example of qualitative research title author and abstract

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• v.13(Suppl 1); 2019 Apr

Writing the title and abstract for a research paper: Being concise, precise, and meticulous is the key

Milind s. tullu.

Department of Pediatrics, Seth G.S. Medical College and KEM Hospital, Parel, Mumbai, Maharashtra, India

This article deals with formulating a suitable title and an appropriate abstract for an original research paper. The “title” and the “abstract” are the “initial impressions” of a research article, and hence they need to be drafted correctly, accurately, carefully, and meticulously. Often both of these are drafted after the full manuscript is ready. Most readers read only the title and the abstract of a research paper and very few will go on to read the full paper. The title and the abstract are the most important parts of a research paper and should be pleasant to read. The “title” should be descriptive, direct, accurate, appropriate, interesting, concise, precise, unique, and should not be misleading. The “abstract” needs to be simple, specific, clear, unbiased, honest, concise, precise, stand-alone, complete, scholarly, (preferably) structured, and should not be misrepresentative. The abstract should be consistent with the main text of the paper, especially after a revision is made to the paper and should include the key message prominently. It is very important to include the most important words and terms (the “keywords”) in the title and the abstract for appropriate indexing purpose and for retrieval from the search engines and scientific databases. Such keywords should be listed after the abstract. One must adhere to the instructions laid down by the target journal with regard to the style and number of words permitted for the title and the abstract.

Introduction

This article deals with drafting a suitable “title” and an appropriate “abstract” for an original research paper. Because the “title” and the “abstract” are the “initial impressions” or the “face” of a research article, they need to be drafted correctly, accurately, carefully, meticulously, and consume time and energy.[ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] Often, these are drafted after the complete manuscript draft is ready.[ 2 , 3 , 4 , 5 , 9 , 10 , 11 ] Most readers will read only the title and the abstract of a published research paper, and very few “interested ones” (especially, if the paper is of use to them) will go on to read the full paper.[ 1 , 2 ] One must remember to adhere to the instructions laid down by the “target journal” (the journal for which the author is writing) regarding the style and number of words permitted for the title and the abstract.[ 2 , 4 , 5 , 7 , 8 , 9 , 12 ] Both the title and the abstract are the most important parts of a research paper – for editors (to decide whether to process the paper for further review), for reviewers (to get an initial impression of the paper), and for the readers (as these may be the only parts of the paper available freely and hence, read widely).[ 4 , 8 , 12 ] It may be worth for the novice author to browse through titles and abstracts of several prominent journals (and their target journal as well) to learn more about the wording and styles of the titles and abstracts, as well as the aims and scope of the particular journal.[ 5 , 7 , 9 , 13 ]

The details of the title are discussed under the subheadings of importance, types, drafting, and checklist.

Importance of the title

When a reader browses through the table of contents of a journal issue (hard copy or on website), the title is the “ first detail” or “face” of the paper that is read.[ 2 , 3 , 4 , 5 , 6 , 13 ] Hence, it needs to be simple, direct, accurate, appropriate, specific, functional, interesting, attractive/appealing, concise/brief, precise/focused, unambiguous, memorable, captivating, informative (enough to encourage the reader to read further), unique, catchy, and it should not be misleading.[ 1 , 2 , 3 , 4 , 5 , 6 , 9 , 12 ] It should have “just enough details” to arouse the interest and curiosity of the reader so that the reader then goes ahead with studying the abstract and then (if still interested) the full paper.[ 1 , 2 , 4 , 13 ] Journal websites, electronic databases, and search engines use the words in the title and abstract (the “keywords”) to retrieve a particular paper during a search; hence, the importance of these words in accessing the paper by the readers has been emphasized.[ 3 , 4 , 5 , 6 , 12 , 14 ] Such important words (or keywords) should be arranged in appropriate order of importance as per the context of the paper and should be placed at the beginning of the title (rather than the later part of the title, as some search engines like Google may just display only the first six to seven words of the title).[ 3 , 5 , 12 ] Whimsical, amusing, or clever titles, though initially appealing, may be missed or misread by the busy reader and very short titles may miss the essential scientific words (the “keywords”) used by the indexing agencies to catch and categorize the paper.[ 1 , 3 , 4 , 9 ] Also, amusing or hilarious titles may be taken less seriously by the readers and may be cited less often.[ 4 , 15 ] An excessively long or complicated title may put off the readers.[ 3 , 9 ] It may be a good idea to draft the title after the main body of the text and the abstract are drafted.[ 2 , 3 , 4 , 5 ]

Types of titles

Titles can be descriptive, declarative, or interrogative. They can also be classified as nominal, compound, or full-sentence titles.

Descriptive or neutral title

This has the essential elements of the research theme, that is, the patients/subjects, design, interventions, comparisons/control, and outcome, but does not reveal the main result or the conclusion.[ 3 , 4 , 12 , 16 ] Such a title allows the reader to interpret the findings of the research paper in an impartial manner and with an open mind.[ 3 ] These titles also give complete information about the contents of the article, have several keywords (thus increasing the visibility of the article in search engines), and have increased chances of being read and (then) being cited as well.[ 4 ] Hence, such descriptive titles giving a glimpse of the paper are generally preferred.[ 4 , 16 ]

Declarative title

This title states the main finding of the study in the title itself; it reduces the curiosity of the reader, may point toward a bias on the part of the author, and hence is best avoided.[ 3 , 4 , 12 , 16 ]

Interrogative title

This is the one which has a query or the research question in the title.[ 3 , 4 , 16 ] Though a query in the title has the ability to sensationalize the topic, and has more downloads (but less citations), it can be distracting to the reader and is again best avoided for a research article (but can, at times, be used for a review article).[ 3 , 6 , 16 , 17 ]

From a sentence construct point of view, titles may be nominal (capturing only the main theme of the study), compound (with subtitles to provide additional relevant information such as context, design, location/country, temporal aspect, sample size, importance, and a provocative or a literary; for example, see the title of this review), or full-sentence titles (which are longer and indicate an added degree of certainty of the results).[ 4 , 6 , 9 , 16 ] Any of these constructs may be used depending on the type of article, the key message, and the author's preference or judgement.[ 4 ]

Drafting a suitable title

A stepwise process can be followed to draft the appropriate title. The author should describe the paper in about three sentences, avoiding the results and ensuring that these sentences contain important scientific words/keywords that describe the main contents and subject of the paper.[ 1 , 4 , 6 , 12 ] Then the author should join the sentences to form a single sentence, shorten the length (by removing redundant words or adjectives or phrases), and finally edit the title (thus drafted) to make it more accurate, concise (about 10–15 words), and precise.[ 1 , 3 , 4 , 5 , 9 ] Some journals require that the study design be included in the title, and this may be placed (using a colon) after the primary title.[ 2 , 3 , 4 , 14 ] The title should try to incorporate the Patients, Interventions, Comparisons and Outcome (PICO).[ 3 ] The place of the study may be included in the title (if absolutely necessary), that is, if the patient characteristics (such as study population, socioeconomic conditions, or cultural practices) are expected to vary as per the country (or the place of the study) and have a bearing on the possible outcomes.[ 3 , 6 ] Lengthy titles can be boring and appear unfocused, whereas very short titles may not be representative of the contents of the article; hence, optimum length is required to ensure that the title explains the main theme and content of the manuscript.[ 4 , 5 , 9 ] Abbreviations (except the standard or commonly interpreted ones such as HIV, AIDS, DNA, RNA, CDC, FDA, ECG, and EEG) or acronyms should be avoided in the title, as a reader not familiar with them may skip such an article and nonstandard abbreviations may create problems in indexing the article.[ 3 , 4 , 5 , 6 , 9 , 12 ] Also, too much of technical jargon or chemical formulas in the title may confuse the readers and the article may be skipped by them.[ 4 , 9 ] Numerical values of various parameters (stating study period or sample size) should also be avoided in the titles (unless deemed extremely essential).[ 4 ] It may be worthwhile to take an opinion from a impartial colleague before finalizing the title.[ 4 , 5 , 6 ] Thus, multiple factors (which are, at times, a bit conflicting or contrasting) need to be considered while formulating a title, and hence this should not be done in a hurry.[ 4 , 6 ] Many journals ask the authors to draft a “short title” or “running head” or “running title” for printing in the header or footer of the printed paper.[ 3 , 12 ] This is an abridged version of the main title of up to 40–50 characters, may have standard abbreviations, and helps the reader to navigate through the paper.[ 3 , 12 , 14 ]

Checklist for a good title

Table 1 gives a checklist/useful tips for drafting a good title for a research paper.[ 1 , 2 , 3 , 4 , 5 , 6 , 12 ] Table 2 presents some of the titles used by the author of this article in his earlier research papers, and the appropriateness of the titles has been commented upon. As an individual exercise, the reader may try to improvise upon the titles (further) after reading the corresponding abstract and full paper.

Checklist/useful tips for drafting a good title for a research paper

Some titles used by author of this article in his earlier publications and remark/comment on their appropriateness

The Abstract

The details of the abstract are discussed under the subheadings of importance, types, drafting, and checklist.

Importance of the abstract

The abstract is a summary or synopsis of the full research paper and also needs to have similar characteristics like the title. It needs to be simple, direct, specific, functional, clear, unbiased, honest, concise, precise, self-sufficient, complete, comprehensive, scholarly, balanced, and should not be misleading.[ 1 , 2 , 3 , 7 , 8 , 9 , 10 , 11 , 13 , 17 ] Writing an abstract is to extract and summarize (AB – absolutely, STR – straightforward, ACT – actual data presentation and interpretation).[ 17 ] The title and abstracts are the only sections of the research paper that are often freely available to the readers on the journal websites, search engines, and in many abstracting agencies/databases, whereas the full paper may attract a payment per view or a fee for downloading the pdf copy.[ 1 , 2 , 3 , 7 , 8 , 10 , 11 , 13 , 14 ] The abstract is an independent and stand-alone (that is, well understood without reading the full paper) section of the manuscript and is used by the editor to decide the fate of the article and to choose appropriate reviewers.[ 2 , 7 , 10 , 12 , 13 ] Even the reviewers are initially supplied only with the title and the abstract before they agree to review the full manuscript.[ 7 , 13 ] This is the second most commonly read part of the manuscript, and therefore it should reflect the contents of the main text of the paper accurately and thus act as a “real trailer” of the full article.[ 2 , 7 , 11 ] The readers will go through the full paper only if they find the abstract interesting and relevant to their practice; else they may skip the paper if the abstract is unimpressive.[ 7 , 8 , 9 , 10 , 13 ] The abstract needs to highlight the selling point of the manuscript and succeed in luring the reader to read the complete paper.[ 3 , 7 ] The title and the abstract should be constructed using keywords (key terms/important words) from all the sections of the main text.[ 12 ] Abstracts are also used for submitting research papers to a conference for consideration for presentation (as oral paper or poster).[ 9 , 13 , 17 ] Grammatical and typographic errors reflect poorly on the quality of the abstract, may indicate carelessness/casual attitude on part of the author, and hence should be avoided at all times.[ 9 ]

Types of abstracts

The abstracts can be structured or unstructured. They can also be classified as descriptive or informative abstracts.

Structured and unstructured abstracts

Structured abstracts are followed by most journals, are more informative, and include specific subheadings/subsections under which the abstract needs to be composed.[ 1 , 7 , 8 , 9 , 10 , 11 , 13 , 17 , 18 ] These subheadings usually include context/background, objectives, design, setting, participants, interventions, main outcome measures, results, and conclusions.[ 1 ] Some journals stick to the standard IMRAD format for the structure of the abstracts, and the subheadings would include Introduction/Background, Methods, Results, And (instead of Discussion) the Conclusion/s.[ 1 , 2 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 17 , 18 ] Structured abstracts are more elaborate, informative, easy to read, recall, and peer-review, and hence are preferred; however, they consume more space and can have same limitations as an unstructured abstract.[ 7 , 9 , 18 ] The structured abstracts are (possibly) better understood by the reviewers and readers. Anyway, the choice of the type of the abstract and the subheadings of a structured abstract depend on the particular journal style and is not left to the author's wish.[ 7 , 10 , 12 ] Separate subheadings may be necessary for reporting meta-analysis, educational research, quality improvement work, review, or case study.[ 1 ] Clinical trial abstracts need to include the essential items mentioned in the CONSORT (Consolidated Standards Of Reporting Trials) guidelines.[ 7 , 9 , 14 , 19 ] Similar guidelines exist for various other types of studies, including observational studies and for studies of diagnostic accuracy.[ 20 , 21 ] A useful resource for the above guidelines is available at www.equator-network.org (Enhancing the QUAlity and Transparency Of health Research). Unstructured (or non-structured) abstracts are free-flowing, do not have predefined subheadings, and are commonly used for papers that (usually) do not describe original research.[ 1 , 7 , 9 , 10 ]

The four-point structured abstract: This has the following elements which need to be properly balanced with regard to the content/matter under each subheading:[ 9 ]

Background and/or Objectives: This states why the work was undertaken and is usually written in just a couple of sentences.[ 3 , 7 , 8 , 9 , 10 , 12 , 13 ] The hypothesis/study question and the major objectives are also stated under this subheading.[ 3 , 7 , 8 , 9 , 10 , 12 , 13 ]

Methods: This subsection is the longest, states what was done, and gives essential details of the study design, setting, participants, blinding, sample size, sampling method, intervention/s, duration and follow-up, research instruments, main outcome measures, parameters evaluated, and how the outcomes were assessed or analyzed.[ 3 , 7 , 8 , 9 , 10 , 12 , 13 , 14 , 17 ]

Results/Observations/Findings: This subheading states what was found, is longer, is difficult to draft, and needs to mention important details including the number of study participants, results of analysis (of primary and secondary objectives), and include actual data (numbers, mean, median, standard deviation, “P” values, 95% confidence intervals, effect sizes, relative risks, odds ratio, etc.).[ 3 , 7 , 8 , 9 , 10 , 12 , 13 , 14 , 17 ]

Conclusions: The take-home message (the “so what” of the paper) and other significant/important findings should be stated here, considering the interpretation of the research question/hypothesis and results put together (without overinterpreting the findings) and may also include the author's views on the implications of the study.[ 3 , 7 , 8 , 9 , 10 , 12 , 13 , 14 , 17 ]

The eight-point structured abstract: This has the following eight subheadings – Objectives, Study Design, Study Setting, Participants/Patients, Methods/Intervention, Outcome Measures, Results, and Conclusions.[ 3 , 9 , 18 ] The instructions to authors given by the particular journal state whether they use the four- or eight-point abstract or variants thereof.[ 3 , 14 ]

Descriptive and Informative abstracts

Descriptive abstracts are short (75–150 words), only portray what the paper contains without providing any more details; the reader has to read the full paper to know about its contents and are rarely used for original research papers.[ 7 , 10 ] These are used for case reports, reviews, opinions, and so on.[ 7 , 10 ] Informative abstracts (which may be structured or unstructured as described above) give a complete detailed summary of the article contents and truly reflect the actual research done.[ 7 , 10 ]

Drafting a suitable abstract

It is important to religiously stick to the instructions to authors (format, word limit, font size/style, and subheadings) provided by the journal for which the abstract and the paper are being written.[ 7 , 8 , 9 , 10 , 13 ] Most journals allow 200–300 words for formulating the abstract and it is wise to restrict oneself to this word limit.[ 1 , 2 , 3 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 22 ] Though some authors prefer to draft the abstract initially, followed by the main text of the paper, it is recommended to draft the abstract in the end to maintain accuracy and conformity with the main text of the paper (thus maintaining an easy linkage/alignment with title, on one hand, and the introduction section of the main text, on the other hand).[ 2 , 7 , 9 , 10 , 11 ] The authors should check the subheadings (of the structured abstract) permitted by the target journal, use phrases rather than sentences to draft the content of the abstract, and avoid passive voice.[ 1 , 7 , 9 , 12 ] Next, the authors need to get rid of redundant words and edit the abstract (extensively) to the correct word count permitted (every word in the abstract “counts”!).[ 7 , 8 , 9 , 10 , 13 ] It is important to ensure that the key message, focus, and novelty of the paper are not compromised; the rationale of the study and the basis of the conclusions are clear; and that the abstract is consistent with the main text of the paper.[ 1 , 2 , 3 , 7 , 9 , 11 , 12 , 13 , 14 , 17 , 22 ] This is especially important while submitting a revision of the paper (modified after addressing the reviewer's comments), as the changes made in the main (revised) text of the paper need to be reflected in the (revised) abstract as well.[ 2 , 10 , 12 , 14 , 22 ] Abbreviations should be avoided in an abstract, unless they are conventionally accepted or standard; references, tables, or figures should not be cited in the abstract.[ 7 , 9 , 10 , 11 , 13 ] It may be worthwhile not to rush with the abstract and to get an opinion by an impartial colleague on the content of the abstract; and if possible, the full paper (an “informal” peer-review).[ 1 , 7 , 8 , 9 , 11 , 17 ] Appropriate “Keywords” (three to ten words or phrases) should follow the abstract and should be preferably chosen from the Medical Subject Headings (MeSH) list of the U.S. National Library of Medicine ( https://meshb.nlm.nih.gov/search ) and are used for indexing purposes.[ 2 , 3 , 11 , 12 ] These keywords need to be different from the words in the main title (the title words are automatically used for indexing the article) and can be variants of the terms/phrases used in the title, or words from the abstract and the main text.[ 3 , 12 ] The ICMJE (International Committee of Medical Journal Editors; http://www.icmje.org/ ) also recommends publishing the clinical trial registration number at the end of the abstract.[ 7 , 14 ]

Checklist for a good abstract

Table 3 gives a checklist/useful tips for formulating a good abstract for a research paper.[ 1 , 2 , 3 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 17 , 22 ]

Checklist/useful tips for formulating a good abstract for a research paper

Concluding Remarks

This review article has given a detailed account of the importance and types of titles and abstracts. It has also attempted to give useful hints for drafting an appropriate title and a complete abstract for a research paper. It is hoped that this review will help the authors in their career in medical writing.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgement

The author thanks Dr. Hemant Deshmukh - Dean, Seth G.S. Medical College & KEM Hospital, for granting permission to publish this manuscript.

Writing an Abstract for Your Research Paper

Definition and Purpose of Abstracts

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

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

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

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

The Contents of an Abstract

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

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

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

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

When to Write Your Abstract

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

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

Choosing Verb Tenses within Your Abstract

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

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

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

Sample Abstract 1

From the social sciences.

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

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

Sample Abstract 2

From the humanities.

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

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

Sample Abstract/Summary 3

From the sciences.

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

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

Sample Abstract 4, a Structured Abstract

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

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

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

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

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

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

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

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

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Title, Abstract and Keywords

The importance of titles.

The title of your manuscript is usually the first introduction readers (and reviewers) have to your work. Therefore, you must select a title that grabs attention, accurately describes the contents of your manuscript, and makes people want to read further.

An effective title should:

• Convey the  main topics  of the study
• Highlight the  importance  of the research
• Be  concise
• Attract  readers

Writing a good title for your manuscript can be challenging. First, list the topics covered by the manuscript. Try to put all of the topics together in the title using as few words as possible. A title that is too long will seem clumsy, annoy readers, and probably not meet journal requirements.

Does Vaccinating Children and Adolescents with Inactivated Influenza Virus Inhibit the Spread of Influenza in Unimmunized Residents of Rural Communities?

This title has too many unnecessary words.

Influenza Vaccination of Children: A Randomized Trial

This title doesn’t give enough information about what makes the manuscript interesting.

Effect of Child Influenza Vaccination on Infection Rates in Rural Communities: A Randomized Trial This is an effective title. It is short, easy to understand, and conveys the important aspects of the research.

Think about why your research will be of interest to other scientists. This should be related to the reason you decided to study the topic. If your title makes this clear, it will likely attract more readers to your manuscript. TIP: Write down a few possible titles, and then select the best to refine further. Ask your colleagues their opinion. Spending the time needed to do this will result in a better title.

Abstract and Keywords

The Abstract is:

• A  summary  of the content of the journal manuscript
• A time-saving  shortcut  for busy researchers
• A guide to the most important parts of your manuscript’s written content

Many readers will only read the Abstract of your manuscript. Therefore, it has to be able to  stand alone . In most cases the abstract is the only part of your article that appears in indexing databases such as Web of Science or PubMed and so will be the most accessed part of your article; making a good impression will encourage researchers to read your full paper.

A well written abstract can also help speed up the peer-review process. During peer review, referees are usually only sent the abstract when invited to review the paper. Therefore, the abstract needs to contain enough information about the paper to allow referees to make a judgement as to whether they have enough expertise to review the paper and be engaging enough for them to want to review it.

Your Abstract should answer these questions about your manuscript:

• What was done?
• Why did you do it?
• What did you find?
• Why are these findings useful and important?

Answering these questions lets readers know the most important points about your study, and helps them decide whether they want to read the rest of the paper. Make sure you follow the proper journal manuscript formatting guidelines when preparing your abstract.

TIP: Journals often set a maximum word count for Abstracts, often 250 words, and no citations. This is to ensure that the full Abstract appears in indexing services.

Keywords  are a tool to help indexers and search engines find relevant papers. If database search engines can find your journal manuscript, readers will be able to find it too. This will increase the number of people reading your manuscript, and likely lead to more citations.

However, to be effective, Keywords must be chosen carefully. They should:

• Represent  the content of your manuscript
• Be  specific  to your field or sub-field

Manuscript title:  Direct observation of nonlinear optics in an isolated carbon nanotube

Poor keywords:  molecule, optics, lasers, energy lifetime

Better keywords:  single-molecule interaction, Kerr effect, carbon nanotubes, energy level structure

Manuscript title:  Region-specific neuronal degeneration after okadaic acid administration Poor keywords:  neuron, brain, OA (an abbreviation), regional-specific neuronal degeneration, signaling

Better keywords:  neurodegenerative diseases; CA1 region, hippocampal; okadaic acid; neurotoxins; MAP kinase signaling system; cell death

Manuscript title:  Increases in levels of sediment transport at former glacial-interglacial transitions

Poor keywords:  climate change, erosion, plant effects Better keywords:  quaternary climate change, soil erosion, bioturbation

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31 Writing Up Qualitative Research

Jane F. Gilgun, School of Social Work, University of Minnesota, Twin Cities

• Published: 04 August 2014
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This chapter provides guidelines for writing journal articles based on qualitative approaches. The guidelines are part of the tradition of the Chicago School of Sociology and the author’s experience as a writer and reviewer. The guidelines include understanding experiences in context, immersion, interpretations grounded in accounts of informants’ lived experiences, and research as action-oriented. The chapter also covers writing articles that report findings based on ethnographies, autoethnographies, performances, poetry, and photography and other graphic media.

How researchers write up results for journal publications depends on the purposes of the research and the methodologies they use. Some topics are standard, such as statements about methods and methodologies, but how to represent other topics, like related research and theory, reflexivity, and informants’ accounts, may vary. For example, articles based on ethnographic research may be structured differently from writing up research whose purpose is theory development. Journal editors and reviewers often are familiar with variations in style of write-ups, but, when they are not, they may ask for modifications that violate the methodological principles of the research. A common reviewer request is for percentages, which has little meaning in almost all forms of qualitative research because the purpose of the research is to identify patterns of meanings and not distributions of variables. For example, Irvine’s (2013) ethnography of the meanings of pets to homeless people shows a variety of meaning without giving the number of participants from which she drew.

Authors sometimes move easily through the review process, but most often they do not, not only because reviewers might not “get it,” but also because authors have left out, underemphasized, or been less than clear about aspects of their research that reviewers and editors believe are important. Working with editors and reviewers frequently results in improved articles.

The purpose of this chapter is to provide guidelines for writing journal articles based on qualitative approaches. My intended audience is composed of researchers, reviewers for journals, and journal editors. Reviewers for funding agencies may also find this chapter useful. I use the terms “journal article” and “research report” as synonyms, even though some journal articles are not reports of research. I have derived the guidelines from ideas associated with the Chicago School of Sociology and my experience as an author and reviewer. Although the Chicago School was, as Becker (1999) wrote, “open to various ways of doing sociology” (p. 10), the ideas in this chapter are part of the tradition, but they are not representative of the entire tradition. Furthermore, the ideas are not fixed but are open-ended because they evolve over time. I have followed the principles of the Chicago School of Sociology throughout my career, augmented by updates to these ideas, experiments with other traditions, and the sense I make of my own experiences as researcher, author, and reviewer.

