identity: Biological Perspective and Social Cognitive Theory .
• Gender: Culturally-Prescribed Role or Biological Sex .
• The Influence of Opioid Misuse on Academic Achievement of Veteran Students .
• The Importance of Ethics in Research .
• The Role of Family and Social Network Support in Mental Health .

Education Literature Review Example

For your education studies , you can write a review of related literature about academic performance to determine factors that affect student achievement and highlight research gaps. One more idea is to create a review of related literature on study habits , considering their role in the student’s life and academic outcomes.

You can also evaluate a computerized grading system in a review of related literature to single out its advantages and barriers to implementation. Or you can complete a review of related literature on instructional materials to identify their most common types and effects on student achievement.

📝 Find some inspiration in the examples below:

• Literature Review on Online Learning Challenges From COVID-19 .
• Education, Leadership, and Management: Literature Review .
• Literature Review: Standardized Testing Bias .
• Bullying of Disabled Children in School .
• Interventions and Letter & Sound Recognition: A Literature Review .
• Social-Emotional Skills Program for Preschoolers .
• Effectiveness of Educational Leadership Management Skills .

Business Research Literature Review

If you’re a business student, you can focus on customer satisfaction in your review of related literature. Discuss specific customer satisfaction features and how it is affected by service quality and prices. You can also create a theoretical literature review about consumer buying behavior to evaluate theories that have significantly contributed to understanding how consumers make purchasing decisions.

📝 Look at the examples to get more exciting ideas:

• Leadership and Communication: Literature Review .
• Human Resource Development: Literature Review .
• Project Management. Literature Review .
• Strategic HRM: A Literature Review .
• Customer Relationship Management: Literature Review .
• Literature Review on International Financial Reporting Standards .
• Cultures of Management: Literature Review .

To conclude, a review of related literature is a significant genre of scholarly works that can be applied in various disciplines and for multiple goals. The sources examined in an RRL provide theoretical frameworks for future studies and help create original research questions and hypotheses.

When you finish your outstanding literature review, don’t forget to check whether it sounds logical and coherent. Our text-to-speech tool can help you with that!

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CHAPTER 2 REVIEW OF RELATED LITERATURE AND STUDIES

Related Papers

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... Rene E. Ofreneo University of the Philippines School of Labor and Industrial Relations ... Erickson, Christopher L.; Kuruvilla, Sarosh ; Ofreneo, Rene E.; and Ortiz, Maria Asuncion , &amp;quot;Recent Developments in Employment Relations in the Philippines&amp;quot; (2001). ...

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This book begins by looking at the status of women in Filipino society and their place in the general socio-economic situation. It continues with sections on education and training in the Philippines and work and training. The next section reviews the constraints to women’s participation in training. In the summary the author gives a general overview of the situation of women and opportunities for work and training in the Philippines and offers some practical suggestions for the enhancement of women’s training and development.

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In this article, I examine the role of CHED and the Technical Panels (TPs) in the “production” of the globally competitive Filipina/o worker. For this paper, I draw on relevant literature on the topic and take nurse education, which is rooted in the colonial system established during the US-American occupation, as an example of how CHED and the TPs could be more linked to labor migration. I use the colonial difference - a space that offers critical insights and interpretation - to illustrate how coloniality remains hidden under the cloak of modernity. Link to the article: http://dx.doi.org/10.1080/00131946.2016.1214913

Asia Pacific Journal of Management and Sustainable Development ISSN 2782-8557(Print)

Ryan O Tayco , Pio Supat

This study aims to determine the employability of the Negros Oriental State University graduates from 2016 to 2020. Employability is measured using different dimensions-from the graduates' side including the perspectives of the employers. A total of 1, 056 NORSU graduates and 68 employers locally and abroad answered the questionnaire through online and offline survey methods. Basic statistics were used and simple linear regression was also used to estimate the relationship between manifestations of respondents in NORSU VMGOs and the job performance as perceived by the employers. Most of the respondents in the study are presently employed and work locally. Many of them stay and accept the job because of the salaries and benefits they received, a career challenge, and related to the course they have taken in college. The study shows that the curriculum used and competencies learned by the NORSU graduates are relevant to their job. Competencies such as communication skills, human relations skills, critical thinking skills, and problem-solving skills are found to be useful by the respondents. It is found that the manifestation of the respondents is very high and homogenous. The same can be said with job performance as perceived by employers in terms of attitudes and values, skills and competencies, and knowledge. Furthermore, job performance and the manifestation of NORSU VMGOs have a significant relationship. That is, those respondents who have higher job performance in terms of attitude and values, skills and competencies, and knowledge have higher manifestations of NORSU VMGOs.

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Generally, “related research” exists when an investigator’s outside activity, interest, or relationship could be affected by the results of the research or it could create bias or the perception of bias in the design, conduct, or reporting of the research. 

Examples of related research include:

• An Investigator provides consulting services to a company and the company supports their University research. Support may include funding, providing services or providing drugs, devices, data, or other materials.   
• An Investigator conducts research that tests or evaluates technology they invented and that has been licensed to a company.   
• An Investigator created a start-up company and conducts University research that could affect the value of that company, including the value of the company’s products, services, or technology.  
• An Investigator owns equity in a company and conducts research that is expected to lead to outcomes that might have a direct effect, in the near future, on the company’s finances, marketing or business interests.

What is a related work? A typology of relationships in research literature

• Original Research
• Open access
• Published: 09 January 2023
• Volume 201 , article number  24 , ( 2023 )

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• Shayan Doroudi   ORCID: orcid.org/0000-0002-0602-1406 1  

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An important part of research is situating one’s work in a body of existing literature, thereby connecting to existing ideas. Despite this, the various kinds of relationships that might exist among academic literature do not appear to have been formally studied. Here I present a graphical representation of academic work in terms of entities and relations, drawing on structure-mapping theory (used in the study of analogies). I then use this representation to present a typology of operations that could relate two pieces of academic work. I illustrate the various types of relationships with examples from medicine, physics, psychology, history and philosophy of science, machine learning, education, and neuroscience. The resulting typology not only gives insights into the relationships that might exist between static publications, but also the rich process whereby an ongoing research project evolves through interactions with the research literature.

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Understanding Research Dynamics

The evolution of knowledge within and across fields in modern physics

Introduction

Avoid common mistakes on your manuscript.

1 Introduction

An important part of the research process is literature search: identifying prior work that is of relevance to the present idea being investigated. In many cases, this is an activity that a researcher may defer until writing up the results of the project, in which case, it is primarily an activity one does because one “has to” rather than an activity that can substantially change the course of the research. In some cases, whether due to negligence or the difficulty of finding related works, a researcher may never come across the fact that someone had previously tackled the same problem or made a similar discovery, and perhaps only years later (if ever) it may be realized (Merton, 1963 ; Ke et al., 2015 ; Sacks, 2002 ). But at its best, this is an activity that leads to new insights into the research problem, generates new ideas, and alters the course of the research. In fact, in some cases, searching for related work can become the research process itself; through connecting various pieces of research literature alone, one can discover previously undiscovered public knowledge (Swanson, 1986 ).

Despite the importance of prior literature in the research process, there has been little effort, if any, dedicated to developing a typology of related works, that is, a typology of relationships that might exist among different pieces of research literature. (Of course, it is entirely possible that such a typology has been constructed, but I have missed it due to an inadequate search of the literature!) In this paper, I propose such a typology to help us better understand the kinds of prior work that might have bearing on a research project. I first present a form of knowledge representation that can theoretically be used to represent any piece of research literature or research project. I then present a typology of relationships that can connect two pieces of research in terms of operations that can apply to the two representations, thereby resulting in a representation of the relationship. I will demonstrate the various operations and how they might be employed with a variety of examples from different fields, including medicine, physics, psychology, history and philosophy of science, machine learning, education, and neuroscience. The same form of representation applies to both published research literature and research projects or topics, whether nascent or fully-fledged. In fact, some of the relationships discussed below make more sense in the context of research projects (or broader research agendas) that can dynamically evolve as relevant literature is encountered, rather than research papers whose underlying representations are static. As such, I will use the terms publication, literature, project, and topic somewhat interchangeably.

The specific representation I use is borrowed from structure-mapping theory (Gentner, 1983 ; Falkenhainer et al., 1989 ), which was originally developed as a way to structurally represent analogies. Structure-mapping theory is particularly useful here, both because we can use it when discussing abstractions and analogies, and because the underlying representation can also handle other types of relationships among literature. I could have instead used other forms of knowledge representation, such as conceptual graphs (Sowa, 1976 ), entailment meshes (Pask et al., 1975 ; Pask, 1988 ), or category theoretic representations like ologs (Spivak & Kent, 2012 ). There may be relative advantages to each of these, but the representation used here is both simple and powerful enough to clearly demonstrate the typology. The exact choice of representation may need further consideration if one wants to perform inference on the representations or utilize them in information retrieval tools. For now, we are not concerned with how one might construct these representations or even the fidelity with which it is possible (though we revisit these questions in Sect. 6 ). The possibility that research projects could in theory be represented in the way described below is sufficient to formulate the typology.

While the form of representation and typology presented below may not be directly used in information retrieval tools, I contend that they may be useful in guiding the overall direction that research on such tools might take (e.g., what kinds of papers should a tool search for?). Moreover, the typology may provide some clarity to researchers going through the literature search process for a project. Constructing a graphical representation of one’s paper may be a useful exercise, and can possibly illuminate different searches that are needed to find related work. Seeing how the representation of one’s paper changes over time can also be a useful documentation of the research and literature search process. Beyond such potential practical uses of the typology, I believe it can simply be beneficial to understand the various ways in which one product of research may relate to another. If alongside the physical and social worlds, the world of research literature “also qualifies as an endless frontier” (Swanson, 1986 , p. 115), then our efforts to make sense of the former should be accompanied by efforts to make sense of the latter.

2 Related work on related work

Related work on related work exists in a number of different disciplines. Literature search is central to all research after all! Fittingly, the typology we develop combines research that exists in different, largely isolated, strands.

In the information sciences and medicine, work on “literature-based discovery” (LBD), dating back to Swanson ( 1986 ), is concerned with making new scientific discoveries by establishing novel connections between different pieces of literature. Swanson ( 1986 ) describes literature-based discovery as a form of scientific discovery that takes place in Karl Popper’s world 3—the “world of the products of the human mind” (Popper, 1978 , p. 144)—whereby search functions are likened to scientific theories and the “logic of undiscovered public knowledge” (p. 116) is analogous to the logic of scientific discovery. In doing so, Swanson ( 1986 ) made a contribution to the philosophy of science, though it seems to have not been recognized in the philosophy of science community. A number of different information-retrieval techniques have been proposed to aid in LBD (Smalheiser, 2017 ; Sebastian et al., 2017 ). Some authors have presented categorizations of different types of “undiscovered public knowledge” or different forms of LBD (Davies, 1989 ; Smalheiser, 2017 ). While these categorizations can be useful in aiding researchers who want to perform literature-based discovery, our typology has a somewhat broader scope in that not all related work necessarily results in LBD. LBD is one potential use case of literature search, and its various methods span across the relationships in the typology presented here, as discussed below.

More broadly, in information retrieval, the notion of “relevance” is central, and some researchers have tried to develop theories around what relevance is—typically conceived of as the relationship between an information need and a document (Saracevic, 1975 , 2016 ; Huang & Soergel, 2013 ). Green ( 1995 ) and Huang and Soergel ( 2013 ) pointed out that most discussions of relevance are around “topic matching,” but that this is only one form of relevance. Green and Bean ( 1995 ) then constructed a typology of different notions of relevance, and Huang ( 2009 ) expanded this to a typology consisting of over 200 notions of relevance. Huang ( 2009 ) considers three broad categories of relationships: (1) “What functional role a piece of information plays in the overall structure of a topic,” (2) “How information contributes to users’ reasoning about a topic,” and (3) “How information connects to a topic semantically” (p. 411). As examples of functional roles, an information source might present a solution to a problem, the cause of an effect, etc. As examples of contributing to reasoning, an information source might provide an analogy to the topic or might be used to deduce something about the topic. While this work is very relevant to the present paper, there are two key differences. First, their work is about the broader concept of relevance between information and needs, while this paper focuses on relevance in academic literature. One would expect that many of the kinds of broader relevance typologies would also hold for research publications, but given the particularities of literature search and the role it plays in the broader process of scientific research, it seems worth studying in its own right. Second, these prior typologies largely focus on the variety of semantic relationships between two topics, while the approach we present here views relevance in terms of operations that operate on knowledge representations of topics. In this sense, the typology I present here can express how to relate different research topics in terms of a small number of mathematically precise operations (that are hopefully easy to remember), rather than a plethora of different possible semantic relationships. The two approaches are complementary, but I contend that the approach taken here is more useful for conceptualizing the evolution of a research project over time.

In computer science and artificial intelligence, there has been a recent thread of work on citation recommendation, concerned with identifying relevant citations given a piece of text and possibly other meta-data (e.g., authors, etc.) (Strohman et al., 2007 ; Liang et al., 2011 ; Ren et al., 2014 ; Bhagavatula et al., 2018 ). Interestingly, this work has not really considered automated techniques for LBD, and it does not cite the vast literature on LBD or on relevance. Indeed, most of the work in this area is concerned with topic matching (finding citations that topically overlap). One notable exception is work by Chan et al. ( 2018 ) and Kang et al. ( 2022 ). Chan et al. ( 2018 ) presented a technique that combines crowdsourcing and machine learning to find analogies between different papers. They utilize a “soft” relational schema, a very coarse-grained representation of a research paper; they explicitly avoid using representations like the one described below, because they can be very difficult to construct for many publications. Kang et al. ( 2022 ) built on this work by training deep learning algorithms on the crowdsourced representations of abstracts to be able to automatically detect the “purpose” and “mechanism” of a paper. An analogy in this context is two papers that have a similar underlying purpose but achieve that purpose through a different mechanism. Kang et al. ( 2022 ) used this to prototype an analogical search engine for scientific literature. While their representation may be useful for LBD, I contend that it can only capture certain kinds of relationships between papers, and, as I discuss further below, some of their methods do not appear to actually look for analogies as per the typology we develop below. As such, our typology can potentially be useful in classifying the different kinds of relationships that various existing LBD and citation recommendation methods can uncover, and the kinds of relationships that they cannot.

3 A representation of a research project

In our representation, a research project or publication \(P \in \Pi \) is represented as a set of entities and relations, \(P = (E, \mathcal {R})\) . An entity conceptually represents any specific topic of relevance to the project, usually expressed as a noun or a noun phrase (e.g., DNA, the civil rights movement, high blood pressure, theorems). Notice that entities can come in different degrees of specificity (e.g., theorems vs. Gödel’s first incompleteness theorem); the important thing is that entities across all topics and publications are represented at the same level of granularity. We allow entities to be hierarchically defined as functions of other entities (e.g., the entity “volume of a cup” can be thought of as the “volume of” function applied to “cup”).

Relations define a relationship between some number of entities, such that the predicate \(R(e_1, e_2, \dots , e_n)\) indicates that \(e_1\) , \(e_2\) , ..., \(e_n\) are related as specified by the relation R . Binary relations are perhaps the most common. For example, in the sentence “stress causes high blood pressure”, “causes” is a relation that takes relates two entities (in this case, “stress” and “high blood pressure”). We might represent this as causes (stress, high blood pressure). As an example of a tertiary relation, consider the sentences “ribosomes translate mRNA into sequences of amino acids” and “Arab translators translated Greek texts into Arabic translations”; they could both be said to use the relation x-translates-y-into-z (though if we think the word “translates” has a very different semantic meaning in these two cases, we could suggest there are two different relations at play here). We also allow for unary relations; for example, “blood pressure is high” can be represented as is-high (blood pressure). Unary relations are called attributes in structure-mapping theory and they effectively allow assigning adjectives to entities; for example, high (blood pressure) would mean “high blood pressure.” With slight abuse of notation, I will use unary relations both as relations (e.g., is-high (blood pressure)) and as attributes (e.g., high (blood pressure)). Finally, we allow for higher-order relations, which take relations as input instead of, or in addition to, entities. causes is a higher-order relation because we can say, for example, causes ( provided ( treatment (subjects), New Curriculum), learn-more-than ( treatment (subjects), control (subjects))).

As with Gentner’s ( 1983 ) structure-mapping theory, the classification of relationships between research has more to do with the structure of the representation (i.e., the presence of certain entities and relations) rather than the semantic meaning of the nodes. However, semantics still play an important role in informing whether a particular relationship is sensible or important in a particular situation. That is, someone without a semantic understanding of a given domain can still apply the operators described below in the sense that one can execute \(4 + 7\) and \(4 \times 7\) , without regard to which operation makes more sense in the given situation. Furthermore, one aspect of semantics is necessary in the application of some of the operators. Namely, there is a general relation, “is a” (or “is an instance of”), which can capture any situation where a particular entity can be categorized as a special case or instance of another entity. Consistent with earlier work on knowledge representation, we will refer to this relation as is-a (Brachman, 1983 ). For example, is-a (Gödel’s first incompleteness theorem, theorem) and is-a (the civil rights movement, historical occurrence). A single entity can be an instance of many entities (e.g., a cat can be considered an animal, a pet, and an Internet phenomenon). The is-a operator is also reflexive (e.g., is-a (cat, cat)). Finally, with slight abuse of notation, we will also have is-a be a higher-order relation that can designate when one relation is an instance of another. For example, is-a ( holds (person, ball), possesses (person, object)), because holding something is a special case of possessing it and a ball is an object. Some of the operators below can only be applied with an understanding of what things are instances of other things; however, when the relationship is more abstract, sometimes even a domain expert will not readily see these connections.

The set of entities and relations that are used in the representation of a research publication will likely not include all entities and relations included in that publication (e.g., all nouns and verbs), but rather they should include the concepts that are focal to that publication. Of course, that is somewhat subjective, but a useful heuristic is to include all entities and relations that are involved in a system of relationships that might be worth providing citations in reference to, as well as any new entities and relations that are being introduced in the paper. For example, in a paper that runs an experiment with seven conditions, the number of seven is probably not an entity that should be included, but in Miller’s ( 1956 ) paper on working memory capacity or a paper on the religious symbolism of the number seven, it likely should be included.

As suggested above, there is no single correct way to represent a research project. In fact, there can be multiple different views of a research project, which induce different representations. Each of these views can be more or less useful depending on how they are to be used. Moreover, even simple relations can be expressed in different ways. For our purposes, there is a relationship between two research projects if there is at least some view of each that permits the relationship. Since we are not concerned with the practical side of how to best represent projects here, we do not worry about how one would go about discovering the “right” views. In practice though, seeing two related papers from the “wrong” viewpoint is one reason why researchers and information retrieval tools might not notice an important relationship.

We can represent these representations graphically using a graph-like structure as shown in Fig.  1 a. Boxes indicate entities, and the text outside of boxes indicate relations. The arrows coming out of a relation point to its arguments in order from left to right. Nested boxes (e.g., “some part of a new thing”) show hierarchically defined entities. For simplicity, we show binary relations as labeled directed edges for asymmetric relations and labeled undirected edges for symmetric relations, as shown in Fig.  1 b.

Examples of how to graphically represent research projects/publications. a An example of a tertiary relation with three entities that would be read as “An old thing can become some part of a new thing through some process.” There are also two unary relations: is-old and is-new . b Two examples of binary relations. The causes relation is asymmetric while the correlated relation is symmetric

This representation could be couched in the language of model-theoretic philosophy of science (Suppes, 1957 , 1960 ), in particular using the partial structures formalism (French, 2000 ; Da Costa & French, 1990 ), which is also often expressed in terms of entities and (partial) relations. Doing so may be appealing since it would connect literature search to an existing framework for discussing scientific theories. The partial structures formalism has also been used in describing analogies and abstractions in science and provides a way to formalize research undergoing change. However, the ideas presented here not only apply to formal scientific theories, but also to non-scientific literature and more nascent representations of scientific topics, and I do not want to associate the typology presented here with a particular interpretation of scientific theories.

4 A typology of related works

We can now describe the different kinds of relationships that can exist between a research project and prior work. Suppose we have a research project \(P = (E_P, \mathcal {R}_P)\) and a piece of literature \(L = (E_L, \mathcal {R}_L)\) . We assume that P is an ongoing project that can potentially change, while L is already published literature and hence static. Below we describe a set of operations that can be used to describe the relationship between P and L . These operations are functions that take the representations of P and L as inputs and output a representation \(\rho \) of the relationship between P and L (as defined by the operation). Since we allow for composing these operations in sequence, some of the operations will actually take as input P , L , and our current representation of the relationship between the two ( \(\rho _i\) ), and will output a modified representation of the relationship ( \(\rho _{i+1}\) ). Moreover, when applying multiple operations in sequence, we may want to keep track of the ongoing relationship, which we can do by merging multiple relationships (i.e., taking the union of entities and the union of relations in the sequence of relationships). After each operation is applied, we can also potentially modify P Footnote 1 , thereby modifying the relationship between P and L as well. The series of operations and modifications reflects the iterative and influential nature of literature search in the research process. The operations, described below, are called intersection, interpretation, expansion, abstraction, reification, analogy, and substitution. Table  1 lists some basic information about the operations, which may be useful when reading the sections below. I do not make any claims that the typology presented here is complete. There might be other operations, or perhaps more useful categorizations of the operations presented here, which can be elucidated upon in future work. In what follows, I will describe each of the operators in words as well as mathematical formalism when needed; readers can safely skip the mathematical formalism and still grasp the key ideas.