The ideas on which I draw include understanding experiences in context, immersion, interpretations grounded in accounts of informants’ lived experiences, and research as action-oriented ( Bulmer, 1984 ; Faris, 1967 ; Gilgun, 1999 d ; 2005 a ; 2012 a ; 2013 b ). To follow these principles, researchers do in-depth studies that take into account the multiple contextual factors that influence meanings and interpretations, seek multiple points of view, and often use multiple methods such as interviews, observations, and document analysis. Researchers do this style of research not only because what they learn is interesting, but because they want to do useful research; that is, research that leads to social actions and even transformations in policies, programs, and interventions. Authors and reviewers pay attention to these principles. Authors convey them in their write-ups, and reviewers look for them as they develop their appraisals.

Excellent writing up of qualitative research matches these principles. In other words, write-ups convey lived experience within multiple contexts, multiple points of view, and analyses that deepen understandings. In addition, if the research is applied, then authors write about how findings may contribute to quality of life. Qualitative researchers from other traditions may follow similar or different guidelines in their write-ups, and I sometimes note other styles of write-ups. Often these variations are related to terminology and not procedures. The reach of the Chicago School of Sociology is wide and deep.

Following these guidelines does not guarantee an easy review process, but this article will be helpful to researchers as they plan and craft their articles and as they respond to reviewers’ and editors’ comments. After almost thirty years of publishing research based on qualitative approaches, almost as many years as a reviewer, and the editing of three collections of qualitative research reports ( Gilgun, Daly, & Handel, 1992 ; Gilgun & Sussman, 1996 : Gilgun & Sands, 2012 ), I am positioned to offer helpful guidelines, not only to authors but also to reviewers and journal editors.

I begin this chapter with a discussion of general principles and then cover the content of typical sections of research reports. Some of the general material fits into various sections of reports, such as methods and findings. In those cases, I do not repeat material already covered and assume that my writing is clear enough so that readers know how the general material fits into particular sections of articles.

Although most of this chapter addresses the writing of conventional research reports, I also cover writing articles that report findings through ethnographies, autoethnographies, performances, poetry, and photography and other graphic media. Ethnographies are based on researchers’ immersion in the field, where they do extensive observations, interviews, and often document analysis (see Block, 2012 ). Geertz’s (1973) notion of “thick description” is associated with ethnographies. Thick description is characterized by research reports that show the matrix of meanings that researchers identify and attempt to represent in their reports. Autoethnographies are in-depth reflective accounts of individual lives that the narrators themselves write ( Ellis, 2009 ). Ethnographies and autoethnographies involve reflections on meanings, contexts, and other wider influences on individual lives. They are studies of intersections of individual lives and wider cultural themes and practices. Reports of these types of research can look different from conventional research reports in that they appear less formal; the usual sections of methods, literature review, findings, and analysis may have different names; and the sections may be in places that fit the logical flow of the research and not the typical structure of introductory material, methods, results, and discussion. Despite these superficial differences, researchers who write these kinds of articles seek to deepen understandings and hope to move audiences to action through conveying lived experience in context and through multiple points of view. They also typically seek transformations of persons and societies. Links between these forms of research and Chicago School traditions are self-evident.

Some General Principles

Research reports that have these characteristics depend on the quality of the data on which the reports are based, the quality of the analysis, and the skills of researchers in conveying the analysis concisely and with “grab” ( Glaser, 1978 ), which means writing that is vivid and memorable ( Gilgun, 2005 b ). Grab brings findings to life. With grab, human experiences jump off the page. Priority is given to the voices of research participants, whom I call informants, with citations and the wisdom of other researchers providing important contextual information. The voices and analyses of researchers do not dominate ( Gilgun, 2005 c ), except in some articles whose purpose is theory development or the presentation of a theory. Researcher analyses often are important, especially in putting forth social action recommendations that stem from the experiences of informants.

A well-done report shows consistency between research traditions and the writing-up of research. For example, reflexivity statements, writing with grab, and copious excerpts from fieldnotes, interviews, and documents of various sorts are consistent with phenomenological approaches whose emphasis is on lived experience and interpretations that informants make of their experiences. Researchers new to qualitative research, however, often mix their traditions without realizing it, which works when the traditions are compatible. When the traditions are not compatible, the write-ups can be confusing and even contradictory ( Gilgun, 2005 d ). Some authors may write in distanced, third-person styles while attempting to convey informants’ lived experiences. These scholars may, therefore, have difficulty getting their articles accepted. Hopefully, this chapter will facilitate the writing of research reports that show consistency across their many parts and save scholars from rejections of work over which they have taken much care.

Details on These General Principles

In this section, I provide more detail on writing up qualitative research. I begin with a discussion of the need for high-quality data, high-quality analysis, and grab. I then move on to the details of the report, such as the place of prior research and theory, contents of methods sections, organization of findings, and the balance between descriptive material and authors’ interpretations. Dilemmas abound. Writing up qualitative research is not for the faint of heart.

High-Quality Data

Since qualitative researchers seek to understand the subjective experiences of research informants in various contexts, high-quality data result in large part from the degree that researchers practice immersion and to the degree that both researchers and informants develop rapport and engage with each other. Through active engagement, informants share their experiences with the kind of detail that brings their experiences to life. How to develop rapport is beyond the scope of this article, but openness and acceptance of whatever informants say are fundamental to engagement. Interviewers do not have to agree with the values that informants’ accounts convey, as when I interview murderers and rapists ( Gilgun, 2008 ), but we do maintain a neutrality that allows the dialogue to continue ( Gilgun & Anderson, 2013 ). The content of interviews is not about us and our preferences, but about understanding informants.

Prolonged engagement can result in quality data. In interview research, prolonged engagement allows for informants’ multiple perspectives to emerge, including inconsistencies, contradictions, ambiguities, and ambivalences. In addition, prolonged engagement facilitates the kind of trust needed for informants to share personal, sensitive information in detail, which are the kinds of data that qualitative researchers seek. Prolonged engagement also gives researchers time to reflect on what they are learning and experiencing through the interviews. This provides opportunities to develop new understandings and test new understandings through subsequent research. Their understandings thus deepen and broaden. Informants, too, can reflect, reconsider, and deepen the accounts they share.

Prolonged engagement means in-depth interviews, typically multiple interviews of more than an hour each. As mentioned earlier, time between interviews allows researchers and informants to reflect on the previous interview and prepare for the next. Researchers can do background reading, discuss emerging ideas with others, and formulate pertinent new questions. Informants may retrieve long-forgotten memories and interpretations through interviews. If they have only one interview, they have no opportunity to share with researchers the material that arises after the single interview is concluded.

There are exceptions to multiple interviews as necessary for immersion and high-quality data. When researchers have expertise in interviewing and when the topic is focused, one interview of between ninety minutes to two hours could provide some depth. Even under these conditions, however, more than one interview is ideal. I did a study that involved one ninety-minute interview with perpetrators of child sexual abuse in order to understand the circumstances under which their abusive behaviors became known to law enforcement. Thus, the interview was focused. The interviewees were volunteers who had talked about the topic many times in the course of their involvement in sex abuse treatment programs. They shared their stories with depth and breadth. I, too, was well-prepared. By then, I had had about twenty-five years of experience interviewing people about personal, sensitive topics. The informants provided accounts not only because the topic was focused, but because they were willing to share and I was willing to listen and to ask questions about their sexually abusive behaviors. With one interview, however, I knew relatively little about their social histories and general worldviews. Thus, I did not have the specifics necessary to place their accounts into context. The material they provided remained valuable and resulted in one publication ( Sharma & Gilgun, 2008 ) and others in planning stages. I prefer two or more interviews because of the importance of contextual data.

In observational studies, prolonged engagement means that researchers do multiple observations over time to obtain the nuances and details that compose human actions. Observational studies often have interview components and also may have document analysis as well. In document analysis, prolonged engagement means researchers base their analyses on an ample storehouse of documents and not just flit in and out of the documents. The quality of document analysis depends on whether the analysis shows multiple perspectives, patterns, and variations within patterns. Ethnographies have these characteristics. Block’s (2012) ethnographic research on AIDS orphans in Lesotho, Africa, is an example of a well-done ethnography.

Sample Size

In principle, the size of the sample and the depth of the interview affect whether researchers can claim immersion. The more depth and breadth each case in a study has, the smaller the sample size can be. For example, researchers can engage in immersion through a single in-depth case study when they do multiple interviews and if multiple facets of the case are examined. Case studies are investigations of single units. The case can be composed of an individual, a couple, a family, a group, a nation, or a region. Single case studies are useful in the illustration, development, and testing of theories, as well as in in-depth descriptions.

The more focused the questions, the larger the sample will be. A study on long-term marriage would require a minimum of two or three interviews because the topic is complicated. The sample would include at least ten participants and up to twenty or thirty, depending on the number of interviews, to account for some of the many patterns that are likely to emerge in a study of a topic this complex. In the one-interview study I did of how sexual abuse came to the notice of law enforcement, one interview was adequate because of the tight focus of the question. Yet, I used a sample size of thirty-two to maximize the possibility of identifying a variety of patterns, which the study accomplished. As mentioned, the one interview, however, did not allow me to contextualize the stories the informants told. Fortunately, I have another large sample that involved multiple, in-depth interviews in which informants discussed multiple contexts over time. This other study was helpful to me in understanding the accounts from the single-interview study.

Recruitment can be difficult. When it is, researchers may not be able to obtain an adequate sample. For example, a sample of seven participants engaging in a single sixty- to ninety-minute interview may not provide enough data on which to base a credible analysis. In a similar vein, articles based on a single or even a few focus groups may not provide enough depth to be informative. Some depth is possible if, in a single-interview study of less than fifteen or twenty interviewees, researchers meet with informants a second time to go over what researchers understand about informants’ accounts. This sometimes is called member-checking , and it provides additional data on which to base the analysis. In summary, the more depth and breadth to a study, the smaller the sample size can be—even as small as one or two—depending on the questions and the complexity of the cases.

Quality of the Analysis

A quality analysis begins with initial planning of the research and continues until the article is accepted for publication. An excellent research report has transparency , meaning the write-up is clear in what researchers did, how they did it, and why. I often tell students they can do almost anything reasonable and ethical, as long as they make a clear account in the write-up.

During planning, some researchers identify those concepts that they can use as sensitizing concepts once in the field. Transparency about the sources of sensitizing concepts characterizes well-done reports. The sources are literature reviews and reflexivity statements. Most researchers, however, have only a limited awareness of the importance of being clear about the sources of sensitizing concepts and other notions that become part of research coding schemes. Sensitizing concepts are notions that researchers identify before beginning their research and that help researchers notice and name social processes that they might not have noticed otherwise ( Blumer, 1986 ). Other researchers wait until data analysis to begin to identify concepts that they may use as codes and that may also become core concepts that organize findings. Either approach is acceptable and depends on purpose and methodologies.

During data collection, researchers reflect on what they are learning, typically talk to other researchers about their emerging understandings, and read relevant research and theory to enlarge and deepen their understandings. Researchers also keep fieldnotes that are a form of reflection. Based on their various reflections, researchers can reformulate interview and research questions and formulate new ones, do within—and across—case comparisons while in the field, and develop new insights into the meanings of the material.

Also, while in the field, researchers identify promising patterns of meanings and identify tentative core concepts, sometimes called categories , which are ideas that organize the copious material that they amass. Once researchers identify tentative core concepts, they seek to test whether they hold up, and, when they do, they further develop the patterns and concepts. Sometimes researchers think they have “struck gold” when they identify a possible core concept or pattern, only to find that the data—or metaphorical vein of gold—peter out (Phyllis Stern, personal communication, November 2002). They then go on to identify and follow-up on other concepts and patterns that show promise of becoming viable.

Core concepts become viable when researchers are able to dimensionalize them ( Schatzman, 1991 ) through selective coding ( Corbin & Strauss, 2008 ). This means that researchers have found data that show the multiple facets of concepts, such as patterns and exceptions to any general patterns. Authors may use other terms to describe what they did, such as thematic analysis. What is important is to describe the processes and produces; and what researchers call them is of less importance.

Core concepts may begin as sensitizing concepts. Researchers sometimes identify, name, and code core concepts through notions that are part of their general stores of knowledge but were not part of the literature review or reflexivity statement. Glaser (1978) called the practice “theoretical sensitivity.” The names researchers choose may be words or phrases informants have used. However derived, core concepts are central to the organization of findings ( Gilgun, 2012 a ).

At some point, data collection stops, but analysis does not. Researchers carry analysis that occurred in the field into the next phases of the research. Immersion at this point means that researchers read and code transcripts of interviews, observations, and any documentary material they find useful. They carry forward the core concepts they identified in the field. An example of a core concept is “resilience,” which in my own research organized a great deal of interview material. The concept of resilience has been an organizing idea in several of the articles I have written and plan to write ( Gilgun, 1996 a ; 1996 b ; 2002 a ; 2002 b ; 2004 a ; 2004 b ; 2005 a ; 2006 , 2008 ; 2010 ; Gilgun & Abrams, 2005 ; Gilgun, Keskinen, Marti, & Rice, 1999 ; Gilgun, Klein, & Pranis, 2000 ).

Corbin and Strauss (2008) stated that selective coding helps researchers to decide if a concept can become a core concept, meaning it organizes a great deal of data that have multiple dimensions. An example of dimensionalization is a study of social workers in Australia whose clients were Aboriginal people. The researchers identified several core concepts, among them critical self-awareness ( Bennet, Zubrzycki, & Bacon, 2011 ). The dimensions of critical self-awareness included understanding motivations to work with Aboriginal people, fears of working with Aboriginal people, and personalization and internalization of the anger that some Aboriginal people express.

Like many other researchers, Bennet et al. (2011) were not working within an explicit Chicago School tradition. They therefore do not use terms such as core concepts, dimensionalization, and selective coding. Instead, they described their procedures as thematic analysis, conceptual mapping, and a search for meaning. However, they did use the term “saturation,” which is part of the Chicago School tradition.

A single core concept or multiple related core concepts compose research reports. The Bennet et al. (2011) article, for example, linked multiple core concepts. The authors showed how critical self-awareness leads to meaningful relationships that in turn connect to “acquiring Aboriginal knowledge” (p. 30).

With viable core concepts and rich data, researchers are positioned to present their findings in ways that are memorable and interesting; that is, with “grab” ( Glaser, 1978 ). “Grab” requires compelling descriptive material: excerpts from interviews, field notes, and various types of documents, as well as researchers’ paraphrases of these materials. An example of a research report with grab is Irvine’s (2013) account of her study of the meanings of pets to homeless people. She provided vivid descriptions of her interactions with the participants and compelling quotes that show what pets mean. Here, an example from Denise’s account of her relationship with her cat Ivy:

I have a history with depression up to suicide ideation, and Ivy, I refer to her as my suicide barrier. And I don’t say that in any light way. I would say, most days, she’s the reason why I keep going.... She is the only source of daily, steady affection and companionship that I have. (p. 19)

These and other quotes, as well as Irvine’s well-written, detailed descriptive material, show what grab means.

Grab equates with excellence in writing. Irvine’s (2013) article is an example. In terms of the grab of her article, her work is in the Chicago School tradition. She wrote in the first person. She told complete stories in which she quoted extensively from the interviews, described the persons she interviewed and the settings in which she interviewed them, and provided biographical sketches. Robert Park and Ernest Burgess, both of whom trained generations of graduate students in qualitative research at the University of Chicago in the first quarter of the twentieth century, held seminars on the use of literary techniques, such as those used in novels and autobiographies, in writing up research ( Bulmer, 1984 ; Gilgun, 1999 d ; 2012 a ). These educators wanted researchers to report on their “first-hand observation.” Park told a class of graduate students to

[g]o and sit in the lounges of the luxury hotels and on the doorsteps of the flophouses; sit on the Gold Coast settees and on the slum shakedowns; sit in the Orchestra Hall and in the Star and Garter Burlesk. In short, gentlemen [sic], go get the seat of your pants dirty. ( McKinney, 1966 , p. 71)

Park suggested to Pauline Young (1928 ; 1932) to “think and feel” like the residents of Russian Town, the subject of her dissertation, published in 1932 ( Faris, 1967 ). Irvine’s work shows these qualities. She immersed herself in the settings, she conducted in-depth interviews, and she conveyed her first-hand experiences in vivid terms.

The Chicago School also encouraged students to write in the first person. A good example is a report by Dollard (1937) , who was concerned about the racial practices of the Southern town where he was doing fieldwork. He said he was afraid that other white people watched as he talked to “Negroes” on his front porch, when he knew that custom regarding the “proper” place of “Negroes” was at the back door. He wrote

My Negro friend brought still another Negro up on the porch to meet me. Should we shake hands? Would he be insulted if I did not, or would he accept the situation? I kept my hands in pockets and did not do it, a device that was often useful in resolving such a situation. (p. 7)

This description is a portrait of a pivotal moment in Dollard’s fieldwork, and it is full of connotations about the racist practices of the time ( Gilgun, 1999 d ; 2012 a ).

Irvine (2013) also wrote in the first person. Here’s an example:

I met Trish on a cold December day in Boulder. She stood on the median at the exit of a busy shopping center with her Jack Russell Terrier bundled up in a dog bed beside her. She was “flying a sign,” or panhandling, with a piece of cardboard neatly lettered in black marker to read, “Sober. Doing the best I can. Please help.” (p. 14)

These two excerpts illustrate a methodological point Small (1916) made in his chapter on the first fifty years of sociological research in the United States: namely, the importance of going beyond “technical treatises” and providing first-person “frank judgments” that can help future generations interpret sociology. Without such contexts, “the historical significance of treatises will be misunderstood” (p. 722). Throughout his chapter, Small wrote in the first-person and provided his views—or frank judgments—on the events he narrated. From then until now, research reports in the Chicago tradition are vivid and contextual, conveying to the extent possible what it was like to be persons in situations.

There are many other examples of well-done research reports. Eck’s (2013) article on never-married men includes the basic elements that are present in almost all reports based on qualitative methods. It is transparent in its procedures, situated within scholarly traditions, well-organized, vivid, and instructive both for those new to qualitative research and for long-term researchers like me. The other articles I cite in this chapter also show many desirable qualities in research reports.

Research Report Sections

The main sections of standard reports based on qualitative methods are the same as for articles based on other types of methods: Introduction, Methods, Findings, and Discussion. The American Psychological Association (APA) manual (2009) provides information on what goes into each of these sections. Research reports in sociology journals follow a similar format, although the citation style is slightly different. The American Sociological Association uses first and last names in the reference section, a practice I support. In articles based on qualitative approaches, researchers sometimes change the names of sections, add or omit some, or reorder them. When changes are made, the general guideline is whether the changes make sense and are consistent with the purpose of the research. As Saldaña (2003) pointed out, researchers choose how to present their findings on the basis of credibility, vividness, and persuasive qualities and not for the sake of novelty. Because some articles report findings as fictionalized accounts, poetry, plays, songs, and performances (including plays), it makes sense that the sections on these findings vary from the standard format that I discuss here.

Although there are no rigid rules about how to write journal articles based on qualitative research, much depends on the methodological perspectives, purposes of the research, and the editorial guidelines of particular journals. For example, if researchers want to develop a theory, it is important to be clear from the beginning of the article to state this as the purpose of the research. The entire article should then focus on how the authors developed the theory. Research and theory cited in the literature review should have direct relevance to the substantive area on which the authors theorized. The methods section should explain what the researchers did to develop the theory. The findings section should begin with a statement of the theory that the researchers developed. The rest of the findings section should usually be composed of three parts. The first is composed of excerpts from those data that support the concepts of the theory. This is the grounding of the theory in something clear and concrete. The second is the authors’ thinking or interpretation of the meanings of each of the concepts. The third is an analysis of how the theory contributes to what is already known, such as how the findings elaborate on and call into question what is known. Thus, a research report on the development of a theory should contain a lot of scholarship that others have developed.

A report based on narrative principles or one based on an ethnography should contain copious excerpts from interviews, citing less scholarship than an article whose purpose is to develop theory. However, it is good practice to bring in related research and theory in the results section when this literature helps in interpretation, when findings have connections to other bodies of thought, and when findings are facets of a larger issue. In my now older publication on incest perpetrators ( Gilgun, 1995 ), the editors suggested that I show that when therapists engage in sexual relationships with clients, they are engaging in abuses of power similar to those of incest perpetrators. I was at first indignant that the editors wanted me to do even more work on the article, but I soon was glad they did. It is important to show that incest or any human phenomenon is not isolated from other phenomenon but is part of a larger picture. Doing so fit my purposes, which was to show how to do theory-testing/theory-guided qualitative research. Showing how findings fit into related research and theory is part of this type of research.

Whenever researchers are ready to submit an article for publication, it is wise to read recent issues of journals in which they would like to publish. If they can identify an article whose structure, methodologies, and general purpose are similar to theirs, they could study how those authors presented their material. If, for example, in a report on narrative research, the introductory material is relatively brief, and the findings and discussion sections compose most of the pages, researchers would do well to format their articles in similar ways. I study journals in which I have interest and model much of my own articles after those published in these journals. I make sure, however, that I cover topics that in my judgment are important to cover.

Prior Research and Theory

In my experience, something as simple as the place of prior research and theory can get complicated in the writing of reports based on qualitative research, even when the purpose of the article is primarily descriptive and is not to construct an explicit theory. In general, related research and theory literature can be presented at the beginning of a report as part of a review of pertinent research and theory, in the findings section when prior work helps in the interpretation and analysis of findings, or in the discussion section, where authors may reflect on how their findings add to, undermine, or correct what is known and even add something new.

Readers expect and journal editors typically want articles to begin with literature review, with some exceptions. A perusal of journals that publish qualitative studies shows this. Yet there are exceptions. Valásquez (2011) began her report on her encounter with scientology with an extended and rather meandering first-person narrative. Her literature review began toward the end of the article. She tailored the review to the report that preceded it. In this article and others, the literature review helped in the interpretation of findings and helped to situate the report in its scholarly contexts. In other articles, the literature review appears in the introductory section. This sets the scholarly context of the research, highlights the significance of topics, and identifies gaps in knowledge. Neither authors nor reviewers should have rigid expectations about where the scholarship of others belongs. It belongs where it makes the most sense and has the most impact.

For many, the placement of literature reviews seems self-evident. Yet, some well-known approaches, such as grounded theory, can set authors up for confusion about where the literature review belongs. This can result in delays in writing up their results. The procedures of grounded theory are open-ended and designed to find new aspects of phenomena—often underresearched—and then develop theories from the findings. At the outset of their work, researchers cannot anticipate what they will find. Therefore, teachers such as Strauss and Glaser advised students not to do literature reviews until they had identified basic social processes that become the focus of the research ( Covan, 2007 ; Glaser & Strauss, 1967 ).

How, then, do researchers write up research reports when they are doing an open-ended study that, by definition, will culminate in unanticipated findings? Do they write their reports as records on how they proceeded chronologically, or do they follow APA style and the dominant tradition that says the literature review comes first? For the most part, I follow the tradition, as, apparently, do most researchers. However, to structure reports in this way sometimes feels strained and artificial. I would prefer to write a more chronological account, in which I can share with readers the lines of inquiry and procedures I followed. The literature review at the beginning of the report, therefore, would be brief. The methods section is quite detailed in how I went about developing the theory. The findings section would have the three-part format I discussed earlier: statement of the theory, presentations of excerpts that support assertions that certain concepts compose the theory, my interpretation of the meanings of the concepts and the excerpts that support them, and then the use of related research and theory to further develop the theory and to situate it in its scholarly traditions.

In all but one of the research reports that I have published, I did the literature after I had identified findings. The one exception was research I did based on the method of analytic induction, in which researchers can use literature reviews to focus their research from the outset ( Gilgun, 1995 , 2007 ). In this research, I used concepts from theories on justice and care to analyze transcripts of interviews I had previously conducted on how perpetrators view child sexual abuse. Even though I was familiar with the transcripts, I found that the concepts of justice and care and their definitions sensitized me to see things in the material that I had not noticed as I did data collection and during previous analyses of the data.

Furthermore, in writing up the results, I brought in research that was not part of the literature review to help me to interpret findings and to show how findings fit with and added to what was already known. I did not place this material in the introductory literature review. Placing related research and theory as parts of the results and discussion sections is common and may be necessary in articles that are reporting on a theory that the authors developed. For descriptive studies whose purpose is not theory-building, such as ethnographies, some findings sections include the addition of research and theory not present in the introductory section. Often, however, authors do not follow this pattern. An example is found in Ahmed (2013) , who described how migrants experience settling into a new country. She presents excerpts from interviews and her interpretation of them, including organizing them into a typology, but she does not bring additional research and theory into her interpretations.

Tensions can arise between how much space to give to literature reviews and how much to allot to presentation of informants’ accounts/findings ( Gilgun, 2005 c ). This happened in the most recent article I co-wrote, which is on mothers’ perspectives on the signs of child sexual abuse ( Gilgun & Anderson, 2013 ). We believed the literature review was important because it not only set up our research but summarized a great deal of information that was important to our intended audience of social service professionals. We also wanted to anticipate the expectations of reviewers and the journal editor. Yet, we put much effort into making the literature review as concise as possible in order to have reasonable space for findings. We wrote the literature review before we did data analysis. When we wrote up the results, the first draft was probably three times longer than any journal article could be.

We had written case studies first to be sure that we understood each case in detail. We had wanted to share what the women said in the kind of detail that had helped us deepen our own understandings, so we cut back on the case material. The article was still too long. We decided to exclude the few instances we had in which women knew of the abuse but tried to handle it themselves or did not believe the children when told. We did more summarizing of the literature review. We eliminated many references.

After much effort, we finally had a manuscript that was the required length of twenty-two pages. It included a literature review that set up the research in good form, an adequate accounting of the method, and findings that conveyed with grab the complexities of the signs and lack of signs of child sexual abuse. We wove points made in the literature review into our interpretations, yet we had to leave out important patterns for the sake of space. The editor’s decision was a revise and resubmit, which we did. The main recommendation was to elaborate on applications. This was a great suggestion, and we dug deep to think about this. We are pleased with the results. We had to do further reading on topics we had not anticipated at the onset of our project, and we squeezed in a few new citations in the discussion section that related to implications of the research. This additional material greatly enhanced the meanings and usefulness of the research.

There is much more to say about qualitative research and literature reviews. Sometimes researchers get stuck, as I have more than once. I have research that I have not yet published because I have been unable to figure out how to do the multiple literature reviews I think I must show how my theory builds on, adds to, and challenges what is already known. I have written up this research as conference papers, where expectations about literature reviews are more relaxed ( Gilgun, 1996c , 1998 , 1999c , 2000 ). One of these. papers was on a comprehensive theory of interpersonal violence ( Gilgun, 2000 ). I wanted to write my theory first and then show how the findings contribute to what is already known. Doing so doesn’t seem so outlandish today, and I now can imagine writing it up exactly as I would want to. At the same time, I wonder if I would? I really don’t know if any journal that would publish a theory of violence would also accept an article that places a literature review after findings. Furthermore, my writing up of the theory would take so many pages that I would not have enough space to do a comprehensive literature review. As of today, the theory I am developing has links to sixteen or more bodies of literature. No way can I publish a journal-length article that will accommodate that much research and theory!

So, here I am, many years into the development of a comprehensive theory, still reflecting on how to create journal articles out of my analysis. I have published many articles in social media outlets exploring ideas that are the basis for the theory. I have put these articles into collections that are available on the internet ( Gilgun, 2012 b ; 2012 c ; 2013 a ). The theory is so complex that writing bits and pieces over the years and having a place to put them have been very helpful.

Finally, some articles may cite few if any related research and theory. This may fit articles whose purpose is to convey lived experience that stands on its own. These articles feature performances, plays, autoethnographies, fictionalized accounts, poetry, and song, among others. Egbe (2013) wrote two poems that she explained were accounts of her experiences of doing research in Nigeria with young smokers. She said she was “dazed by the vast opportunity this method gives a researcher to dig deep into a research problem and be submerged into the world of participants” (p. 353). Her two-page article is composed of two poems and her explanation. The article showed grab, evidence of immersion, experiences in contexts, and multiple perspectives. Her work, therefore, followed well-established guidelines for writing up qualitative research. Egbe not only omitted a literature review, but she did not write about how to use the results of her research, assuming that its uses are self-evident. Obviously, she thought a literature review unnecessary; the reviewers and journal editors agreed with her.

Reflexivity Statements

A growing number of journals encourage researchers to include reflexivity statements in research reports. Researchers may place these in the introductory material of an article, after the literature review and before the methods section; this probably is the most important place to put them because reflexivity statements often influence the focus and design of the research, including the choice of sensitizing concepts and codes. Reflexivity statements may also appear in the methods and findings and methods sections when important. Reflexivity statements are accounts of researchers’ experiences with the topic of research; accounts of their expectations regarding informant issues and their relationships to informants, especially in regard to power differentials and other ethical concerns; and accounts of their reflections on various issues related to possible experiences that informants may have had. They also may include the experience they had while participating in the research ( D’Cruz, Gillingham, & Melendez, 2007 ; Presser, 2005 ). My article on doing research on violence is an extended reflexivity statement ( Gilgun, 2008 ). There appears to be no standard content for reflexivity statements and no standard places for them to appear. Personal and professional experiences and reflections on power differentials may be the emergent standard. Whatever decisions researchers make about reflexivity statements, they alert audiences to researchers’ perspectives, which can be helpful to readers as they attempt to make sense of research reports.

An example of a reflexivity statement is found in Winter (2010) work. Winter is a practitioner turned researcher who had a previous relationship as a guardian ad litem with the children with whom she later conducted the research that she was reporting. Winter was reflexive about the implications of her prior relationship with these children. I imagine, based on my own experience, that she put only a fraction of her thinking into her article. Not only did she write in her reflexivity statement that she had a prior relationship with the children, but she also wrote about the ethical issues involved.

Ethical issues have a place in reflexivity statements. I have run into ethical questions over the course of my research career. One situation that stands out is the encounter I had with a mother and her eleven-year-old daughter who had participated in my dissertation research on child sexual abuse ( Gilgun, 1983 ). The mother cried and told her daughter how sorry she was that she had been unable to protect her from sexual abuse. The girl was touched but did not seem to know what to do. I suggested that she go stand by her mother. When she got close, the mother and daughter hugged each other and cried. This is a significant event with ethical implications that I included in the findings section of my dissertation and in a subsequent research report ( Gilgun, 1984 ). The ethical issue is, first, whether I should have stepped out of my role as detached researcher and guided the girl to go to her mother, and, second, whether I should have made my blurring of boundaries public by publishing them.

As far as the placement of reflexivity statements, the initial statement has a logical location after the literature review because the reflexivity statement contributes to the development of the research questions, the identification of sensitizing concepts, the interview schedule, and the overall design of research procedures. Accounts of ongoing reflexivity could be part the findings section and of the discussion section. Reflexivity statements are not a standard part of research reports, but they can contribute to readers’ understandings of the research.

Along with the literature review, reflexivity statements contribute to practical and applied significance statements and may also help to identify gaps in knowledge. Literature reviews and reflexivity statements contain key concepts. The concepts that researchers define at the end of introductory sections typically become codes during analysis, although researchers may not label the concepts as codes either in the introductory section or in the methods section. I am unsure why such labeling has not become routine. When concepts carry the label code , this clarifies where codes come from. Without naming codes and stating where they come from, much of analysis is mystified. Many reports read as if the codes appear out of nowhere during analysis. Even Glaser’s (1978) notion of theoretical sensitivity mystifies the origins of codes. How, for example, do researchers become theoretically sensitive? What if researchers are beginning their scholarly careers? How theoretically sensitive are they ( Covan, 2007 )? What are the implications for the quality of the analysis?

Research Questions, Hypotheses, and Definitions

The final part of the introductory section of a research report is devoted to research questions, hypotheses to be tested (if any), and definitions of core concepts. In general, in qualitative research, hypotheses are statements of relationships between concepts. Theories usually are composed of two or more hypotheses, although, at times, some researchers may use the term theory to designate a single hypothesis ( Gilgun, 2005 b ). Concepts are extractions from concrete data. Sometimes concepts are called second-order concepts and data first-order concepts .

Research questions may be absent. In their place are purpose statements that make the focus of the report clear. Hypotheses are rarely present in qualitative research. When they are, the purpose of the research is to test them and typically to develop them more fully. This type of research has in the past been called analytic induction ( Gilgun, 1995 e), whereas a more up-to-date version of qualitative hypothesis testing and theory-guided research is called deductive qualitative analysis ( Gilgun, 2005 d ; 2013 ). Analytic induction and deductive qualitative analysis are part of the Chicago School tradition.

Methods Section

Most methods sections for reports based on qualitative approaches have the same elements as any other research report. Descriptions of the sample, recruitment, interview schedule, and plans for data analysis are standard. The APA manual provides guidelines ( American Psychological Association, 2009 ) that fit many types of qualitative research reports. However, reports based on autoethnographies, poetry, and performances may have brief or no methods sections. As is clear by now, the report’s contents depend on the purposes and methodologies of the research and on the editorial requirements of journals.

Accounts of Methodologies

In writing up qualitative research, methods sections usually contain a brief overview of the research methodology, which is the set of principles that guided the research. The following is an account of the methodology used in a research report on cancer treatment in India:

For this project we drew upon interpretive traditions within qualitative research. This involved us taking an in-depth exploratory approach to data collection, aimed at documenting the subjective and complex experiences of the respondents. Our aim was to achieve a detailed understanding of the varying positions adhered to, and to locate those within a broader spectrum underlying beliefs and/or agendas. ( Broom & Doron, 2013 , p. 57)

Sometimes, statements of methodology are much more elaborate, but in research reports, such a statement is sufficient, again depending on the editorial policies of particular journals. A few citations, which this article had, round out an adequate statement of methodology.

However, many reports are written in a clear and straightforward way with scant or no account of methodologies. Examples are the work of Eck (2013) and Spermon, Darlington, and Gibney (2013) . These kinds of well-done write-ups might eventually be considered generic. Spermon et al. said their study was phenomenological, which sets up assumptions that the report will be primarily descriptive. In actuality, the intent was to develop theory. Such mixing of methodologies may be the wave of the future; in many ways, distinctions between phenomenological studies whose purposes are descriptive and those whose purposes are to build theory are blurred. Such blurring may have been the case for decades because it is possible and often desirable to build theories based on phenomenological perspectives; that is, in-depth descriptions of lived experience. However, authors are wise to state in one place what their methodologies are and how they put them to use, such as for descriptive purposes or for theory-building.

Description of Sample

Placing descriptions of sample size and the demographics of the sample in the methods sections is typical. As mentioned earlier, evaluation of sample size depends on the depth and breadth of the study. The more depth a study has, the smaller the number of cases can be. The more breadth and the sharper the focus, the larger sample sizes typically are. Samples on which a study is based must provide enough material on which to base a credible article. A sample size of one may be adequate if researchers show their work demonstrates the basic principles of almost all forms of qualitative research: perspectives of persons who participate in the research, researcher immersion into the settings or the life stories of persons interviewed, multiple perspectives, contextual information of various types, and applications. Autoethnographies often have an n of one, but joint autoethnographies are possible. Ethnographies may not give a sample size, as was the case in the performance ethnography of Valásquez (2011) who wrote in the first person about her experience with scientology. In her first-person ethnography, Irvine (2013) also did not mention sample size. She said that the narratives she used for the article were from a larger study on the meanings of animals to people who have no homes. She did not describe the usual demographics of age, gender, social class, and ethnicity.

Most articles describe the demographics of the sample. In a recently accepted article ( Gilgun & Anderson, 2013 ), I saw no relevance in mentioning the size of the larger sample from which we drew in order to tell the stories of how mothers responded to their learning that their husbands or life partners had sexually abused their children. We included an exact count of the larger sample because we assumed that it would be the journal’s expectations. We also gave particulars of the demographics. Except for social class and ethnicity, we saw little relevance for the other descriptors. These status variables were relevant to us because most of the sample was white and middle or upper class. This is important because much research on child sexual abuse is done with poor people, and there are stereotypes that poor families and families of color are more likely to experience incest than are white middle and upper class families. Overall, as with some other issues related to writing, the adequacy of the sample description depends on the methodological principles of the research and the journal’s editorial policies.

Recruitment

Accounts of recruitment procedures are important because researchers want to show that their work is ethical. Respect for the autonomy or freedom of choice of participants needs to be demonstrated. In addition, often the persons in whose lives we are interested have vulnerabilities. To show that the research procedures have not exploited these vulnerabilities is part of ethical considerations. Most articles have these accounts. Furthermore, when there are accounts of recruitment procedures, it becomes obvious why the sample is not randomly selected. Irvine’s (2013) account of recruitment is exemplary. She recruited through veterinary clinics that took care of the pets of homeless persons. She did not approach potential participants herself. Doing so risked making refusals difficult. The staff informed persons of the research and its purposes. If individuals said they were interested, they gave permission for the staff to give their names to researchers. The research interviews took place in the clinics.

The ethics of recruitment revolve around values, such as respect for autonomy, dignity, and worth. Other ethical issues that are important to mention in reports include the use of incentives for participation. Although many human subjects committees now require monetary incentives for participation, this has ethical implications. Irvine (2013) solved this by giving gift cards after the interviews were completed. Reports on ethical issues have a place in methods sections.

Data Collection and Analysis

Accounts of data collection and analysis are part of the methods section. Data collection procedures should be detailed for many reasons. Primary among them is the need for transparency in terms of the ethical standards the researchers followed, as well as the need to allow for replication of the study. Such details also provide guidelines for others who might be interested in using the methods. In addition, there are many different schools of thought and procedures for each of the methods used with the three general types of data collection: interviews, observations, and documents. It is helpful to state which particular data collection procedures the researchers used. Researchers often provide examples of the kinds of questions asked and procedures used for recording observations and excerpts from documents. Some researchers may omit such an accounting, as with some autoethnographies and articles that turn research material into performances.

How researchers analyzed data is part of the methods sections. As with data collection, there are so many types of analysis that researchers need to describe the particular forms that they used. For figuring out how to report on data analysis, researchers would do well to study articles in journals in which they want to publish. Irvine (2013) used a method of analysis I have never heard of called “personal narrative analysis” (p. 8). She gave enough detail to provide the general idea of what she did and a sufficient number of citations for additional information.

The level of detail can vary. In some sociology journals, for example, researchers may say little about analysis and sometimes little about data collection. This is because the journal editors, reviewers, and those who publish in and read the articles have assumptions that they for the most part take for granted. Even in these journals, however, researchers may want to account for their analytic procedures, especially if they are writing on topics outside of what is usual in such journals.

Other journals require a great deal of detail. In those instances, researchers first decide what they think is essential and then shape their accounts to fit what appears to be usual practice in the journal. The following paragraphs describe data analysis in a recently accepted article on signs of child sexual abuse in families ( Gilgun & Anderson, 2013 ).

Data Analysis

In the analysis of data, the first author read the transcripts multiple times and coded them for instances related to disclosures of child sexual abuse and associated signs of the abuse, such as how and when the women first learned of the abuse or suspected it was occurring in their families, their responses, and their reflections on the signs of abuse they might have missed, as well as child and perpetrator behaviors that they did not realize were related to child sexual abuse. Their initial and longer term responses and reflections were also coded. The second author independently read and coded about one-third of the transcripts using this coding scheme to arrive at a 100 percent agreement.

Sources of the codes were our professional experiences in the area of child sexual abuse, the review of research, and the first author’s familiarity with the content of the interviews because she had been the interviewer. These codes served as sensitizing concepts, which, as Blumer (1986) explained, are ideas that guide researchers to see aspects of phenomena that they might otherwise not notice. Although altering researchers’ ideas to what might be significant serves an obvious useful purpose, sensitizing concepts might also may blind researchers to other aspects of phenomena that might be important. Therefore, we also used negative case analysis, which is a procedure that guides researchers to look for aspects of phenomena that contradict or do not fit with emerging understandings. In this way, researchers are positioned to see patterns, variations within patterns, exceptions, and contradictions in findings ( Becker et al., 1961 ; Bogdan & Biklen, 2007 ; Cressey, 1953 ; Lindesmith, 1947 ).

As we wrote this section, we were aware of the limited space that we had to fill. Yet we were committed to accounting for where our codes came from for reviewers and editors who may be unfamiliar with pre-established codes. As discussed earlier, many reports are written as if codes appear by magic. We decided that, in this report, we would be as clear as possible about where our codes came from. We also reasoned that we would have to call on the authority of well-respected methodologists if reviewers and editors had questions about what we had done. Furthermore, we were aware of the dated nature of the references; we could do nothing about that because there has not been much written recently about pre-established codes. I have written about this quite a bit, but as one of the authors, I not only had to be anonymous during the review process, but I could not be the sole authority.

Generalizability

Many reviewers and editors have questions about the generalizability of the results of qualitative research. Authors themselves sometimes question the generalizability of their own findings. That’s why it remains important to provide clear guidelines in research reports about how the authors view the usefulness of their findings. The following ideas may be helpful to authors as they write their reports and to reviewers who are positioned as gatekeepers. The results of qualitative research are not meant to be generalized in a probabilistic sense. But because dropouts and refusals limit the randomness of samples, most forms of research can’t be generalized in a probabilistic sense.

Conversely, as Cronbach (1975) wrote almost forty years ago, the results of any form of research are working hypotheses that must be tested in local settings. Thus, the applicability of qualitative or any other kind of research can be demonstrated only through attempts at application. Do the findings illuminate other situations? Do the results provide researchers, policy makers, and direct practitioners with ideas on how to proceed? Those who apply the research expect to have to adjust findings to fit particular new situations. Many researchers and some journal editors and reviewers know through common sense and everyday experience how to use the results of qualitative research. Our personal lives are extended case studies. What we learn in one situation, we carry over into another. We know we have to test what we have learned in past situations for fit with new situations. If we do not, we impose our ideas on situations that may demand new perspectives. This common practice of applying results to all situations is disrespectful of local conditions and autonomy of persons. We want to avoid such disrespect in how we suggest readers use the results of our research.

Trustworthiness and Authenticity

Pointing out the trustworthiness of procedures and the findings that result from them sometimes are parts of methods sections. Related to trustworthiness are issues of authenticity ( Guba & Lincoln, 2005 ). Both trustworthiness and authenticity arise from immersion, seeking to understand the perspectives of others in context, reflexivity, and seeking multiple points of view. Researchers who have applied these principles will produce reports that are trustworthy and authentic. In addition, the reports will have grab. Extended discussions related to these issues are beyond the scope of this chapter and the scope of research reports as well.

I get more requests for revisions of methods sections, especially for accounts of data collection and analysis, than for any other parts of a manuscript. This is not surprising, given the multiple possible variations. I never know who the reviewers will be and what their expectations are. I rely first on my beliefs about what I want in the procedures section and then I study articles the journal has already publishes. I include what journal editors appear to expect, but I also add information that I think is important, even when it is not part of what I see in methods sections.

Findings Sections

Findings sections in research reports include both descriptive and conceptual material. Descriptive material is composed of researchers’ paraphrasing and summarizing of what they found and excerpts from interviews, fieldnotes, and documents. The descriptive material, at its best, is detailed and lively; it not only is informative, it has grab. This material contributes to understandings of human experiences in context. In addition, descriptive material is the basis of researchers’ theorizing and it also provides documentation and illustrations of assertions that researchers make.

Conceptual material comprises the analysis and is made up of inferences such as the general statements, concepts, and hypotheses that researchers develop from the material (data). One way to think about the relationship between descriptive and conceptual material is to think of descriptive material as composed of first-order concepts and conceptual material as composed of second-order concepts. Each type depends on the other. Credible conceptual material is based on descriptive material, some of which is contained in the article. Qualitative research yields mountains of data, a fraction of which can be placed into a published article.

As with other sections of research reports, findings sections have many possible variations that depend on the purpose of the research and the methodologies on which the research is based. Thus, the findings can range from heavily descriptive to heavily conceptual. Heavily conceptual research reports arise from research whose purpose is theoretical, in which researchers set out to test, refine, reformulate, or develop theory. Theoretical reports require some descriptive material to show the basis of theoretical statements, but they are often relatively short on descriptive material.

Reports that are primarily descriptive are composed of excerpts from data. Theoretical material appears in often subtle ways, such as in the form of concepts that organize findings. Irvine’s (2013) study of homeless people and their pets is largely descriptive, composed of excerpts from the interviews and Irvine’s paraphrases and narration of what she did, how, and when. The findings were narrative case studies based on interviews and observations. The details of the narratives were vivid and had the kind of grab that Glaser (1978) recommended. They showed multiples perspectives and variations on what it meant to homeless informants to have pets in their lives. The first three pages were a review of relevant literature and a presentation of method. The last five pages were a discussion of the findings.

As lengthy as the descriptive material is, conceptual material frames the entire report. In the literature review, Irvine introduced notions of positive identity, generativity, and redemption. She used them to analyze her data and organize findings, which were the narrative case studies. She used the concept of redemption as the core or organizing concept, going into some detail about how the research material supports the significance of this idea of pets as redemptive for homeless people.

This analysis is based squarely on the descriptive material. For instance, Irvine wrote that in the stories she presented in her article, “animals provide the vehicle for redemption.” She illustrated this point with a quote from one of the narratives and then reminded readers that the narratives “contain variations on the theme” of “ life is better because this animal is in it ” (p. 20; emphasis in original). Readers do not take this on faith because the basis of this general statement in presented multiple times in the case studies. Irvine has much more material on which she based these ideas, but there is not enough room in a journal-length article to show all of her evidence.

An example of an article that is theoretical in purpose and short on descriptive material is found in the work of Cordeau (2012) . She developed a grounded theory of the “transition from student to professional nurse” when student nurses work with “mannequins as simulated patients” (p. 90). Based on interviews, observations, and reports that the students wrote on their clinical experiences, the study was composed of about 10 percent descriptive material. This material included excerpts interviews and student reports. In the results section, she used this descriptive material to illustrate and possibly document the grounded theory she constructed. The theory’s “core category” was “linking,” which had four components, called properties. She documented the properties, primarily with her own thinking about her research material and also with excerpts from interviews, observations, and student reports.

Like Irvine’s (2013) study, the purpose of Cordeau’s (2012) work was applied where she wanted to build theory that would contribute to the development of clinical expertise in nursing students. She also devoted about one page of her study to applications.

Core Concepts

I’ve previously provided an extended discussion of core concepts. This section highlights some key points and illustrates them. Core concepts, often called core categories , organize findings. I prefer the term concept because concept is the term used in discussing theory, such as “concepts are the building blocks of theory,” and theory is one of several possible products of qualitative research. Researchers decide on which concepts are core in the course of analysis. Researchers are ready to write up their reports when they have settled on, named, and dimensionalized one or more core concepts. The terms “core concepts” and “core categories” are associated with grounded theory ( Charmaz, 2006 ; Corbin & Strauss, 2008 ), but they are useful in other types of qualitative research, such as interpretive phenomenology and narrative analysis. Core concepts both organize findings and, typically, bring together a great deal of information. The term “dimension” means that researchers account for as many aspects of the core concepts as they can in order to show the multiple perspectives and patterns that typically compose concepts.

In reporting on core concepts, I recommend that researchers name them, introduce them, describe them using excerpts from the research material, comment on them, and then situate each of the concepts and their commentaries within their scholarly contexts. As discussed earlier, this shows how the findings fit with what is already known, or add to, force modification of, or refute what is known. Although many researchers, do not situate findings in their scholarly contexts, they usually cover the other topics.

No matter how authors report findings, they should do so with grab. An example of a report exemplary for its grab is the work of Scott (2003) on what it means to be a professional with a physical disability. Scott began her article not with a literature review but with three reviewer comments on other articles she had written. She then stated that the present article was a response to these comments. She followed up with a description of three male students who waited to speak to her after class about her disability and the notion of embodiment that she discussed in class. She brought in related literature throughout the article. Through her own reflections, reports on how others have responded to her, reports on the accounts that three other women with disabilities gave to her as a person with cerebral palsy, and her literature review, Scott not only showed the meanings of disabilities to persons who have them, but also what others say about their own disabilities, what some people who are able-bodied say about women with disabilities, and how all of this connects to what is known about disabilities and to wide-spread beliefs about disabilities. Her article is full of grab, such as the header that read, “The Day I Became Human.” With the authors’ own experience as the centerpiece, this article exemplifies write-ups that demonstrate the meanings of lived experience in various contexts, immersion, grab, and implications for social action. The analysis she presented as part of her findings is exemplary.

In the production of quality research, no matter the type of write-up, there are no short cuts. Research reports based on poetry, for example, are held to the same standards as any other article: grab, immersion, lived experience in context, and implications for action. In addition, such research reports typically locate themselves within social and human sciences traditions. Furman’s (2007) reflections and analysis of poetry that he wrote over the course of many years provide an example of how poetry can be used in qualitative analysis. This kind of research is a type of document analysis. In performance studies, researchers create a theater production of informant’s accounts of their experiences whose purpose is to transform audiences and move them to action ( Saldaña, 2003 ). The performances are the equivalent of research reports and when they are effective, they have the four characteristics of qualitative research under discussion.