4.1 Intersection

The first and probably most prevalent operation is intersection , which outputs a subset of entities that are shared by P and L and a subset of relations shared by the two. Specifically, intersection outputs a representation \(\rho = (E_{PL}, \mathcal {R}_{PL})\) , where \(E_{PL} \subseteq E_P \cap E_L\) and \(\mathcal {R}_{PL} \subseteq \mathcal {R}_P \cap \mathcal {R}_L\) . The exact subset depends on what is determined to be relevant between the two representations. A simple special case of this would be when P and L share just a single entity. For example, suppose P and L both have to do with DNA, but one is about DNA to solve computational problems (Adleman, 1994 ) and the other is about DNA vaccines for coronavirus (Callaway, 2020 ). It is unlikely that these publications have other entities in common. In many such cases, publications are not worth citing, and such an intersection would actually not be relevant. A relationship is worth noting when the degree of overlap is large enough; this can be measured by associating some degree of importance to each entity in P and taking the sum (or some non-linear function) of importances across all the entities in \(\rho \) .

In some cases, overlap in a single entity may be enough to warrant citation or even to alter the course of a research project. For example, one of the examples that Swanson ( 1986 ) gives for undiscovered public knowledge has to do with a potential research publication on the “all swans are white hypothesis,” a hypothesis that states that all swans are white. This hypothesis could be supported inductively if there was a lack of any documented evidence of black swans. As Swanson ( 1986 ) says:

Suppose for the sake of argument that scientists living in a remote part of the world were to publish, in a local wildlife journal, some observations about a family of black swans living on a nearby lake. We suppose further that the report comes from a half-dozen people who are reliable observers, and that they are unaware that other people in the world think that all swans are white. (p. 109)

As shown in Fig.  2 , the potential all-swans-are-white hypothesis publication ( P ) is represented using three entities and two relations, although it can be interpreted as two entities and the relationship between them (“the all-swans-are-white hypothesis is proved by the fact that there is no evidence of black swans”); on the other hand, the article in the wildlife journal ( L ) only concerns itself with black swans and possibly other topics of local interest. As such, the two articles overlap in only one entity: black swans. It just so happens that the existence of black swans is a critical refutation of the theory (i.e., “evidence of black swans” is a very important entity in P ), and so this single article can change the course of the research project (e.g., the authors publish a refutation of the all-swans-are-white hypothesis rather than a proclamation of it).

Notice that the intersection of the two articles was “black swans” not “evidence of black swans.” (The wildlife journal is not trying to present evidence of black swans; it is discussing a piece of wildlife whose existence they never called into question.) The intersection of “black swans” by itself is not necessarily meaningful. Another paper that discusses black swans but provides no evidence for them is of less value to P . How then can we capture the obvious fact that L presents evidence of black swans, even though it is not captured in its representation? The answer lies in the interpretation operation.

Swanson’s ( 1986 ) black swans example as an example of intersection

4.2 Interpretation

An interpretation takes an existing relationship between P and L and adds additional entities and/or relations from P (not included in L ) that can help interpret the current relationship. Namely, if \(\rho _{i} = (E_{\rho _{i}}, \mathcal {R}_{\rho _{i}})\) is the output of a previous operation, then \(\rho _{i+1} = (E_{\rho _{i}} \cup E_{PS}, \mathcal {R}_{\rho _{i}} \cup \mathcal {R}_{PS})\) , where \(E_{PS} \subseteq E_P \setminus E_L\) and \(\mathcal {R}_{PS} \subseteq \mathcal {R}_P \setminus \mathcal {R}_L\) . (I use PS and LS as subscripts to denote subsets of P and L .) A natural use of interpretation is to apply it after an intersection. For example, in the black swans example above, we can interpret the intersection of P and L as being “evidence of black swans.” Clearly, L does present evidence of black swans, but it was not interpreted that way until it was interpreted in light of P . Notice that if a researcher conducting project P were to construct the representation of L , they might do so according to their interpretation, whereby “evidence of black swans” would appear in L . Therefore, interpretation steps may often be implicit or hidden in the particular view of L that a researcher adopts. In this paper, I try to represent prior work in a way that is faithful to the original authors’ meaning, though we must recognize that views of prior work will always be informed by our worldview.

4.3 Expansion

An expansion takes an existing relationship between P and L and adds additional entities and/or relations from L (not included in P ) to potentially expand the content of P or to bring new insights into the picture. Notice that structurally, the expansion operation is equivalent to the interpretation operation with P and L swapped; however, semantically, the two are often quite different. An expansion will often result in a change in P . As a result, it makes the most sense when P is an ongoing research topic (or a follow-up investigation to published work), rather than a final publication. Once P has changed to \(P'\) to incorporate the new entities and relations, what was once an expansion between P and L may be viewed as an intersection between \(P'\) and L . Therefore expansions play developmental roles in the research process, which are often not captured in publications. That is, many research projects may have changed course as a result of particular publications, but the final publication may only refer to the relationship to prior work at the time of publication, rather than the developmental influence of that prior work.

For example, in the related works section above, I acknowledged connections to Chan et al. ( 2018 ); these connections would be viewed as intersections (e.g., both papers have to do with academic literature, analogies, knowledge representation, etc.). However, what I did not state was that reading Chan et al. ( 2018 ) led me to read about structure-mapping theory (Gentner, 1983 ), and the two publications combined (and considered in relation to Swanson ( 1986 )) resulted in the beginnings of this paper. That is, before this paper was even conceived of, the aforementioned prior works resulted in a series of expansions, which turned into the present piece only after many iterations, which involved a series of other operations applied to various publications (some of which are cited, and some of which may not be). This reflects the role of literature search in the messy process that is research. I suspect that researchers rarely document the series of expansions (and other steps) that lead to the final state of a publication.

In fact, at times, some prior work may only play the role of a stepping stone to discovering other, more relevant, prior works. That is, an expansion of P by \(L_1\) may result in an exploration of the new entities in the expansion, which results in discovering \(L_2\) , which intersects with P . At that point, \(L_1\) may no longer really be relevant; that is, the extent of \(L_1\) ’s relevance may be better captured by \(L_2\) .

One broad category of expansions falls under Swanson’s ( 1986 ) second example—“A Missing Link in the Logic of Discovery” or what is often referred to as the ABC model. As Swanson ( 1986 ) originally expressed it:

Suppose the following two reports are published separately and independently, the authors of each report being unaware of the other report: (i) a report that process A causes the result B, and (ii) a separate report that B causes the result C. It follows of course that A leads to, causes, or implies C. That is, the proposition that A causes C objectively exists, at least as a hypothesis. (p. 110)

Swanson gave a specific example of a discovery he made (the first of his several literature-based discoveries in medicine): connecting (a) literature on how fish oil causes a reduction of blood viscosity with (b) literature on how reducing blood viscosity leads to an improvement in symptoms of Raynaud’s syndrome. The intersection of these two literatures is the entity “reduction of blood viscosity.” An expansion adds the causal link to “relief from Raynaud’s syndrome” and that link is then interpreted in light of the connection to “dietary fish oil.” Connecting these two literatures via these steps can result in a change in P as shown in Fig.  3 . Notice that the addition of a new causal relation between dietary fish oil and relief from Raynaud’s syndrome was inferred from this expansion, but had never been experimentally shown or even published about. Two years later, a clinical trial independently confirmed this hypothesis (Swanson & Smalheiser, 1996 ).

Swanson’s ( 1986 ) example of the ABC model as an example of expansion

Literature-based discovery often involves this kind of linking between two “non-interactive literatures,” literatures that are rarely, if ever, cited in the same publications (Swanson & Smalheiser, 1996 ). However, expansion need not always be between two non-interactive literatures. Indeed, researchers may often be unaware of highly relevant work within their own research community (or other interactive literatures) that build upon the concepts they are investigating. Such cases can often be caught by the researchers themselves when conducting a more expansive literature review, or by reviewers during the peer review process, but likely often go undetected.

4.4 Abstraction

An abstraction applies if P contains a subset of entities and relations that are instances of entities and relations in L . In other words, we have an abstraction when L contains a more abstract or generalized representation of part of P . An abstraction can still consist of concrete entities and relations as long as they are more general or more abstract than the entities and relations in P (e.g., as suggested above is-a (cat, animal), is-a (cat, Internet phenomenon), and is-a (the civil rights movement, historical occurrence) can all be single entity abstractions).

Describing an abstraction mathematically requires a bit more care than for previous operations since abstractions must be semantically “consistent” across the entities and relations involved. Formally, an abstraction applies if there is a subset of entities and relations in P —say \(E_{PS} \subseteq E_P\) and \(\mathcal {R}_{PS} \subseteq \mathcal {R}_P\) —and a subset of entities and relations in L —say \(E_{LS} \subseteq E_L\) and \(\mathcal {R}_{LS} \subseteq \mathcal {R}_L\) —such that the following four conditions hold:

For all \(e \in E_{PS}\) , there exists a \(\tilde{e} \in E_{LS}\) such that e is an instance of \(\tilde{e}\) .

For all \(R \in \mathcal {R}_{PS}\) , there exists a \(\tilde{R} \in \mathcal {R}_{LS}\) such that R is an instance of \(\tilde{R}\) .

For all \(R \in \mathcal {R}_{PS}\) , if \(R(e_1, e_2, \dots , e_n)\) , then \(\tilde{R}(\tilde{e}_1, \tilde{e}_2, \dots , \tilde{e}_n)\) , where \(R, e_1, \dots , e_n\) are instances of \(\tilde{R}, \tilde{e},_1 \dots , \tilde{e}_n\) respectively.

At least some \(e \not = \tilde{e}\) or some \(R \not = \tilde{R}\) .

The last condition is required to make sure the abstraction is not simply mapping identical representations (in which case it would just be an intersection). The resulting representation is \(\rho = (E_{PS} \cup E_{LS}, \mathcal {R}_{PS} \cup \mathcal {R}_{LS} \cup \textsc {is-a})\) , where \(\textsc {is-a}(e, \tilde{e})\) and \(\textsc {is-a}(R(e_1, e_2, \dots , e_n), \tilde{R}(\tilde{e}_1, \tilde{e}_2, \dots , \tilde{e}_n))\) , for all e , \(\tilde{e}\) , R , and \(\tilde{R}\) as defined in the conditions above.

Abstractions need not be profound. Consider the black swans example again. The way I presented it above was actually a bit disingenuous: black swans are not the only evidence that disproves the all-swans-are-white hypothesis; any non-white swans would. Thus it might be more accurate to replace the “black swans” entity with “non-white swans” in Fig.  2 a. The relationship between P and L then first involves an abstraction (instead of an intersection)—namely is-a (black swans, non-white swans)—followed by an interpretation, as shown in Fig.  4 . This is a rather trivial kind of abstraction, which likely happens all the time when interpreting prior work in the context of current work.

The black swans example revisited. The relationship between P and L is now an interpretation of an abstraction of L . Notice that we used “are” instead of “is a” simply because the entities are expressed in plural

A more substantial form of abstraction is whenever P reports on empirical findings that can be subsumed into an existing theory described by L . For example, if researchers find that students in a collaborative problem-solving activity learned more than students who were working on the activity on their own, then they might see the ICAP hypothesis (Chi & Wylie, 2014 ), which posits that interactive learning is better than constructive learning, as an abstraction.

Finally, perhaps the most interesting (but also rarest) form of abstraction is when a body of research is interpreted or a problem is solved using some abstract formalism or framework that exists in the literature (often in a different field). For example, a notable example in the history of science is the introduction of group theory to quantum mechanics to solve certain problems related to symmetry (French, 2000 ; Scholz, 2006 ). According to French ( 2000 ):

the relationship between mathematics and physics is represented in terms of an embedding of a scientific theory into a mathematical structure. This effectively gives the theory access to ‘surplus’ mathematical structure which can play an essential role in the further development of theory. (p. 104)

This “surplus structure”—a term originally from Redhead ( 1975 )—is represented in our typology by expansion steps that can follow the abstraction. Namely, once a connection is made between L (say group theory) and P (a particular problem in physics), an expansion can be applied to bring new mathematical machinery from L to bear on P . Furthermore, an interpretation of the abstraction of L in light of P might result in new insights that could lead to further developments in L (if we do not consider L to be static literature). As French ( 2000 ) states, “it is important to acknowledge that both group theory and quantum mechanics were in a state of flux at the time they were brought into contact and both subsequently underwent further development” (p. 110).

4.5 Reification

A reification is the inverse of an abstraction. That is, a reification has the same definition of an abstraction, except that P and L are exchanged. We can say P is reified by L if L is abstracted by P . A reification can occur when prior work might contain a concrete example of a phenomenon, which one’s present work presents in more abstract or general terms. Reifications will often be used when interpreting prior empirical findings in light of a new theoretical framework. For example, when articulating his theory of the structure of scientific revolutions, Kuhn ( 2012 ) drew on myriad concrete historical examples from the history of physics, astronomy, chemistry, and other fields. These findings are reifications of particular components of Kuhn’s theory (e.g., paradigms, anomalies, paradigm shifts, etc.).

A reification can also make sense when one is in a formative stage of a project where some of the specifics have not yet been determined. For example, consider Tu Youyou’s work on finding a cure for malaria in the 1970s for which she won the Nobel Prize in 2015. The problem that Tu and her team were working on is represented in Fig.  5 a. According to Tu ( 2015 ):

After thoroughly reviewing the traditional Chinese medical literature and folk recipes and interviewing experienced Chinese medical practitioners, I collected over two thousand herbal, animal and mineral prescriptions within three months after initiation of the project.

One of the substances that showed some initial promise was sweet wormwood ( qinghao ), which was shown in the literature to cure intermittent fevers, as shown in Fig.  5 b. Therefore sweet wormwood is a reification of a potential cure for malaria, as shown in Fig.  5 c, and this can be interpreted in the broader research of finding a cure for malaria, as shown in Fig.  5 d. Yu went on to identify artemisinin as an actual cure for malaria, but there was an additional step of literature-based discovery needed first, which we will return to later.

The discovery of sweet wormwood as a cure for malaria as an example of reification

4.6 Analogy

An analogy applies when P and L both have a subset of entities and relations that have a shared abstraction. More formally, using the same notation as above, an analogy applies if there exists some other representation \(A = (E_A, \mathcal {R}_A)\) (representing an abstraction) and the following four conditions hold Footnote 2 :

For all \(\tilde{e} \in E_A\) , there exists an \(e \in E_{PS}\) and an \(e' \in E_{LS}\) such that e and \(e'\) are both instances of \(\tilde{e}\) .

For all \(\tilde{R} \in \mathcal {R}_A\) , there exists an \(R \in \mathcal {R}_{PS}\) and an \(R' \in \mathcal {R}_{LS}\) such that R and \(R'\) are both instances of \(\tilde{R}\) .

For all \(\tilde{R} \in \mathcal {R}_{A}\) and for every pair \(R \in \mathcal {R}_{PS}\) and \(R' \in \mathcal {R}_{LS}\) such that R and \(R'\) are both instances of \(\tilde{R}\) , if \(\tilde{R}(\tilde{e}_1, \tilde{e}_2, \dots , \tilde{e}_n)\) then \(R(e_1, e_2, \dots , e_n)\) and \(R'(e'_1, e'_2, \dots , e'_n)\) , where \(e_i\) and \(e'_i\) are instances of \(\tilde{e}_i\) for all i and R and \(R'\) are instances of \(\tilde{R}\) .

At least some \(e \not = e'\) or some \(R \not = R'\) .

We say that \(\textsc {analogous}(e,e')\) if and only if condition 1 holds for e and \(e'\) and similarly we say that \(\textsc {analogous}(R(e_1, e_2, \dots , e_n), R'(e'_1, e'_2, \dots , e'_n))\) if and only if the conditions 2 and 3 above hold for those entities and relations. The representation that results from an analogy operation is \(\rho = (E_{PS} \cup E_{LS}, \mathcal {R}_{PS} \cup \mathcal {R}_{LS} \cup \textsc {analogous})\) .

Analogies can span from shallow analogies between two instances of a similar phenomenon in the same field to deep analogies across scientific fields that share little apparent relation to one another on the surface. The further removed that P and L are from the abstraction A , the deeper the analogy becomes (and typically, the harder to notice). Concretely identifying the abstraction implicit in an analogy is not necessary, and in some cases, it can actually be difficult to do, but I suggest that doing so may be a useful exercise (and could lead to refining the analogy).

Like expansions, analogies can sometimes result in modifying P by looking at the research project in a whole new light. Like expansions, this also means the way in which an analogy might have helped develop P over time may not always be apparent from the final product. Even if a publication discusses an analogy, it may not always be clear if that analogy was instrumental in developing the idea in the first place or if it was an afterthought that the two ideas were related.

An example of an analogy where the impact of prior work on a research project is actually made explicit is the analogy between Thomas Kuhn’s historical philosophy of science and Jean Piaget’s psychological and epistemological theory of how a child develops knowledge. In The Structure of Scientific Revolutions , Kuhn ( 2012 ) gives us a brief sense of his indebtedness to Piaget:

A footnote encountered by chance led me to the experiments by which Jean Piaget has illuminated both the various worlds of the growing child and the process of transition from one to the next. (p. xi)

The extent of this has recently been clarified by historians examining Kuhn’s other works and archival materials (Galison, 2016 ; Burman, 2020 ). For example, Kuhn ( 1977 , as cited in Burman, 2020) states:

Almost twenty years ago I first discovered, very nearly at the same time, both the intellectual interest of the history of science and the psychological studies of Jean Piaget. Ever since that time the two have interacted closely in my mind and in my work. (p. 21)

So what was the nature of this close interaction? One can draw a clear analogy between the two. At risk of oversimplification, a representation of the analogy between Kuhn’s theory and Piaget’s is shown in Fig.  6 , adapted from a mapping given by MacIsaac ( 1991 ). This is not at all to say that this is the precise analogy that Kuhn drew which led to a refinement of his theory as presented in The Structure of Scientific Revolutions . However, he probably made similar mappings that changed over time as he developed his theory. Similar analogies can also be drawn from Kuhn’s theory to gestalt theory and Bruner and Postman’s ( 1949 ) psychological theory of how people perceive incongruities, both of which Kuhn ( 2012 ) explicitly builds off of. Interestingly enough, the Piagetian analogy, while very influential on the development of Kuhn’s theory, was not retained in the final representation of his book, while the analogies to gestalt theory and Bruner and Postman ( 1949 ) were explicitly an important part of his narrative. Note that the relations in P and L are identical in this case, but this need not be the case in general; in fact, they may only be identical because I constructed them that way, but perhaps if the representations were to be derived independently, the relations would be non-identical, but share a common abstraction.

The representation of the analogy between Kuhn’s The Structure of Scientific Revolutions and Piagetian theory. The analogous relations are shown as dotted lines without labels for ease of reading

To provide a more recent example of analogy, we can consider the relationship between the recent machine learning literature on fairness ( P ) in relation to older literature from the 1960s-1970s on fairness in educational and employment testing ( L ). As Hutchinson and Mitchell ( 2019 ) point out, the two literatures share much in common including many mathematical definitions of fairness. To formalize this, Hutchinson and Mitchell ( 2019 ) explicitly construct an analogy between the two literatures:

Test items (questions) are analogous to model features, and item responses analogous to specific activations of those features. Scoring a test is typically a simple linear model which produces a (possibly weighted) sum of the item scores....Because of this correspondence, much of the math is directly comparable; and many of the underlying ideas in earlier fairness work trivially map on to modern day ML fairness. “History doesn’t repeat itself, but it often rhymes”; and by hearing this rhyme, we hope to gain insight into the future of ML fairness. (p. 49)

Their last sentence suggests that the goal of pointing out the relationship between these two literatures are further steps of expansion and interpretation, or in other words, exploiting the “surplus of structure.” Indeed, the authors surface several definitions from test fairness that had not been proposed in machine learning (i.e., an expansion). Notice that in this case, the underlying abstraction may not be immediately obvious (e.g., what is the abstraction underlying both a test item and a feature?); in fact, in some cases, there may not be a simple word or phrase to describe the abstraction, but the fact that a clear analogy can be drawn indicates that there must be some more abstract underlying representation.

Finally, in my own research, I have found that there is an analogy between debates in education research and the bias-variance tradeoff in machine learning (Doroudi, 2020 ). Here an analogy was determined by directly formulating the abstraction (a generalized version of the bias-variance decomposition theorem). This abstraction has four components that any instance must specify: a target, an approximator, a random mechanism, and a source of randomness; once these components are specified, one can derive other phenomena (e.g., the meaning of bias, variance, etc.). This naturally sounds very abstract, but it is more concrete once instantiated in specific contexts. Table  2 gives an example of the analogy between these concepts in machine learning and debates around pedagogy. Once this analogy is drawn, it may be possible to expand techniques that are developed in machine learning to bear on educational debates (Doroudi, 2020 ). One benefit of making the abstraction concrete is that the same abstraction can be used to draw analogies to other fields as well.