Discussion Sections

In traditional research reports, the discussion section follows the results section. In discussion sections, authors reflect on findings, including what the findings are, how findings contribute to understandings of phenomena of interest, the lines of inquiry the results open up, and implications for policy and practice. Other generic topics to consider are those related to the focus of the journal. For example, if the journal’s focus is related to health, then authors show how findings are related to health.

Discussion sections present the author with opportunities to advocate for how his or her research can be used. The applied purposes of Irvine’s (2013) research come through when she devoted an entire page to make observations about implications. She pointed out how her research contributes to a transformation of images of homeless persons as isolated to images of them as engaged in relationships not only with their pets but with other persons, too. She noted that rehousing homeless persons requires a change in policy that would allow them to have pets. Furthermore, she said that caring for a pet “can turn things around” (p. 24).

In the discussion section I wrote with Anderson ( Gilgun & Anderson, 2013 ), we addressed methodological issues, such as the probable existence of other patterns in addition to those we identified and the nonrandom nature of our sample. We also acknowledged the difficulties in working with families in which child sexual abuse has occurred. Since qualitative researchers want to understand lived experiences, we had to prepare ourselves to deal effectively in research areas that are difficult emotionally for us as researchers. Although we may acknowledge the emotional challenges of some topics in reflexivity statements, discussion sections are opportunities for authors to acknowledge the difficulties of using the results we produce. In the article I wrote with Anderson, we made such an acknowledgment, one that we hoped would facilitate more effective practice. We wrote

Practitioners themselves may experience shock, rage, and disgust. The practice of neutrality, in its therapeutic sense, is important in these cases ( Gil & Johnson, 1993 ; Rober, 2011 ). Neutrality means that practitioners maintain their analytic stances while at the same time they remain attuned not only to service users but also to themselves. When practicing neutrality, service providers regulate their own emotional responses in order to remain emotionally available to service users. Neutrality also means that service providers remain open-minded so that they can hear stories that they may not expect to hear; in other words, to make room for the unexpected ( Rober, 2011 ). Attunement to inner processes is a form of reflection that can facilitate the development of trust between service users and providers. When providers are reflective, they are less likely to tune out, close down, and otherwise stop listening to what services users express. When they listen and hear what service users say, they are more likely to facilitate the best possible outcomes in difficult situations ( Weingarten, 2012 ).

Doing research on lived experience can be difficult for informants and for researchers. Acknowledgment of the implications of these difficulties for users of the research has a place in discussion sections.

In summary, most articles are fairly straightforward in their write-ups: focused literature reviews, reflexivity statements in many cases, clear statements of purpose, clarity about sources of research questions and/or hypotheses, identification and definition of key concepts, identification of codes the researcher develops from literature reviews and reflexivity statements, succinct accounting of methods, and findings organized logically by core concepts around which the researcher organizes the multiple dimensions of those concepts. Excellent writing makes articles interesting and accessible. Some kinds of write-ups deviate from these components, but they are held to the same standards of immersion, experiences in context, multiple perspectives, and implications for action and other applications. When authors have the good fortune to have a recommendation to revise and resubmit, suggestions for revisions often improve the quality of the article.

The seemingly endless variations that are possible in the write-up of qualitative research makes writing and reviewing manuscripts challenging, especially when compared to traditions in which rigid rules prevail. However, it is important that approaches to qualitative research continue to evolve to meet with our ever-changing understandings of human phenomena. The clarity and transparency of reports are the fundamental guidelines for making judgments about quality. I often tell my students that the guidelines for doing qualitative research are flexible, and what is important is to be clear about what you did, why you did it, and what you came up with.

The notion of grab is central to write-up. Since qualitative research seeks to understand lived experiences, it is logical that findings report on the lived experiences in vivid terms, replete with quotes from data. This is not to undermine the importance of analysis, but grab is possible even in write-ups that require a great deal of analysis. Grab becomes possible because researchers must provide the evidence for the theories and concepts they develop.

When there are questions about priorities related to informants’ voices, researchers’ interpretations, and prior research, I hope that authors, reviewers, and editors remember that as important as analysis and previous work may be, the voices of informants bring these other important parts of manuscripts to life. Researchers make decisions about whose voices take priority.

There is no one way to respond to these dilemmas. Authors must make their own decisions about what is important to them and then search for journals that will welcome what they want to convey. It’s important to consider pushing the boundaries and writing an article in a way that the researcher thinks will best convey his or her findings.

The importance of quality data, quality analysis, and “grab” are foundational. I began this chapter with a discussion of the balance between description and analysis. I then considered core concepts as organizers of findings, the place of literature reviews, styles of presenting methods and methodologies, and the balance between the voices of informants and researchers. I concluded with the many variations in types of reports that result from the various purposes that qualitative research projects can have. There are many different types of qualitative research and many styles of write-ups. This chapter may sensitize readers to enduring issues in the writing of research reports. Like qualitative research itself, there are multiple points of view on how to write up qualitative research.

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Home » 500+ Qualitative Research Titles and Topics

500+ Qualitative Research Titles and Topics

Table of Contents

Qualitative research is a methodological approach that involves gathering and analyzing non-numerical data to understand and interpret social phenomena. Unlike quantitative research , which emphasizes the collection of numerical data through surveys and experiments, qualitative research is concerned with exploring the subjective experiences, perspectives, and meanings of individuals and groups. As such, qualitative research topics can be diverse and encompass a wide range of social issues and phenomena. From exploring the impact of culture on identity formation to examining the experiences of marginalized communities, qualitative research offers a rich and nuanced perspective on complex social issues. In this post, we will explore some of the most compelling qualitative research topics and provide some tips on how to conduct effective qualitative research.

Qualitative Research Titles

Qualitative research titles often reflect the study’s focus on understanding the depth and complexity of human behavior, experiences, or social phenomena. Here are some examples across various fields:

• “Understanding the Impact of Project-Based Learning on Student Engagement in High School Classrooms: A Qualitative Study”
• “Navigating the Transition: Experiences of International Students in American Universities”
• “The Role of Parental Involvement in Early Childhood Education: Perspectives from Teachers and Parents”
• “Exploring the Effects of Teacher Feedback on Student Motivation and Self-Efficacy in Middle Schools”
• “Digital Literacy in the Classroom: Teacher Strategies for Integrating Technology in Elementary Education”
• “Culturally Responsive Teaching Practices: A Case Study in Diverse Urban Schools”
• “The Influence of Extracurricular Activities on Academic Achievement: Student Perspectives”
• “Barriers to Implementing Inclusive Education in Public Schools: A Qualitative Inquiry”
• “Teacher Professional Development and Its Impact on Classroom Practice: A Qualitative Exploration”
• “Student-Centered Learning Environments: A Qualitative Study of Classroom Dynamics and Outcomes”
• “The Experience of First-Year Teachers: Challenges, Support Systems, and Professional Growth”
• “Exploring the Role of School Leadership in Fostering a Positive School Culture”
• “Peer Relationships and Learning Outcomes in Cooperative Learning Settings: A Qualitative Analysis”
• “The Impact of Social Media on Student Learning and Engagement: Teacher and Student Perspectives”
• “Understanding Special Education Needs: Parent and Teacher Perceptions of Support Services in Schools

Health Science

• “Living with Chronic Pain: Patient Narratives and Coping Strategies in Managing Daily Life”
• “Healthcare Professionals’ Perspectives on the Challenges of Rural Healthcare Delivery”
• “Exploring the Mental Health Impacts of COVID-19 on Frontline Healthcare Workers: A Qualitative Study”
• “Patient and Family Experiences of Palliative Care: Understanding Needs and Preferences”
• “The Role of Community Health Workers in Improving Access to Maternal Healthcare in Rural Areas”
• “Barriers to Mental Health Services Among Ethnic Minorities: A Qualitative Exploration”
• “Understanding Patient Satisfaction in Telemedicine Services: A Qualitative Study of User Experiences”
• “The Impact of Cultural Competence Training on Healthcare Provider-Patient Communication”
• “Navigating the Transition to Adult Healthcare Services: Experiences of Adolescents with Chronic Conditions”
• “Exploring the Use of Alternative Medicine Among Patients with Chronic Diseases: A Qualitative Inquiry”
• “The Role of Social Support in the Rehabilitation Process of Stroke Survivors”
• “Healthcare Decision-Making Among Elderly Patients: A Qualitative Study of Preferences and Influences”
• “Nurse Perceptions of Patient Safety Culture in Hospital Settings: A Qualitative Analysis”
• “Experiences of Women with Postpartum Depression: Barriers to Seeking Help”
• “The Impact of Nutrition Education on Eating Behaviors Among College Students: A Qualitative Approach”
• “Understanding Resilience in Survivors of Childhood Trauma: A Narrative Inquiry”
• “The Role of Mindfulness in Managing Work-Related Stress Among Corporate Employees: A Qualitative Study”
• “Coping Mechanisms Among Parents of Children with Autism Spectrum Disorder”
• “Exploring the Psychological Impact of Social Isolation in the Elderly: A Phenomenological Study”
• “Identity Formation in Adolescence: The Influence of Social Media and Peer Groups”
• “The Experience of Forgiveness in Interpersonal Relationships: A Qualitative Exploration”
• “Perceptions of Happiness and Well-Being Among University Students: A Cultural Perspective”
• “The Impact of Art Therapy on Anxiety and Depression in Adult Cancer Patients”
• “Narratives of Recovery: A Qualitative Study on the Journey Through Addiction Rehabilitation”
• “Exploring the Psychological Effects of Long-Term Unemployment: A Grounded Theory Approach”
• “Attachment Styles and Their Influence on Adult Romantic Relationships: A Qualitative Analysis”
• “The Role of Personal Values in Career Decision-Making Among Young Adults”
• “Understanding the Stigma of Mental Illness in Rural Communities: A Qualitative Inquiry”
• “Exploring the Use of Digital Mental Health Interventions Among Adolescents: A Qualitative Study”
• “The Psychological Impact of Climate Change on Young Adults: An Exploration of Anxiety and Action”
• “Navigating Identity: The Role of Social Media in Shaping Youth Culture and Self-Perception”
• “Community Resilience in the Face of Urban Gentrification: A Case Study of Neighborhood Change”
• “The Dynamics of Intergenerational Relationships in Immigrant Families: A Qualitative Analysis”
• “Social Capital and Economic Mobility in Low-Income Neighborhoods: An Ethnographic Approach”
• “Gender Roles and Career Aspirations Among Young Adults in Conservative Societies”
• “The Stigma of Mental Health in the Workplace: Employee Narratives and Organizational Culture”
• “Exploring the Intersection of Race, Class, and Education in Urban School Systems”
• “The Impact of Digital Divide on Access to Healthcare Information in Rural Communities”
• “Social Movements and Political Engagement Among Millennials: A Qualitative Study”
• “Cultural Adaptation and Identity Among Second-Generation Immigrants: A Phenomenological Inquiry”
• “The Role of Religious Institutions in Providing Community Support and Social Services”
• “Negotiating Public Space: Experiences of LGBTQ+ Individuals in Urban Environments”
• “The Sociology of Food: Exploring Eating Habits and Food Practices Across Cultures”
• “Work-Life Balance Challenges Among Dual-Career Couples: A Qualitative Exploration”
• “The Influence of Peer Networks on Substance Use Among Adolescents: A Community Study”

Business and Management

• “Navigating Organizational Change: Employee Perceptions and Adaptation Strategies in Mergers and Acquisitions”
• “Corporate Social Responsibility: Consumer Perceptions and Brand Loyalty in the Retail Sector”
• “Leadership Styles and Organizational Culture: A Comparative Study of Tech Startups”
• “Workplace Diversity and Inclusion: Best Practices and Challenges in Multinational Corporations”
• “Consumer Trust in E-commerce: A Qualitative Study of Online Shopping Behaviors”
• “The Gig Economy and Worker Satisfaction: Exploring the Experiences of Freelance Professionals”
• “Entrepreneurial Resilience: Success Stories and Lessons Learned from Failed Startups”
• “Employee Engagement and Productivity in Remote Work Settings: A Post-Pandemic Analysis”
• “Brand Storytelling: How Narrative Strategies Influence Consumer Engagement”
• “Sustainable Business Practices: Stakeholder Perspectives in the Fashion Industry”
• “Cross-Cultural Communication Challenges in Global Teams: Strategies for Effective Collaboration”
• “Innovative Workspaces: The Impact of Office Design on Creativity and Collaboration”
• “Consumer Perceptions of Artificial Intelligence in Customer Service: A Qualitative Exploration”
• “The Role of Mentoring in Career Development: Insights from Women in Leadership Positions”
• “Agile Management Practices: Adoption and Impact in Traditional Industries”

Environmental Studies

• “Community-Based Conservation Efforts in Tropical Rainforests: A Qualitative Study of Local Perspectives and Practices”
• “Urban Sustainability Initiatives: Exploring Resident Participation and Impact in Green City Projects”
• “Perceptions of Climate Change Among Indigenous Populations: Insights from Traditional Ecological Knowledge”
• “Environmental Justice and Industrial Pollution: A Case Study of Community Advocacy and Response”
• “The Role of Eco-Tourism in Promoting Conservation Awareness: Perspectives from Tour Operators and Visitors”
• “Sustainable Agriculture Practices Among Smallholder Farmers: Challenges and Opportunities”
• “Youth Engagement in Climate Action Movements: Motivations, Perceptions, and Outcomes”
• “Corporate Environmental Responsibility: A Qualitative Analysis of Stakeholder Expectations and Company Practices”
• “The Impact of Plastic Pollution on Marine Ecosystems: Community Awareness and Behavioral Change”
• “Renewable Energy Adoption in Rural Communities: Barriers, Facilitators, and Social Implications”
• “Water Scarcity and Community Adaptation Strategies in Arid Regions: A Grounded Theory Approach”
• “Urban Green Spaces: Public Perceptions and Use Patterns in Megacities”
• “Environmental Education in Schools: Teachers’ Perspectives on Integrating Sustainability into Curricula”
• “The Influence of Environmental Activism on Policy Change: Case Studies of Grassroots Campaigns”
• “Cultural Practices and Natural Resource Management: A Qualitative Study of Indigenous Stewardship Models”

Anthropology

• “Kinship and Social Organization in Matrilineal Societies: An Ethnographic Study”
• “Rituals and Beliefs Surrounding Death and Mourning in Diverse Cultures: A Comparative Analysis”
• “The Impact of Globalization on Indigenous Languages and Cultural Identity”
• “Food Sovereignty and Traditional Agricultural Practices Among Indigenous Communities”
• “Navigating Modernity: The Integration of Traditional Healing Practices in Contemporary Healthcare Systems”
• “Gender Roles and Equality in Hunter-Gatherer Societies: An Anthropological Perspective”
• “Sacred Spaces and Religious Practices: An Ethnographic Study of Pilgrimage Sites”
• “Youth Subcultures and Resistance: An Exploration of Identity and Expression in Urban Environments”
• “Cultural Constructions of Disability and Inclusion: A Cross-Cultural Analysis”
• “Interethnic Marriages and Cultural Syncretism: Case Studies from Multicultural Societies”
• “The Role of Folklore and Storytelling in Preserving Cultural Heritage”
• “Economic Anthropology of Gift-Giving and Reciprocity in Tribal Communities”
• “Digital Anthropology: The Role of Social Media in Shaping Political Movements”
• “Migration and Diaspora: Maintaining Cultural Identity in Transnational Communities”
• “Cultural Adaptations to Climate Change Among Coastal Fishing Communities”

Communication Studies

• “The Dynamics of Family Communication in the Digital Age: A Qualitative Inquiry”
• “Narratives of Identity and Belonging in Diaspora Communities Through Social Media”
• “Organizational Communication and Employee Engagement: A Case Study in the Non-Profit Sector”
• “Cultural Influences on Communication Styles in Multinational Teams: An Ethnographic Approach”
• “Media Representation of Women in Politics: A Content Analysis and Audience Perception Study”
• “The Role of Communication in Building Sustainable Community Development Projects”
• “Interpersonal Communication in Online Dating: Strategies, Challenges, and Outcomes”
• “Public Health Messaging During Pandemics: A Qualitative Study of Community Responses”
• “The Impact of Mobile Technology on Parent-Child Communication in the Digital Era”
• “Crisis Communication Strategies in the Hospitality Industry: A Case Study of Reputation Management”
• “Narrative Analysis of Personal Stories Shared on Mental Health Blogs”
• “The Influence of Podcasts on Political Engagement Among Young Adults”
• “Visual Communication and Brand Identity: A Qualitative Study of Consumer Interpretations”
• “Communication Barriers in Cross-Cultural Healthcare Settings: Patient and Provider Perspectives”
• “The Role of Internal Communication in Managing Organizational Change: Employee Experiences”

Information Technology

• “User Experience Design in Augmented Reality Applications: A Qualitative Study of Best Practices”
• “The Human Factor in Cybersecurity: Understanding Employee Behaviors and Attitudes Towards Phishing”
• “Adoption of Cloud Computing in Small and Medium Enterprises: Challenges and Success Factors”
• “Blockchain Technology in Supply Chain Management: A Qualitative Exploration of Potential Impacts”
• “The Role of Artificial Intelligence in Personalizing User Experiences on E-commerce Platforms”
• “Digital Transformation in Traditional Industries: A Case Study of Technology Adoption Challenges”
• “Ethical Considerations in the Development of Smart Home Technologies: A Stakeholder Analysis”
• “The Impact of Social Media Algorithms on News Consumption and Public Opinion”
• “Collaborative Software Development: Practices and Challenges in Open Source Projects”
• “Understanding the Digital Divide: Access to Information Technology in Rural Communities”
• “Data Privacy Concerns and User Trust in Internet of Things (IoT) Devices”
• “The Effectiveness of Gamification in Educational Software: A Qualitative Study of Engagement and Motivation”
• “Virtual Teams and Remote Work: Communication Strategies and Tools for Effectiveness”
• “User-Centered Design in Mobile Health Applications: Evaluating Usability and Accessibility”
• “The Influence of Technology on Work-Life Balance: Perspectives from IT Professionals”

Tourism and Hospitality

• “Exploring the Authenticity of Cultural Heritage Tourism in Indigenous Communities”
• “Sustainable Tourism Practices: Perceptions and Implementations in Small Island Destinations”
• “The Impact of Social Media Influencers on Destination Choice Among Millennials”
• “Gastronomy Tourism: Exploring the Culinary Experiences of International Visitors in Rural Regions”
• “Eco-Tourism and Conservation: Stakeholder Perspectives on Balancing Tourism and Environmental Protection”
• “The Role of Hospitality in Enhancing the Cultural Exchange Experience of Exchange Students”
• “Dark Tourism: Visitor Motivations and Experiences at Historical Conflict Sites”
• “Customer Satisfaction in Luxury Hotels: A Qualitative Study of Service Excellence and Personalization”
• “Adventure Tourism: Understanding the Risk Perception and Safety Measures Among Thrill-Seekers”
• “The Influence of Local Communities on Tourist Experiences in Ecotourism Sites”
• “Event Tourism: Economic Impacts and Community Perspectives on Large-Scale Music Festivals”
• “Heritage Tourism and Identity: Exploring the Connections Between Historic Sites and National Identity”
• “Tourist Perceptions of Sustainable Accommodation Practices: A Study of Green Hotels”
• “The Role of Language in Shaping the Tourist Experience in Multilingual Destinations”
• “Health and Wellness Tourism: Motivations and Experiences of Visitors to Spa and Retreat Centers”

Qualitative Research Topics

Qualitative Research Topics are as follows:

• Understanding the lived experiences of first-generation college students
• Exploring the impact of social media on self-esteem among adolescents
• Investigating the effects of mindfulness meditation on stress reduction
• Analyzing the perceptions of employees regarding organizational culture
• Examining the impact of parental involvement on academic achievement of elementary school students
• Investigating the role of music therapy in managing symptoms of depression
• Understanding the experience of women in male-dominated industries
• Exploring the factors that contribute to successful leadership in non-profit organizations
• Analyzing the effects of peer pressure on substance abuse among adolescents
• Investigating the experiences of individuals with disabilities in the workplace
• Understanding the factors that contribute to burnout among healthcare professionals
• Examining the impact of social support on mental health outcomes
• Analyzing the perceptions of parents regarding sex education in schools
• Investigating the experiences of immigrant families in the education system
• Understanding the impact of trauma on mental health outcomes
• Exploring the effectiveness of animal-assisted therapy for individuals with anxiety
• Analyzing the factors that contribute to successful intergenerational relationships
• Investigating the experiences of LGBTQ+ individuals in the workplace
• Understanding the impact of online gaming on social skills development among adolescents
• Examining the perceptions of teachers regarding technology integration in the classroom
• Analyzing the experiences of women in leadership positions
• Investigating the factors that contribute to successful marriage and long-term relationships
• Understanding the impact of social media on political participation
• Exploring the experiences of individuals with mental health disorders in the criminal justice system
• Analyzing the factors that contribute to successful community-based programs for youth development
• Investigating the experiences of veterans in accessing mental health services
• Understanding the impact of the COVID-19 pandemic on mental health outcomes
• Examining the perceptions of parents regarding childhood obesity prevention
• Analyzing the factors that contribute to successful multicultural education programs
• Investigating the experiences of individuals with chronic illnesses in the workplace
• Understanding the impact of poverty on academic achievement
• Exploring the experiences of individuals with autism spectrum disorder in the workplace
• Analyzing the factors that contribute to successful employee retention strategies
• Investigating the experiences of caregivers of individuals with Alzheimer’s disease
• Understanding the impact of parent-child communication on adolescent sexual behavior
• Examining the perceptions of college students regarding mental health services on campus
• Analyzing the factors that contribute to successful team building in the workplace
• Investigating the experiences of individuals with eating disorders in treatment programs
• Understanding the impact of mentorship on career success
• Exploring the experiences of individuals with physical disabilities in the workplace
• Analyzing the factors that contribute to successful community-based programs for mental health
• Investigating the experiences of individuals with substance use disorders in treatment programs
• Understanding the impact of social media on romantic relationships
• Examining the perceptions of parents regarding child discipline strategies
• Analyzing the factors that contribute to successful cross-cultural communication in the workplace
• Investigating the experiences of individuals with anxiety disorders in treatment programs
• Understanding the impact of cultural differences on healthcare delivery
• Exploring the experiences of individuals with hearing loss in the workplace
• Analyzing the factors that contribute to successful parent-teacher communication
• Investigating the experiences of individuals with depression in treatment programs
• Understanding the impact of childhood trauma on adult mental health outcomes
• Examining the perceptions of college students regarding alcohol and drug use on campus
• Analyzing the factors that contribute to successful mentor-mentee relationships
• Investigating the experiences of individuals with intellectual disabilities in the workplace
• Understanding the impact of work-family balance on employee satisfaction and well-being
• Exploring the experiences of individuals with autism spectrum disorder in vocational rehabilitation programs
• Analyzing the factors that contribute to successful project management in the construction industry
• Investigating the experiences of individuals with substance use disorders in peer support groups
• Understanding the impact of mindfulness meditation on stress reduction and mental health
• Examining the perceptions of parents regarding childhood nutrition
• Analyzing the factors that contribute to successful environmental sustainability initiatives in organizations
• Investigating the experiences of individuals with bipolar disorder in treatment programs
• Understanding the impact of job stress on employee burnout and turnover
• Exploring the experiences of individuals with physical disabilities in recreational activities
• Analyzing the factors that contribute to successful strategic planning in nonprofit organizations
• Investigating the experiences of individuals with hoarding disorder in treatment programs
• Understanding the impact of culture on leadership styles and effectiveness
• Examining the perceptions of college students regarding sexual health education on campus
• Analyzing the factors that contribute to successful supply chain management in the retail industry
• Investigating the experiences of individuals with personality disorders in treatment programs
• Understanding the impact of multiculturalism on group dynamics in the workplace
• Exploring the experiences of individuals with chronic pain in mindfulness-based pain management programs
• Analyzing the factors that contribute to successful employee engagement strategies in organizations
• Investigating the experiences of individuals with internet addiction disorder in treatment programs
• Understanding the impact of social comparison on body dissatisfaction and self-esteem
• Examining the perceptions of parents regarding childhood sleep habits
• Analyzing the factors that contribute to successful diversity and inclusion initiatives in organizations
• Investigating the experiences of individuals with schizophrenia in treatment programs
• Understanding the impact of job crafting on employee motivation and job satisfaction
• Exploring the experiences of individuals with vision impairments in navigating public spaces
• Analyzing the factors that contribute to successful customer relationship management strategies in the service industry
• Investigating the experiences of individuals with dissociative amnesia in treatment programs
• Understanding the impact of cultural intelligence on intercultural communication and collaboration
• Examining the perceptions of college students regarding campus diversity and inclusion efforts
• Analyzing the factors that contribute to successful supply chain sustainability initiatives in organizations
• Investigating the experiences of individuals with obsessive-compulsive disorder in treatment programs
• Understanding the impact of transformational leadership on organizational performance and employee well-being
• Exploring the experiences of individuals with mobility impairments in public transportation
• Analyzing the factors that contribute to successful talent management strategies in organizations
• Investigating the experiences of individuals with substance use disorders in harm reduction programs
• Understanding the impact of gratitude practices on well-being and resilience
• Examining the perceptions of parents regarding childhood mental health and well-being
• Analyzing the factors that contribute to successful corporate social responsibility initiatives in organizations
• Investigating the experiences of individuals with borderline personality disorder in treatment programs
• Understanding the impact of emotional labor on job stress and burnout
• Exploring the experiences of individuals with hearing impairments in healthcare settings
• Analyzing the factors that contribute to successful customer experience strategies in the hospitality industry
• Investigating the experiences of individuals with gender dysphoria in gender-affirming healthcare
• Understanding the impact of cultural differences on cross-cultural negotiation in the global marketplace
• Examining the perceptions of college students regarding academic stress and mental health
• Analyzing the factors that contribute to successful supply chain agility in organizations
• Understanding the impact of music therapy on mental health and well-being
• Exploring the experiences of individuals with dyslexia in educational settings
• Analyzing the factors that contribute to successful leadership in nonprofit organizations
• Investigating the experiences of individuals with chronic illnesses in online support groups
• Understanding the impact of exercise on mental health and well-being
• Examining the perceptions of parents regarding childhood screen time
• Analyzing the factors that contribute to successful change management strategies in organizations
• Understanding the impact of cultural differences on international business negotiations
• Exploring the experiences of individuals with hearing impairments in the workplace
• Analyzing the factors that contribute to successful team building in corporate settings
• Understanding the impact of technology on communication in romantic relationships
• Analyzing the factors that contribute to successful community engagement strategies for local governments
• Investigating the experiences of individuals with attention deficit hyperactivity disorder (ADHD) in treatment programs
• Understanding the impact of financial stress on mental health and well-being
• Analyzing the factors that contribute to successful mentorship programs in organizations
• Investigating the experiences of individuals with gambling addictions in treatment programs
• Understanding the impact of social media on body image and self-esteem
• Examining the perceptions of parents regarding childhood education
• Analyzing the factors that contribute to successful virtual team management strategies
• Investigating the experiences of individuals with dissociative identity disorder in treatment programs
• Understanding the impact of cultural differences on cross-cultural communication in healthcare settings
• Exploring the experiences of individuals with chronic pain in cognitive-behavioral therapy programs
• Analyzing the factors that contribute to successful community-building strategies in urban neighborhoods
• Investigating the experiences of individuals with alcohol use disorders in treatment programs
• Understanding the impact of personality traits on romantic relationships
• Examining the perceptions of college students regarding mental health stigma on campus
• Analyzing the factors that contribute to successful fundraising strategies for political campaigns
• Investigating the experiences of individuals with traumatic brain injuries in rehabilitation programs
• Understanding the impact of social support on mental health and well-being among the elderly
• Exploring the experiences of individuals with chronic illnesses in medical treatment decision-making processes
• Analyzing the factors that contribute to successful innovation strategies in organizations
• Investigating the experiences of individuals with dissociative disorders in treatment programs
• Understanding the impact of cultural differences on cross-cultural communication in education settings
• Examining the perceptions of parents regarding childhood physical activity
• Analyzing the factors that contribute to successful conflict resolution in family relationships
• Investigating the experiences of individuals with opioid use disorders in treatment programs
• Understanding the impact of emotional intelligence on leadership effectiveness
• Exploring the experiences of individuals with learning disabilities in the workplace
• Analyzing the factors that contribute to successful change management in educational institutions
• Investigating the experiences of individuals with eating disorders in recovery support groups
• Understanding the impact of self-compassion on mental health and well-being
• Examining the perceptions of college students regarding campus safety and security measures
• Analyzing the factors that contribute to successful marketing strategies for nonprofit organizations
• Investigating the experiences of individuals with postpartum depression in treatment programs
• Understanding the impact of ageism in the workplace
• Exploring the experiences of individuals with dyslexia in the education system
• Investigating the experiences of individuals with anxiety disorders in cognitive-behavioral therapy programs
• Understanding the impact of socioeconomic status on access to healthcare
• Examining the perceptions of parents regarding childhood screen time usage
• Analyzing the factors that contribute to successful supply chain management strategies
• Understanding the impact of parenting styles on child development
• Exploring the experiences of individuals with addiction in harm reduction programs
• Analyzing the factors that contribute to successful crisis management strategies in organizations
• Investigating the experiences of individuals with trauma in trauma-focused therapy programs
• Examining the perceptions of healthcare providers regarding patient-centered care
• Analyzing the factors that contribute to successful product development strategies
• Investigating the experiences of individuals with autism spectrum disorder in employment programs
• Understanding the impact of cultural competence on healthcare outcomes
• Exploring the experiences of individuals with chronic illnesses in healthcare navigation
• Analyzing the factors that contribute to successful community engagement strategies for non-profit organizations
• Investigating the experiences of individuals with physical disabilities in the workplace
• Understanding the impact of childhood trauma on adult mental health
• Analyzing the factors that contribute to successful supply chain sustainability strategies
• Investigating the experiences of individuals with personality disorders in dialectical behavior therapy programs
• Understanding the impact of gender identity on mental health treatment seeking behaviors
• Exploring the experiences of individuals with schizophrenia in community-based treatment programs
• Analyzing the factors that contribute to successful project team management strategies
• Investigating the experiences of individuals with obsessive-compulsive disorder in exposure and response prevention therapy programs
• Understanding the impact of cultural competence on academic achievement and success
• Examining the perceptions of college students regarding academic integrity
• Analyzing the factors that contribute to successful social media marketing strategies
• Investigating the experiences of individuals with bipolar disorder in community-based treatment programs
• Understanding the impact of mindfulness on academic achievement and success
• Exploring the experiences of individuals with substance use disorders in medication-assisted treatment programs
• Investigating the experiences of individuals with anxiety disorders in exposure therapy programs
• Understanding the impact of healthcare disparities on health outcomes
• Analyzing the factors that contribute to successful supply chain optimization strategies
• Investigating the experiences of individuals with borderline personality disorder in schema therapy programs
• Understanding the impact of culture on perceptions of mental health stigma
• Exploring the experiences of individuals with trauma in art therapy programs
• Analyzing the factors that contribute to successful digital marketing strategies
• Investigating the experiences of individuals with eating disorders in online support groups
• Understanding the impact of workplace bullying on job satisfaction and performance
• Examining the perceptions of college students regarding mental health resources on campus
• Analyzing the factors that contribute to successful supply chain risk management strategies
• Investigating the experiences of individuals with chronic pain in mindfulness-based pain management programs
• Understanding the impact of cognitive-behavioral therapy on social anxiety disorder
• Understanding the impact of COVID-19 on mental health and well-being
• Exploring the experiences of individuals with eating disorders in treatment programs
• Analyzing the factors that contribute to successful leadership in business organizations
• Investigating the experiences of individuals with chronic pain in cognitive-behavioral therapy programs
• Understanding the impact of cultural differences on intercultural communication
• Examining the perceptions of teachers regarding inclusive education for students with disabilities
• Investigating the experiences of individuals with depression in therapy programs
• Understanding the impact of workplace culture on employee retention and turnover
• Exploring the experiences of individuals with traumatic brain injuries in rehabilitation programs
• Analyzing the factors that contribute to successful crisis communication strategies in organizations
• Investigating the experiences of individuals with anxiety disorders in mindfulness-based interventions
• Investigating the experiences of individuals with chronic illnesses in healthcare settings
• Understanding the impact of technology on work-life balance
• Exploring the experiences of individuals with learning disabilities in academic settings
• Analyzing the factors that contribute to successful entrepreneurship in small businesses
• Understanding the impact of gender identity on mental health and well-being
• Examining the perceptions of individuals with disabilities regarding accessibility in public spaces
• Understanding the impact of religion on coping strategies for stress and anxiety
• Exploring the experiences of individuals with chronic illnesses in complementary and alternative medicine treatments
• Analyzing the factors that contribute to successful customer retention strategies in business organizations
• Investigating the experiences of individuals with postpartum depression in therapy programs
• Understanding the impact of ageism on older adults in healthcare settings
• Examining the perceptions of students regarding online learning during the COVID-19 pandemic
• Analyzing the factors that contribute to successful team building in virtual work environments
• Investigating the experiences of individuals with gambling disorders in treatment programs
• Exploring the experiences of individuals with chronic illnesses in peer support groups
• Analyzing the factors that contribute to successful social media marketing strategies for businesses
• Investigating the experiences of individuals with ADHD in treatment programs
• Understanding the impact of sleep on cognitive and emotional functioning
• Examining the perceptions of individuals with chronic illnesses regarding healthcare access and affordability
• Investigating the experiences of individuals with borderline personality disorder in dialectical behavior therapy programs
• Understanding the impact of social support on caregiver well-being
• Exploring the experiences of individuals with chronic illnesses in disability activism
• Analyzing the factors that contribute to successful cultural competency training programs in healthcare settings
• Understanding the impact of personality disorders on interpersonal relationships
• Examining the perceptions of healthcare providers regarding the use of telehealth services
• Investigating the experiences of individuals with dissociative disorders in therapy programs
• Understanding the impact of gender bias in hiring practices
• Exploring the experiences of individuals with visual impairments in the workplace
• Analyzing the factors that contribute to successful diversity and inclusion programs in the workplace
• Understanding the impact of online dating on romantic relationships
• Examining the perceptions of parents regarding childhood vaccination
• Analyzing the factors that contribute to successful communication in healthcare settings
• Understanding the impact of cultural stereotypes on academic achievement
• Exploring the experiences of individuals with substance use disorders in sober living programs
• Analyzing the factors that contribute to successful classroom management strategies
• Understanding the impact of social support on addiction recovery
• Examining the perceptions of college students regarding mental health stigma
• Analyzing the factors that contribute to successful conflict resolution in the workplace
• Understanding the impact of race and ethnicity on healthcare access and outcomes
• Exploring the experiences of individuals with post-traumatic stress disorder in treatment programs
• Analyzing the factors that contribute to successful project management strategies
• Understanding the impact of teacher-student relationships on academic achievement
• Analyzing the factors that contribute to successful customer service strategies
• Investigating the experiences of individuals with social anxiety disorder in treatment programs
• Understanding the impact of workplace stress on job satisfaction and performance
• Exploring the experiences of individuals with disabilities in sports and recreation
• Analyzing the factors that contribute to successful marketing strategies for small businesses
• Investigating the experiences of individuals with phobias in treatment programs
• Understanding the impact of culture on attitudes towards mental health and illness
• Examining the perceptions of college students regarding sexual assault prevention
• Analyzing the factors that contribute to successful time management strategies
• Investigating the experiences of individuals with addiction in recovery support groups
• Understanding the impact of mindfulness on emotional regulation and well-being
• Exploring the experiences of individuals with chronic pain in treatment programs
• Analyzing the factors that contribute to successful conflict resolution in romantic relationships
• Investigating the experiences of individuals with autism spectrum disorder in social skills training programs
• Understanding the impact of parent-child communication on adolescent substance use
• Examining the perceptions of parents regarding childhood mental health services
• Analyzing the factors that contribute to successful fundraising strategies for non-profit organizations
• Investigating the experiences of individuals with chronic illnesses in support groups
• Understanding the impact of personality traits on career success and satisfaction
• Exploring the experiences of individuals with disabilities in accessing public transportation
• Analyzing the factors that contribute to successful team building in sports teams
• Investigating the experiences of individuals with chronic pain in alternative medicine treatments
• Understanding the impact of stigma on mental health treatment seeking behaviors
• Examining the perceptions of college students regarding diversity and inclusion on campus.

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Selection of authors, titles and writing a manuscript abstract

Affiliation.

• 1 Department of Urology, Faculty of Medicine, Bezmialem Vakıf University, İstanbul, Turkey.
• PMID: 26328127
• PMCID: PMC4548569
• DOI: 10.5152/tud.2013.045

Title of the study, authors list and abstract are not only the most widely read parts in an article but also determine whether the remaining sections are worth to be read. The first author in an article should be the planner and performer of the research. Researchers who have actively conducted the study and written the manuscript should be sorted in the authors list according to the importance of their individual contribution. However, researchers who do not directly contribute to the study but take part in data collection process should not be included in the ideal authors list. The title should be comprised of a brief statement or one or two short sentences that best reflect the original and gripping aspects of the study. On the other hand, abstract should provide answers to the following questions in advance of reading the full text: Why this study was performed?, What was the procedure?, What was found?, and What were the outcomes? In this manuscript, writing of an abstract was reviewed in addition to author and title selection.

Keywords: Author selection; title selection; writing an abstract.

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Criteria for Good Qualitative Research: A Comprehensive Review

• Regular Article
• Open access
• Published: 18 September 2021
• Volume 31 , pages 679–689, ( 2022 )

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This review aims to synthesize a published set of evaluative criteria for good qualitative research. The aim is to shed light on existing standards for assessing the rigor of qualitative research encompassing a range of epistemological and ontological standpoints. Using a systematic search strategy, published journal articles that deliberate criteria for rigorous research were identified. Then, references of relevant articles were surveyed to find noteworthy, distinct, and well-defined pointers to good qualitative research. This review presents an investigative assessment of the pivotal features in qualitative research that can permit the readers to pass judgment on its quality and to condemn it as good research when objectively and adequately utilized. Overall, this review underlines the crux of qualitative research and accentuates the necessity to evaluate such research by the very tenets of its being. It also offers some prospects and recommendations to improve the quality of qualitative research. Based on the findings of this review, it is concluded that quality criteria are the aftereffect of socio-institutional procedures and existing paradigmatic conducts. Owing to the paradigmatic diversity of qualitative research, a single and specific set of quality criteria is neither feasible nor anticipated. Since qualitative research is not a cohesive discipline, researchers need to educate and familiarize themselves with applicable norms and decisive factors to evaluate qualitative research from within its theoretical and methodological framework of origin.

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Introduction

“… It is important to regularly dialogue about what makes for good qualitative research” (Tracy, 2010 , p. 837)

To decide what represents good qualitative research is highly debatable. There are numerous methods that are contained within qualitative research and that are established on diverse philosophical perspectives. Bryman et al., ( 2008 , p. 262) suggest that “It is widely assumed that whereas quality criteria for quantitative research are well‐known and widely agreed, this is not the case for qualitative research.” Hence, the question “how to evaluate the quality of qualitative research” has been continuously debated. There are many areas of science and technology wherein these debates on the assessment of qualitative research have taken place. Examples include various areas of psychology: general psychology (Madill et al., 2000 ); counseling psychology (Morrow, 2005 ); and clinical psychology (Barker & Pistrang, 2005 ), and other disciplines of social sciences: social policy (Bryman et al., 2008 ); health research (Sparkes, 2001 ); business and management research (Johnson et al., 2006 ); information systems (Klein & Myers, 1999 ); and environmental studies (Reid & Gough, 2000 ). In the literature, these debates are enthused by the impression that the blanket application of criteria for good qualitative research developed around the positivist paradigm is improper. Such debates are based on the wide range of philosophical backgrounds within which qualitative research is conducted (e.g., Sandberg, 2000 ; Schwandt, 1996 ). The existence of methodological diversity led to the formulation of different sets of criteria applicable to qualitative research.

Among qualitative researchers, the dilemma of governing the measures to assess the quality of research is not a new phenomenon, especially when the virtuous triad of objectivity, reliability, and validity (Spencer et al., 2004 ) are not adequate. Occasionally, the criteria of quantitative research are used to evaluate qualitative research (Cohen & Crabtree, 2008 ; Lather, 2004 ). Indeed, Howe ( 2004 ) claims that the prevailing paradigm in educational research is scientifically based experimental research. Hypotheses and conjectures about the preeminence of quantitative research can weaken the worth and usefulness of qualitative research by neglecting the prominence of harmonizing match for purpose on research paradigm, the epistemological stance of the researcher, and the choice of methodology. Researchers have been reprimanded concerning this in “paradigmatic controversies, contradictions, and emerging confluences” (Lincoln & Guba, 2000 ).

In general, qualitative research tends to come from a very different paradigmatic stance and intrinsically demands distinctive and out-of-the-ordinary criteria for evaluating good research and varieties of research contributions that can be made. This review attempts to present a series of evaluative criteria for qualitative researchers, arguing that their choice of criteria needs to be compatible with the unique nature of the research in question (its methodology, aims, and assumptions). This review aims to assist researchers in identifying some of the indispensable features or markers of high-quality qualitative research. In a nutshell, the purpose of this systematic literature review is to analyze the existing knowledge on high-quality qualitative research and to verify the existence of research studies dealing with the critical assessment of qualitative research based on the concept of diverse paradigmatic stances. Contrary to the existing reviews, this review also suggests some critical directions to follow to improve the quality of qualitative research in different epistemological and ontological perspectives. This review is also intended to provide guidelines for the acceleration of future developments and dialogues among qualitative researchers in the context of assessing the qualitative research.

The rest of this review article is structured in the following fashion: Sect.  Methods describes the method followed for performing this review. Section Criteria for Evaluating Qualitative Studies provides a comprehensive description of the criteria for evaluating qualitative studies. This section is followed by a summary of the strategies to improve the quality of qualitative research in Sect.  Improving Quality: Strategies . Section  How to Assess the Quality of the Research Findings? provides details on how to assess the quality of the research findings. After that, some of the quality checklists (as tools to evaluate quality) are discussed in Sect.  Quality Checklists: Tools for Assessing the Quality . At last, the review ends with the concluding remarks presented in Sect.  Conclusions, Future Directions and Outlook . Some prospects in qualitative research for enhancing its quality and usefulness in the social and techno-scientific research community are also presented in Sect.  Conclusions, Future Directions and Outlook .

For this review, a comprehensive literature search was performed from many databases using generic search terms such as Qualitative Research , Criteria , etc . The following databases were chosen for the literature search based on the high number of results: IEEE Explore, ScienceDirect, PubMed, Google Scholar, and Web of Science. The following keywords (and their combinations using Boolean connectives OR/AND) were adopted for the literature search: qualitative research, criteria, quality, assessment, and validity. The synonyms for these keywords were collected and arranged in a logical structure (see Table 1 ). All publications in journals and conference proceedings later than 1950 till 2021 were considered for the search. Other articles extracted from the references of the papers identified in the electronic search were also included. A large number of publications on qualitative research were retrieved during the initial screening. Hence, to include the searches with the main focus on criteria for good qualitative research, an inclusion criterion was utilized in the search string.

From the selected databases, the search retrieved a total of 765 publications. Then, the duplicate records were removed. After that, based on the title and abstract, the remaining 426 publications were screened for their relevance by using the following inclusion and exclusion criteria (see Table 2 ). Publications focusing on evaluation criteria for good qualitative research were included, whereas those works which delivered theoretical concepts on qualitative research were excluded. Based on the screening and eligibility, 45 research articles were identified that offered explicit criteria for evaluating the quality of qualitative research and were found to be relevant to this review.

Figure  1 illustrates the complete review process in the form of PRISMA flow diagram. PRISMA, i.e., “preferred reporting items for systematic reviews and meta-analyses” is employed in systematic reviews to refine the quality of reporting.

PRISMA flow diagram illustrating the search and inclusion process. N represents the number of records

Criteria for Evaluating Qualitative Studies

Fundamental criteria: general research quality.

Various researchers have put forward criteria for evaluating qualitative research, which have been summarized in Table 3 . Also, the criteria outlined in Table 4 effectively deliver the various approaches to evaluate and assess the quality of qualitative work. The entries in Table 4 are based on Tracy’s “Eight big‐tent criteria for excellent qualitative research” (Tracy, 2010 ). Tracy argues that high-quality qualitative work should formulate criteria focusing on the worthiness, relevance, timeliness, significance, morality, and practicality of the research topic, and the ethical stance of the research itself. Researchers have also suggested a series of questions as guiding principles to assess the quality of a qualitative study (Mays & Pope, 2020 ). Nassaji ( 2020 ) argues that good qualitative research should be robust, well informed, and thoroughly documented.

Qualitative Research: Interpretive Paradigms

All qualitative researchers follow highly abstract principles which bring together beliefs about ontology, epistemology, and methodology. These beliefs govern how the researcher perceives and acts. The net, which encompasses the researcher’s epistemological, ontological, and methodological premises, is referred to as a paradigm, or an interpretive structure, a “Basic set of beliefs that guides action” (Guba, 1990 ). Four major interpretive paradigms structure the qualitative research: positivist and postpositivist, constructivist interpretive, critical (Marxist, emancipatory), and feminist poststructural. The complexity of these four abstract paradigms increases at the level of concrete, specific interpretive communities. Table 5 presents these paradigms and their assumptions, including their criteria for evaluating research, and the typical form that an interpretive or theoretical statement assumes in each paradigm. Moreover, for evaluating qualitative research, quantitative conceptualizations of reliability and validity are proven to be incompatible (Horsburgh, 2003 ). In addition, a series of questions have been put forward in the literature to assist a reviewer (who is proficient in qualitative methods) for meticulous assessment and endorsement of qualitative research (Morse, 2003 ). Hammersley ( 2007 ) also suggests that guiding principles for qualitative research are advantageous, but methodological pluralism should not be simply acknowledged for all qualitative approaches. Seale ( 1999 ) also points out the significance of methodological cognizance in research studies.

Table 5 reflects that criteria for assessing the quality of qualitative research are the aftermath of socio-institutional practices and existing paradigmatic standpoints. Owing to the paradigmatic diversity of qualitative research, a single set of quality criteria is neither possible nor desirable. Hence, the researchers must be reflexive about the criteria they use in the various roles they play within their research community.

Improving Quality: Strategies

Another critical question is “How can the qualitative researchers ensure that the abovementioned quality criteria can be met?” Lincoln and Guba ( 1986 ) delineated several strategies to intensify each criteria of trustworthiness. Other researchers (Merriam & Tisdell, 2016 ; Shenton, 2004 ) also presented such strategies. A brief description of these strategies is shown in Table 6 .

It is worth mentioning that generalizability is also an integral part of qualitative research (Hays & McKibben, 2021 ). In general, the guiding principle pertaining to generalizability speaks about inducing and comprehending knowledge to synthesize interpretive components of an underlying context. Table 7 summarizes the main metasynthesis steps required to ascertain generalizability in qualitative research.

Figure  2 reflects the crucial components of a conceptual framework and their contribution to decisions regarding research design, implementation, and applications of results to future thinking, study, and practice (Johnson et al., 2020 ). The synergy and interrelationship of these components signifies their role to different stances of a qualitative research study.

Essential elements of a conceptual framework

In a nutshell, to assess the rationale of a study, its conceptual framework and research question(s), quality criteria must take account of the following: lucid context for the problem statement in the introduction; well-articulated research problems and questions; precise conceptual framework; distinct research purpose; and clear presentation and investigation of the paradigms. These criteria would expedite the quality of qualitative research.

How to Assess the Quality of the Research Findings?

The inclusion of quotes or similar research data enhances the confirmability in the write-up of the findings. The use of expressions (for instance, “80% of all respondents agreed that” or “only one of the interviewees mentioned that”) may also quantify qualitative findings (Stenfors et al., 2020 ). On the other hand, the persuasive reason for “why this may not help in intensifying the research” has also been provided (Monrouxe & Rees, 2020 ). Further, the Discussion and Conclusion sections of an article also prove robust markers of high-quality qualitative research, as elucidated in Table 8 .

Quality Checklists: Tools for Assessing the Quality

Numerous checklists are available to speed up the assessment of the quality of qualitative research. However, if used uncritically and recklessly concerning the research context, these checklists may be counterproductive. I recommend that such lists and guiding principles may assist in pinpointing the markers of high-quality qualitative research. However, considering enormous variations in the authors’ theoretical and philosophical contexts, I would emphasize that high dependability on such checklists may say little about whether the findings can be applied in your setting. A combination of such checklists might be appropriate for novice researchers. Some of these checklists are listed below:

The most commonly used framework is Consolidated Criteria for Reporting Qualitative Research (COREQ) (Tong et al., 2007 ). This framework is recommended by some journals to be followed by the authors during article submission.

Standards for Reporting Qualitative Research (SRQR) is another checklist that has been created particularly for medical education (O’Brien et al., 2014 ).

Also, Tracy ( 2010 ) and Critical Appraisal Skills Programme (CASP, 2021 ) offer criteria for qualitative research relevant across methods and approaches.