4.7 Substitution

The analogy operator as described above can be applied in cases that do not semantically appear to be analogies. For example, consider two papers that use different methods to achieve the same outcome; many of the entities and relations may be the same across the two representations, but the entity (or entities) representing the methods would be different. Colloquially we would probably not say there is an analogy between the two approaches. For this reason, we make a distinction between substitutions and analogies. A substitution operates exactly in the same way as an analogy, but it should be applied when it is more sensible. The analogous relation can be replaced with the substitutes relation for semantic clarity. Therefore, unlike the other operators, the distinction between the analogy and substitution operators is semantic. However, there are typically clear structural differences between the two. In a substitution, typically only one or a few entities and relations will change, and the rest will be identical across P and L . Moreover, a substitution is similar to what Gentner ( 1983 ) terms a literal similarity. Namely, Gentner ( 1983 ) suggests that the difference between a literal similarity and an analogy is typically that a literal similarity will involve a greater number of identical attributes (or unary relations).

Consider the following four scenarios that loosely describe different papers:

Convolutional neural networks are trained to classify histopathological images of breast tissue as benign or malignant (Spanhol et al., 2016 ).

Support vector machines are trained to classify histopathological images of breast tissue as benign or malignant (Aswathy & Jagannath, 2021 ).

Human crowdworkers are trained to classify histopathological images of breast tissue as benign or malignant (Eickhoff, 2014 ).

Pigeons are trained to classify histopathological images of breast tissue as benign or malignant (Levenson et al., 2015 ).

In cases 1 and 2, it would be a stretch to say that there is an analogy between “convolutional neural networks” and “support vector machines,” which are both machine learning algorithms that can be applied to the same classification tasks. Thus, here is a clear case of substitution. However, with case 4, even though one could argue a pigeon is being substituted for a machine learning algorithm, the idea of training pigeons and the idea of training machine learning algorithms both have long histories and are often used for different purposes. Thus, it seems more natural to say pigeons are analogical to neural networks or support vector machines in these scenarios (with the underlying abstraction being a learning agent). Pigeons and support vector machines have a lot fewer attributes in common than convolutional neural networks and support vector machines. Unlike pigeons, the latter two are both algorithms implemented in computer code that were designed specifically for classification tasks. Pigeons, on the other hand, are animals, fly, eat, and make sounds. Some attributes of pigeons are actually important for the training process but not shared by any standard machine learning algorithms, such as their hunger. While we might say getting hungry is analogous to the “reward seeking” or “loss minimizing” property of machine learning algorithms, there is no literal hunger in those algorithms.

Case 3 is less clear-cut. While human crowdworkers are also significantly different from machine learning algorithms, crowdsourcing is often used for tasks where state-of-the-art machine learning is not good enough or a machine learning engineer might want to compare the performance of their algorithm against crowdworkers. On the other hand, human crowdworkers and pigeons share a lot of similar attributes that are lacking in machine learning algorithms. These ambiguities point out that ultimately the decision of whether an analogy or a substitution applies is in the eyes of the beholder. In other words, the degree of overlap in attributes depends on what attributes are most salient to the researcher. If a crowdworker is seen as an alternative to artificial intelligence and its humanity is not at the forefront, then perhaps a substitution would apply. On the other hand, researchers interested in using pigeons’ visual properties as a substitute for human labelers (Levenson et al., 2015 ) could also see a substitution between crowdworkers and pigeons.

As mentioned earlier, Kang et al.’s ( 2022 ) analogical search engine looks for papers that overlap in terms of purpose with a researchers’ study (as represented in the form of a search query). However, if the purpose is virtually identical, then replacing one mechanism for another may often be a substitution, not an analogy, as seen in cases 1 and 2 above. In some cases, such as using pigeons vs. neural networks to classify images, swapping mechanisms may result in an analogy. On the other hand, when the purpose is only similar (but not identical), there is no guarantee that the purpose-mechanism relationship will be analogical across different papers. Footnote 3 Thus, while Kang et al. ( 2022 ) find that their search engine is more likely to identify papers that trigger creative adaptations of the original idea (when compared to a standard keyword-based search engine), it is important to distinguish related work that might result in generating novel ideas and related work that actually has an analogical relationship with the present work.

Returning to Tu’s work on discovering a cure for malaria, she found that wormwood “showed some effects in inhibiting malaria parasites during initial screening, but the result was inconsistent and not reproducible.” Scouring over the relevant literature, she then identified a relevant sentence in Ge Hong’s fifth century A Handbook of Prescriptions for Emergencies : “A handful of Qinghao immersed in two liters of water, wring out the juice and drink it all” (Tu, 2015 ). Tu realized that while herbs are typically boiled, Ge’s recipe did not advocate for boiling it so perhaps the heat killed the active components in the wormwood. This led to a new method for extracting artemisinin from wormwood. To model this we would have to add entities to Fig.  5 that account for the method by which the drug is extracted. In that case, Ge’s method can be seen as a substitution for Tu’s original method. This substitution led to a drastic change in the research direction, eventually resulting in a cure for malaria.

Example of literature search as a sequence of operators applied to a research question on how memory is stored in synapses

5 Putting the pieces together

Now that we have seen the various operations that can relate two pieces of research to one another, it is worth discussing how these operations might be used in sequence over the scope of a research project. To do so, I provide a hypothetical example. As a disclaimer, the example is not from an area I have any expertise in; in fact, I encountered the relationships described below in the process of writing this paper (although not in the exact sequence described below). On the one hand, this suggests that the example may be oversimplified; on the other hand, perhaps it gives a somewhat authentic account of a non-expert navigating a new research field.

Suppose we are interested in conducting a literature review related to the question “how are memories stored in synapses?” This research question can be represented as “memories are stored in synapses through some mechanism” as shown at the top of Fig.  7 . Some of the steps described below are also represented in Fig.  7 ; in those cases, I will mention the number of the step in parentheses. Operator names are italicized below. If the reader wants to assess their understanding of the operators (or perhaps assess the degree to which there could be subjectivity in which operators apply), the reader can guess which operator applies for each step of the figure before reading the rest of this section.

When embarking on this literature search process, we are likely already aware of some answers to the question. For example, “some mechanism” could be reified by “synaptic plasticity” (Step 1). But synaptic plasticity is quite broad and could be reified further by several more specific forms of plasticity, such as “long-term potentiation” (Step 2) and “long-term depression.” Further literature search might reveal a plethora of other mechanisms such as “protein synthesis,” “epigenetic mechanisms,” or “the standard model of synaptic consolidation.” However, these mechanisms are not necessarily mutually exclusive, perhaps leading to a revision of the question formulation to “memories are stored in synapses through a combination of X, Y, ...” (or some more hierarchical representation). On the other hand, some proposed mechanisms may be competing, like “the standard model of synaptic consolidation” and “multiple trace theory” (i.e., one can be substituted for the other). Moreover, we might realize that the “memory” entity can also be reified into particular kinds of memory, like “episodic memory” or “semantic memory.”

Searching the literature further may reveal that there are recent suggestions that memory is not (only) stored in synapses, but could be stored in sub-cellular materials. This might result in a substitution of certain molecules (e.g., “RNA”) for synapse (Step 3 \('\) ). Alternatively, to keep our options open we may apply an abstraction of “synapse,” such as “parts of the brain” (Step 3). “Parts of the brain” can then be reified with many different entities, like “RNA” (Step 4). But it can also be substituted for regions of the brain where memories are stored, like the hippocampus. This may subsequently lead to the realization that rather than just asking how memories are stored, we should also be asking where memories are stored, leading to an expansion of the initial representation.

So far we have primarily considered literature that directly bears on the initial question. But sometimes surprising related works can also be discovered through intersections . For example, once we have established that RNA may be involved in memory, a colleague who is a molecular biologist might point out that there is an intersection with the literature on RNA interference (Step 5). Indeed, Smalheiser et al. ( 2001 ) noticed connections between a series of controversial 1960s studies on RNA-mediated memory transfer and RNAi; Smalheiser was a pioneer of literature-based discovery. We might then posit a relation that was neither present in our initial representation nor in related work: RNAi is potentially involved in the memory storage mechanism (i.e., “some mechanism” in our representation). Although it took over a decade, Smalheiser eventually found evidence to suggest that RNAi could indeed be involved in memory transfer (Smalheiser, 2017 ).

Finally, upon contemplating the initial representation further, the researcher may recognize an analogy to “how is memory stored in computer hardware?” (Step 6) or “how is memory stored in artificial neural networks?” Studying the literature in either of these areas may lead to the addition of new hypothesized mechanisms through an interpretation in light of the analogies. Notice that while in some cases a researcher notices an analogy when examining related literature, in other cases a researcher might think of an analogy, and then search for related literature. The related literature could either be about the analog (e.g., how memory is encoded in artificial neural networks) or about the analogy itself (Langille & Gallistel, 2020 ,e.g., how do theories of memory storage in the human brain relate to theories of memory storage in computer science). In the latter case, we have an intersection applied to the entire analogy .

6 The typology in practice

In this section, we discuss some important considerations for how the representation and typology could be used in practice. In theory, an understanding of the various ways in which one piece of literature may relate to a research topic can inform directions in information retrieval and citation recommendation. Such systems could potentially represent papers in terms of entities and relations by using named entity recognition (Nadeau & Sekine, 2007 ) and relation extraction (Bach & Badaskar, 2007 ); they can also leverage a growing body of work on using knowledge graphs for information retrieval (Reinanda et al., 2020 ). The typology can then inform the kinds of relationships that such systems can explore and possibly recommend to users. However, we reiterate that there is no single way to represent a paper or single way of applying the operators to identify relationships to prior work. As noted above, the choice of what operators apply and hence which relationships to related works will be noticed depends on the view one takes of one’s work and related work. One way to potentially mitigate this challenge is by having users specify their current view of their work in terms of its representation, or perhaps by allowing them to simultaneously represent their work in multiple ways. Furthermore, recognizing that different researchers and papers will use slightly different terms to refer to identical or very similar entities and relations, search engines could try to treat semantically similar phrases as being identical or provide a pre-selected set of entities and relations that they recommend users use.

However, even if the representations of papers are completely aligned, the task of retrieving good analogies and abstractions may be computationally intractable in the worst case (Wareham et al., 2011 ). Indeed, in automated analogical search, simplifications are made to make finding potential analogies more tractable. For example, the MAC/FAC algorithm—which is rooted in structure-mapping theory—first finds several examples that have the most surface-level overlap in terms of relations and then identifies the analogy Footnote 4 that is structurally strongest (Forbus et al., 1995 ). In Kang et al.’s ( 2022 ) analogical search engine, they look for papers that have a similar purpose, where similarity is measured by neural network embeddings rather than looking for a formally analogical structure. Although such algorithms may not be perfect, they could still potentially surface candidate analogies that would be given to a researcher who would ultimately identify when an analogy operator is applicable and useful.

Given the ongoing challenges in automated search, perhaps the typology would be more useful as a conceptual tool for researchers. Huang and Soergel ( 2013 ) found that “teaching users about the different kinds of topical relevance relationships may open their minds and make them better searchers and users of information.” Similarly, perhaps the typology presented here could be used as a tool to familiarize researchers with the different ways in which their research may relate to prior work, and how to use search tools to find such works. As mentioned before, simply representing one’s paper as a network of entities and relations may be a useful exercise to help researchers realize new insights about their research; future experimental studies could confirm whether this is true. Moreover, in discussing the potential value of their analogical search engine, Kang et al. ( 2022 ) mention the importance of “how deeply the human users can reflect on the retrieved analogs...and recognize how different notions of relevance may exist for their own problem context, despite potential dissimilarity on the surface” (p. 125). They suggest that “one approach to explaining relevance might be to surface a small number of core common features between an analog and a problem query” (p. 126). The representation presented here provides a natural way of showing users the potential relevance of related work. For example, when one searches for literature (even using a traditional search engine), representations could be generated on demand for the resulting papers such that they maximally align with the user’s query (at least in terms of number of entities and relations, if not in terms of higher-order relationships). Moreover, if the user specifies multiple research projects, a search engine could potentially represent each paper in terms of the representation that best aligns with each project.

7 Conclusion

I have tried to make the case that literature search is a complex process that can influence and be influenced by research in a variety of ways. By describing research papers and projects in terms of concrete representations, we can formally articulate how different pieces of research might relate to one another. As discussed in the last section, this could have practical ramifications in terms of how search engines could better support the literature search process or how to design training for researchers to improve the way they approach literature search.

Beyond practical applications, the typology presented here could give us insight into the ways in which literature search might iteratively change the course of a research project as a sequence of operations. Although it goes beyond the scope of this paper, it might be worth briefly considering some of the ways in which a research project might be modified as a result of these operations. One form of modification is simply adding new entities and relations to P as a result of an expansion; we can view this as a natural extension of the expansion operator. Several other forms of modifications can fall under the category of logical inference (i.e., deduction , induction , and abduction ). For example, in the black swans example, evidence of black swans triggers a modus ponens argument that proves the “all-swans-are-white” hypothesis is false, thereby changing P . Similarly, in Swanson’s ABC model, we can discern the presence of a new relation through the transitivity of the causal relation. If the representations are well-specified, one can imagine creating an inference engine that can automatically detect such changes in P after coming into contact with related work.

However, literature search cannot be considered in isolation from the other aspects of scientific discovery. Another form of modification to P might be the result of an experimentation operation, whereby a deduced relation is tested. We saw this both in the case of medical research that confirmed the causal link deduced by Swanson, and Tu’s experimental confirmation that wormwood can cure malaria. Finally, there is the construction operation, whereby a new entity or relation is created. Construction can result from either literature search (e.g., where an interpretation of some finding results in the discovery of a new finding, or where the expansion of an analogy results in an analogous entity that was not previously conceived of) or from research itself (e.g., the discovery of a new molecule or a new experimental finding). A thorough understanding of the processes of inference, experimentation, and construction is beyond the scope of this paper, but they begin to give us a hint as to how literature search is an iterative process that interacts with other aspects of the research process.

As pointed out by Swanson ( 1986 ), world 3 is also a world where scientific discovery takes place, by interacting with world 1 (the physical world) and world 2 (the subjective world of mental states). Philosophy of science should try to understand how these worlds interact in the process of scientific discovery; this paper is a step in that direction.

Availability of data and material:

This can be formalized using the partial structures formalism mentioned above (Da Costa & French, 1990 ).

Gentner ( 1983 ) did not explicitly define an analogy in terms of an abstraction, but I believe it is useful to recognize that there is always implicitly an abstraction present, and in many cases, it might be useful to reason about what that abstraction is. Gentner ( 1983 ) further differentiates between abstractions, analogies, and literal similarities. These are differentiated by how many attributes and relations are shared between the two and the degree of abstractness of the entities (i.e., in an abstraction, entities are more abstract). While this is sensible, we allow for abstractions that are more concrete, so long as the entities in one representation are still instances of the entities in the other.

For example, one participant’s research question was how to “Grow plants better by optimizing entry of nanoparticle fertilizers into the plant” (p. 14). One paper identified by analogical search was about identifying plants by applying image analysis techniques to their leaves. It is not clear what the similar purpose is in this case, but regardless, the paper does not obviously share an analogical relationship with the research question. While this paper inspired a novel idea that the researcher thought would be relevant to her project, the relationship is captured by an intersection (through the “plant” entity) and possibly the application of interpretation and expansion operators.

Technically it looks for matches in terms of literal similarity to mimic people’s tendencies to find literally similar matches, but the algorithm could be easily modified to search for analogies.

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Wareham, T., Evans, P., & van Rooij, I. (2011). What does (and doesn’t) make analogical problem solving easy? A complexity-theoretic perspective. The Journal of Problem Solving, 3 (2), 30–70. https://doi.org/10.7771/1932-6246.1091

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This material is based upon work supported by the National Science Foundation under Grant No. (2033868). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

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Doroudi, S. What is a related work? A typology of relationships in research literature. Synthese 201 , 24 (2023). https://doi.org/10.1007/s11229-022-03976-5

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How to Synthesize Written Information from Multiple Sources

Shona McCombes

Content Manager

B.A., English Literature, University of Glasgow

Shona McCombes is the content manager at Scribbr, Netherlands.

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Saul Mcleod, PhD

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BSc (Hons) Psychology, MRes, PhD, University of Manchester

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

On This Page:

When you write a literature review or essay, you have to go beyond just summarizing the articles you’ve read – you need to synthesize the literature to show how it all fits together (and how your own research fits in).

Synthesizing simply means combining. Instead of summarizing the main points of each source in turn, you put together the ideas and findings of multiple sources in order to make an overall point.

At the most basic level, this involves looking for similarities and differences between your sources. Your synthesis should show the reader where the sources overlap and where they diverge.

Unsynthesized Example

Franz (2008) studied undergraduate online students. He looked at 17 females and 18 males and found that none of them liked APA. According to Franz, the evidence suggested that all students are reluctant to learn citations style. Perez (2010) also studies undergraduate students. She looked at 42 females and 50 males and found that males were significantly more inclined to use citation software ( p < .05). Findings suggest that females might graduate sooner. Goldstein (2012) looked at British undergraduates. Among a sample of 50, all females, all confident in their abilities to cite and were eager to write their dissertations.

Synthesized Example

Studies of undergraduate students reveal conflicting conclusions regarding relationships between advanced scholarly study and citation efficacy. Although Franz (2008) found that no participants enjoyed learning citation style, Goldstein (2012) determined in a larger study that all participants watched felt comfortable citing sources, suggesting that variables among participant and control group populations must be examined more closely. Although Perez (2010) expanded on Franz’s original study with a larger, more diverse sample…

Step 1: Organize your sources

After collecting the relevant literature, you’ve got a lot of information to work through, and no clear idea of how it all fits together.

Before you can start writing, you need to organize your notes in a way that allows you to see the relationships between sources.

One way to begin synthesizing the literature is to put your notes into a table. Depending on your topic and the type of literature you’re dealing with, there are a couple of different ways you can organize this.

Summary table

A summary table collates the key points of each source under consistent headings. This is a good approach if your sources tend to have a similar structure – for instance, if they’re all empirical papers.

Each row in the table lists one source, and each column identifies a specific part of the source. You can decide which headings to include based on what’s most relevant to the literature you’re dealing with.

For example, you might include columns for things like aims, methods, variables, population, sample size, and conclusion.

For each study, you briefly summarize each of these aspects. You can also include columns for your own evaluation and analysis.

The summary table gives you a quick overview of the key points of each source. This allows you to group sources by relevant similarities, as well as noticing important differences or contradictions in their findings.

Synthesis matrix

A synthesis matrix is useful when your sources are more varied in their purpose and structure – for example, when you’re dealing with books and essays making various different arguments about a topic.

Each column in the table lists one source. Each row is labeled with a specific concept, topic or theme that recurs across all or most of the sources.

Then, for each source, you summarize the main points or arguments related to the theme.

The purposes of the table is to identify the common points that connect the sources, as well as identifying points where they diverge or disagree.

Step 2: Outline your structure

Now you should have a clear overview of the main connections and differences between the sources you’ve read. Next, you need to decide how you’ll group them together and the order in which you’ll discuss them.

For shorter papers, your outline can just identify the focus of each paragraph; for longer papers, you might want to divide it into sections with headings.

There are a few different approaches you can take to help you structure your synthesis.

If your sources cover a broad time period, and you found patterns in how researchers approached the topic over time, you can organize your discussion chronologically .

That doesn’t mean you just summarize each paper in chronological order; instead, you should group articles into time periods and identify what they have in common, as well as signalling important turning points or developments in the literature.

If the literature covers various different topics, you can organize it thematically .

That means that each paragraph or section focuses on a specific theme and explains how that theme is approached in the literature.

Source Used with Permission: The Chicago School

If you’re drawing on literature from various different fields or they use a wide variety of research methods, you can organize your sources methodologically .

That means grouping together studies based on the type of research they did and discussing the findings that emerged from each method.

If your topic involves a debate between different schools of thought, you can organize it theoretically .

That means comparing the different theories that have been developed and grouping together papers based on the position or perspective they take on the topic, as well as evaluating which arguments are most convincing.

Step 3: Write paragraphs with topic sentences

What sets a synthesis apart from a summary is that it combines various sources. The easiest way to think about this is that each paragraph should discuss a few different sources, and you should be able to condense the overall point of the paragraph into one sentence.

This is called a topic sentence , and it usually appears at the start of the paragraph. The topic sentence signals what the whole paragraph is about; every sentence in the paragraph should be clearly related to it.

A topic sentence can be a simple summary of the paragraph’s content:

“Early research on [x] focused heavily on [y].”

For an effective synthesis, you can use topic sentences to link back to the previous paragraph, highlighting a point of debate or critique:

“Several scholars have pointed out the flaws in this approach.” “While recent research has attempted to address the problem, many of these studies have methodological flaws that limit their validity.”

By using topic sentences, you can ensure that your paragraphs are coherent and clearly show the connections between the articles you are discussing.

As you write your paragraphs, avoid quoting directly from sources: use your own words to explain the commonalities and differences that you found in the literature.

Don’t try to cover every single point from every single source – the key to synthesizing is to extract the most important and relevant information and combine it to give your reader an overall picture of the state of knowledge on your topic.

Step 4: Revise, edit and proofread

Like any other piece of academic writing, synthesizing literature doesn’t happen all in one go – it involves redrafting, revising, editing and proofreading your work.

Checklist for Synthesis

•   Do I introduce the paragraph with a clear, focused topic sentence?
•   Do I discuss more than one source in the paragraph?
•   Do I mention only the most relevant findings, rather than describing every part of the studies?
•   Do I discuss the similarities or differences between the sources, rather than summarizing each source in turn?
•   Do I put the findings or arguments of the sources in my own words?
•   Is the paragraph organized around a single idea?
•   Is the paragraph directly relevant to my research question or topic?
•   Is there a logical transition from this paragraph to the next one?