Further, researchers have also outlined different criteria as hallmarks of high-quality qualitative research. For instance, the “Road Trip Checklist” (Epp & Otnes, 2021 ) provides a quick reference to specific questions to address different elements of high-quality qualitative research.

Conclusions, Future Directions, and Outlook

This work presents a broad review of the criteria for good qualitative research. In addition, this article presents an exploratory analysis of the essential elements in qualitative research that can enable the readers of qualitative work to judge it as good research when objectively and adequately utilized. In this review, some of the essential markers that indicate high-quality qualitative research have been highlighted. I scope them narrowly to achieve rigor in qualitative research and note that they do not completely cover the broader considerations necessary for high-quality research. This review points out that a universal and versatile one-size-fits-all guideline for evaluating the quality of qualitative research does not exist. In other words, this review also emphasizes the non-existence of a set of common guidelines among qualitative researchers. In unison, this review reinforces that each qualitative approach should be treated uniquely on account of its own distinctive features for different epistemological and disciplinary positions. Owing to the sensitivity of the worth of qualitative research towards the specific context and the type of paradigmatic stance, researchers should themselves analyze what approaches can be and must be tailored to ensemble the distinct characteristics of the phenomenon under investigation. Although this article does not assert to put forward a magic bullet and to provide a one-stop solution for dealing with dilemmas about how, why, or whether to evaluate the “goodness” of qualitative research, it offers a platform to assist the researchers in improving their qualitative studies. This work provides an assembly of concerns to reflect on, a series of questions to ask, and multiple sets of criteria to look at, when attempting to determine the quality of qualitative research. Overall, this review underlines the crux of qualitative research and accentuates the need to evaluate such research by the very tenets of its being. Bringing together the vital arguments and delineating the requirements that good qualitative research should satisfy, this review strives to equip the researchers as well as reviewers to make well-versed judgment about the worth and significance of the qualitative research under scrutiny. In a nutshell, a comprehensive portrayal of the research process (from the context of research to the research objectives, research questions and design, speculative foundations, and from approaches of collecting data to analyzing the results, to deriving inferences) frequently proliferates the quality of a qualitative research.

Prospects : A Road Ahead for Qualitative Research

Irrefutably, qualitative research is a vivacious and evolving discipline wherein different epistemological and disciplinary positions have their own characteristics and importance. In addition, not surprisingly, owing to the sprouting and varied features of qualitative research, no consensus has been pulled off till date. Researchers have reflected various concerns and proposed several recommendations for editors and reviewers on conducting reviews of critical qualitative research (Levitt et al., 2021 ; McGinley et al., 2021 ). Following are some prospects and a few recommendations put forward towards the maturation of qualitative research and its quality evaluation:

In general, most of the manuscript and grant reviewers are not qualitative experts. Hence, it is more likely that they would prefer to adopt a broad set of criteria. However, researchers and reviewers need to keep in mind that it is inappropriate to utilize the same approaches and conducts among all qualitative research. Therefore, future work needs to focus on educating researchers and reviewers about the criteria to evaluate qualitative research from within the suitable theoretical and methodological context.

There is an urgent need to refurbish and augment critical assessment of some well-known and widely accepted tools (including checklists such as COREQ, SRQR) to interrogate their applicability on different aspects (along with their epistemological ramifications).

Efforts should be made towards creating more space for creativity, experimentation, and a dialogue between the diverse traditions of qualitative research. This would potentially help to avoid the enforcement of one's own set of quality criteria on the work carried out by others.

Moreover, journal reviewers need to be aware of various methodological practices and philosophical debates.

It is pivotal to highlight the expressions and considerations of qualitative researchers and bring them into a more open and transparent dialogue about assessing qualitative research in techno-scientific, academic, sociocultural, and political rooms.

Frequent debates on the use of evaluative criteria are required to solve some potentially resolved issues (including the applicability of a single set of criteria in multi-disciplinary aspects). Such debates would not only benefit the group of qualitative researchers themselves, but primarily assist in augmenting the well-being and vivacity of the entire discipline.

To conclude, I speculate that the criteria, and my perspective, may transfer to other methods, approaches, and contexts. I hope that they spark dialog and debate – about criteria for excellent qualitative research and the underpinnings of the discipline more broadly – and, therefore, help improve the quality of a qualitative study. Further, I anticipate that this review will assist the researchers to contemplate on the quality of their own research, to substantiate research design and help the reviewers to review qualitative research for journals. On a final note, I pinpoint the need to formulate a framework (encompassing the prerequisites of a qualitative study) by the cohesive efforts of qualitative researchers of different disciplines with different theoretic-paradigmatic origins. I believe that tailoring such a framework (of guiding principles) paves the way for qualitative researchers to consolidate the status of qualitative research in the wide-ranging open science debate. Dialogue on this issue across different approaches is crucial for the impending prospects of socio-techno-educational research.

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Yadav, D. Criteria for Good Qualitative Research: A Comprehensive Review. Asia-Pacific Edu Res 31 , 679–689 (2022). https://doi.org/10.1007/s40299-021-00619-0

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Qualitative Research Topics & Ideas For Students

Do you have difficulty finding a qualitative research title for your project? If you are, you need not worry because you are not alone. However, there are many unique qualitative titles you can explore for your research. You just need a few qualitative research title examples to get you started. Qualitative research is focused on data obtained through a researcher’s first-hand observations, natural setting recording, artifacts, case studies, documents, questionnaires, and interviews. The findings in qualitative research are usually non-numerical. Also, it is common in humanities and social sciences. This post provides over 100 qualitative research topics you can consider.

• The Best Qualitative Research Topics That Impress the Teacher

Exceptional Qualitative Research Topics In Social Science

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An excellent research topic will help you earn a good grade. Consider any example of a qualitative research title from the following options:

• The impacts of social media on physical social engagement in society
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• Sexual workers: the stigma they experience
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• Food insecurity in third-world nations

The topic of your research paper can influence how easily you can conduct your study and draw conclusions.

Here are fantastic examples of qualitative research titles:

• Female harm: how it is influenced by culture
• The socioeconomic impacts of free education
• The link between food insecurity and poor performance in schools
• Alcoholism among college students: a critical study
• How to mitigate child labor in our society
• The root causes of child labor in Latin America
• The stigma of living with transmissive medical conditions
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Choosing a qualitative research topic is not a task you should take lightly because it can influence your performance. Here are some noteworthy qualitative research titles examples:

• Basic patient care policies in developing nations
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• Adult learning: what does it entail?
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• Student bullying: the psychological impacts
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• research writing in high schools: its significance

Are you looking for qualitative research topic examples to start your study? Below are some creative examples to consider:

• Remote tests: are they as effective as in-class tests?
• The value of social activities in academic institutions
• Why should healthcare be free in all countries?
• The implications of racist laws on society
• The reception of COVID-19 vaccines and treatments
• What is the difference between foreign policies in first-world and third-world nations?
• Racism and Colorism: what is the difference?
• Dissecting the causes of low voter turnouts in the 21 st century
• The challenges of social media on kid’s brain development
• The inclusion of black women in American politics and its impacts

When competing with several brilliant minds, a good research topic can do you greatly. The following qualitative research examples titles are a great place to start:

• Should school uniforms be discarded for high schoolers?
• The need for equal representation in global politics
• The implications of police brutality on politics
• The role of parental care in foster kids
• The distinction between Islamic values and Christian values
• The correlation between political instability and migration
• Sex trafficking and violence against women: what is the link?
• How can global governments eradicate homelessness?
• Fraternities and sororities: are they still relevant?
• The role of literature in promoting societal changes

Qualitative research is popular in the education field and other social sciences. Choose a qualitative research title example on the subject of education from the following list:

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• How can teachers help students with disabilities improve their learning?
• The link between social activities and comprehension among students
• Research writing in high schools: is it necessary?
• How has virtual learning influenced teacher-student relationships?
• The implications of allowing smartphones in classes
• Should all schools introduce sign language lessons in their curriculum?
• Student loans: their impacts on black students
• The impacts of race on college acceptance rates
• Poverty and education: what is the link?
• Ethnic and socioeconomic causes of poor school attendance in developing worlds
• Various teaching methods and their efficiency
• Efficient teaching methods for children below two years
• Why do students perform better in humanities than in sciences?
• The difference between college acceptance and completion in most nations
• Remote learning in developing countries
• What are the best ways of approaching bullying in schools?
• How do teachers promote inequality among students?
• Does social class influence academic performance negatively or positively?
• How do teachers shape their students’ personalities?

Coming up with a qualitative research title can be hard because of the numerous subject areas and the issue of uniqueness. Therefore, we have prepared the following qualitative title examples for you:

• How to promote oral learning in classrooms
• Political instability in developing countries: its economic impacts
• The impacts of weather on social activities
• Boredom and poor-decision making: the connection
• Exploring the connection between attachment types and love languages
• Socioeconomic impacts of instability on a country
• How does social media impact the perception of reality
• Reality TV shows: are they a true reflection of reality?
• How culture applies to different age groups
• Is social media influencing the loss of cultural values?

You can base your research topic on a specific region or nation, like the Philippines. A sample qualitative research title can get you started. You can pick a sample qualitative research title from the ideas below:

• Why are so many Philippines residents migrating to America?
• The impact of politics on migration in the Philippines
• How has violence led to food insecurity in rural areas in the Philippines?
• The Philippine education system: an overview
• How cultural norms influence social activities in the Philippines
• Gender roles in the Philippines society
• How popular Filipino cultures have served as agents of social change in the nation
• The link between male dominance and GBV in the Philippines
• Barriers to clean hygiene in health centers in the Philippines
• The spread of COVID in rural areas in the Philippines

Most top performers in research subjects attribute their success to choosing the best title for qualitative research. Here are some qualitative research topics about humanities and social science to promote good performance:

• The impact of poor market rivalry on supply and demand
• The role of parents in shaping kids’ morals
• Is social media the root cause of poor societal morals?
• How does alcohol impact a person’s normal behavior?
• How often should adults engage in sporting activities?
• Children’s eating habits and their influences
• Low socioeconomic backgrounds and their impacts on self-esteem
• The effect of the COVID-19 pandemic on the world’s views on viral diseases
• How can school-going kids manage depression
• Causes of mental challenges among school-going kids

Finding a good topic for qualitative research is a critical task that requires a lot of thought and research. However, we have simplified the process with the following qualitative topic ideas:

• Pop music and erratic youth behavior: is there a link?
• How do public figures influence cultures?
• Ideas for improving healthcare in developing nations
• Possible solutions for alleviating the food crisis in developing nations
• New ways of mitigating viral diseases
• Social media trends among the elderly
• Quarantine as a mitigation approach for infectious diseases
• Promoting social justice in patriarchal societies
• Worrying trends among the young population
• Emerging marketing trends in 2023

Qualitative research for college and high school students helps improve reading, writing, and intellectual skills. Here are some qualitative research examples and topic ideas for students :

• How to detect and prevent natural disasters beforehand
• Can the whole world have the same education system?
• What is the most effective therapy for patients recuperating from brain surgery?
• Possible solutions for promoting ethical practices in telehealth
• Can addicts overcome addiction without therapy?
• The latest technology trends and their impacts?
• How can global governments promote mental health awareness?
• Have smartphones caused reduced attention spans among users?
• Sexual violence in rural areas
• The introduction of Islam in African nations

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Qualitative research is an investigative analysis of intangible or inexact data, mostly non-numerical. The title of qualitative research you choose will guide your entire research process and influence its conclusions. Do you need a paper or an example of a research title qualitative topic? Our expert team is ready to write it for you.

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Primary care involvement in clinical research – prerequisites, motivators, and barriers: results from a study series

• Julian Wangler 1 &
• Michael Jansky 1  

Archives of Public Health volume  82 , Article number:  41 ( 2024 ) Cite this article

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Long-term reinforcement in the role of primary care and improvement the healthcare system as a whole requires the involvement of GPs in clinical research processes. However, many clinical studies fail due to failure to achieve sample population targets amongst GPs and their patients. This issue has been identified and discussed, but effective strategies to overcome it are still lacking. One of the reasons is that the positions, requirements, and experiences of GPs on participating in clinical research have hardly been examined up to now.

The years 2021 and 2022 saw three quantitative and qualitative surveys amongst GPs in Germany with the aim of shedding light on the attitudes, experiences, and potential issues regarding the involvement of primary care in clinical research projects and participation in cluster-randomised controlled trials (cRCTs) in a general sense. This overview summarises and abstracts conclusions gained from the exploratory series of studies and compares the results with the current research situation. From here, this contribution will then develop an approach towards optimising the integration of GPs into clinical research.

Most of the GPs asked associated clinical research with opportunities and potential such as closing gaps in healthcare, using evidence-based instruments, optimising diagnostic and therapeutic management, and reinforcement of multiprofessional healthcare. Even so, many GPs unsure as to how far primary care in particular would stand to benefit from studies of this type in the long term. Respondents were also divided on willingness to participate in clinical research. GPs having already participated in Innovation Fund projects generally saw a benefit regarding intervention and cost–benefit relationship. However, some also reported major hurdles and stress factors such as excessive documentation and enrolment requirements, greater interference in practice routines, and sometimes poor integration into project processes such as in communication and opportunities to play an active role in the project.

Conclusions

Results from the studies presented provide indications as to how GPs perceive clinical research projects and cRCTs as a whole and from their existing project experience, and on the requirements that studies would have to meet for GPs to be willing to participate. In particular, making sure that clinical studies fully conform with GPs would play a major role; this especially applies to freedom to make medical decisions, limitation of documentation obligations, interference in regular practice routine, greater involvement in research planning, and long-term reinforcement in the role of primary care. Clinical research projects and cRCTs should be planned, designed, and communicated for clear and visible relevance to everyday primary care.

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Primary care plays an indispensable role in ensuring a functioning healthcare system. This applies to continuous (long-term) healthcare across the entire range of clinical conditions and complaints as well as patient types. However, it also applies to GPs in guiding their patients through the healthcare system by specifically referring them to other levels of care. Primary care participation in clinical research processes will play a central role in expanding primary care and other healthcare roles in a consistent and methodical fashion while also testing novel forms of healthcare and improving the healthcare system as a whole [ 1 , 2 ]. New healthcare models – especially in the low-prevalence area – need to encompass sufficiently large patient cohorts for evidentially significant results, making primary care involvement inevitable in many cases [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ].

However, GP-based interventions face significant hurdles in projects in clinical as well as healthcare research despite the significant role of primary care in clinical research and the potential benefits that may result. It is often a challenge to recruit a sufficient number of GPs for these studies, which usually involves a sophisticated cluster-randomised design in cRCTs [ 2 , 10 ]. Various research projects and application areas have indeed shown recruitment of GPs to be a limiting factor in performing clinical projects involving primary care [ 1 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Especially cRCTs usually require sufficiently sized study cohorts with failure to achieve patient recruitment goals often leading to insufficient statistical significance and even premature study termination [ 17 , 18 ].

There are various reasons for insufficient overall recruitment and research participation amongst GPs [ 8 , 13 , 19 ]. Many studies have identified a lack of time and resources [ 11 , 14 , 15 , 20 , 21 ] and fear of administrative and documentation effort [ 21 , 22 , 23 ] as the main reasons for GPs to decide against participating in research projects. A lack of relationship with research and the problems this involves in understanding and implementing research methods have also been given as possible reasons [ 1 , 3 , 4 , 6 ]. One qualitative study found GPs sometimes facing problems in enrolling patients in clinical research projects and supporting them throughout the intervention as they did not see themselves as being equipped with the comprehensive clinical research competence necessary [ 20 ].

Another factor is that Germany lacks a longstanding tradition of involving GPs in clinical research activities, unlike other countries [ 16 , 24 ]. The healthcare and innovation systems may not be directly comparable, but studies in other Western countries have shown issues associated with recruiting and involving GPs in clinical research projects [ 5 , 10 , 13 , 14 , 16 , 22 , 23 , 25 , 26 , 27 ]. As an example, only every third health study in primary care achieves its target patient cohort in Anglo-American countries as insufficient numbers of GPs can be recruited and/or too many leave the research projects early [ 5 , 17 , 28 ].

Summarising, recruiting GPs for major clinical healthcare studies is one of the greatest challenges facing healthcare research. This issue has already been identified and discussed, but there is still a lack of effective strategies towards overcoming it [ 2 , 4 , 6 , 9 , 10 , 12 , 16 ]. One of the reasons is that the positions, requirements, and experiences of GPs on participating in clinical research have hardly been examined up to now.

Research interest and aim of study

Addressing the attitudes, experiences and potential issues involved in including primary care in clinical research projects and participation in cRCTs in a general sense crucially requires ascertaining the perspective of GPs.

The overview article summarises and abstracts the conclusions gained from an exploratory series of studies as well as the authors' own research experiences. The results are intended for comparison against the research situation up to now. This articles centres on the following issues:

What attitudes do GPs have towards clinical research and its benefits for primary care?

How far do GPs see barriers against participating in clinical research projects?

Under what conditions would GPs be willing to participate in clinical research projects and cRCTs?

What experiences have GPs had after participating in clinical research projects and cRCTs? Adding up the columns, what conclusions have they drawn from project participation?

What would GPs like to see in the way of optimisation to increase the attractiveness of participation in clinical studies or cRCTs in the future?

In principle, the series of studies was about all types and forms of clinical research projects, i.e. not necessarily just about therapeutic interventions, but also, for example, about questions of quality of life, drug therapy and drug therapy safety, cross-sectoral care, medical guidelines and application adherence, geriatric care, telemedicine and eHealth/mHealth, delegation and substitution of services, care for vulnerable groups (e.g. family caregivers), communication with patients and promotion of health literacy, care in structurally weak or rural areas etc.

Overall, we aim to contribute to a better understanding of barriers and facilitators of the recruitment of GPs and their patients. With this in mind, we used the findings presented as a synopsis towards developing approaches towards optimising integration of GPs in clinical research.

The studies included in this overall assessment include detailed surveys amongst GPs in Germany and in their willingness to participate clinical research activities and cRCTs as well as their experiences specifically in this regard. From the findings gathered together and presented in this contribution, we have drawn conclusions as to how clinical research projects might be designed towards making participation as attractive as possible amongst GPs.

Study design and recruitment

This analysis includes three surveys on German GPs posing a variety of questions with central areas of focus regarding participation in clinical research. All sub-studies were deliberately designed to be exploratory in nature, reflecting the paucity of research on this subject (see Fig.  1 ).

Order of the individual studies [ 29 , 30 , 31 ]

All 13,170 GPs with active practices in the federal states of Baden-Württemberg, Hesse, and Rhineland-Palatinate were invited to an online survey between July and November 2021 [ 30 ]; the survey was based on a smaller qualitative preliminary study that had already taken place [ 29 ]. This initial study served to collect general information on the topic in order to create the conditions for conducting a large quantitative study. The main study asked GPs for their attitudes, expectations on participation, and experiences from clinical research and especially the Innovation Fund, which serves as the central health policy instrument for promoting and financing new forms of healthcare in standard healthcare. Footnote 1

A third study was qualitative in nature and functioned as an in-depth study, specifically aiming to capture the perspective of general practitioners with research experience. A total of 36 semi-standardised individual interviews with GPs already having participated in clinical and Innovation Fund projects were conducted between September 2021 and February 2022 [ 31 ] alongside the quantitative survey. Eleven regional physicians’ networks in the federal states of Rhineland-Palatinate, Hesse, North Rhine-Westphalia, and Schleswig–Holstein were involved in the recruitment process. This study mainly focused on investigating actual experiences amongst GPs from participating in research studies on the health services. With the help of the mentioned regional doctors’ networks, contact was established with a total of 36 GPs; interviews were conducted with all of them.

None of the studies included used any form of incentives.

Development of survey instruments

Questionnaires and interview guidelines were developed for the quantitative and qualitative surveys on GPs as to their general participation and willingness to participate in clinical research and cRCTs; these questionnaires and guidelines took into account the authors’ previous research and recruitment experience in the Innovation Fund and evidence-based instruments [ 36 , 37 , 38 , 39 , 40 ] and general desk research (including Lech et al. [ 1 ] and Heytens et al. [ 34 ]). Both the quantitative main survey and the qualitative study contain the following main content areas: a) attitudes towards clinical research projects and their benefits; b) willingness to participate and corresponding prerequisites; c) experiences from taking part in specific projects; d) perceived optimisation potential.

The quantitative study contains a total of 25 questions. In addition to the standardized questions, which were often 4-point Likert scales, a series of open questions were used. The sociodemographic characteristics recorded were gender, age, practice environment, type of practice and patients per quarter. A pretest was carried out prior to data collection. For this purpose, the questionnaire was presented to 50 randomly selected GPs. The pretest showed that the questionnaire was easy to understand, structured and has complete answer categories.

The qualitative in-depth study included 20 questions. The focus was more on the experiences of GPs in clinical research projects. Here, too, a pretest was carried out in advance to check the comprehensibility and practicality of the guidelines.

Data analysis

The SPSS 23.0 statistical package was used for evaluating the data from the quantitative survey studies. Apart from the descriptive analysis, Student’s t -test for independent samples was used to analyse for significant differences between the two groups. STROBE was used as the reporting statement for the main study.

Qualitative content analysis according to Mayring [ 41 ] was used as a basis for evaluating the qualitative interviews and open questions in the questionnaires. After transcription, we evaluated the interviews in a team using the MAXQDA software. In preparation, the written consultations were summarised with the essential information to gain an overview of the fundamental material. The text was then extracted in individual sentences or paragraphs depending on importance and expressiveness with units to be used in analysis previously determined (context, interview code, original text, paraphrasing, generalisation). The most important core statements were isolated, abstracted and summarised before forming categories. The categorical system created (see Multimedia Appendix 1 ) was based on the priorities set in the guidelines, repeatedly checked, and modified as necessary in the course of evaluation. We used the COREQ methodology as reporting statement for the qualitative study.

Sample overview

The 3,556 fully completed questionnaires corresponding to a response rate of 27% were included in analysis [ 30 ]. Table 1 compares the sample obtained with reference data from the German Association of Statutory Health Insurance Physicians (KV) on the structure of GPs in Germany.

The qualitative sample [ 31 ] comprised the following (see Table  2 ):

General results

Table 3 summarises the salient findings of the studies mentioned. These findings will be discussed alongside the research issues listed in the following.

Attitudes towards clinical research and healthcare benefit

Around half the GPs surveyed had an explicitly favourable attitude towards clinical research in all studies covered; the other physicians saw this rather negatively or did not take a clear position, which was mostly due to their stated unfamiliarity with scientific research [ 29 , 30 , 31 ]. Notably, the proportion of those reporting a favourable verdict in the quantitative study was significantly higher among urban than rural physicians (60% vs. 38%, p  < 0.001). Around every third general practitioner associated clinical research with major benefit, while another third saw minor to moderate benefit [ 30 ].

Closer inspection reveals that a large proportion of those surveyed associated clinical research with considerable opportunities for the healthcare system, especially regarding identifying and closing gaps in care, using evidence-based instruments and procedures, and therefore optimising diagnostics and/or therapeutic management. Another benefit of clinical research projects according to respondents was their own contribution to reinforcing multiprofessional and cross-sectoral care, and therefore also the sequence of healthcare steps between the various medical and nursing protagonists.

“I do think it gives us an opportunity to benefit from targeted and sustainable improvements in taking care of our patients.” (I-8 m)

“Complex clinical research – Germany has long since been a bit of a developing country in getting general practitioners on board. This is where the vast majority of patients receive healthcare in everyday life. […] So, it’s definitely a step in the right direction.” (I-17f)

Even so, many GPs also doubted that clinical research would be an easy fit for the requirements of primary care, where pragmatic and social considerations (“talking medicine”) play a far greater role than a strictly research-based focus. Some therefore wondered how far primary care could benefit on a larger scale from involvement in this type of research project. Respondents especially mentioned addressing specific primary care needs and (sustainable) accuracy in interventions.

Apart from that, many respondents expressed concerns that clinical research could lead to issues in primary care in the long term with funds in the healthcare system being reallocated towards specialised structures even with the dependency of clinical research on primary care for studying larger patient cohorts and testing interventions. Some respondents during the interviews reported on their own experiences with projects involving new health protagonists such as special case managers with the concern that these new multiprofessional positions might ultimately come at the expense of primary care budgets and lead to “an over-engineered and bloated healthcare system” (I-24 m) [ 31 ].

“Clinical research encourages a kind of proliferation and chaos. New professional groups are constantly popping up, challenging the guiding role of general practitioners." (I-30 m)

With this in mind, the level of support from those surveyed was relatively low as to the prospect of clinical research projects and cRCTs leading to long-term reinforcement in the role of primary care. Physicians in urban areas anticipated this significantly more frequently than rural physicians (51% vs. 28%, p  < 0.001) [ 30 ].

“The whole thing could also have a negative side. […] For example, I see a risk that these studies might ultimately bypass the reality of general practitioners too much and be of little use to us, or even a burden in the worst case.” (I-11 m)

"We’ve already seen that happen. GPs are recruited, but they’re more of a means to an end […] to feed study planners with patients.” (I-14 m)

Apart from that, a number of GPs expressed concerns that clinical research “does not necessarily support projects that the healthcare system needs;” rather, that it often focused on “politically selected topics and issues” (I-11 m). Some of the respondents also expressed doubts as to whether new healthcare models, such as those being tested in cRCTs, would ultimately find their way into standard care in practice [ 29 , 31 ].

"Remember that these studies are subject to funding programmes lasting a few years. This is a high bar to overcome in successfully providing evidence of an intervention’s efficacy. I think many of these projects would just fizzle out for a whole variety of reasons." (I-25f)

Perceived barriers to the participation of primary care in clinical research

Respondents saw the various aspects of additional workload as the greatest barriers facing general practitioner involvement in clinical research activities [ 29 , 31 ]. This included increased amounts of work and significantly increased time and resource pressure for the entire practice team. The cost to flexibility in everyday practice due to research project commitments and intervention specifications was also seen as an issue.

"I’ve heard about this from a close colleague in general practice. He applied a clinically developed algorithm towards improving early diagnosis of liver disease. Sounds easy enough. But you can’t imagine the chaos that all the action guidelines caused in his medical practice. It sounded really awful.” (I-33m)

These perceptions are based on the fear of lasting detriment to established routine at the practice. Many respondents took the view that “general practitioners can’t afford to compromise on regular patient care for some special project especially in these times of high patient numbers and general practitioners in acutely short supply in some cases” (I-27f) [ 31 ]. A reduction in the total number of patient contacts and treatment programmes would therefore not be an acceptable condition for participation in research projects, according to many respondents.

“The thing is, you either join the project in full or not at all. That means either you’re willing to take on this added burden, or you’re not. But what if you want to contribute as a GP, but you can't get involved as much as the project requires in time or seasonality? Count me out. Because there’s no in-between in project participation, I mean as in flexibility." (I-29f)

This came with a high level of concern facing comprehensive and potentially escalating documentation and administrative obligations, such as in registering patients and filling out case files for the project. A few respondents also reported fearing substantial financial losses from participating in clinical research projects. Another key barrier was the lack of a research background amongst many GPs, so finding their way around the clinical procedure – especially in cRCTs – would mean a “transition and additional effort that shouldn’t be underestimated” (I-17f).

No fewer GPs saw a barrier in that those responsible for the project often failed to demonstrate any concrete benefit or added value for primary care from the intervention; practical implications for primary care remained unclear when recruiting from general practices for a study [ 31 ].

“Maybe it’s my lack of basic knowhow in research. But I'd like to know exactly what's in it for my patients and, of course, for me as a physician, before getting involved in something like this. I’m sure the project managers know what they have in mind, but they have a problem communicating it.” (I-11m)

Willingness to participate, prerequisites, and reasons for participating in clinical research projects

According to the large-scale written survey of GPs, 31% of respondents were generally willing to consider participating in a clinical study or cRCT in the future, and another 24% reported that they had already participated in at least one associated study [ 30 ]. In contrast, 45% were fundamentally unwilling to participate in any clinical research project. Comparing age groups, 47% of physicians younger than the median age of 55 saw participation in a clinical research project would as an option vs. 20% of physicians aged 55 and over ( p  < 0.001).

In an open question, the respective physicians explained their willingness to participate as mainly due to curiosity and involvement in scientific research (35%), interest in or prior knowledge in the specific topic (35%), and a desire to help improve healthcare and quality of life for patients (45%).

Respondents not willing to participate explained their stance with consistently high workloads (54%), concerns about excess burden when participating in research activities (44%), and doubts as to the benefits of clinical scientific research in some cases (29%). Amongst GPs for whom taking part in clinical studies or cRCTs was out of the question, most doubted that these studies would find their way into standard healthcare (57%) or that they would be of any substantial benefit to primary care (58%).

Prerequisites played an important role for physicians responding that they would consider participating in a study or had already participated in one or more projects. Apart from likely diagnostic or therapeutic benefit for patients, they mainly focused on issues regarding the (limited) additional burden (such as preparation and follow-up, documentation, patient registration), appropriate remuneration, and structural improvement to the primary care setting. Respondents also saw importance in projects contributing to breaking down sector boundaries in the healthcare system and, above all, not interfering with normal operations and responsibilities in their medical practice [ 29 , 30 , 31 ]. Rural physicians in the quantitative survey emphasised the prerequisite that the project must not cause changes in practice routines far more often than city physicians (64% vs. 30% amongst city physicians, p  < 0.001) [ 30 ].

“Committing yourself to studies like this isn’t trivial. They should see how they can accommodate general practitioners here. I think there’s still too little of that.” (I-6f)

Experiences from taking part in specific projects

According to their own replies, 24% (875) of those respondents in the quantitative survey had already been involved or were currently participating in at least one clinical research project or cRCT [ 30 ]. The respondents comprised 92% urban and 8% rural physicians. Of the 875 respondents, 33% were individual and 67% group practices. Regarding age, 73% were younger than the mean, and 35% were networked with other physicians.

The information gained from surveyed reveals that most of the projects in which the physicians were participating or had participated focused on optimising a specific area of patient care, drug therapy or drug therapy safety, polypharmacy, extending regional and multiprofessional care networks, or promoting evidence-based medicine or compliance with guidelines. Many of the projects also involved telemedicine as well as enabling the delegation of care services. Projects focused on care in vulnerable groups such as caring relatives or people with disabilities or on promoting health and communication skills were less frequent.

However, some respondents emphasised that they had initially weighed up the feasibility of taking part in a large research study against their heavy workload [ 31 ].

“You have to think carefully about whether you can afford to take part in a study like this. You have to play it out in your head, even if things don’t turn out to be that serious.” (I-19m)

Two-thirds of respondents involved in the project reported that they needed to train members of the medical practice staff due to participation [ 30 ]. This especially applied to physicians participating in projects focused on drug therapy or specific medical conditions. Of the respondents, 80% reported severe (27%) or moderate (53%) complications vs. 20% with no complications as a result of participating in one or more research projects at their medical practice.

“That was an issue. The practice staff had to undergo a huge amount of preparation, the short-term training requirement was heavy… we were not informed about the type and extent of training from the start, and the training was scheduled at too short notice. This made normal office routine more difficult.” (I-26f)

The physicians involved in the project reported they were especially impressed by the results (from treatment) and optimised patient care (69%), improvement in cooperation with other care providers and sectors (52%), and enhancement of their diagnostic and therapeutic skills (40%) in response to an open question in the quantitative survey [ 30 ].

In contrast, physicians saw increased time pressure (66%) as well as considerable documentation requirements such as in registering patients and heavy paperwork in many cases (64%) as negatives alongside interference with practice routines and established procedures arising from project participation (55%) as well as too little involvement in research processes and decisions relating to the project for some of the physicians (43%). A few reported pressure from the project management to “recruit an unrealistically large patient cohort” (I-2 m).

“The hurdles and additional burdens shouldn’t be underestimated. I can understand why not all doctors can take part.” (I-25 m)

Some GPs complained that they did not have the research skills for rapid quick integration into the project or easy grasp of the procedure. On the other hand, some criticised the apparent lack of priority in bringing physicians up to speed on the research requirements such as in corresponding preparation courses [ 29 , 31 ].

"Apart from that, we as general practitioners – especially in Germany – don’t have the academic background to keep up with these activities. This is a real problem that has to be addressed in medical studies in the long term if we really want to train general practitioners with an affinity for research.” (I-32f)

Verdicts on project participation

GPs having participated in clinical studies or cRCTs draw a favourable overall conclusion in the general quantitative survey [ 30 ] on the benefits of the intervention tested. Of the respondents, 72% reported that care and treatment for the patients involved benefited very highly at 13% or rather highly at 45% vs. 18% less highly, 16% not at all, 8% difficult to say. Likewise, 66% rated the project participation benefit as clearly (43%) or slightly (23%) outweighing the effort involved vs. 11% about the same, 12% effort slightly outweighing benefit, 11% effort clearly outweighing benefit. Respondents rated projects covering healthcare in economically underdeveloped areas, drug therapy/safety, delegation and substitution, and cross-sector healthcare favourably for added value.

Of all the respondents having already participated in clinical trials or cRCTs, 15% reported prematurely ceasing participation. The main reasons they gave were excessive additional burden, (documentation) effort, and interference with practice routine. Some also mentioned inadequate opportunity for decision-making and participation. The qualitative interviews came to the same result [ 31 ].

“This project just got out of hand. They were constantly increasing the requirements for me as a physician without asking me. At some point, it became too much of a burden.” (I-31f)

Even so, most of the respondents stated that they would generally consider participating in other projects in the future provided the project promised worthwhile benefits for primary care from their point of view.

Potential for optimisation

Physicians surveyed having taken part in clinical studies or cRCTs named several improvements they would like to see [ 30 ]. These involved strict limitations to documentation obligations (65%), a simple documentation system (62%), clearer organisation in project coordination (56%), making more flexibility possible in medical decision-making such as in calling in patients as well as decisions related to treatment, less severe interference with practice routines (49%), and reinforcement and improvement in structuring communication and cooperation between the physicians and other healthcare protagonists (37%). Finally, the physicians stated that they would appreciate (more) cost-based remuneration (34%).

The responses also demonstrate that the position of GPs should be reinforced further at various stages of a clinical study. Of all respondents, 57% saw importance in involving GPs more than before in study design and development. This would also include project-internal formats for structured participation such as research workshops as well as institutionalised exchange with colleagues and the research consortium.

“General practitioners simply just need to be more involved than before in designing and developing new studies and healthcare models. Once that happens, the studies will be more compatible with primary care and they’ll achieve their aims earlier." (I-23f)

GPs considered it particularly important for research projects to ensure the possibility of delegation allowing individual physicians to entrust practice staff members with project activities. Ensuring this across the board would save time and resources in the intervention. GPs also saw importance in integrated and coordinated training for the whole practice staff to prepare for workable project participation and avoid stressful individual situations. Flexible adjustment possibilities in project requirements such as varying levels and types of participation adjusted to capacity and degree of workload – such as reducing project obligations to account for seasonal factors – would also help prevent premature study cessation of GPs while also lowering barriers to entry [ 29 , 30 , 31 ].

GPs would appreciate more overall recognition of their commitment to clinical research. Some respondents suggested clinical practice status as recognition. A few respondents also raised the possibility of further academic titles as a result of years of involvement in clinical research.

Principal findings and comparison with prior work

We have presented the main results from various surveys amongst GPs on their pervious experiences and future willingness to participate in clinical studies and cRCTs as a synopsis in the course of this contribution. These findings show GPs to be divided on whether to participate in studies of this type.

Overall, the attitudes of many GPs were notably favourable with regard to the fundamental benefits and added value from clinical research, and that opportunities for corresponding research projects were being taken such as towards identifying and closing healthcare gaps, intensifying application-oriented healthcare research, using evidence-based research instruments and procedures, optimising diagnostic and therapeutic management, and reinforcing multiprofessional healthcare. The research literature repeatedly described methodically embedding primary care into cross-sector, interdisciplinary structures as a major asset in clinical care models [ 15 , 25 , 36 ].

However, some GPs took a more critical and distanced attitude towards long-term goal orientation in corresponding studies. Some respondents were unsure as to what extent structures created by clinical-scientific care models could contribute in practice towards making the healthcare system more effective in the long term. Some expressed uncertainty as to whether primary care could actually benefit from such research participation in the long term.

Urban physicians in the quantitative survey sample [ 30 ] identified clear benefits from clinical studies and cRCTs, but their rural counterparts took a more cautious stance. This tallies with the general research findings that GPs in rural areas perceive lower added value in evidence-based structures and instruments [ 39 , 40 ]. Likewise, most of the 24% of respondents having already taken part in cRCTs were located in urban areas with a greater variety of care services, which is often a prerequisite in effective clinical research [ 7 , 9 ]. We did not find any significant gender differences in the studies we carried out; this contrasts with other individual studies on willingness of GPs to participate in research networks such as Virnau et al. [ 2 , 42 , 43 ]. Apart from the difference between urban and rural physicians, age is a factor that this study has in common: Openness to clinical research projects amongst the respondents decreased with age [ 2 , 21 ].

Physicians fundamentally open to or already participating in research projects raised a number of requirements in this regard. Apart from added value for patient care, they emphasised manageable and plannable additional burden, impact on practice routines remaining tolerable, and structural reinforcement in the role of primary care. This tallies with results from previous surveys of primary care research networks (to be established) (see for example: [ 9 , 24 , 44 , 45 , 46 , 47 , 48 , 49 ]). A study by Güthlin et al. showed GPs to be especially interested in complex research projects if the topic seemed relevant to them and participation promised an actual benefit for the staff and patients of the practice. With this in mind, it hardly comes as a surprise that physicians having participated in clinical care models give especially favourable assessments of studies on topics such as rural care, drug therapy/safety, delegation, or sector cooperation. Other studies have also shown GPs to consider areas such as polypharmacy, drug safety and adverse drug effects, and multiprofessional cooperation models to be especially important [ 1 , 2 , 15 , 29 , 31 ]. Apart from that, many GPs currently would not want their medical practice just “researched,” but would rather help shape how these research projects are conducted [ 44 , 45 ].

The conclusion reached by most of the GPs involved from participation in the corresponding studies is clearly favourable. This applies to healthcare and increase in treatment quality for the patients involved and to the cost–benefit relationship. Physicians found it easier to assess care needs of patients and their relatives, and recommend assistance services. Finally, there was a noticeable increase in subjective capability to perform diagnostics and disease management, and to apply the S3 guideline. Even so, some respondents described negative experiences and stress factors as reflected in documentation requirements and administrative effort, temporary but substantial changes in practice routine, deficits in project communication, and remuneration not matching the effort involved.

The results from the survey may be seen as confirmation of increased willingness amongst GPs to participate in empirical, evidence-oriented studies with the aim of optimising healthcare [ 15 ]. Especially younger GPs in urban catchment areas are increasingly basing their work on standardised, evidence-based interventions [ 39 , 40 ]. Even so, a substantial proportion of general medical practices are fundamentally unwilling or remain reluctant regarding these research projects [ 1 , 7 , 8 , 9 ]. This has resulted in a regional shortage of GPs available for recruitment in complex studies as reflected by project experience from the Innovation Fund in existence in Germany since 2015, often failing to achieve the original target cohorts of physicians and patients [ 50 ]. Lech et al. [ 1 ] provided one example in a contribution reporting on a cluster-randomised study to optimise outpatient dementia care. The authors reported difficulties in recruiting GPs despite using a wide range of recruitment strategies.

There is mounting evidence that combining these projects with everyday general practice care is not a smooth process, although the reasons for barriers and challenges to recruiting GPs for clinical research have hardly been investigated to date. This involves, on the one hand, immediate difficulties from an underlying shortage of time and resources in general practices [ 45 ]. GPs need to make the time required for project activities during consultation hours, which represents a major barrier to any research interest [ 43 , 51 ]. This barrier may be raised further by the fear of a potentially escalating additional workload such as what GPs see as high-threshold registration of patients for the project, alongside documentation requirements and dealing with complex documentation systems such as digital case files. A low, tightly planned time investment for GPs and their staff always boosts the attractiveness of clinical research and may be of benefit to future recruitment [ 52 ]. On the other hand, GPs cast doubts on the match and fit of interventions in everyday primary care. This applies to project plans often conceived from a clinical-scientific perspective that then led to complications and limitations in primary care [ 44 , 45 , 49 , 50 , 53 ].

A review by Fletcher et al. [ 3 ] on GP-based clinical research identified barriers such as poor communication by study coordinators, difficulties experienced by GPs in understanding research methods, concerns about possible harm to patients, and the feeling of being overwhelmed by too many research requests without being perceived as genuine research partners. Routinised communication between all the stakeholders in every project phase plays an especially important role in enabling and improving practice-oriented research [ 54 ]. Apart from that, reliable and persistent contacts such as at university hospitals play a major part as an indispensable prerequisite for workable and cooperative relationships between resident GPs and clinical project management [ 55 ].

There are also indications that topics covered in clinical research projects do not always match the interests and perceived issues shared by gene, making it impossible to convince them to participate [ 2 , 21 , 43 , 56 , 57 , 58 ]. This points to the need for continuous interaction between hospital-based primary care and GPs for continuous identification of everyday topics related to healthcare as relevant to GPs and their patients [ 59 , 60 , 61 , 62 ].

Beyond the issues already covered, Lech et al. [ 1 ] also discussed requirements for a specific recruitment of GPs. The contribution emphasised the benefit of greater concentration on (regional) physicians’ networks to specific recruitment in cRCT studies due to increased research interest, specific topic reference, and close coordination between the participating physicians [ 47 , 63 , 64 ]. A substantial proportion of the physicians involved in the studies were also members of a physicians’ network in the surveys presented [ 29 , 30 , 31 , 36 , 37 ].

Finally, consideration should be given to remuneration for participating GPs. GPs and their staff would welcome some financial reward for participating in clinical research even if most do not anticipate major financial losses from time spent in participation. One possibility would be increasing the remuneration amount with the number of patients enrolled into the study [ 65 ]. Apart from that, many GPs would benefit from reimbursement of additional expenses; this would help to ensure continuity and sustainability in clinical research networks [ 63 ]. Norway provides an example of good practice where physicians participating in research projects receive an annual fee for ongoing administrative work in addition to an hourly fee for each study participation [ 66 ].

Most important takeaways from the studies presented

As shown, the findings obtained the from survey included in this contribution are largely consistent with existing research literature on primary care involvement in clinical research and cRCTs [ 55 , 67 , 68 ]. However, specific weightings and focal points in general practitioner positions as well as additional insights towards motivating GPs to take part in complex clinical research projects emerged during the studies. Figure  2  summarises the central takeaways.

Approaches developed towards optimising integration of primary care in clinical studies (own diagram)

GPs expressed a desire for a manageable and predictable additional workload such as in the complexity of the intervention to be used and in administrative and documentation tasks without excessively interfering with established practice routines [ 36 , 39 , 40 , 49 ]. GPs also wished for more individual flexibility in action and decision-making extending beyond participation in research activities to involvement in evidence-based structures and instruments such as disease management programmes and guidelines. Examples of this included authorisation to take alternative approaches considered beforehand in these research projects or temporarily cutting down on project commitment without having to withdraw from the study entirely.

In addition, many GPs also expressed a strong desire for more involvement in shaping project activities and more inclusion in clearly structured communications during research projects. Many GPs advocate constant updates on research-related matters from project management, but also institutionalised, multi-layered exchange and feedback opportunities within the research network. GPs also found it important to use integrated and methodical training programmes and, wherever possible, detailed delegation plans for practice teams to demonstrate possibilities for implementing interventions while saving time and resources as far as possible. All this indicates that clinical research projects have still not always been compatible with the salient primary care setting up to now [ 16 , 44 , 53 ].

GPs saw it as desirable to approach rewards for participation in clinical studies not only in terms of remuneration alone, but also as a form of academic and research recognition. Some respondents saw a definite motivational factor in the possibility of receiving official certification as a university-associated research practice or a specific academic title in recognition of years of commitment to clinical research [ 44 , 47 ].

Finally, training, and further training as a whole should undergo significant extension towards participation in research studies. The studies performed demonstrated that a sizable proportion of GPs were unsure about their research qualifications and current level of knowledge, leading to doubts as to their personal suitability for active research involvement. Germany has only seen increased efforts towards integrating research competence more firmly as a component of Medical Studies programmes in recent years [ 1 , 37 ].

The main findings have demonstrated how it might be possible to recruit more GPs in the future. In the opinion of the authors, consistent implementation of these resulting clusters will not only exert a favourable effect on motivation to join but also to remain in the project while also improving process and result quality in cRCT studies. This would also create more favourable general conditions for GPs to take an active part in clinical research in the future. It would also be important to align research projects with topics that GPs see as relevant for these optimisation approaches to materialise, and also to convey the specific benefit and added value for primary care in a clear fashion when addressing physicians [ 2 ].

Strengths and limitations

The studies presented in this contribution are to the best of our knowledge amongst the few empirical studies that have been published so far with an in-depth focus on attitudes, acceptance, expectations, and experiences of GPs towards participation in clinical research projects. However, the study cannot make any representative claims in the strict sense due to the limited number of cases and regional recruiting focus. We must also take into account that the focus of the surveys was also largely placed within the Innovation Fund context. Particularly extensive, complex, and also cost-intensive clinical research projects in Germany are financed by the Innovation Fund, which is not necessarily representative of any other type of clinical research.

In addition, the proportion of GPs involved in research is noticeably overrepresented in the quantitative survey sample compared to the total number of GPs, so selection bias needs to be considered. This implies that the survey addressed physicians with a greater interest or commitment to the topic at hand in contrast to physicians with no connection to clinical research, who have presumably participated to a lesser extent in clinical research. The responses from respondents on the main topics of respective research projects should also be seen within this context. The ranking order of responses listed reflects the topical interest of GPs, but the number of projects available for the respective subject areas may cause a bias in this information.

Even so, the heterogeneous random sample taken approximated to the general population of GPs in important aspects (see Table  1 ). The exploratory approach combining quantitative and qualitative components allowed a wide range of general practitioner perspectives, attitudes, and experiences to be documented.

The present study has not looked into how far the projects the responding GPs took part in were implemented, co-managed, or coordinated by primary care institutes. These institutes have gathered a wealth of experience in research collaboration with GPs. Future studies should therefore focus on whether the study conditions for GPs could be more favourable in cooperation with primary care institutes.

The present studies have also left aside the extent to which settings other than clinical studies may be more suited to primary care regarding willingness to become involved in scientific research. Studies from primary care suggest that the research practice model may potentially achieve more effective recruitment and participation [ 44 , 47 ]. In this respect, results from the present study may be compared with results from a survey to be suggested here documenting the experiences of GPs specifically in the research practice setting. This type of survey would be feasible on a larger scale in view of the recent emergence of larger research networks coordinated by primary care institutes.

Results from the studies presented provide indications as to how GPs perceive clinical research projects and cRCTs as a whole and from their existing project experience, and on the requirements that studies would have to meet for GPs to be willing to participate. Future research projects on primary care-based interventions should redouble their efforts at reflecting the positions, needs, and experiences of GPs. This would enable us to even out the hurdles and challenges perceived by GPs in participating in projects of this type. In particular, making sure that clinical studies fully conform with GPs would play a significant role; this especially applies to the medical decision-making freedom, limitation of documentation obligations, impediment to medical practice routine, greater involvement in research planning, and long-term reinforcement in the role of primary care. Clinical research projects and cRCTs should be planned, designed, and communicated for clear and visible relevance to everyday primary care.

Availability of data and materials

All major data generated or analyzed during this study are included in this published article. Additional information can be provided on request made to the corresponding author.

The year 2015 saw the Innovation Fund established as part of the Federal Joint Committee (G-BA) [ 32 ]. As a health policy instrument, the aim of the fund is to promote evidence-based development in pay-as-you-go healthcare by developing new healthcare concepts [ 32 , 33 , 34 , 35 ]. An annual funding volume of €200 million has been secured for the project in the current funding phase with funding provided by the statutory health insurance companies and health fund.

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TacticAI: an AI assistant for football tactics

• Zhe Wang   ORCID: orcid.org/0000-0002-0748-5376 1   na1 ,
• Petar Veličković   ORCID: orcid.org/0000-0002-2820-4692 1   na1 ,
• Daniel Hennes   ORCID: orcid.org/0000-0002-3646-5286 1   na1 ,
• Nenad Tomašev   ORCID: orcid.org/0000-0003-1624-0220 1 ,
• Laurel Prince 1 ,
• Michael Kaisers 1 ,
• Yoram Bachrach 1 ,
• Romuald Elie 1 ,
• Li Kevin Wenliang 1 ,
• Federico Piccinini 1 ,
• William Spearman 2 ,
• Ian Graham 3 ,
• Jerome Connor 1 ,
• Yi Yang 1 ,
• Adrià Recasens 1 ,
• Mina Khan 1 ,
• Nathalie Beauguerlange 1 ,
• Pablo Sprechmann 1 ,
• Pol Moreno 1 ,
• Nicolas Heess   ORCID: orcid.org/0000-0001-7876-9256 1 ,
• Michael Bowling   ORCID: orcid.org/0000-0003-2960-8418 4 ,
• Demis Hassabis 1 &
• Karl Tuyls   ORCID: orcid.org/0000-0001-7929-1944 5  

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Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose TacticAI, an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. TacticAI incorporates both a predictive and a generative component, allowing the coaches to effectively sample and explore alternative player setups for each corner kick routine and to select those with the highest predicted likelihood of success. We validate TacticAI on a number of relevant benchmark tasks: predicting receivers and shot attempts and recommending player position adjustments. The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC. We show that TacticAI’s model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90% of the time, and that TacticAI offers an effective corner kick retrieval system. TacticAI achieves these results despite the limited availability of gold-standard data, achieving data efficiency through geometric deep learning.