Further Information

How to Synthesise: a Step-by-Step Approach

Help…I”ve Been Asked to Synthesize!

Learn how to Synthesise (combine information from sources)

How to write a Psychology Essay

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Research Implications & Recommendations

A Plain-Language Explainer With Examples + FREE Template

By: Derek Jansen (MBA) | Reviewers: Dr Eunice Rautenbach | May 2024

The research implications and recommendations are closely related but distinctly different concepts that often trip students up. Here, we’ll unpack them using plain language and loads of examples , so that you can approach your project with confidence.

Overview: Implications & Recommendations

• What are research implications ?
• What are research recommendations ?
• Examples of implications and recommendations
• The “ Big 3 ” categories
• How to write the implications and recommendations
• Template sentences for both sections
• Key takeaways

Implications & Recommendations 101

Let’s start with the basics and define our terms.

At the simplest level, research implications refer to the possible effects or outcomes of a study’s findings. More specifically, they answer the question, “ What do these findings mean?” . In other words, the implications section is where you discuss the broader impact of your study’s findings on theory, practice and future research.

This discussion leads us to the recommendations section , which is where you’ll propose specific actions based on your study’s findings and answer the question, “ What should be done next?” . In other words, the recommendations are practical steps that stakeholders can take to address the key issues identified by your study.

In a nutshell, then, the research implications discuss the broader impact and significance of a study’s findings, while recommendations provide specific actions to take, based on those findings. So, while both of these components are deeply rooted in the findings of the study, they serve different functions within the write up.

Need a helping hand?

Examples: Implications & Recommendations

The distinction between research implications and research recommendations might still feel a bit conceptual, so let’s look at one or two practical examples:

Let’s assume that your study finds that interactive learning methods significantly improve student engagement compared to traditional lectures. In this case, one of your recommendations could be that schools incorporate more interactive learning techniques into their curriculums to enhance student engagement.

Let’s imagine that your study finds that patients who receive personalised care plans have better health outcomes than those with standard care plans. One of your recommendations might be that healthcare providers develop and implement personalised care plans for their patients.

Now, these are admittedly quite simplistic examples, but they demonstrate the difference (and connection ) between the research implications and the recommendations. Simply put, the implications are about the impact of the findings, while the recommendations are about proposed actions, based on the findings.

The “Big 3” Categories

Now that we’ve defined our terms, let’s dig a little deeper into the implications – specifically, the different types or categories of research implications that exist.

Broadly speaking, implications can be divided into three categories – theoretical implications, practical implications and implications for future research .

Theoretical implications relate to how your study’s findings contribute to or challenge existing theories. For example, if a study on social behaviour uncovers new patterns, it might suggest that modifications to current psychological theories are necessary.

Practical implications , on the other hand, focus on how your study’s findings can be applied in real-world settings. For example, if your study demonstrated the effectiveness of a new teaching method, this would imply that educators should consider adopting this method to improve learning outcomes.

Practical implications can also involve policy reconsiderations . For example, if a study reveals significant health benefits from a particular diet, an implication might be that public health guidelines be re-evaluated.

Last but not least, there are the implications for future research . As the name suggests, this category of implications highlights the research gaps or new questions raised by your study. For example, if your study finds mixed results regarding a relationship between two variables, it might imply the need for further investigation to clarify these findings.

To recap then, the three types of implications are the theoretical, the practical and the implications on future research. Regardless of the category, these implications feed into and shape the recommendations , laying the foundation for the actions you’ll propose.

How To Write The  Sections

Now that we’ve laid the foundations, it’s time to explore how to write up the implications and recommendations sections respectively.

Let’s start with the “ where ” before digging into the “ how ”. Typically, the implications will feature in the discussion section of your document, while the recommendations will be located in the conclusion . That said, layouts can vary between disciplines and institutions, so be sure to check with your university what their preferences are.

For the implications section, a common approach is to structure the write-up based on the three categories we looked at earlier – theoretical, practical and future research implications. In practical terms, this discussion will usually follow a fairly formulaic sentence structure – for example:

This research provides new insights into [theoretical aspect], indicating that…

The study’s outcomes highlight the potential benefits of adopting [specific practice] in..

This study raises several questions that warrant further investigation, such as…

Moving onto the recommendations section, you could again structure your recommendations using the three categories. Alternatively, you could structure the discussion per stakeholder group – for example, policymakers, organisations, researchers, etc.

Again, you’ll likely use a fairly formulaic sentence structure for this section. Here are some examples for your inspiration: 

Based on the findings, [specific group] should consider adopting [new method] to improve…

To address the issues identified, it is recommended that legislation should be introduced to…

Researchers should consider examining [specific variable] to build on the current study’s findings.

Remember, you can grab a copy of our tried and tested templates for both the discussion and conclusion sections over on the Grad Coach blog. You can find the links to those, as well as loads of other free resources, in the description 🙂

FAQs: Implications & Recommendations

How do i determine the implications of my study.

To do this, you’ll need to consider how your findings address gaps in the existing literature, how they could influence theory, practice, or policy, and the potential societal or economic impacts.

When thinking about your findings, it’s also a good idea to revisit your introduction chapter, where you would have discussed the potential significance of your study more broadly. This section can help spark some additional ideas about what your findings mean in relation to your original research aims. 

Should I discuss both positive and negative implications?

Absolutely. You’ll need to discuss both the positive and negative implications to provide a balanced view of how your findings affect the field and any limitations or potential downsides.

Can my research implications be speculative?

Yes and no. While implications are somewhat more speculative than recommendations and can suggest potential future outcomes, they should be grounded in your data and analysis. So, be careful to avoid overly speculative claims.

How do I formulate recommendations?

Ideally, you should base your recommendations on the limitations and implications of your study’s findings. So, consider what further research is needed, how policies could be adapted, or how practices could be improved – and make proposals in this respect.

How specific should my recommendations be?

Your recommendations should be as specific as possible, providing clear guidance on what actions or research should be taken next. As mentioned earlier, the implications can be relatively broad, but the recommendations should be very specific and actionable. Ideally, you should apply the SMART framework to your recommendations.

Can I recommend future research in my recommendations?

Absolutely. Highlighting areas where further research is needed is a key aspect of the recommendations section. Naturally, these recommendations should link to the respective section of your implications (i.e., implications for future research).

Wrapping Up: Key Takeaways

We’ve covered quite a bit of ground here, so let’s quickly recap.

• Research implications refer to the possible effects or outcomes of a study’s findings.
• The recommendations section, on the other hand, is where you’ll propose specific actions based on those findings.
• You can structure your implications section based on the three overarching categories – theoretical, practical and future research implications.
• You can carry this structure through to the recommendations as well, or you can group your recommendations by stakeholder.

Remember to grab a copy of our tried and tested free dissertation template, which covers both the implications and recommendations sections. If you’d like 1:1 help with your research project, be sure to check out our private coaching service, where we hold your hand throughout the research journey, step by step.

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Experimental Research: Definition, Types, Design, Examples

Appinio Research · 14.05.2024 · 31min read

Experimental research is a cornerstone of scientific inquiry, providing a systematic approach to understanding cause-and-effect relationships and advancing knowledge in various fields. At its core, experimental research involves manipulating variables, observing outcomes, and drawing conclusions based on empirical evidence. By controlling factors that could influence the outcome, researchers can isolate the effects of specific variables and make reliable inferences about their impact. This guide offers a step-by-step exploration of experimental research, covering key elements such as research design, data collection, analysis, and ethical considerations. Whether you're a novice researcher seeking to understand the basics or an experienced scientist looking to refine your experimental techniques, this guide will equip you with the knowledge and tools needed to conduct rigorous and insightful research.

What is Experimental Research?

Experimental research is a systematic approach to scientific inquiry that aims to investigate cause-and-effect relationships by manipulating independent variables and observing their effects on dependent variables. Experimental research primarily aims to test hypotheses, make predictions, and draw conclusions based on empirical evidence.

By controlling extraneous variables and randomizing participant assignment, researchers can isolate the effects of specific variables and establish causal relationships. Experimental research is characterized by its rigorous methodology, emphasis on objectivity, and reliance on empirical data to support conclusions.

Importance of Experimental Research

• Establishing Cause-and-Effect Relationships : Experimental research allows researchers to establish causal relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. This provides valuable insights into the underlying mechanisms driving phenomena and informs theory development.
• Testing Hypotheses and Making Predictions : Experimental research provides a structured framework for testing hypotheses and predicting the relationship between variables . By systematically manipulating variables and controlling for confounding factors, researchers can empirically test the validity of their hypotheses and refine theoretical models.
• Informing Evidence-Based Practice : Experimental research generates empirical evidence that informs evidence-based practice in various fields, including healthcare, education, and business. Experimental research contributes to improving outcomes and informing decision-making in real-world settings by identifying effective interventions, treatments, and strategies.
• Driving Innovation and Advancement : Experimental research drives innovation and advancement by uncovering new insights, challenging existing assumptions, and pushing the boundaries of knowledge. Through rigorous experimentation and empirical validation, researchers can develop novel solutions to complex problems and contribute to the advancement of science and technology.
• Enhancing Research Rigor and Validity : Experimental research upholds high research rigor and validity standards by employing systematic methods, controlling for confounding variables, and ensuring replicability of findings. By adhering to rigorous methodology and ethical principles, experimental research produces reliable and credible evidence that withstands scrutiny and contributes to the cumulative body of knowledge.

Experimental research plays a pivotal role in advancing scientific understanding, informing evidence-based practice, and driving innovation across various disciplines. By systematically testing hypotheses, establishing causal relationships, and generating empirical evidence, experimental research contributes to the collective pursuit of knowledge and the improvement of society.

Understanding Experimental Design

Experimental design serves as the blueprint for your study, outlining how you'll manipulate variables and control factors to draw valid conclusions.

Experimental Design Components

Experimental design comprises several essential elements:

• Independent Variable (IV) : This is the variable manipulated by the researcher. It's what you change to observe its effect on the dependent variable. For example, in a study testing the impact of different study techniques on exam scores, the independent variable might be the study method (e.g., flashcards, reading, or practice quizzes).
• Dependent Variable (DV) : The dependent variable is what you measure to assess the effect of the independent variable. It's the outcome variable affected by the manipulation of the independent variable. In our study example, the dependent variable would be the exam scores.
• Control Variables : These factors could influence the outcome but are kept constant or controlled to isolate the effect of the independent variable. Controlling variables helps ensure that any observed changes in the dependent variable can be attributed to manipulating the independent variable rather than other factors.
• Experimental Group : This group receives the treatment or intervention being tested. It's exposed to the manipulated independent variable. In contrast, the control group does not receive the treatment and serves as a baseline for comparison.

Types of Experimental Designs

Experimental designs can vary based on the research question, the nature of the variables, and the desired level of control. Here are some common types:

• Between-Subjects Design : In this design, different groups of participants are exposed to varying levels of the independent variable. Each group represents a different experimental condition, and participants are only exposed to one condition. For instance, in a study comparing the effectiveness of two teaching methods, one group of students would use Method A, while another would use Method B.
• Within-Subjects Design : Also known as repeated measures design , this approach involves exposing the same group of participants to all levels of the independent variable. Participants serve as their own controls, and the order of conditions is typically counterbalanced to control for order effects. For example, participants might be tested on their reaction times under different lighting conditions, with the order of conditions randomized to eliminate any research bias .
• Mixed Designs : Mixed designs combine elements of both between-subjects and within-subjects designs. This allows researchers to examine both between-group differences and within-group changes over time. Mixed designs help study complex phenomena that involve multiple variables and temporal dynamics.

Factors Influencing Experimental Design Choices

Several factors influence the selection of an appropriate experimental design:

• Research Question : The nature of your research question will guide your choice of experimental design. Some questions may be better suited to between-subjects designs, while others may require a within-subjects approach.
• Variables : Consider the number and type of variables involved in your study. A factorial design might be appropriate if you're interested in exploring multiple factors simultaneously. Conversely, if you're focused on investigating the effects of a single variable, a simpler design may suffice.
• Practical Considerations : Practical constraints such as time, resources, and access to participants can impact your choice of experimental design. Depending on your study's specific requirements, some designs may be more feasible or cost-effective   than others .
• Ethical Considerations : Ethical concerns, such as the potential risks to participants or the need to minimize harm, should also inform your experimental design choices. Ensure that your design adheres to ethical guidelines and safeguards the rights and well-being of participants.

By carefully considering these factors and selecting an appropriate experimental design, you can ensure that your study is well-designed and capable of yielding meaningful insights.

Experimental Research Elements

When conducting experimental research, understanding the key elements is crucial for designing and executing a robust study. Let's explore each of these elements in detail to ensure your experiment is well-planned and executed effectively.

Independent and Dependent Variables

In experimental research, the independent variable (IV) is the factor that the researcher manipulates or controls, while the dependent variable (DV) is the measured outcome or response. The independent variable is what you change in the experiment to observe its effect on the dependent variable.

For example, in a study investigating the effect of different fertilizers on plant growth, the type of fertilizer used would be the independent variable, while the plant growth (height, number of leaves, etc.) would be the dependent variable.

Control Groups and Experimental Groups

Control groups and experimental groups are essential components of experimental design. The control group serves as a baseline for comparison and does not receive the treatment or intervention being studied. Its purpose is to provide a reference point to assess the effects of the independent variable.

In contrast, the experimental group receives the treatment or intervention and is used to measure the impact of the independent variable. For example, in a drug trial, the control group would receive a placebo, while the experimental group would receive the actual medication.

Randomization and Random Sampling

Randomization is the process of randomly assigning participants to different experimental conditions to minimize biases and ensure that each participant has an equal chance of being assigned to any condition. Randomization helps control for extraneous variables and increases the study's internal validity .

Random sampling, on the other hand, involves selecting a representative sample from the population of interest to generalize the findings to the broader population. Random sampling ensures that each member of the population has an equal chance of being included in the sample, reducing the risk of sampling bias .

Replication and Reliability

Replication involves repeating the experiment to confirm the results and assess the reliability of the findings . It is essential for ensuring the validity of scientific findings and building confidence in the robustness of the results. A study that can be replicated consistently across different settings and by various researchers is considered more reliable. Researchers should strive to design experiments that are easily replicable and transparently report their methods to facilitate replication by others.

Validity: Internal, External, Construct, and Statistical Conclusion Validity

Validity refers to the degree to which an experiment measures what it intends to measure and the extent to which the results can be generalized to other populations or contexts. There are several types of validity that researchers should consider:

• Internal Validity : Internal validity refers to the extent to which the study accurately assesses the causal relationship between variables. Internal validity is threatened by factors such as confounding variables, selection bias, and experimenter effects. Researchers can enhance internal validity through careful experimental design and control procedures.
• External Validity : External validity refers to the extent to which the study's findings can be generalized to other populations or settings. External validity is influenced by factors such as the representativeness of the sample and the ecological validity of the experimental conditions. Researchers should consider the relevance and applicability of their findings to real-world situations.
• Construct Validity : Construct validity refers to the degree to which the study accurately measures the theoretical constructs of interest. Construct validity is concerned with whether the operational definitions of the variables align with the underlying theoretical concepts. Researchers can establish construct validity through careful measurement selection and validation procedures.
• Statistical Conclusion Validity : Statistical conclusion validity refers to the accuracy of the statistical analyses and conclusions drawn from the data. It ensures that the statistical tests used are appropriate for the data and that the conclusions drawn are warranted. Researchers should use robust statistical methods and report effect sizes and confidence intervals to enhance statistical conclusion validity.

By addressing these elements of experimental research and ensuring the validity and reliability of your study, you can conduct research that contributes meaningfully to the advancement of knowledge in your field.

How to Conduct Experimental Research?

Embarking on an experimental research journey involves a series of well-defined phases, each crucial for the success of your study. Let's explore the pre-experimental, experimental, and post-experimental phases to ensure you're equipped to conduct rigorous and insightful research.

Pre-Experimental Phase

The pre-experimental phase lays the foundation for your study, setting the stage for what's to come. Here's what you need to do:

• Formulating Research Questions and Hypotheses : Start by clearly defining your research questions and formulating testable hypotheses. Your research questions should be specific, relevant, and aligned with your research objectives. Hypotheses provide a framework for testing the relationships between variables and making predictions about the outcomes of your study.
• Reviewing Literature and Establishing Theoretical Framework : Dive into existing literature relevant to your research topic and establish a solid theoretical framework. Literature review helps you understand the current state of knowledge, identify research gaps, and build upon existing theories. A well-defined theoretical framework provides a conceptual basis for your study and guides your research design and analysis.

Experimental Phase

The experimental phase is where the magic happens – it's time to put your hypotheses to the test and gather data. Here's what you need to consider:

• Participant Recruitment and Sampling Techniques : Carefully recruit participants for your study using appropriate sampling techniques . The sample should be representative of the population you're studying to ensure the generalizability of your findings. Consider factors such as sample size , demographics , and inclusion criteria when recruiting participants.
• Implementing Experimental Procedures : Once you've recruited participants, it's time to implement your experimental procedures. Clearly outline the experimental protocol, including instructions for participants, procedures for administering treatments or interventions, and measures for controlling extraneous variables. Standardize your procedures to ensure consistency across participants and minimize sources of bias.
• Data Collection and Measurement : Collect data using reliable and valid measurement instruments. Depending on your research questions and variables of interest, data collection methods may include surveys , observations, physiological measurements, or experimental tasks. Ensure that your data collection procedures are ethical, respectful of participants' rights, and designed to minimize errors and biases.

Post-Experimental Phase

In the post-experimental phase, you make sense of your data, draw conclusions, and communicate your findings  to the world . Here's what you need to do:

• Data Analysis Techniques : Analyze your data using appropriate statistical techniques . Choose methods that are aligned with your research design and hypotheses. Standard statistical analyses include descriptive statistics, inferential statistics (e.g., t-tests, ANOVA), regression analysis , and correlation analysis. Interpret your findings in the context of your research questions and theoretical framework.
• Interpreting Results and Drawing Conclusions : Once you've analyzed your data, interpret the results and draw conclusions. Discuss the implications of your findings, including any theoretical, practical, or real-world implications. Consider alternative explanations and limitations of your study and propose avenues for future research. Be transparent about the strengths and weaknesses of your study to enhance the credibility of your conclusions.
• Reporting Findings : Finally, communicate your findings through research reports, academic papers, or presentations. Follow standard formatting guidelines and adhere to ethical standards for research reporting. Clearly articulate your research objectives, methods, results, and conclusions. Consider your target audience and choose appropriate channels for disseminating your findings to maximize impact and reach.

By meticulously planning and executing each experimental research phase, you can generate valuable insights, advance knowledge in your field, and contribute to scientific progress.

A s you navigate the intricate phases of experimental research, leveraging Appinio can streamline your journey toward actionable insights. With our intuitive platform, you can swiftly gather real-time consumer data, empowering you to make informed decisions with confidence. Say goodbye to the complexities of traditional market research and hello to a seamless, efficient process that puts you in the driver's seat of your research endeavors.

Ready to revolutionize your approach to data-driven decision-making? Book a demo today and discover the power of Appinio in transforming your research experience!

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Experimental Research Examples

Understanding how experimental research is applied in various contexts can provide valuable insights into its practical significance and effectiveness. Here are some examples illustrating the application of experimental research in different domains:

Market Research

Experimental studies are crucial in market research in testing hypotheses, evaluating marketing strategies, and understanding consumer behavior . For example, a company may conduct an experiment to determine the most effective advertising message for a new product. Participants could be exposed to different versions of an advertisement, each emphasizing different product features or appeals.

By measuring variables such as brand recall, purchase intent, and brand perception, researchers can assess the impact of each advertising message and identify the most persuasive approach.

Software as a Service (SaaS)

In the SaaS industry, experimental research is often used to optimize user interfaces, features, and pricing models to enhance user experience and drive engagement. For instance, a SaaS company may conduct A/B tests to compare two versions of its software interface, each with a different layout or navigation structure.

Researchers can identify design elements that lead to higher user satisfaction and retention by tracking user interactions, conversion rates, and customer feedback . Experimental research also enables SaaS companies to test new product features or pricing strategies before full-scale implementation, minimizing risks and maximizing return on investment.

Business Management

Experimental research is increasingly utilized in business management to inform decision-making, improve organizational processes, and drive innovation. For example, a business may conduct an experiment to evaluate the effectiveness of a new training program on employee productivity. Participants could be randomly assigned to either receive the training or serve as a control group.

By measuring performance metrics such as sales revenue, customer satisfaction, and employee turnover, researchers can assess the training program's impact and determine its return on investment. Experimental research in business management provides empirical evidence to support strategic initiatives and optimize resource allocation.

In healthcare , experimental research is instrumental in testing new treatments, interventions, and healthcare delivery models to improve patient outcomes and quality of care. For instance, a clinical trial may be conducted to evaluate the efficacy of a new drug in treating a specific medical condition. Participants are randomly assigned to either receive the experimental drug or a placebo, and their health outcomes are monitored over time.

By comparing the effectiveness of the treatment and placebo groups, researchers can determine the drug's efficacy, safety profile, and potential side effects. Experimental research in healthcare informs evidence-based practice and drives advancements in medical science and patient care.

These examples illustrate the versatility and applicability of experimental research across diverse domains, demonstrating its value in generating actionable insights, informing decision-making, and driving innovation. Whether in market research or healthcare, experimental research provides a rigorous and systematic approach to testing hypotheses, evaluating interventions, and advancing knowledge.

Experimental Research Challenges

Even with careful planning and execution, experimental research can present various challenges. Understanding these challenges and implementing effective solutions is crucial for ensuring the validity and reliability of your study. Here are some common challenges and strategies for addressing them.

Sample Size and Statistical Power

Challenge : Inadequate sample size can limit your study's generalizability and statistical power, making it difficult to detect meaningful effects. Small sample sizes increase the risk of Type II errors (false negatives) and reduce the reliability of your findings.

Solution : Increase your sample size to improve statistical power and enhance the robustness of your results. Conduct a power analysis before starting your study to determine the minimum sample size required to detect the effects of interest with sufficient power. Consider factors such as effect size, alpha level, and desired power when calculating sample size requirements. Additionally, consider using techniques such as bootstrapping or resampling to augment small sample sizes and improve the stability of your estimates.

To enhance the reliability of your experimental research findings, you can leverage our Sample Size Calculator . By determining the optimal sample size based on your desired margin of error, confidence level, and standard deviation, you can ensure the representativeness of your survey results. Don't let inadequate sample sizes hinder the validity of your study and unlock the power of precise research planning!

Confounding Variables and Bias

Challenge : Confounding variables are extraneous factors that co-vary with the independent variable and can distort the relationship between the independent and dependent variables. Confounding variables threaten the internal validity of your study and can lead to erroneous conclusions.

Solution : Implement control measures to minimize the influence of confounding variables on your results. Random assignment of participants to experimental conditions helps distribute confounding variables evenly across groups, reducing their impact on the dependent variable. Additionally, consider using matching or blocking techniques to ensure that groups are comparable on relevant variables. Conduct sensitivity analyses to assess the robustness of your findings to potential confounders and explore alternative explanations for your results.

Researcher Effects and Experimenter Bias

Challenge : Researcher effects and experimenter bias occur when the experimenter's expectations or actions inadvertently influence the study's outcomes. This bias can manifest through subtle cues, unintentional behaviors, or unconscious biases , leading to invalid conclusions.

Solution : Implement double-blind procedures whenever possible to mitigate researcher effects and experimenter bias. Double-blind designs conceal information about the experimental conditions from both the participants and the experimenters, minimizing the potential for bias. Standardize experimental procedures and instructions to ensure consistency across conditions and minimize experimenter variability. Additionally, consider using objective outcome measures or automated data collection procedures to reduce the influence of experimenter bias on subjective assessments.

External Validity and Generalizability

Challenge : External validity refers to the extent to which your study's findings can be generalized to other populations, settings, or conditions. Limited external validity restricts the applicability of your results and may hinder their relevance to real-world contexts.

Solution : Enhance external validity by designing studies closely resembling real-world conditions and populations of interest. Consider using diverse samples  that represent  the target population's demographic, cultural, and ecological variability. Conduct replication studies in different contexts or with different populations to assess the robustness and generalizability of your findings. Additionally, consider conducting meta-analyses or systematic reviews to synthesize evidence from multiple studies and enhance the external validity of your conclusions.

By proactively addressing these challenges and implementing effective solutions, you can strengthen the validity, reliability, and impact of your experimental research. Remember to remain vigilant for potential pitfalls throughout the research process and adapt your strategies as needed to ensure the integrity of your findings.

Advanced Topics in Experimental Research

As you delve deeper into experimental research, you'll encounter advanced topics and methodologies that offer greater complexity and nuance.

Quasi-Experimental Designs

Quasi-experimental designs resemble true experiments but lack random assignment to experimental conditions. They are often used when random assignment is impractical, unethical, or impossible. Quasi-experimental designs allow researchers to investigate cause-and-effect relationships in real-world settings where strict experimental control is challenging. Common examples include:

• Non-Equivalent Groups Design : This design compares two or more groups that were not created through random assignment. While similar to between-subjects designs, non-equivalent group designs lack the random assignment of participants, increasing the risk of confounding variables.
• Interrupted Time Series Design : In this design, multiple measurements are taken over time before and after an intervention is introduced. Changes in the dependent variable are assessed over time, allowing researchers to infer the impact of the intervention.
• Regression Discontinuity Design : This design involves assigning participants to different groups based on a cutoff score on a continuous variable. Participants just above and below the cutoff are treated as if they were randomly assigned to different conditions, allowing researchers to estimate causal effects.

Quasi-experimental designs offer valuable insights into real-world phenomena but require careful consideration of potential confounding variables and limitations inherent to non-random assignment.

Factorial Designs

Factorial designs involve manipulating two or more independent variables simultaneously to examine their main effects and interactions. By systematically varying multiple factors, factorial designs allow researchers to explore complex relationships between variables and identify how they interact to influence outcomes. Common types of factorial designs include:

• 2x2 Factorial Design : This design manipulates two independent variables, each with two levels. It allows researchers to examine the main effects of each variable as well as any interaction between them.
• Mixed Factorial Design : In this design, one independent variable is manipulated between subjects, while another is manipulated within subjects. Mixed factorial designs enable researchers to investigate both between-subjects and within-subjects effects simultaneously.

Factorial designs provide a comprehensive understanding of how multiple factors contribute to outcomes and offer greater statistical efficiency compared to studying variables in isolation.

Longitudinal and Cross-Sectional Studies

Longitudinal studies involve collecting data from the same participants over an extended period, allowing researchers to observe changes and trajectories over time. Cross-sectional studies , on the other hand, involve collecting data from different participants at a single point in time, providing a snapshot of the population at that moment. Both longitudinal and cross-sectional studies offer unique advantages and challenges:

• Longitudinal Studies : Longitudinal designs allow researchers to examine developmental processes, track changes over time, and identify causal relationships. However, longitudinal studies require long-term commitment, are susceptible to attrition and dropout, and may be subject to practice effects and cohort effects.
• Cross-Sectional Studies : Cross-sectional designs are relatively quick and cost-effective, provide a snapshot of population characteristics, and allow for comparisons across different groups. However, cross-sectional studies cannot assess changes over time or establish causal relationships between variables.

Researchers should carefully consider the research question, objectives, and constraints when choosing between longitudinal and cross-sectional designs.

Meta-Analysis and Systematic Reviews

Meta-analysis and systematic reviews are quantitative methods used to synthesize findings from multiple studies and draw robust conclusions. These methods offer several advantages:

• Meta-Analysis : Meta-analysis combines the results of multiple studies using statistical techniques to estimate overall effect sizes and assess the consistency of findings across studies. Meta-analysis increases statistical power, enhances generalizability, and provides more precise estimates of effect sizes.
• Systematic Reviews : Systematic reviews involve systematically searching, appraising, and synthesizing existing literature on a specific topic. Systematic reviews provide a comprehensive summary of the evidence, identify gaps and inconsistencies in the literature, and inform future research directions.

Meta-analysis and systematic reviews are valuable tools for evidence-based practice, guiding policy decisions, and advancing scientific knowledge by aggregating and synthesizing empirical evidence from diverse sources.

By exploring these advanced topics in experimental research, you can expand your methodological toolkit, tackle more complex research questions, and contribute to deeper insights and understanding in your field.

Experimental Research Ethical Considerations

When conducting experimental research, it's imperative to uphold ethical standards and prioritize the well-being and rights of participants. Here are some key ethical considerations to keep in mind throughout the research process:

• Informed Consent : Obtain informed consent from participants before they participate in your study. Ensure that participants understand the purpose of the study, the procedures involved, any potential risks or benefits, and their right to withdraw from the study at any time without penalty.
• Protection of Participants' Rights : Respect participants' autonomy, privacy, and confidentiality throughout the research process. Safeguard sensitive information and ensure that participants' identities are protected. Be transparent about how their data will be used and stored.
• Minimizing Harm and Risks : Take steps to mitigate any potential physical or psychological harm to participants. Conduct a risk assessment before starting your study and implement appropriate measures to reduce risks. Provide support services and resources for participants who may experience distress or adverse effects as a result of their participation.
• Confidentiality and Data Security : Protect participants' privacy and ensure the security of their data. Use encryption and secure storage methods to prevent unauthorized access to sensitive information. Anonymize data whenever possible to minimize the risk of data breaches or privacy violations.
• Avoiding Deception : Minimize the use of deception in your research and ensure that any deception is justified by the scientific objectives of the study. If deception is necessary, debrief participants fully at the end of the study and provide them with an opportunity to withdraw their data if they wish.
• Respecting Diversity and Cultural Sensitivity : Be mindful of participants' diverse backgrounds, cultural norms, and values. Avoid imposing your own cultural biases on participants and ensure that your research is conducted in a culturally sensitive manner. Seek input from diverse stakeholders to ensure your research is inclusive and respectful.
• Compliance with Ethical Guidelines : Familiarize yourself with relevant ethical guidelines and regulations governing research with human participants, such as those outlined by institutional review boards (IRBs) or ethics committees. Ensure that your research adheres to these guidelines and that any potential ethical concerns are addressed appropriately.
• Transparency and Openness : Be transparent about your research methods, procedures, and findings. Clearly communicate the purpose of your study, any potential risks or limitations, and how participants' data will be used. Share your research findings openly and responsibly, contributing to the collective body of knowledge in your field.

By prioritizing ethical considerations in your experimental research, you demonstrate integrity, respect, and responsibility as a researcher, fostering trust and credibility in the scientific community.

Conclusion for Experimental Research

Experimental research is a powerful tool for uncovering causal relationships and expanding our understanding of the world around us. By carefully designing experiments, collecting data, and analyzing results, researchers can make meaningful contributions to their fields and address pressing questions. However, conducting experimental research comes with responsibilities. Ethical considerations are paramount to ensure the well-being and rights of participants, as well as the integrity of the research process. Researchers can build trust and credibility in their work by upholding ethical standards and prioritizing participant safety and autonomy. Furthermore, as you continue to explore and innovate in experimental research, you must remain open to new ideas and methodologies. Embracing diversity in perspectives and approaches fosters creativity and innovation, leading to breakthrough discoveries and scientific advancements. By promoting collaboration and sharing findings openly, we can collectively push the boundaries of knowledge and tackle some of society's most pressing challenges.

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Understanding External Validity and its Role in Research Design

This essay discusses external validity in scientific research and its significance in ensuring that study findings are applicable beyond specific experimental conditions. It examines how external validity helps researchers determine whether results can be generalized to different populations, settings, and contexts. The essay highlights challenges in achieving high external validity due to the specificity of research environments, participant characteristics, and timing. It also explains strategies like replication studies and field experiments that help increase the generalizability of results. The essay emphasizes the importance of balancing internal and external validity, acknowledging that while highly controlled studies can ensure internal accuracy, they might not reflect real-world conditions. Ultimately, external validity is crucial for producing research that informs effective policies, clinical guidelines, and societal changes.

How it works

Within the domain of scientific inquiry, the concept of external validity assumes paramount importance, serving as a linchpin for ensuring the relevance and broad applicability of research findings beyond the confines of a singular study. Put succinctly, external validity scrutinizes the extent to which the outcomes of a given investigation can be extrapolated to diverse populations, environments, temporal epochs, or contextual milieus. This conceptual framework assumes significance by bridging the chasm between meticulously controlled research settings and the kaleidoscopic intricacies of real-world scenarios, thereby furnishing researchers with the assurance that their conclusions possess trans-situational validity.

External validity assumes a pivotal role in buttressing the overall veracity of scientific inquiry, furnishing scientists with the means to discern whether their revelations hold substantive import and utility for wider swathes of humanity. Nonetheless, attaining elevated levels of external validity often presents an arduous undertaking owing to the idiosyncratic nature of experimental methodologies. Numerous investigations are conducted within controlled environs or with highly circumscribed samples to mitigate the influence of confounding variables, thereby complicating the process of extrapolating findings to disparate cohorts. For instance, a psychological study confined to the precincts of university campuses might furnish insights germane solely to that demographic stratum, precluding facile generalization to alternative age cohorts or cultural constellations.

Pivotal determinants impinging upon external validity encompass the demographic attributes of study participants, the ambient ambiance of experimental locales, and the temporal dynamics of data accrual. Researchers routinely resort to random sampling methodologies to obviate selection biases and assemble a heterogeneous cohort reflective of the broader populace. The ambient setting assumes salience as outcomes gleaned within sterile laboratory settings might not seamlessly transmute into real-world vicinities. Furthermore, external validity can be influenced by the temporal dimension of investigations, particularly when grappling with phenomena susceptible to temporal vicissitudes induced by cultural, economic, or technological flux.

Researchers endeavor to fortify external validity through recourse to replication studies, which seek to reproduce experimental paradigms under varying conditions to corroborate the robustness of findings. Engaging in multi-site investigations spanning disparate geographical realms or demographic constituencies also serves to validate the generalizability of findings across multifarious locales and participant profiles. Augmenting the verisimilitude of study designs by approximating real-world conditions more faithfully or deploying field experiments conducted within natural habitats constitutes an additional stratagem employed to enhance external validity.

Nonetheless, a judicious equilibrium between internal and external validity is indispensable. While internal validity safeguards against spurious attributions of causality by delineating the genuine effects attributable to independent variables as opposed to extraneous factors, an undue emphasis on this facet may inadvertently compromise external validity. For instance, a rigorously controlled laboratory investigation might succeed in eradicating most confounding variables but might concurrently engender an artificial milieu divorced from real-world veracity.

On occasion, external validity is consciously sacrificed to scrutinize a phenomenon within a specific subgroup or contextual milieu. Clinical trials, for instance, might pivot towards patients exhibiting highly circumscribed medical afflictions or demographic profiles. While such findings might lack broad generalizability, they furnish invaluable insights germane to the target demographic.

In summation, the salience of external validity cannot be overstated. Research endeavors endeavoring to inform policy formulation, clinical dictums, or societal transformations necessitate a degree of generalizability to obviate the formulation of recommendations that are ineffectual or even deleterious. Researchers ought to meticulously orchestrate their investigations, cognizant of the demographic cohorts and environmental terrains that their findings aspire to impact. By acknowledging and redressing the vicissitudes of external validity, scientists can engender more resilient and universally applicable research findings that withstand the rigors of real-world complexity.

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• Open access
• Published: 21 May 2024

Exploring shared decision-making needs in lung cancer screening among high-risk groups and health care providers in China: a qualitative study

• Xiujing Lin 1 ,
• Fangfang Wang 1 ,
• Yonglin Li 1 ,
• Fang Lei 2 ,
• Weisheng Chen 3 ,
• Rachel H. Arbing 4 ,
• Wei-Ti Chen   ORCID: orcid.org/0000-0002-2342-045X 4 &
• Feifei Huang   ORCID: orcid.org/0000-0003-0197-8687 1  

BMC Cancer volume  24 , Article number:  613 ( 2024 ) Cite this article

Metrics details

The intricate balance between the advantages and risks of low-dose computed tomography (LDCT) impedes the utilization of lung cancer screening (LCS). Guiding shared decision-making (SDM) for well-informed choices regarding LCS is pivotal. There has been a notable increase in research related to SDM. However, these studies possess limitations. For example, they may ignore the identification of decision support and needs from the perspective of health care providers and high-risk groups. Additionally, these studies have not adequately addressed the complete SDM process, including pre-decisional needs, the decision-making process, and post-decision experiences. Furthermore, the East-West divide of SDM has been largely ignored. This study aimed to explore the decisional needs and support for shared decision-making for LCS among health care providers and high-risk groups in China.

Informed by the Ottawa Decision-Support Framework, we conducted qualitative, face-to-face in-depth interviews to explore shared decision-making among 30 lung cancer high-risk individuals and 9 health care providers. Content analysis was used for data analysis.

We identified 4 decisional needs that impair shared decision-making: (1) LCS knowledge deficit; (2) inadequate supportive resources; (3) shared decision-making conceptual bias; and (4) delicate doctor-patient bonds. We identified 3 decision supports: (1) providing information throughout the LCS process; (2) providing shared decision-making decision coaching; and (3) providing decision tools.

Conclusions

This study offers valuable insights into the decisional needs and support required to undergo LCS among high-risk individuals and perspectives from health care providers. Future studies should aim to design interventions that enhance the quality of shared decision-making by offering LCS information, decision tools for LCS, and decision coaching for shared decision-making (e.g., through community nurses). Simultaneously, it is crucial to assess individuals’ needs for effective deliberation to prevent conflicts and regrets after arriving at a decision.

Peer Review reports

Low-dose computed tomography (LDCT) is an effective tool for early lung cancer detection and has been proven to enhance survival rates in individuals at high-risk for lung cancer [ 1 , 2 ]. However, global LDCT usage is limited, with only 2-35% of eligible individuals undergoing screening [ 3 , 4 , 5 , 6 , 7 ], in contrast to 16-68% of eligible candidates undergoing colorectal cancer screening [ 8 ]. Improvements in LDCT screening rates for high-risk groups have been modest. The intricate balance between the advantages and risks of LDCT impedes the utilization of lung cancer screening (LCS) [ 9 ]. Notably, compared to their non-screened counterparts, high-risk individuals who underwent LDCT had a remarkable 24% decrease in lung cancer mortality [ 2 ]. However, the benefits of LDCT come with potential drawbacks, such as radiation-induced cancer, needless examinations, invasive procedures stemming from false positives, overdiagnosis, incidental discoveries, and psychological burdens [ 10 ]. These complexities render the LDCT screening decision-making process multifaceted and reliant on personal preferences. Hence, guiding high-risk groups toward well-informed choices regarding LCS is pivotal and represents a substantial mechanism for advancing the secondary prevention of lung cancer.

Shared decision-making is defined as “a collaborative approach for health care providers and patients in making informed health decisions”, which involves considering evidence regarding the benefits and risks of medical options, as well as individuals’ preferences and values [ 11 ]. This decision-making process allows both health care providers and individuals as well as their family members to engage in deliberation which leads to identifying the most appropriate decision for the situation [ 12 ]. Multiple guidelines strongly recommend shared decision-making as an essential step before patients undergo LDCT. Shared decision-making is also stipulated as a prerequisite for LDCT reimbursement by the Centers for Medicare and Medicaid Services in the United States [ 13 , 14 , 15 , 16 ]. Regrettably, the utilization of shared decision-making in clinical practice is currently not optimal [ 17 , 18 ]. Patients do not know what LDCT is, and they often report a lack of about the risks and benefits of LDCT. As a result, patients often have concerns about the risks of LDCT, and health care providers frequently fail to inquire about individuals’ preferences [ 19 ]. Consequently, there has been a notable increase in the literature focusing on barriers to shared decision-making from the perspectives of both health care providers and lung cancer high-risk groups. For example, studies have shown that the barriers to shared decision-making include different perceptions about the use of shared decision-making and a lack of time to communicate with providers. However, there are some limitations in terms of methodology and the comparative nature of the studies that focus on LCS shared decision-making. First, previously published studies focused on identifying barriers to shared decision-making and neglected decision support from physicians and patients. For instance, one study found that a lack of professionalism in health care providers is a barrier to shared decision-making, yet no studies have examined specific LCS shared decision-making decision supports for health care providers [ 19 ]. Second, current research centers on short-term decision-making experiences, such as cognitive consequences experienced immediately following shared decision-making. However, studies have not adequately addressed the complete shared decision-making process – pre-decisional needs, the decision-making process itself, and post-decision experiences, such as decision regret. Third, the COVID-19 pandemic has introduced a new risk of LDCT usage (exposure to the health-care environment) [ 20 ]. The added risk alters the benefit-risk ratio of LDCT under pre-COVID-19 guideline recommendations. Fourth, shared decision-making, developed in Western societies, is rarely discussed in China. The national climate and medical systems of China and Western countries differ greatly [ 21 ], and the lack of evidence on LCS shared decision-making in China indicates a need for an assessment of shared decision-making in those who require LDCT.

This study aimed to explore the decisional needs and decision support of shared decision-making for LCS among Chinese high-risk individuals and their health care providers using data collected through in-depth one-on-one interviews.

Theoretical framework

The Ottawa Decision-Support Framework (ODSF) is an evidence-based conceptual framework that is structured around three key components [ 22 ]: (1) assessing decisional needs, such as insufficient knowledge, complex decision types, and limited resources; (2) providing decision support, which encompasses clinical counseling, decision-making tools, and decision coaching; and (3) evaluating decisional outcomes, which includes assessing the quality of the decision-making process and its impact. According to the ODSF, successful decision support should be guided by an assessment of the individual’s knowledge and his/her ability to make his/her own decision to reduce their unmet needs and achieve a final health decision with the support of health care providers and family members. The ODSF has been successfully used within several populations with health needs to guide health decisions and provide decision support [ 23 , 24 ].

This qualitative study emphasizes the “who, what, and where” of events or experiences [ 25 ]. The central research question posed was, “What are the decisional needs and supports of LCS shared decision-making among individuals at high-risk of lung cancer and health care providers?” Consequently, a descriptive qualitative approach was deemed appropriate for exploring the decisional needs and supports for LCS shared decision-making among individuals at high-risk of lung cancer and health care providers [ 26 ]. This descriptive qualitative study adhered to the Consolidated Criteria for Reporting Qualitative Studies (COREQ) checklist [ 27 ]. Ethical approval for this study was obtained from the ethics committee of Fujian Medical University (Approval No. 2,023,098).

Inclusion and exclusion criteria

Aligned with the guidelines for the early detection of lung cancer in China [ 14 ], the inclusion criteria used for the high-risk group for lung cancer were as follows: (a) aged between 50 and 74 years; (b) had at least one of the following risk factors for lung cancer: a smoking history ≥ 30 pack-years, which includes current smokers or individuals who quit smoking within the last 15 years; prolonged exposure to passive smoking (living or working with smokers for 20 years or more); a history of COPD; a history of occupational exposure to asbestos, radon, beryllium, chromium, cadmium, nickel, silicon, soot, or coal soot for a minimum of 1 year; or a family history of lung cancer; (c) verbal confirmation of undergoing LCS shared decision-making; (d) undergone LDCT within the past 5 years; (e) Able to converse in Mandarin; (f) absence of cognitive or psychological disorders; and (g) willingness to share their personal stories. The exclusion criteria used for the high-risk group for lung cancer were as follows: (a) previous history of lung cancer; and (b) cognitive or psychological disorders (such as depression and anxiety). The inclusion criteria used for health care providers were as follows: (a) certified physicians or nurses; (b) expertise in LCS; and (c) willingness to share their experiences. Healthcare providers who were receiving external training were excluded from participation in the study.

Qualitative data collection

The data were collected from March 2023 to May 2023. A purposive sampling method was used to identify and recruit individuals at high-risk for lung cancer, as well as local health care providers from five community healthcare centers and two surgical oncology departments of tertiary hospitals. Study flyers provided information on the purpose of the study and the inclusion and exclusion criteria and were distributed to potential participants on site. After participants expressed their interest in the study, they were screened for eligibility to participate and their informed consent was secured. Next, a one-on-one interview was scheduled and a questionnaire was completed by participants to obtain their demographic data (gender, age, residential area, smoking status, etc.). One-on-one interviews were conducted in Mandarin, digitally recorded, with study data stored on a passworded encrypted laptop. Each interview lasted approximately 20 to 40 min. A private room in the clinic was used for all the in-depth interviews.

The interview questions were formulated based on the ODSF and after a comprehensive literature review [ 28 ], with extensive discussions among researchers of the study (Feifei Huang, PhD, RN, Professor, specializing in lung cancer prevention and psycho-oncology; Weisheng Chen, MD, specializing in lung cancer prevention, diagnosis and treatment; and Wei-Ti Chen PhD, RN, CNM, FAAN, specializing in intervention design and qualitative data collection). To ensure the acceptability and credibility of the interview guide, the interview questions were pilot tested with four participants in total, including two health care providers and two individuals at high-risk of lung cancer. As a result, some misconceptions regarding the interview questions were identified and subsequently modified. For instance, we replaced the term “decision tools” with “patient decision aids” to help participants to better understand the posed questions. The final interview questions are outlined in Table 1 . Tables 2 and 3 summarize key demographic data collected on the high-risk individuals and health care providers, respectively.

The sample size was determined by data saturation, that is, recruitment ended at the point where no new themes emerged from the participants’ experiences [ 29 ]. Data saturation was reached at approximately the twenty-seventh in-depth interview with a high-risk lung cancer individual, with another three high-risk lung cancer individuals being interviewed to ensure that the data reached complete saturation. Data saturation was reached at approximately the seventh in-depth interview with healthcare providers, with another two healthcare providers interviewed to ensure data saturation.

Data analysis

Since the interviews were conducted in Mandarin, a bilingual coding technique was used to keep the data in the original Chinese format, and the coding assignments were in English (e.g., decision negotiation). To ensure accuracy and minimize potential translation errors, two bilingual researchers (Chinese and English) reviewed and confirmed the translations [ 30 ]. The process of data analysis began with data collection. To analyze the data, content analysis was guided by the ODSF and Nvivo software version 12 was used [ 31 ]. The classification of themes was performed both inductively (derived from the quotes of research participants) and deductively (derived from the ODSF theoretical framework) under the principle of complementarity. The detailed steps of the data analysis process are illustrated in Fig. 1 .

Directed content analysis flowchart

Trustworthiness

Credibility, dependability, confirmability and transferability were employed to assure the trustworthiness of this study’s findings [ 32 ]. To enhance credibility, the researcher dedicated ample time to establishing meaningful interactions with the participants, thereby building trust for effective data collection. Regarding dependability, two researchers cross-checked and rectified codes that did not precisely reflect participants’ perspectives. Furthermore, an audit trail and reflexivity techniques were used during the data analysis process, which included tracking the interview and data analysis notes and memos. To ensure confirmability, the supervisor reviewed and selected quotations, codes, and categories, thereby validating the accuracy of the coding process. In terms of transferability, participants were purposefully selected from both urban and rural areas to incorporate a wide range of perspectives. Herein, a comprehensive description of the entire research process is presented to facilitate reproducibility of the study.

Out of a total of 44 participants consented, five participants (4 high-risk individuals and 1 health care provider) dropped out of the study due to their busy schedules and lack of interest in participating. A total of 39 eligible volunteers composed the study sample. Among them, 30 individuals were classified as at high-risk for lung cancer with an average age of 61.27 ± 7.92 years, while nine health care providers had an average age of 36.78 ± 7.45 years. Five health care provider participants specialized in lung cancer prevention, diagnosis, and treatment, and four specialized in general medical education and community cancer screening education. Detailed demographic information on the participants can be found in Tables  2 and 3 .

A total of 546 unique codes related to LCS shared decision-making were identified. Following the framework of the ODSF, participants’ decisional needs and supports for shared decision-making were categorized (refer to Fig.  2 ; Table  4 ).

Participants’ viewpoints on shared decision-making based on ODSF

Decisional needs

We identified four categories related to the theme of decisional needs, including LCS knowledge deficits, inadequate supportive resources, shared decision-making conceptual bias, and delicate doctor-patient bonds.

Theme 1: LCS knowledge deficit

Many high-risk study participants expressed that they did not have access to reliable and authoritative medical information. Many of the high-risk participants shared their inability to access LCS-related information and their limited capacity to distinguish accurate LCS information from misinformation. Furthermore, participants mentioned that a negative personal view of life influenced their active engagement in shared decision-making with health care providers and/or family, which diminished their comprehensive understanding of LCS.

“Some people are negative, they believe God’s will can decide everything, so when they faced a decision, they will ask the gods instead of making a decision according to their actual situation” H13 (high-risk individual, female, 53 years-old).

Theme 2: inadequate supportive resources

Participants emphasized that shared decision-making was hindered by financial, transportation and time-related barriers to hospital visits. Furthermore, unfamiliarity with the process of seeking medical treatment also presented an obstacle to shared decision-making. Notably, participants expressed negative emotions related to the LDCT test which influenced their shared decision-making. In particular, the LDCT process was not well received by individuals who had claustrophobia. Participants described feeling claustrophobic during the process of the imagological examination. The requirement for patients to lie flat during the examination, combined with the confined and dim space, can lead to feelings of depression and suffocation. Additionally, the machine’s noise and concerns about potential risks (such as radiation and false positives) from having LDCT scans may have heightened patients’ negative emotions and fears.

“Since I smoke, I’m always scared of getting bad test results. If the results are bad, it’s just really scary, I don’t think I have the sanity to make shared decisions with my doctors. I need help.” H11 (a high-risk individual, female, 54 years-old).

“I struggle with claustrophobia, and every time I have a test, I feel really trapped. It would be difficult for me to have shared decision-making when I have a claustrophobia. It felt like my mind was blank.” H12 (a high-risk individual, male, 52 years-old).

Several participants mentioned experiencing anxiety regarding the test results. They expressed their apprehension about potential adverse outcomes and indicated that this anxiety affected their ability to engage in shared decision-making with their doctors. Moreover, after experiencing claustrophobia, some participants expressed that they felt an inability to make shared decisions with their doctors in a rational manner.

Theme 3: Shared decision-making conceptual bias

Some participants mentioned that they were not familiar with the specific term ‘shared decision-making’. Health care providers shared the perspective that excessive communication with the high-risk group about their condition might lead to a refusal of subsequent treatment, potentially jeopardizing their health.

“I believe that when it comes to professional matters, it’s best to rely on trained professionals. Most patients don’t have expert medical knowledge, and even if they do, they might be hesitant about certain exams. That, in my opinion, doesn’t do much good for their health.” M8 (a general practitioner, female, 36 years-old).

Additionally, participants had misconceptions about shared decision-making. For example, health care providers had misconceptions about shared decision-making in LDCT screenings – some believed that shared decision-making meant merely providing information about the benefits and risks of LDCT; others confused the concepts of informed consent and shared decision-making all together; and a few providers viewed encouraging high-risk groups to conduct LDCT screening to be a part of shared decision-making. Some participants believed shared decision-making to be merely a procedural step to schedule a test appointment.

“I think shared decision-making means thoroughly informing those in high-risk groups about the pros and cons of a particular exam and ultimately letting them make the call.” M5 (a physician specialist, male, 25 years-old).

“When we suggest undergoing a medical examination, doctors might assume that this visit is a necessary step for patients to get a chance to be examined, not a step for shared decision-making. As a result, they may believe that there’s no necessity for patient education.” H13 (a high-risk individual, female, 53 years-old).

Theme 4: delicate doctor-patient bonds

Both health care providers and high-risk individuals emphasized that time constraints pose a significant barrier to shared decision-making. Some participants noted that doctors, who often express concerns about work-related burnout, were hesitant to provide comprehensive information about LDCT.

“I believe that doctor burnout contributes to their reluctance to discuss lung cancer screening with patients.” H9 (a high-risk individual, male, 57 years-old).

Furthermore, health care providers and participants encountered challenges with communication. Health care providers struggled to simplify complex information for easy understanding, while participants had difficulty clearly expressing their needs.

“Effective communication is essential for both doctors and patients. The doctor’s ability to convey information and the patient’s capacity to express their needs are crucial. Insufficient communication skills represent a challenge for both parties.” M6 (a physician specialist, male, 27 years-old).

Participants also mentioned that they were hesitant to express their thoughts to doctors whom they do not know well.

“Building trust is not a simple task. When patients and I have a strong connection and they trust us enough to share their true thoughts, it significantly reduces barriers to shared decision-making. On the other hand, some doctors who aren’t deeply connected with the community may struggle to gain patients’ trust, leading to communication challenges that hinder shared decision-making.” M2 (a nurse in grade A tertiary hospital, female, 41 years-old).

Others believe that the professional competence of doctors plays a pivotal role in shared decision-making in LCS. People often opt for doctors from tertiary hospitals who were perceived to have a higher level of professionalism, which is conducive to shared decision-making.

“Personally, I believe that the expertise of doctors in county-level hospitals may not be as advanced, which affects my level of trust in them. I tend to find doctors in top-tier tertiary hospitals to be more credible.” H12 (a high-risk individual, male, 52 years-old).

Decision support

Three categories related to the theme of decision support were identified: provide information throughout the LCS process, providing a shared decision-making coach, and provide decision tools.

Theme 1: provide information throughout the LCS process

Participants shared that they would like to know information about LDCT before and after undergoing the screening test. Desired information prior to screening included: eligibility criteria for LCS; benefits and risks of LDCT, the LDCT process itself, primary and secondary prevention of lung cancer, the cost of LDCT, potential emergencies and appropriate responses during LDCT, guidelines for Medicare reimbursement related to LDCT, and the medical visit steps. Most participants wanted information after the screening to include the interpretation and monitoring of LDCT results as well as the recommended frequency of LDCT.

Theme 2: providing a shared decision-making decision coach

Several participants said that it is necessary to enhance shared decision-making beliefs to better support the decision-making process for LCS, which is inherently a preference-sensitive decision.

“In China, shared decision-making isn’t commonly practiced. Many physicians here may not be familiar with the concept, even though it’s something they should consider adopting. Personally, I strongly believe in the importance of implementing shared decision-making.” H6 (a high-risk individual, male, 58 years-old).

High-risk individuals emphasize the importance of establishing a foundation for knowledge before engaging in shared decision-making. Participants advocated for a basic understanding of medical concepts, with decision counselors possessing specialized medical expertise.

“Before participating in shared decision-making, I’d like to gain some basic medical knowledge.” H4 (a high-risk individual, female, 53 years-old).

Due to time and energy constraints, clinicians found it challenging to engage in shared decision-making. However, the community doctors in our study stated that they had more time to communicate and share opinions and that their closer patient-provider relationships could facilitate the shared decision-making process in China.

“We only present the benefit and harm of LDCT briefly. We don’t have enough time to describe these in more detail. You know, lung cancer pathology and knowledge of imaging are too complex for high-risk individuals of lung cancer. For individuals who don’t have professional backgrounds, it is impossible for them to understand totally, what we can do is try to get them to understand as much as possible in a limited time.” M5 (a doctor in grade A tertiary hospital, male, 25 years-old).

“It’s important to involve community health providers in shared decision-making for a couple of reasons. Firstly, we tend to establish a strong rapport with patients, and they often trust us more compared to clinicians. Additionally, we have the advantage of spending more time communicating with patients, which makes us better suited to facilitate shared decision-making.” M9 (a general practitioner, male, 42 years-old).

Theme 3: providing decision tools

Participants expressed the need for decision tools and made several suggestions for decision tools to better cater to diverse groups. Decision tools are instruments that aid users in clarifying the congruence between their decisions and their individual values by presenting relevant options along with their associated benefits and potential drawbacks. Through the use of decision tools, users are assisted in arriving at clear, high-quality decisions.

The participants had several suggestions for providing decision tools. First, various information modalities such as videos, images, and written content should be integrated into tools to accommodate varying education levels and preferences. Second, tailored information that aligns with LCS decision-making is preferred. Third, a three-way interaction model involving patients, decision tools, and health care providers could enhance effectiveness. Fourth, medical knowledge should be presented in a comprehensible manner to improve accessibility. Additionally, access to more detailed information is necessary. Fifth, the time spent using decision tools should be less than 20 min to prevent impatience. Sixth, most participants emphasized addressing credibility concerns, through incorporating medical professionals into the tool’s development team, emphasizing authoritative sources, and involving experts from reputable hospitals. Finally, most participants acknowledged that value clarification exercises should be integrated to help users articulate their personal screening preferences to ensure a comprehensive approach to decision support.

Shared decision-making plays a crucial role in enhancing the understanding of LCS and LDCT in high-risk groups. Shared decision-making can also establish realistic expectations for health outcomes and ultimately improve decision-making for the best treatment or screening option [ 33 ]. This qualitative study provides insights into the decisional needs and necessary support for shared decision-making in LDCT screening, from the perspectives of health care providers and high-risk individuals in China. Specifically, LDCT screening decisions should evaluate the knowledge, availability of supportive resources, health care providers’ understanding of shared decision-making concepts, and quality of doctor-patient relationships. At present, both providers and screeners require decision support surrounding LDCT information and need shared decision-making coaching to effectively arrive at a decision. This study finding is valuable for shaping the design of future interventions that aim to facilitate decision-making and has the potential to increase the use of LDCT screening in Chinese society.

Our findings also contribute to the classification refinement of the ODSF. Regarding LCS knowledge, we have observed that high-risk groups not only lack specific knowledge of LCS, but also face challenges accessing relevant information and struggle with their capacity to distinguish accurate LCS information from misinformation. Previous multimodel public health interventions have focused on education related to specific LCS knowledge and ignored the need to access correct information, insufficiently addressing the needs of populations at high-risk of lung cancer [ 34 ]. Therefore, in addition to limited knowledge, limited access to information and lack of identification undermine the contributions of high-risk groups in shared decision-making.

In terms of support and resources, it is essential to consider not only conventional limitations such as financial and health system resources, but also the psychological well-being of high-risk populations. The proportion of smokers is greater among those at high-risk for lung cancer than among those at high-risk for other types of cancers (such as breast cancer and colorectal cancer) [ 35 ]. Being a smoker can affect the execution of shared decision-making due to perceived stigma, lung cancer fatalism, and heightened levels of worry and fear of contracting lung cancer [ 35 ]. Additionally, concerns about potential risks associated with LDCT serve as a barrier to the shared decision-making process with health care providers [ 9 ].

Our findings provide new insights into the core constructs of decisional needs, including awareness of shared decision-making and doctor-patient bonds. Additionally, shared decision-making awareness studies have demonstrated that bias can lead to differences in individual preferences, which can hinder the initiation of shared decision-making and result in higher levels of decision conflict [ 36 ]. Additionally, studies have shown that poor doctor-patient communication can lead to low-quality shared decision-making. For example, dismissive clinicians who dominate decision-making encounters, use negative verbal or nonverbal cues, or fail to respect patients’ concerns have been shown to act as barriers to shared decision-making for many patients [ 37 ]. Conversely, clinicians who strive to understand individual needs and preferences can foster a sense of partnership and facilitate their involvement in shared decision-making processes [ 38 ]. It has also been found that allocating limited time for consultations as well as poor communication skills results in ineffective shared decision-making [ 39 ]. Limitations in skill and time can impede the ability to be fully informed by health care providers, to process and reflect on the information received, and to engage in meaningful discussions between providers and individuals [ 37 ]. Furthermore, the presence of trust is identified as a facilitator of shared decision-making. Establishing a trusting relationship with health care providers encourages patients to feel more comfortable asking questions, sharing personal information, and discussing their concerns [ 39 ].

Currently, the use of shared decision-making in clinical practice is suboptimal in China [ 11 ]. Fortunately, our study provides potential mitigation strategies. First, the need for comprehensive decision tools that appeal to diverse groups of patients was emphasized by both high-risk groups and health providers. A decision tool can furnish information, facilitate patient-doctor dialog, and enhance therapeutic outcomes [ 33 ]. However, the availability of decision tools for LCS is limited and their applications are less than ideal, partly due to their failure to be tailored to personal needs. For instance, most LCS decision tools are presented as single-page materials or premade videos, which may not fully address participants’ needs. Our findings highlight the demand for personalized decision tools for LCS in China. Second, some participants suggested that decision counselors should not be limited solely to clinicians; community health care providers can also serve as counselors for decision-making. This aligns with the concept that shared decision-making requires multisectoral collaboration [ 40 ]. Community nurses in particular, share similar ethnic, linguistic, and geographic backgrounds with the residents they serve compared to other nurses. Consequently, they are more likely to encounter high-risk populations in the community [ 41 ]. Additionally, due to the nature of their work, they have more time to engage in shared decision-making discussions with high-risk groups. Research has revealed that community nurses, in their roles as coordinators, educators, researchers, navigators, and practitioners, can play multidimensional roles essential for leading successful LCS [ 42 ]. Hence, future research should actively promote the development of community nurses as counsellors for LCS to alleviate the burden on hospital-based physicians. Third, both health care providers and high-risk groups should receive education on shared decision-making. Our findings reveal that both sides still possess a vague understanding of shared decision-making, often conflating it with informed consent (patient-led) and paternalism (physician-led) models. Unlike in Western countries, humanistic medicine education in China is lacking, resulting in an inadequate grasp of patient-centered medical-ethical principles among health providers and patients [ 21 ]. Future interventions in China should emphasize humanistic medicine to establish the foundation of shared decision-making.

Our findings are rooted in Chinese culture, which, along with broader Asian cultural influences, places a significant emphasis on Confucianism and sociocultural values such as family support, care, and respect for familial hierarchy and authority [ 43 ]. Therefore, the insights provided by this paper may be applicable to other Asian countries. Despite the rapid development of SDM research in the West, the actual implementation of SDM in clinical practice is not as favorable [ 44 ]. One contributing factor is that highly developed patient decision aids often overly focus on standardized processes, deviating from a more humanistic approach that can be applied universally [ 44 ]. Moreover, the ongoing wave of globalization has resulted in increasingly multicultural societies, necessitating a broader scope of SDM coverage that includes individuals from diverse cultural backgrounds. Therefore, avoiding cultural stereotypes and actively inquiring about patients’ preferences become especially crucial. The results of our study contribute valuable insights into individual decisional needs and decision support from the perspectives of both individuals at high-risk for lung cancer and health care providers. These perspectives can assist patient decision aids in avoiding excessive standardization. Simultaneously, the perspective embedded in our findings is well-suited to accommodate the multicultural nature of Western countries. Future studies should seek to bridge the gap in SDM between Eastern and Western contexts.

Limitations

There are several limitations in this study. First, since the high-risk lung cancer individuals in our study did not undergo LCS shared decision-making recently, their views on LCS shared decision-making may have been subject to recall bias. Second, all study participants were from Fujian Province, which is a southeastern province in China. It is possible that recruitment from a broader geographical area may have led to a wider range of perspectives and experiences and thus influenced the point at which data saturation was reached. Third, as a qualitative, in-depth interview study, generalizations of findings to a larger population are not possible. Future quantitative studies should explore decision-making experiences among a broad range of high-risk groups and health care providers in China to enhance data triangulation and thus, the credibility and reliability of the study’s findings.

Guiding high-risk groups toward well-informed choices regarding LCS represents a substantial gain toward advancing secondary prevention of lung cancer. This descriptive qualitative study offers valuable insights into decision-making regarding LDCT screening among Chinese high-risk groups and their health care providers. The findings from this study highlight the decisional needs and decision support for shared decision-making for LCS using the ODSF conceptual framework. Future studies should target intervention development to offer decision support by evaluating individuals’ decisional needs, enabling them to make choices confidently, and with minimal conflict and decisional regret. In addition, this study may also serve as a starting point for the development of more effective decision tools for LDCT screening.

Availability of data and materials

The de-identified datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

Low-dose computed tomography

Lung cancer screening

The Ottawa Decision-Support Framework

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Acknowledgements

The authors are grateful to all the participants in this study.

This work was supported by the National Natural Science Foundation of China [grant number 72304068] and the General Project of Fujian Provincial Nature Science Foundation (grant number 2021J01133126).

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XJL had full access to all of the data and takes responsibility for the integrity of the data and the accuracy of the data analysis. FFW and YLL contributed to the study design, data collection, data analysis and interpretation, and writing of the manuscript. FL. and WSC contributed to the recruitment, data collection and interpretation, and writing of the manuscript. WTC contributed to the study design, coordination, interpretation, and writing of the manuscript. FFH contributed to the overall study design, interpretation, and writing of the manuscript. All authors approved the final version of the manuscript.

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Lin, X., Wang, F., Li, Y. et al. Exploring shared decision-making needs in lung cancer screening among high-risk groups and health care providers in China: a qualitative study. BMC Cancer 24 , 613 (2024). https://doi.org/10.1186/s12885-024-12360-0

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Received : 13 January 2024

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DOI : https://doi.org/10.1186/s12885-024-12360-0

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• Published: 14 May 2024

Exploring predictors and prevalence of postpartum depression among mothers: Multinational study

• Samar A. Amer   ORCID: orcid.org/0000-0002-9475-6372 1 ,
• Nahla A. Zaitoun   ORCID: orcid.org/0000-0002-5274-6061 2 ,
• Heba A. Abdelsalam 3 ,
• Abdallah Abbas   ORCID: orcid.org/0000-0001-5101-5972 4 ,
• Mohamed Sh Ramadan 5 ,
• Hassan M. Ayal 6 ,
• Samaher Edhah Ahmed Ba-Gais 7 ,
• Nawal Mahboob Basha 8 ,
• Abdulrahman Allahham 9 ,
• Emmanuael Boateng Agyenim 10 &
• Walid Amin Al-Shroby 11  

BMC Public Health volume  24 , Article number:  1308 ( 2024 ) Cite this article

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Postpartum depression (PPD) affects around 10% of women, or 1 in 7 women, after giving birth. Undiagnosed PPD was observed among 50% of mothers. PPD has an unfavorable relationship with women’s functioning, marital and personal relationships, the quality of the mother-infant connection, and the social, behavioral, and cognitive development of children. We aim to determine the frequency of PPD and explore associated determinants or predictors (demographic, obstetric, infant-related, and psychosocial factors) and coping strategies from June to August 2023 in six countries.

An analytical cross-sectional study included a total of 674 mothers who visited primary health care centers (PHCs) in Egypt, Yemen, Iraq, India, Ghana, and Syria. They were asked to complete self-administered assessments using the Edinburgh Postnatal Depression Scale (EPDS). The data underwent logistic regression analysis using SPSS-IBM 27 to list potential factors that could predict PPD.

The overall frequency of PPD in the total sample was 92(13.6%). It ranged from 2.3% in Syria to 26% in Ghana. Only 42 (6.2%) were diagnosed. Multiple logistic regression analysis revealed there were significant predictors of PPD. These factors included having unhealthy baby adjusted odds ratio (aOR) of 11.685, 95% CI: 1.405–97.139, p  = 0.023), having a precious baby (aOR 7.717, 95% CI: 1.822–32.689, p  = 0.006), who don’t receive support (aOR 9.784, 95% CI: 5.373–17.816, p  = 0.001), and those who are suffering from PPD. However, being married and comfortable discussing mental health with family relatives are significant protective factors (aOR = 0.141 (95% CI: 0.04–0.494; p  = 0.002) and (aOR = 0.369, 95% CI: 0.146–0.933, p  = 0.035), respectively.

The frequency of PPD among the mothers varied significantly across different countries. PPD has many protective and potential factors. We recommend further research and screenings of PPD for all mothers to promote the well-being of the mothers and create a favorable environment for the newborn and all family members.

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Introduction

Postpartum depression (PPD) is among the most prevalent mental health issues [ 1 ]. The onset of depressive episodes after childbirth occurs at a pivotal point in a woman’s life and can last for an extended period of 3 to 6 months; however, this varies based on several factors [ 2 ]. PPD can develop at any time within the first year after childbirth and last for years [ 2 ]. It refers to depressive symptoms that a mother experiences during the postpartum period, which are vastly different from “baby blues,” which many mothers experience within three to five days after the birth of their child [ 3 ].

Depressive episodes are twice as likely to occur during pregnancy compared to other times in a woman’s life, and they frequently go undetected and untreated [ 4 ]. According to estimates, almost 50% of mothers with PPD go undiagnosed [ 4 ]. The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for PPD include mood instability, loss of interest, feelings of guilt, sleep disturbances, sleep disorders, and changes in appetite [ 5 ], as well as decreased libido, crying spells, anxiety, irritability, feelings of isolation, mental liability, thoughts of hurting oneself and/or the infant, and even suicidal ideation [ 6 ].

Approximately 1 in 10 women will experience PPD after giving birth, with some studies reporting 1 in 7 women [ 7 ]. Globally, the prevalence of PPD is estimated to be 17.22% (95% CI: 16.00–18.05) [ 4 ], with a prevalence of up to 15% in the previous year in eighty different countries or regions [ 1 ]. This estimate is lower than the 19% prevalence rate of PPD found in studies from low- and middle-income countries and higher than the 13% prevalence rate (95% CI: 12.3–13.4%) stated in a different meta-analysis of data from high-income countries [ 8 ].

The occurrence of postpartum depression is influenced by various factors, including social aspects like marital status, education level, lack of social support, violence, and financial difficulties, as well as other factors such as maternal age (particularly among younger women), obstetric stressors, parity, and unplanned pregnancy [ 4 ]. When a mother experiences depression, she may face challenges in forming a satisfying bond with her child, which can negatively affect both her partner and the emotional and cognitive development of infants and adolescents [ 4 ]. As a result, adverse effects may be observed in children during their toddlerhood, preschool years, and beyond [ 9 ].

Around one in seven women can develop PPD [ 7 ]. While women experiencing baby blues tend to recover quickly, PPD tends to last longer and severely affects women’s ability to return to normal function. PPD affects the mother and her relationship with the infant [ 7 ]. The prevalence of postpartum depression varies depending on the assessment method, timing of assessment, and cultural disparities among countries [ 7 ]. To address these aspects, we conducted a cross-sectional study focusing on mothers who gave birth within the previous 18 months. Objectives: to determine the frequency of PPD and explore associated determinants or predictors, including demographic, obstetric, infant-related, and psychosocial factors, and coping strategies from June to August 2023 in six countries.

Study design and participants

This is an analytical cross-sectional design and involved 674 mothers during the childbearing period (CBP) from six countries, based on the authors working settings, namely Egypt, Syria, Yemen, Ghana, India, and Iraq. It was conducted from June to August 2023. It involved all mothers who gave birth within the previous 18 months, citizens of one of the targeted countries, and those older than 18 years and less than 40 years. Women who visited for a routine postpartum follow-up visit and immunization of their newborns were surveyed.

Multiple pregnancies, illiteracy, or anyone deemed unfit to participate in accordance with healthcare authorities, mothers who couldn’t access or use the Internet, mothers who couldn’t read or speak Arabic or English and couldn’t deal with the online platform or smart devices, mothers whose babies were diagnosed with serious health problems, were stillborn, or experienced intrauterine fetal death, and participants with complicated medical, mental, or psychological disorders that interfered with completing the questionnaire were all exclusion criteria. There were no incentives offered to encourage participation.

Sample size and techniques

The sample size was estimated according to the following equation: n = Z 2 P (1-P)/d 2 . This calculation was based on the results of a systematic review and meta-analysis in 2020 of 17% as the worldwide prevalence of PPD and 12% as the worldwide incidence of PPD, as well as a 5% precision percentage, 80% power of the study, a 95% confidence level, and an 80% response rate [ 11 ]. The total calculated sample size is 675. The sample was diverse in terms of nationality, with the majority being Egyptian (16.3%), followed by Yemeni (24.3%) and Indian (19.1%), based on many factors discussed in the limitation section.

The sampling process for recruiting mothers utilized a multistage approach. Two governorates were randomly selected from each country. Moreover, we selected one rural and one urban area from each governorate. Through random selection, participants were chosen for the study. Popular and officially recognized online platforms, including websites and social media platforms such as Facebook, Twitter, WhatsApp groups, and registered emails across various health centers, were utilized for reaching out to participants. Furthermore, a community-based sample was obtained from different public locations, including well-baby clinics, PHCs, and family planning units.

Mothers completed the questionnaire using either tablets or cellphones provided by the data collectors or by scanning the QR code. All questions were mandatory to prevent incomplete forms. Once they provided their informed consent, they received the questionnaire, which they completed and submitted. To enhance the response rate, reminder messages and follow-up communications were employed until the desired sample size was achieved or until the end of August. To avoid seasonal affective disorders, the meteorological autumn season began on the 1st day of September, which may be associated with Autum depressive symptoms that may confound or affect our results.

Data collection tool

Questionnaire development and structure.

The questionnaire was developed and adapted based on data obtained from previous studies [ 7 , 8 , 9 , 10 , 11 , 12 ]. Initially, it was created in English and subsequently translated into Arabic. To ensure accuracy, a bilingual panel consisting of two healthcare experts and an externally qualified medical translator translated the English version into Arabic. Additionally, two English-speaking translators performed a back translation, and the original panel was consulted if any concerns arose.

Questionnaire validation

To collect the data, an online, self-administered questionnaire was utilized, designed in Arabic with a well-structured format. We conducted an assessment of the questionnaire’s reliability and validity to ensure a consistent interpretation of the questions. The questionnaire underwent validation by psychiatrists, obstetricians, and gynecologists. Furthermore, in a pilot study involving 20 women of CBA, the questionnaire’s clarity and comprehensibility were evaluated. It is important to note that the findings from the pilot study were not included in our main study.

The participants were asked to rate the questionnaire’s organization, clarity, and length, as well as provide a general opinion. Following that, certain questions were revised in light of their input. To check for reliability and reproducibility, the questionnaire was tested again on the same people one week later. The final data analysis will not include the data collected during the pilot test. We calculated a Cronbach’s alpha of 0.76 for the questionnaire.

The structure of the questionnaire

After giving their permission to take part in the study. The questionnaire consisted of the following sections:

Study information and electronic solicitation of informed consent.

Demographic and health-related factors: age, gender, place of residence, educational level, occupation, marital status, weight, height, and the fees of access to healthcare services.

Obstetric history: number of pregnancies, gravida, history of abortions, number of live children, history of dead children, inter-pregnancy space (y), current pregnancy status, type of the last delivery, weight gain during pregnancy (kg), baby age (months), premature labor, healthy baby, baby admitted to the NICU, Feeding difficulties, pregnancy problems, postnatal problems, and natal problems The nature of baby feeding.

Assessment of postpartum depression (PPD) levels using the Edinburgh 10-question scale: This scale is a simple and effective screening tool for identifying individuals at risk of perinatal depression. The EPDS (Edinburgh Postnatal Depression Scale) is a valuable instrument that helps identify the likelihood of a mother experiencing depressive symptoms of varying severity. A score exceeding 13 indicates an increased probability of a depressive illness. However, clinical discretion should not be disregarded when interpreting the EPDS score. This scale captures the mother’s feelings over the past week, and in cases of uncertainty, it may be beneficial to repeat the assessment after two weeks. It is important to note that this scale is not capable of identifying mothers with anxiety disorders, phobias, or personality disorders.

For Questions 1, 2, and 4 (without asterisks): Scores range from 0 to 3, with the top box assigned a score of 0 and the bottom box assigned a score of 3. For Questions 3 and 5–10 (with asterisks): Scores are reversed, with the top box assigned a score of 3 and the bottom box assigned a score of 0. The maximum score achievable is 30, and a probability of depression is considered when the score is 10 or higher. It is important to always consider item 10, which pertains to suicidal ideation [ 12 ].

Psychological and social characteristics: received support or treatment for PPD, awareness of symptoms and risk factors, experienced cultural stigma or judgment about PPD in the community, suffer from any disease or mental or psychiatric disorder, have you ever been diagnosed with PPD, problems with the husband, and financial problems.

Coping strategies and causes for not receiving the treatment and reactions to PPD, in descending order: social norms, cultural or traditional beliefs, personal barriers, 48.5% geographical or regional disparities in mental health resources, language or communication barriers, and financial constraints.

Statistical analysis

The collected data was computerized and statistically analyzed using the SPSS program (Statistical Package for Social Science), version 27. The data was tested for normal distribution using the Shapiro-Walk test. Qualitative data was represented as frequencies and relative percentages. Quantitative data was expressed as mean ± SD (standard deviation) if it was normally distributed; otherwise, median and interquartile range (IQR) were used. The Mann-Whitney test (MW) was used to calculate the difference between quantitative variables in two groups for non-parametric variables. Correlation analysis (using Spearman’s method) was used to assess the relationship between two nonparametric quantitative variables. All results were considered statistically significant when the significant probability was < 0.05. The chi-square test (χ 2 ) and Fisher exact were used to calculate the difference between qualitative variables.

The frequency of PPD among mothers (Fig.  1 )

The frequency of PPD among the studied mothers

The frequency of PPD in the total sample using the Edinburgh 10-question scale was 13.5% (Table S1) and 92 (13.6%). Which significantly ( p  = 0.001) varied across different countries, being highest among Ghana mothers 13 (26.0%) out of 50 and Indians 28 (21.7%) out of 129. Egyptian 21 (19.1) out of 110, Yemen 14 (8.5%) out of 164, Iraq 13 (7.7%) out of 168, and Syria 1 (2.3%) out of 43 in descending order. Nationality is also significantly associated with PPD ( p  = 0.001).

Demographic, and health-related characteristics and their association with PPD (Table  1 )

The study included 674 participants. The median age was 27 years, with 407 (60.3%) of participants falling in the >25 to 40-year-old age group. The majority of participants were married, 650 (96.4%), had sufficient monthly income, 449 (66.6%), 498 (73.9%), had at least a preparatory or high school level of education, and were urban. Regarding health-related factors, 270 (40.01%) smoked, 645 (95.7%) smoked, 365 (54.2%) got the COVID-19 vaccine, and 297 (44.1%) got COVID-19. Moreover, 557 (82.6%) had no comorbidities, 623 (92.4%) had no psychiatric illness or family history, and they charged for health care services for themselves 494 (73.3%).

PPD is significant ( p  < 0.05). Higher among single or widowed women 9 (56.3%) and mothers who had both medical, mental, or psychological problems 2 (66.7%), with ex-cigarette smoking 5 (35.7%) ( p  = 0.033), alcohol consumption ( p  = 0.022) and mothers were charged for the health care services for themselves 59 (11.9%).

Obstetric, current pregnancy, and infant-related characteristics and their association with PPD (Table  2 )

The majority of the studied mothers were on no hormonal treatment or contraceptive pills 411 (60.9%), the current pregnancy was unplanned and wanted 311 (46.1%), they gained 10 ≥ kg 463 (68.6%), 412 (61.1%) delivered vaginal, a healthy baby 613 (90.9%), and, on breastfeeding, only 325 (48.2%).

There was a significant ( P  < 0.05) association observed between PPD, which was significantly higher among mothers on contraceptive methods, and those who had 1–2 live births (76.1%) and mothers who had interpregnancy space for less than 2 years. 86 (93.5%), and those who had a history of dead children. Moreover, among those who had postnatal problems (27.2%).

The psychosocial characteristics and their association with PPD (Table  3 )

Regarding the psychological and social characteristics of the mothers, the majority of mothers were unaware of the symptoms of PPD (75%), and only 236 (35.3%) experienced cultural stigma or judgment about PPD in the community. About 41 (6.1%) were diagnosed with PPD during the previous pregnancy, and only 42 (6.2%) were diagnosed and on medications.

A p -value of less than 0.001 demonstrates a highly statistically significant association with the presence of PPD. Mothers with PPD were significantly more likely to have a history of or be currently diagnosed with PPD, as well as financial and marital problems. Experienced cultural stigma or judgment about PPD and received more support.

Coping strategies and causes for not receiving the treatment and reaction to PPD (Table  3 ; Fig.  2 )

Causes for not receiving the treatment and reaction to PPD

Around half of the mothers didn’t feel comfortable discussing mental health: 292 (43.3%) with a physician, 307 (45.5%) with a husband, 326 (48.4%) with family, and 472 (70.0%) with the community. Moreover, mothers with PPD felt significantly more comfortable discussing mental health in descending order: 46 (50.0%) with a physician, 41 (44.6%) with a husband, and 39 (42.3%) with a family (Table  3 ).

There were different causes for not receiving the treatment and reactions to PPD, in descending order: 65.7% social norms, 60.5% cultural or traditional beliefs, 56.5% personal barriers, 48.5% geographical or regional disparities in mental health resources, 47.4% language or communication barriers, and 39.7% financial constraints.

Prediction of PPD (significant demographics, obstetric, current pregnancy, and infant-related, and psychosocial), and coping strategies derived from multiple logistic regression analysis (Table  4 ).

Significant demographic predictors of ppd.

Marital Status (Married or Single): The adjusted odds ratio (aOR) among PPD mothers who were married in comparison to their single counterparts was 0.141 (95% CI: 0.04–0.494; p -value = 0.002).

Nationality: For PPD Mothers of Yemeni nationality compared to those with Egyptian nationality, the aOR was 0.318 (95% CI: 0.123–0.821, p  = 0.018). Similarly, for Syrian nationality in comparison to Egyptian nationality, the aOR was 0.111 (95% CI: 0.0139–0.887, p  = 0.038), and for Iraqi nationality compared to Egyptian nationality, the aOR was 0.241 (95% CI: 0.0920–0.633, p  = 0.004).

Significant obstetric, current pregnancy, and infant-related characteristics predictors of PPD

Current Pregnancy Status (Precious Baby—Planned): The aOR for the occurrence of PPD among women with a “precious baby” relative to those with a “planned” pregnancy was 7.717 (95% CI: 1.822–32.689, p  = 0.006).

Healthy Baby (No-Yes): The aOR for the occurrence of PPD among women with unhealthy babies in comparison to those with healthy ones is 11.685 (95% CI: 1.405–97.139, p  = 0.023).

Postnatal Problems (No–Yes): The aOR among PPD mothers reporting postnatal problems relative to those not reporting such problems was 0.234 (95% CI: 0.0785–0.696, p  = 0.009).

Significant psychological and social predictors of PPD

Receiving support or treatment for PPD (No-Yes): The aOR among PPD mothers who were not receiving support or treatment relative to those receiving support or treatment was 9.784 (95% CI: 5.373–17.816, p  = 0.001).

Awareness of symptoms and risk factors (No-Yes): The aOR among PPD mothers who lack awareness of symptoms and risk factors relative to those with awareness was 2.902 (95% CI: 1.633–5.154, p  = 0.001).

Experienced cultural stigma or judgement about PPD in the community (No-Yes): The aOR among PPD mothers who had experienced cultural stigma or judgment in the community relative to those who have not was 4.406 (95% CI: 2.394–8.110, p  < 0.001).

Suffering from any disease or mental or psychiatric disorder: For “Now I am suffering—not at all,” the aOR among PPD mothers was 12.871 (95% CI: 3.063–54.073, p  = 0.001). Similarly, for “Had a past history but was treated—not at all,” the adjusted odds ratio was 16.6 (95% CI: 2.528–108.965, p  = 0.003), and for “Had a family history—not at all,” the adjusted odds ratio was 3.551 (95% CI: 1.012–12.453, p  = 0.048).

Significant coping predictors of PPD comfort: discussing mental health with family (maybe yes)

The aOR among PPD mothers who were maybe more comfortable discussing mental health with family relatives was 0.369 (95% CI: 0.146–0.933, p  = 0.035).

PDD is a debilitating mental disorder that has many potential and protective risk factors that should be considered to promote the mental and psychological well-being of the mothers and to create a favorable environment for the newborn and all family members. This multinational cross-sectional survey was conducted in six different countries to determine the frequency of PDD using EPDS and to explore its predictors. It was found that PPD was a prevalent problem that varied across different nations.

The frequency of PPD across the studied countries

Using the widely used EPDS to determine the current PPD, we found that the overall frequency of PPD in the total sample was 92 (13.6%). Which significantly ( p  = 0.001) varied across different countries, being highest among Ghana mothers 13 (26.0%) out of 50 and Indians 28 (21.7%) out of 129. Egyptian 21 (19.1) out of 110, Yemen 14 (8.5%) out of 164, Iraq 13 (7.7%) out of 169, and Syria 1 (2.3%) out of 43 in descending order. This prevalence was similar to that reported by Hairol et al. (2021) in Malaysia (14.3%) [ 13 ], Yusuff et al. (2010) in Malaysia (14.3%) [ 14 ], and Nakku et al. (2006) in New Delhi (12.75%) [ 15 ].

While the frequency of PPD varied greatly based on the timing, setting, and existence of many psychosocial and post-partum periods, for example, it was higher than that reported in Italy (2012), which was 4.7% [ 16 ], in Turkey (2017) was 9.1%/110 [ 17 ], 9.2% in Sudan [ 18 ], Eritrea (2020) was 7.4% [ 19 ], in the capital Kuala Lumpur (2001) was (3.9%) [ 20 ], in Malaysia (2002) was (9.8%) [ 21 ], and in European countries. (2021) was 13–19% [ 22 ].

Lower frequencies were than those reported; PPD is a predominant problem in Asia, e.g., in Pakistan, the three-month period after childbirth, ranging from 28.8% in 2003 to 36% in 2006 to 94% in 2007, while after 12 months after childbirth, it was 62% in 2021 [ 23 – 24 ]. While in 2022 Afghanistan 45% after their first labour [ 25 ] in Canada (2015) was 40% [ 26 ], in India, the systematic review in 2022 was 22% of Primipara [ 27 ], in Malaysia (2006) was 22.8% [ 28 ], in India (2019) was 21.5% [ 29 ], in the Tigray zone in Ethiopia (2017) was 19% [ 30 ], varied in Iran between 20.3% and 35% [ 31 – 32 ], and in China was 499 (27.37%) out of 1823 [ 33 ]. A possible explanation might be the differences in the study setting and the type of design utilized. Other differences should be considered, like different populations with different socioeconomic characteristics and the variation in the timing of post-partum follow-up. It is vital to consider the role of culture, the impact of patients’ beliefs, and the cultural support for receiving help for PPD.

Demographic and health-related associations, or predictors of PPD (Tables  1 and 4 )

Regarding age, our study found no significant difference between PPD and non-PPD mothers with regard to age. In agreement with our study [ 12 , 34 , 35 ], other studies [ 36 , 37 , 38 ] found an inverse association between women’s age and PPD, with an increased risk of PPD (increases EPDS scores) at a younger age significantly, as teenage mothers, being primiparous, encounter difficulty during the postpartum period due to their inability to cope with financial and emotional difficulties, as well as the challenge of motherhood. Cultural factors and social perspectives of young mothers in different countries could be a reason for this difference. [ 38 – 39 ] and Abdollahi et al. [ 36 ] reported that older mothers were a protective factor for PPD (OR = 0.88, 95% CI: 0.84–0.92].

Regarding marital status, after controlling for other variables, married mothers exhibited a significantly diminished likelihood of experiencing PPD in comparison to single women (0.141; 95% CI: 0.04–0.494; p  = 0.002). Also, Gebregziabher et al. [ 19 ] reported that there were statistically significant differences in proportions between mothers’ PPD and marital status.

Regarding the mother’s education, in agreement with our study, Ahmed et al. [ 34 ] showed that there was no statistically significant difference between PPD and a mother’s education. While Agarwala et al. [ 29 ] showed that a higher level of mother’s education. increases the risk of PPD, Gebregziabher et al. [ 19 ] showed that the housewives were 0.24 times less likely to develop PPD as compared to the employed mothers (aOR = 0.24, 95% CI: 0.06–0.97; p  = 0.046); those mothers who perceived their socioeconomic status (SES) as low were 13 times more likely to develop PPD as compared to the mothers who had good SES (aOR = 13.33, 95% CI: 2.66–66.78; p  = 0.002).

Regarding the SES or monthly income, while other studies [ 18 , 40 ] found that there was a statistically significant association between PPD mothers and different domains of SES, 34% of depressed women were found to live under low SES conditions in comparison to only 15.4% who were found to live in high SES and experienced PPD. In disagreement with our study, Hairol et al. [ 12 ] demonstrated that the incidence of PPD was significantly p  = 0.01 higher for participants from the low-income group (27.27%) who were 2.58 times more likely to have PDD symptoms (OR: 2.58, 95% CI: 1.23–5.19; p  = 0.01 compared to those from the middle- and high-income groups (8.33%), and low household income (OR = 3.57 [95% CI: 1.49–8.5] increased the odds of PPD [ 41 ].

Adeyemo et al. (2020),and Al Nasr et al. (2020) revealed that there was no significant difference between the occurrence of PPD and socio-demographic characteristics. This difference may be due to a different sample size and ethnicity [ 42 , 43 ]. In agreement with our findings, Abdollahi et al. [ 36 ] demonstrated that after multiple logistic regression analyses, there were increased odds of PPD with a lower state of general health (OR = 1.08 [95% CI: 1.06–1.11]), gestational diabetes (OR = 2.93 [95% CI = 1.46–5.88]), and low household income (OR = 3.57 [95% CI: 1.49–8.5]). The odds of PPD decreased.

Regarding access to health care, in agreement with studies conducted at Gondar University Hospital, Ethiopia [ 18 ], North Carolina, Colorado [ 21 ], Khartoum, Sudan [ 44 ], Asaye et al. [ 45 ], the current study found that participants who did not have free access to the healthcare system were riskier for the development of PPD. the study results may be affected by the care given during the antenatal care (ANC) visits. This can be explained by the fact that PPD was four times higher than that of mothers who did not have ANC, where counseling and anticipatory guidance care are given that build maternal self-esteem and resiliency, along with knowledge about normal and problematic complications to discuss at care visits and their right to mental and physical wellness, including access to care. The increased access to care (including postpartum visits) will increase the diagnosis of PPD and provide guidance, reassurance, and appropriate referrals. Healthcare professionals have the ability to both educate and empower mothers as they care for their babies, their families, and themselves [ 46 ].

Regarding nationality, for PPD mothers of Yemeni nationality compared to those of Egyptian nationality, the aOR is 0.318 (95% CI: 0.123–0.821, p  = 0.018). Similarly, for Syrian nationality in comparison to Egyptian nationality, the aOR is 0.111 (95% CI: 0.0139–0.887, p  = 0.038), and for Iraqi nationality compared to Egyptian nationality, the aOR is 0.241 (95% CI: 0.0920–0.633, p  = 0.004). These findings indicated that, while accounting for other covariates, individuals from the aforementioned nationalities were less predisposed to experiencing PPD than their Egyptian counterparts. These findings can be explained by the fact that, in Egypt, the younger age of marriage, especially in rural areas, poor mental health services, being illiterate, dropping out of school early, unemployment, and the stigma of psychiatric illnesses are cultural factors that hinder the diagnosis and treatment of PPD [ 40 ].

Obstetric, current pregnancy, and infant-related characteristics and their association or predictors of PPD (Tables  2 and 4 )

In the present study, the number of dead children was significantly associated with PPD. This report was supported by studies conducted with Gujarati postpartum women [ 41 ] and rural southern Ethiopia [ 43 ]. This might be because mothers who have dead children pose different psychosocial problems and might regret it for fear of complications developing during their pregnancy. Agarwala et al. [ 29 ] found that a history of previous abortions and having more than two children increased the risk of developing PPD due to a greater psychological burden. The inconsistencies in the findings of these studies indicate that the occurrence of postpartum depression is not solely determined by the number of childbirths.

In obstetric and current pregnancy , there was no significant difference regarding the baby’s age, number of miscarriages, type of last delivery, premature labour, healthy baby, baby admitted to the neonatal intensive care unit (NICU), or feeding difficulties. In agreement with Al Nasr et al. [ 42 ], inconsistent with Asaye et al. [ 45 ], they showed that concerning multivariable logistic regression analysis, abortion history, birth weight, and gestational age were significant associated factors of postpartum depression at a value of p <  0.05.

However, a close association was noted between the mode of delivery and the presence of PPD in mothers, with p  = 0.107. There is a high tendency towards depression seen in mothers who have delivered more than three times (44%). In disagreement with what was reported by Adeyemo et al. [ 41 ], having more than five children ( p  = 0.027), cesarean section delivery ( p  = 0.002), and mothers’ poor state of health since delivery ( p  < 0.001) are associated with an increase in the risk of PPD [ 47 ]. An increased risk of cesarean section as a mode of delivery was observed (OR = 1.958, p  = 0.049) in a study by Al Nasr et al. [ 42 ].

We reported breastfeeding mothers had a lower, non-significant frequency of PPD compared to non-breast-feeding mothers (36.6% vs. 45%). In agreement with Ahmed et al. [ 34 ], they showed that with respect to breastfeeding and possible PPD, about 67.3% of women who depend on breastfeeding reported no PPD, while 32.7% only had PP. Inconsistency with Adeyemo et al. [ 41 ], who reported that unexclusive breastfeeding ( p  = 0.003) was associated with PPD, while Shao et al. [ 40 ] reported that mothers who were exclusively formula feeding had a higher prevalence of PPD.

Regarding postnatal problems, our results revealed that postnatal problems display a significant association with PPD. In line with our results, Agarwala et al. [ 29 ] and Gebregziabher et al. [ 19 ] showed that mothers who experienced complications during childbirth, those who became ill after delivery, and those whose babies were unhealthy had a statistically significant higher proportion of PPD.

Hormone-related contraception methods were found to have a statistically significant association with PPD, consistent with the literature [ 46 ]; this can be explained by the hormones and neurotransmitters as biological factors that play significant roles in the onset of PPD. Estrogen hormones act as regulators of transcription from brain neurotransmitters and modulate the action of serotonin receptors. This hormone stimulates neurogenesis, the process of generating new neurons in the brain, and promotes the synthesis of neurotransmitters. In the hypothalamus, estrogen modulates neurotransmitters and governs sleep and temperature regulation. Variations in the levels of this hormone or its absence are linked to depression [ 19 ].

Participants whose last pregnancy was unplanned were 3.39 times more likely to have postpartum depression (aOR = 3.39, 95% CI: 1.24–9.28; p  = 0.017). Mothers who experienced illness after delivery were more likely to develop PPD as compared to their counterparts (aOR = 7.42, 95% CI: 1.44–34.2; p  = 0.016) [ 40 ]. In agreement with Asaye et al. [ 45 ] and Abdollahi et al. [ 36 ], unplanned pregnancy has been associated with the development of PPD (aOR = 2.02, 95% CI: 1.24, 3.31) and OR = 2.5 [95% CI: 1.69–3.7] than those of those who had planned, respectively.

The psychosocial characteristics and their association with PPD

Mothers with a family history of mental illness were significantly associated with PPD. This finding was in accordance with studies conducted in Istanbul, Turkey [ 47 ], and Bahrain [ 48 ]. Other studies also showed that women with PPD were most likely to have psychological symptoms during pregnancy [ 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. A meta-analysis of 24,000 mothers concluded that having depression and anxiety during pregnancy and a previous history of psychiatric illness or a history of depression are strong risk factors for developing PPD [ 50 , 51 , 52 ]. Asaye et al. [ 45 ], mothers whose relatives had mental illness history were (aOR = 1.20, 95% CI: 1.09, 3.05 0) be depressed than those whose relatives did not have mental illness history.

This can be attributed to the links between genetic predisposition and mood disorders, considering both nature and nurture are important to address PDD. PPD may be seen as a “normal” condition for those who are acquainted with relatives with mood disorders, especially during the CBP. A family history of mental illness can be easily elicited in the ANC first visit history and requires special attention during the postnatal period. There are various risk factors for PPD, including stressful life events, low social support, the infant’s gender preference, and low income [ 53 ].

Concerning familial support and possible PPD, a statistically significant association was found between them. We reported that mothers who did not have social support (a partner or the father of the baby) had higher odds (aOR = 5.8, 95% CI: 1.33–25.29; p  = 0.019) of experiencing PPD. Furthermore, Al Nasr et al. [ 42 ] revealed a significant association between the PPD and an unsupportive spouse ( P value = 0.023). while it was noted that 66.5% of women who received good familial support after giving birth had no depression, compared to 33.5% who only suffered from possible PPD [ 40 ]]. Also, Adeyemo et al. [ 41 ] showed that some psychosocial factors were significantly associated with having PPD: having an unsupportive partner ( p  < 0.001), experiencing intimate partner violence ( p  < 0.001), and not getting help in taking care of their baby ( p  < 0.001). Al Nasr et al. (2020) revealed that the predictor of PPD was an unsupportive spouse (OR = 4.53, P  = 0.049) [ 48 ].

Regarding the perceived stigma, in agreement with our study, Bina (2020) found that shame, stigma, the fear of being labeled mentally ill, and language and communication barriers were significant factors in women’s decisions to seek treatment or accept help [ 53 ]. Other mothers were hesitant about mental health services [ 54 ]. It is noteworthy that some PPD mothers refused to seek treatment due to perceived insufficient time and the inconvenience of attending appointments [ 55 ].

PPD was significantly higher among mothers with financial problems or problems with their husbands. This came in agreement with Ahmed et al. [ 34 ], who showed that, regarding stressful conditions and PPD, there was a statistically significant association with a higher percentage of PPD among mothers who had a history of stressful conditions (59.3%), compared to those with no history of stressful conditions (40.7%). Furthermore, Al Nasr et al. (2020) revealed that stressful life events contributed significantly ( P value = 0.003) to the development of PPD in the sample population. Al Nasr et al. stressful life events (OR = 2.677, p  = 0.005) [ 42 ].

Coping strategies: causes of fearing and not seeking

Feeling at ease discussing mental health topics with one’s husband, family, community, and physician and experiencing cultural stigma or judgment regarding PPD within the community was significantly associated with the presence of PPD. In the current study, there were different reasons for not receiving the treatment, including cultural or traditional beliefs, language or communication barriers, social norms, and geographical or regional disparities in mental health resources. Haque and Malebranche [ 56 ] portrayed culture and the various conceptualizations of the maternal role as barriers to women seeking help and treatment.

In the present study, marital status, nationality, current pregnancy status, healthy baby, postnatal problems, receiving support or treatment for PPD, having awareness of symptoms and risk factors of PPD, suffering from any disease or mental or psychiatric disorder, comfort discussing mental health with family, and experiencing cultural stigma or judgment about PPD in the community were the significant predictors of PPD. In agreement with Ahmed et al. [ 34 ], the final logistic regression model contained seven predictors for PPD symptoms: SES, history of depression, history of PPD, history of stressful conditions, familial support, unwanted pregnancy, and male preference.

PPD has been recognized as a public health problem and may cause negative consequences for infants. It is estimated that 20 to 40% of women living in low-income countries experience depression during pregnancy or the postpartum period. The prevalence of PPD shows a wide variation, affecting 8–50% of postnatal mothers across countries [ 19 ].

Strengths and limitations

Strengths of our study include its multinational scope, which involved participants from six different countries, enhancing the generalizability of the findings. The study also boasted a large sample size of 674 participants, increasing the statistical power and reliability of the results. Standardized measures, such as the Edinburgh Postnatal Depression Scale (EPDS), were used for assessing postpartum depression, ensuring consistency and comparability across diverse settings. Additionally, the study explored a comprehensive range of predictors and associated factors of postpartum depression, including demographic, obstetric, health-related, and psychosocial characteristics. Rigorous analysis techniques, including multiple logistic regression analyses, were employed to identify significant predictors of postpartum depression, controlling for potential confounders and providing robust statistical evidence.

However, the study has several limitations that should be considered. Firstly, its cross-sectional design limits causal inference, as it does not allow for the determination of temporal relationships between variables. Secondly, the reliance on self-reported data, including information on postpartum depression symptoms and associated factors, may be subject to recall bias and social desirability bias. Thirdly, the use of convenience sampling methods may introduce selection bias and limit the generalizability of the findings to a broader population. Lastly, cultural differences in the perception and reporting of postpartum depression symptoms among participants from different countries could influence the results.

Moreover, the variation in sample size and response rates among countries can be attributed to two main variables. (1) The methodology showed that the sample size was determined by considering several parameters, such as allocating proportionately to the mothers who gave birth and fulfilling the selection criteria during the data collection period served by each health center. (2) The political turmoil in Syria affects how often and how well people can use the Internet, especially because the data was gathered using an online survey link, leading to a relatively low number of responses from those areas. (3) Language barrier in Ghana: as we used the Arabic and English-validated versions of the EPDS, Ghana is a multilingual country with approximately eighty languages spoken. Although English is considered an official language, the primarily spoken languages in the southern region are Akan, specifically the Akuapem Twi, Asante Twi, and Fante dialects. In the northern region, primarily spoken are the Mole-Dagbani ethnic languages, Dagaare and Dagbanli. Moreover, there are around seventy ethnic groups, each with its own unique language [ 57 ]. (4) At the end of the data collection period, to avoid seasonal affective disorders, the meteorological autumn season began on the 1st day of September, which may be associated with autumm depressive symptoms that may confound or affect our results. Furthermore, the sampling methods were not universal across all Arabic countries, potentially constraining the generalizability of our findings.

Recommendations

The antenatal programme should incorporate health education programmes about the symptoms of PPD. Health education programs about the symptoms of PPD should be included in the antenatal program.

Mass media awareness campaigns have a vital role in raising public awareness about PPD-related issues. Mass media.

The ANC first visit history should elicit a family history of mental illness, enabling early detection of risky mothers. Family history of mental illness can be easily elicited in the ANC first visit history.

For effective management of PPD, effective support (from husband, friends, and family) is an essential component. For effective management of PPD effectiveness of support.

The maternal (antenatal, natal, and postnatal) services should be provided for free and of high quality The maternal (antenatal, natal, postnatal) services should be provided free and of high quality.

It should be stressed that although numerous studies have been carried out on PPD, further investigation needs to be conducted on the global prevalence and incidence of depressive symptoms in pregnant women and related risk factors, especially in other populations.

Around 14% of the studied mothers had PPD, and the frequency varies across different countries and half of them do not know. Our study identified significant associations and predictors of postpartum depression (PPD) among mothers. Marital status was significantly associated with PPD, with married mothers having lower odds of experiencing PPD compared to single mothers. Nationality also emerged as a significant predictor, with Yemeni, Syrian, and Iraqi mothers showing lower odds of PPD compared to Egyptian mothers. Significant obstetric, current pregnancy, and infant-related predictors included the pregnancy status, the health status of the baby, and the presence of postnatal problems. Among psychological and social predictors, receiving support or treatment for PPD, awareness of symptoms and risk factors, experiencing cultural stigma or judgment about PPD, and suffering from any disease or mental disorder were significantly associated with PPD. Additionally, mothers who were maybe more comfortable discussing mental health with family relatives had lower odds of experiencing PPD.

These findings underscore the importance of considering various demographic, obstetric, psychosocial, and coping factors in the identification and management of PPD among mothers. Targeted interventions addressing these predictors could potentially mitigate the risk of PPD and improve maternal mental health outcomes.

Data availability

Yes, I have research data to declare.The data is available when requested from the corresponding author [email protected].

Abbreviations

Adjusted Odds Ratio

• Postpartum depression

Primary Health Care centers

Socioeconomic Status

program (Statistical Package for Social Science

The Edinburgh Postnatal Depression Scale

The Neonatal Intensive Care Unit

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Acknowledgements

We would like to express our deep thanks to Rovan Hossam Abdulnabi Ali for her role in completing this study and her unlimited support. Special thanks to Dr. Mohamed Liaquat Raza for his role in reviewing the questionnaire. Moreover, we would like to thank all the mothers who participated in this study.

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Department of Public Health and Community Medicine, Faculty of Medicine, Zagazig University, Zagazig, Egypt

Samar A. Amer

Department of Family Medicine, Faculty of Medicine, Zagazig University, Zagazig, Egypt

Nahla A. Zaitoun

Department of Psychiatry, Faculty of Medicine, Zagazig University, Zagazig, Egypt

Heba A. Abdelsalam

Faculty of Medicine, Al-Azhar University, Damietta, Egypt

Abdallah Abbas

Department of Obstetrics and Gynecology, Faculty of Medicine, Zagazig University, Zagazig, Egypt

Mohamed Sh Ramadan

Hammurabi Medical College, University of Babylon, Al-Diwaniyah, Iraq

Hassan M. Ayal

Hardamout University College of Medicine, Almukalla, Yemen

Samaher Edhah Ahmed Ba-Gais

Department of General Medicine, Shadan Institute of Medical Science, Hyderabad, India

Nawal Mahboob Basha

College of Medicine, Sulaiman Alrajhi University, Albukayriah, Al-Qassim, Saudi Arabia

Abdulrahman Allahham

Department of Virology, Noguchi Memorial Institute for Medical Research, University of Ghana Legon, Accra, Ghana

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Contributions

Conceptualization: Samar A. Amer (SA); Methodology: SA, Nahal A. Zaitoun (NZ); Validation: Mohamed Ramadan Ali Shaaban (MR), Hassan Majid Abdulameer Aya (HM), Samaher Edhah Ahmed Ba-Gais (SG), Nawal Mahboob Basha (NB), Abdulrahman Allahham (AbAl), Emmanuael Boateng Agyenim (EB); Formal analysis: Abdallah Abbas (AA); Data curation: MR, HM, SG, NB, AbAl, NZ, and EB; Writing original draft preparation: SA, Heba Ahmed Abdelsalam (HAA), and NZ; Writing review and editing: MR, AA, Walid Amin Elshrowby (WE); Visualization: SA, AA; Supervision: SA; Project administration: AA. All authors have read and agreed to the published version of the manuscript.

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All participants were provided with electronic informed consent after receiving clear explanations regarding the study’s objectives, data confidentiality, voluntary participation, and the right to withdraw. The questionnaire did not contain any sensitive questions, and data collection was performed anonymously. We affirm that all relevant ethical guidelines have been adhered to, and any necessary approvals from the ethics committee have been obtained. Approval was received from the ethical committee of the family medicine department, the faculty of medicine at Zagazig University, and from the patients included in the study. IRP#ZU-IRP#11079-8/10-2023.

Practicing ethical decision-making is crucial for providing clinical treatment. Such decisions are frequently made challenging due to a lack of knowledge and the mother’s ability to handle the associated complexities and uncertainties that affect the patient’s current level of functioning and ability to take care of her child. At the end of the survey, we raised concerns regarding the red flags, such as suicidal thoughts, and called for a revisit for the psychiatrist’s evaluation of the discussion of the risks, benefits, and alternatives to using medication.

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Amer, S.A., Zaitoun, N.A., Abdelsalam, H.A. et al. Exploring predictors and prevalence of postpartum depression among mothers: Multinational study. BMC Public Health 24 , 1308 (2024). https://doi.org/10.1186/s12889-024-18502-0

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