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Introduction

Association football, or simply football or soccer, is a widely popular and highly professionalised sport, in which two teams compete to score goals against each other. As each football team comprises up to 11 active players at all times and takes place on a very large pitch (also known as a soccer field), scoring goals tends to require a significant degree of strategic team-play. Under the rules codified in the Laws of the Game 1 , this competition has nurtured an evolution of nuanced strategies and tactics, culminating in modern professional football leagues. In today’s play, data-driven insights are a key driver in determining the optimal player setups for each game and developing counter-tactics to maximise the chances of success 2 .

When competing at the highest level the margins are incredibly tight, and it is increasingly important to be able to capitalise on any opportunity for creating an advantage on the pitch. To that end, top-tier clubs employ diverse teams of coaches, analysts and experts, tasked with studying and devising (counter-)tactics before each game. Several recent methods attempt to improve tactical coaching and player decision-making through artificial intelligence (AI) tools, using a wide variety of data types from videos to tracking sensors and applying diverse algorithms ranging from simple logistic regression to elaborate neural network architectures. Such methods have been employed to help predict shot events from videos 3 , forecast off-screen movement from spatio-temporal data 4 , determine whether a match is in-play or interrupted 5 , or identify player actions 6 .

The execution of agreed-upon plans by players on the pitch is highly dynamic and imperfect, depending on numerous factors including player fitness and fatigue, variations in player movement and positioning, weather, the state of the pitch, and the reaction of the opposing team. In contrast, set pieces provide an opportunity to exert more control on the outcome, as the brief interruption in play allows the players to reposition according to one of the practiced and pre-agreed patterns, and make a deliberate attempt towards the goal. Examples of such set pieces include free kicks, corner kicks, goal kicks, throw-ins, and penalties 2 .

Among set pieces, corner kicks are of particular importance, as an improvement in corner kick execution may substantially modify game outcomes, and they lend themselves to principled, tactical and detailed analysis. This is because corner kicks tend to occur frequently in football matches (with ~10 corners on average taking place in each match 7 ), they are taken from a fixed, rigid position, and they offer an immediate opportunity for scoring a goal—no other set piece simultaneously satisfies all of the above. In practice, corner kick routines are determined well ahead of each match, taking into account the strengths and weaknesses of the opposing team and their typical tactical deployment. It is for this reason that we focus on corner kick analysis in particular, and propose TacticAI, an AI football assistant for supporting the human expert with set piece analysis, and the development and improvement of corner kick routines.

TacticAI is rooted in learning efficient representations of corner kick tactics from raw, spatio-temporal player tracking data. It makes efficient use of this data by representing each corner kick situation as a graph—a natural representation for modelling relationships between players (Fig.  1 A, Table  2 ), and these player relationships may be of higher importance than the absolute distances between them on the pitch 8 . Such a graph input is a natural candidate for graph machine learning models 9 , which we employ within TacticAI to obtain high-dimensional latent player representations. In the Supplementary Discussion section, we carefully contrast TacticAI against prior art in the area.

A How corner kick situations are converted to a graph representation. Each player is treated as a node in a graph, with node, edge and graph features extracted as detailed in the main text. Then, a graph neural network operates over this graph by performing message passing; each node’s representation is updated using the messages sent to it from its neighbouring nodes. B How TacticAI processes a given corner kick. To ensure that TacticAI’s answers are robust in the face of horizontal or vertical reflections, all possible combinations of reflections are applied to the input corner, and these four views are then fed to the core TacticAI model, where they are able to interact with each other to compute the final player representations—each internal blue arrow corresponds to a single message passing layer from ( A ). Once player representations are computed, they can be used to predict the corner’s receiver, whether a shot has been taken, as well as assistive adjustments to player positions and velocities, which increase or decrease the probability of a shot being taken.

Uniquely, TacticAI takes advantage of geometric deep learning 10 to explicitly produce player representations that respect several symmetries of the football pitch (Fig.  1 B). As an illustrative example, we can usually safely assume that under a horizontal or vertical reflection of the pitch state, the game situation is equivalent. Geometric deep learning ensures that TacticAI’s player representations will be identically computed under such reflections, such that this symmetry does not have to be learnt from data. This proves to be a valuable addition, as high-quality tracking data is often limited—with only a few hundred matches played each year in every league. We provide an in-depth overview of how we employ geometric deep learning in TacticAI in the “Methods” section.

From these representations, TacticAI is then able to answer various predictive questions about the outcomes of a corner—for example, which player is most likely to make first contact with the ball, or whether a shot will take place. TacticAI can also be used as a retrieval system—for mining similar corner kick situations based on the similarity of player representations—and a generative recommendation system, suggesting adjustments to player positions and velocities to maximise or minimise the estimated shot probability. Through several experiments within a case study with domain expert coaches and analysts from Liverpool FC, the results of which we present in the next section, we obtain clear statistical evidence that TacticAI readily provides useful, realistic and accurate tactical suggestions.

To demonstrate the diverse qualities of our approach, we design TacticAI with three distinct predictive and generative components: receiver prediction, shot prediction, and tactic recommendation through guided generation, which also correspond to the benchmark tasks for quantitatively evaluating TacticAI. In addition to providing accurate quantitative insights for corner kick analysis with its predictive components, the interplay between TacticAI’s predictive and generative components allows coaches to sample alternative player setups for each routine of interest, and directly evaluate the possible outcomes of such alternatives.

We will first describe our quantitative analysis, which demonstrates that TacticAI’s predictive components are accurate at predicting corner kick receivers and shot situations on held-out test corners and that the proposed player adjustments do not strongly deviate from ground-truth situations. However, such an analysis only gives an indirect insight into how useful TacticAI would be once deployed. We tackle this question of utility head-on and conduct a comprehensive case study in collaboration with our partners at Liverpool FC—where we directly ask human expert raters to judge the utility of TacticAI’s predictions and player adjustments. The following sections expand on the specific results and analysis we have performed.

In what follows, we will describe TacticAI’s components at a minimal level necessary to understand our evaluation. We defer detailed descriptions of TacticAI’s components to the “Methods” section. Note that, all our error bars reported in this research are standard deviations.

Benchmarking TacticAI

We evaluate the three components of TacticAI on a relevant benchmark dataset of corner kicks. Our dataset consists of 7176 corner kicks from the 2020 to 2021 Premier League seasons, which we randomly shuffle and split into a training (80%) and a test set (20%). As previously mentioned, TacticAI operates on graphs. Accordingly, we represent each corner kick situation as a graph, where each node corresponds to a player. The features associated with each node encode the movements (velocities and positions) and simple profiles (heights and weights) of on-pitch players at the timestamp when the corresponding corner kick was being taken by the attacking kicker (see the “Methods” section), and no information of ball movement was encoded. The graphs are fully connected; that is, for every pair of players, we will include the edge connecting them in the graph. Each of these edges encodes a binary feature, indicating whether the two players are on opposing teams or not. For each task, we generated the relevant dataset of node/edge/graph features and corresponding labels (Tables  1 and 2 , see the “Methods” section). The components were then trained separately with their corresponding corner kick graphs. In particular, we only employ a minimal set of features to construct the corner kick graphs, without encoding the movements of the ball nor explicitly encoding the distances between players into the graphs. We used a consistent training-test split for all benchmark tasks, as this made it possible to benchmark not only the individual components but also their interactions.

Accurate receiver and shot prediction through geometric deep learning

One of TacticAI’s key predictive models forecasts the receiver out of the 22 on-pitch players. The receiver is defined as the first player touching the ball after the corner is taken. In our evaluation, all methods used the same set of features (see the “Receiver prediction” entry in Table  1 and the “Methods” section). We leveraged the receiver prediction task to benchmark several different TacticAI base models. Our best-performing model—achieving 0.782 ± 0.039 in top-3 test accuracy after 50,000 training steps—was a deep graph attention network 11 , 12 , leveraging geometric deep learning 10 through the use of D 2 group convolutions 13 . We supplement this result with a detailed ablation study, verifying that both our choice of base architecture and group convolution yielded significant improvements in the receiver prediction task (Supplementary Table  2 , see the subsection “Ablation study” in the “Methods” section). Considering that corner kick receiver prediction is a highly challenging task with many factors that are unseen by our model—including fatigue and fitness levels, and actual ball trajectory—we consider TacticAI’s top-3 accuracy to reflect a high level of predictive power, and keep the base TacticAI architecture fixed for subsequent studies. In addition to this quantitative evaluation with the evaluation dataset, we also evaluate the performance of TacticAI’s receiver prediction component in a case study with human raters. Please see the “Case study with expert raters” section for more details.

For shot prediction, we observe that reusing the base TacticAI architecture to directly predict shot events—i.e., directly modelling the probability \({\mathbb{P}}(\,{{\mbox{shot}}}| {{\mbox{corner}}}\,)\) —proved challenging, only yielding a test F 1 score of 0.52 ± 0.03, for a GATv2 base model. Note that here we use the F 1 score—the harmonic mean of precision and recall—as it is commonly used in binary classification problems over imbalanced datasets, such as shot prediction. However, given that we already have a potent receiver predictor, we decided to use its output to give us additional insight into whether or not a shot had been taken. Hence, we opted to decompose the probability of taking a shot as

where \({\mathbb{P}}(\,{{\mbox{receiver}}}| {{\mbox{corner}}}\,)\) are the probabilities computed by TacticAI’s receiver prediction system, and \({\mathbb{P}}(\,{{\mbox{shot}}}| {{\mbox{receiver}}},{{\mbox{corner}}}\,)\) models the conditional shot probability after a specific player makes first contact with the ball. This was implemented through providing an additional global feature to indicate the receiver in the corresponding corner kick (Table  1 ) while the architecture otherwise remained the same as that of receiver prediction (Supplementary Fig.  2 , see the “Methods” section). At training time, we feed the ground-truth receiver as input to the model—at inference time, we attempt every possible receiver, weighing their contributions using the probabilities given by TacticAI’s receiver predictor, as per Eq. ( 1 ). This two-phased approach yielded a final test F 1 score of 0.68 ± 0.04 for shot prediction, which encodes significantly more signal than the unconditional shot predictor, especially considering the many unobservables associated with predicting shot events. Just as for receiver prediction, this performance can be further improved using geometric deep learning; a conditional GATv2 shot predictor with D 2 group convolutions achieves an F 1 score of 0.71 ± 0.01.

Moreover, we also observe that, even just through predicting the receivers, without explicitly classifying any other salient features of corners, TacticAI learned generalisable representations of the data. Specifically, team setups with similar tactical patterns tend to cluster together in TacticAI’s latent space (Fig.  2 ). However, no clear clusters are observed in the raw input space (Supplementary Fig.  1 ). This indicates that TacticAI can be leveraged as a useful corner kick retrieval system, and we will present our evaluation of this hypothesis in the “Case study with expert raters” section.

We visualise the latent representations of attacking and defending teams in 1024 corner kicks using t -SNE. A latent team embedding in one corner kick sample is the mean of the latent player representations on the same attacking ( A – C ) or defending ( D ) team. Given the reference corner kick sample ( A ), we retrieve another corner kick sample ( B ) with respect to the closest distance of their representations in the latent space. We observe that ( A ) and ( B ) are both out-swing corner kicks and share similar patterns of their attacking tactics, which are highlighted with rectangles having the same colours, although they bear differences with respect to the absolute positions and velocities of the players. All the while, the latent representation of an in-swing attack ( C ) is distant from both ( A ) and ( B ) in the latent space. The red arrows are only used to demonstrate the difference between in- and out-swing corner kicks, not the actual ball trajectories.

Lastly, it is worth emphasising that the utility of the shot predictor likely does not come from forecasting whether a shot event will occur—a challenging problem with many imponderables—but from analysing the difference in predicted shot probability across multiple corners. Indeed, in the following section, we will show how TacticAI’s generative tactic refinements can directly influence the predicted shot probabilities, which will then corresponds to highly favourable evaluation by our expert raters in the “Case study with expert raters” section.

Controlled tactic refinement using class-conditional generative models

Equipped with components that are able to potently relate corner kicks with their various outcomes (e.g. receivers and shot events), we can explore the use of TacticAI to suggest adjustments of tactics, in order to amplify or reduce the likelihood of certain outcomes.

Specifically, we aim to produce adjustments to the movements of players on one of the two teams, including their positions and velocities, which would maximise or minimise the probability of a shot event, conditioned on the initial corner setup, consisting of the movements of players on both teams and their heights and weights. In particular, although in real-world scenarios both teams may react simultaneously to the movements of each other, in our study, we focus on moderate adjustments to player movements, which help to detect players that are not responding to a tactic properly. Due to this reason, we simplify the process of tactic refinement through generating the adjustments for only one team while keeping the other fixed. The way we train a model for this task is through an auto-encoding objective: we feed the ground-truth shot outcome (a binary indicator) as an additional graph-level feature to TacticAI’s model (Table  1 ), and then have it learn to reconstruct a probability distribution of the input player coordinates (Fig.  1 B, also see the “Methods” section). As a consequence, our tactic adjustment system does not depend on the previously discussed shot predictor—although we can use the shot predictor to evaluate whether the adjustments make a measurable difference in shot probability.

This autoencoder-based generative model is an individual component that separates from TacticAI’s predictive systems. All three systems share the encoder architecture (without sharing parameters), but use different decoders (see the “Methods” section). At inference time, we can instead feed in a desired shot outcome for the given corner setup, and then sample new positions and velocities for players on one team using this probability distribution. This setup, in principle, allows for flexible downstream use, as human coaches can optimise corner kick setups through generating adjustments conditioned on the specific outcomes of their interest—e.g., increasing shot probability for the attacking team, decreasing it for the defending team (Fig.  3 ) or amplifying the chance that a particular striker receives the ball.

TacticAI makes it possible for human coaches to redesign corner kick tactics in ways that help maximise the probability of a positive outcome for either the attacking or the defending team by identifying key players, as well as by providing temporally coordinated tactic recommendations that take all players into consideration. As demonstrated in the present example ( A ), for a corner kick in which there was a shot attempt in reality ( B ), TacticAI can generate a tactically-adjusted setting in which the shot probability has been reduced, by adjusting the positioning of the defenders ( D ). The suggested defender positions result in reduced receiver probability for attacking players 2–5 (see bottom row), while the receiver probability of Attacker 1, who is distant from the goalpost, has been increased ( C ). The model is capable of generating multiple such scenarios. Coaches can inspect the different options visually and additionally consult TacticAI’s quantitative analysis of the presented tactics.

We first evaluate the generated adjustments quantitatively, by verifying that they are indistinguishable from the original corner kick distribution using a classifier. To do this, we synthesised a dataset consisting of 200 corner kick samples and their corresponding conditionally generated adjustments. Specifically, for corners without a shot event, we generated adjustments for the attacking team by setting the shot event feature to 1, and vice-versa for the defending team when a shot event did happen. We found that the real and generated samples were not distinguishable by an MLP classifier, with an F 1 score of 0.53 ± 0.05, indicating random chance level accuracy. This result indicates that the adjustments produced by TacticAI are likely similar enough to real corner kicks that the MLP is unable to tell them apart. Note that, in spite of this similarity, TacticAI recommends player-level adjustments that are not negligible—in the following section we will illustrate several salient examples of this. To more realistically validate the practical indistinguishability of TacticAI’s adjustments from realistic corners, we also evaluated the realism of the adjustments in a case study with human experts, which we will present in the following section.

In addition, we leveraged our TacticAI shot predictor to estimate whether the proposed adjustments were effective. We did this by analysing 100 corner kick samples in which threatening shots occurred, and then, for each sample, generated one defensive refinement through setting the shot event feature to 0. We observed that the average shot probability significantly decreased, from 0.75 ± 0.14 for ground-truth corners to 0.69 ± 0.16 for adjustments ( z  = 2.62,  p  < 0.001). This observation was consistent when testing for attacking team refinements (shot probability increased from 0.18 ± 0.16 to 0.31 ± 0.26 ( z  = −4.46,  p  < 0.001)). Moving beyond this result, we also asked human raters to assess the utility of TacticAI’s proposed adjustments within our case study, which we detail next.

Case study with expert raters

Although quantitative evaluation with well-defined benchmark datasets was critical for the technical development of TacticAI, the ultimate test of TacticAI as a football tactic assistant is its practical downstream utility being recognised by professionals in the industry. To this end, we evaluated TacticAI through a case study with our partners at Liverpool FC (LFC). Specifically, we invited a group of five football experts: three data scientists, one video analyst, and one coaching assistant. Each of them completed four tasks in the case study, which evaluated the utility of TacticAI’s components from several perspectives; these include (1) the realism of TacticAI’s generated adjustments, (2) the plausibility of TacticAI’s receiver predictions, (3) effectiveness of TacticAI’s embeddings for retrieving similar corners, and (4) usefulness of TacticAI’s recommended adjustments. We provide an overview of our study’s results here and refer the interested reader to Supplementary Figs.  3 – 5 and the  Supplementary Methods for additional details.

We first simultaneously evaluated the realism of the adjusted corner kicks generated by TacticAI, and the plausibility of its receiver predictions. Going through a collection of 50 corner kick samples, we first asked the raters to classify whether a given sample was real or generated by TacticAI, and then they were asked to identify the most likely receivers in the corner kick sample (Supplementary Fig.  3 ).

On the task of classifying real and generated samples, first, we found that the raters’ average F 1 score of classifying the real vs. generated samples was only 0.60 ± 0.04, with individual F 1 scores ( \({F}_{1}^{A}=0.54,{F}_{1}^{B}=0.64,{F}_{1}^{C}=0.65,{F}_{1}^{D}=0.62,{F}_{1}^{E}=0.56\) ), indicating that the raters were, in many situations, unable to distinguish TacticAI’s adjustments from real corners.

The previous evaluation focused on analysing realism detection performance across raters. We also conduct a study that analyses realism detection across samples. Specifically, we assigned ratings for each sample—assigning +1 to a sample if it was identified as real by a human rater, and 0 otherwise—and computed the average rating for each sample across the five raters. Importantly, by studying the distribution of ratings, we found that there was no significant difference between the average ratings assigned to real and generated corners ( z  = −0.34,  p  > 0.05) (Fig.  4 A). Hence, the real and generated samples were assigned statistically indistinguishable average ratings by human raters.

In task 1, we tested the statistical difference between the real corner kick samples and the synthetic ones generated by TacticAI from two aspects: ( A.1 ) the distributions of their assigned ratings, and ( A.2 ) the corresponding histograms of the rating values. Analogously, in task 2 (receiver prediction), ( B.1 ) we track the distributions of the top-3 accuracy of receiver prediction using those samples, and ( B.2 ) the corresponding histogram of the mean rating per sample. No statistical difference in the mean was observed in either cases (( A.1 ) ( z  = −0.34,  p  > 0.05), and ( B.1 ) ( z  = 0.97,  p  > 0.05)). Additionally, we observed a statistically significant difference between the ratings of different raters on receiver prediction, with three clear clusters emerging ( C ). Specifically, Raters A and E had similar ratings ( z  = 0.66,  p  > 0.05), and Raters B and D also rated in similar ways ( z  = −1.84,  p  > 0.05), while Rater C responded differently from all other raters. This suggests a good level of variety of the human raters with respect to their perceptions of corner kicks. In task 3—identifying similar corners retrieved in terms of salient strategic setups—there were no significant differences among the distributions of the ratings by different raters ( D ), suggesting a high level of agreement on the usefulness of TacticAI’s capability of retrieving similar corners ( F 1,4  = 1.01,  p  > 0.1). Finally, in task 4, we compared the ratings of TacticAI’s strategic refinements across the human raters ( E ) and found that the raters also agreed on the general effectiveness of the refinements recommended by TacticAI ( F 1,4  = 0.45,  p  > 0.05). Note that the violin plots used in B.1 and C – E model a continuous probability distribution and hence assign nonzero probabilities to values outside of the allowed ranges. We only label y -axis ticks for the possible set of ratings.

For the task of identifying receivers, we rated TacticAI’s predictions with respect to a rater as +1 if at least one of the receivers identified by the rater appeared in TacticAI’s top-3 predictions, and 0 otherwise. The average top-3 accuracy among the human raters was 0.79 ± 0.18; specifically, 0.81 ± 0.17 for the real samples, and 0.77 ± 0.21 for the generated ones. These scores closely line up with the accuracy of TacticAI in predicting receivers for held-out test corners, validating our quantitative study. Further, after averaging the ratings for receiver prediction sample-wise, we found no statistically significant difference between the average ratings of predicting receivers over the real and generated samples ( z  = 0.97,  p  > 0.05) (Fig.  4 B). This indicates that TacticAI was equally performant in predicting the receivers of real corners and TacticAI-generated adjustments, and hence may be leveraged for this purpose even in simulated scenarios.

There is a notably high variance in the average receiver prediction rating of TacticAI. We hypothesise that this is due to the fact that different raters may choose to focus on different salient features when evaluating the likely receivers (or even the amount of likely receivers). We set out to validate this hypothesis by testing the pair-wise similarity of the predictions by the human raters through running a one-away analysis of variance (ANOVA), followed by a Tukey test. We found that the distributions of the five raters’ predictions were significantly different ( F 1,4  = 14.46,  p  < 0.001) forming three clusters (Fig.  4 C). This result indicates that different human raters—as suggested by their various titles at LFC—may often use very different leads when suggesting plausible receivers. The fact that TacticAI manages to retain a high top-3 accuracy in such a setting suggests that it was able to capture the salient patterns of corner kick strategies, which broadly align with human raters’ preferences. We will further test this hypothesis in the third task—identifying similar corners.

For the third task, we asked the human raters to judge 50 pairs of corners for their similarity. Each pair consisted of a reference corner and a retrieved corner, where the retrieved corner was chosen either as the nearest-neighbour of the reference in terms of their TacticAI latent space representations, or—as a feature-level heuristic—the cosine similarities of their raw features (Supplementary Fig.  4 ) in our corner kick dataset. We score the raters’ judgement of a pair as +1 if they considered the corners presented in the case to be usefully similar, otherwise, the pair is scored with 0. We first computed, for each rater, the recall with which they have judged a baseline- or TacticAI-retrieved pair as usefully similar—see description of Task 3 in the  Supplementary Methods . For TacticAI retrievals, the average recall across all raters was 0.59 ± 0.09, and for the baseline system, the recall was 0.36 ± 0.10. Secondly, we assess the statistical difference between the results of the two methods by averaging the ratings for each reference–retrieval pair, finding that the average rating of TacticAI retrievals is significantly higher than the average rating of baseline method retrievals ( z  = 2.34,  p  < 0.05). These two results suggest that TacticAI significantly outperforms the feature-space baseline as a method for mining similar corners. This indicates that TacticAI is able to extract salient features from corners that are not trivial to extract from the input data alone, reinforcing it as a potent tool for discovering opposing team tactics from available data. Finally, we observed that this task exhibited a high level of inter-rater agreement for TacticAI-retrieved pairs ( F 1,4  = 1.01,  p  > 0.1) (Fig.  4 D), suggesting that human raters were largely in agreement with respect to their assessment of TacticAI’s performance.

Finally, we evaluated TacticAI’s player adjustment recommendations for their practical utility. Specifically, each rater was given 50 tactical refinements together with the corresponding real corner kick setups—see Supplementary Fig.  5 , and the “Case study design” section in the  Supplementary Methods . The raters were then asked to rate each refinement as saliently improving the tactics (+1), saliently making them worse (−1), or offering no salient differences (0). We calculated the average rating assigned by each of the raters (giving us a value in the range [− 1, 1] for each rater). The average of these values across all five raters was 0.7 ± 0.1. Further, for 45 of the 50 situations (90%), the human raters found TacticAI’s suggestion to be favourable on average (by majority voting). Both of these results indicate that TacticAI’s recommendations are salient and useful to a downstream football club practitioner, and we set out to validate this with statistical tests.

We performed statistical significance testing of the observed positive ratings. First, for each of the 50 situations, we averaged its ratings across all five raters and then ran a t -test to assess whether the mean rating was significantly larger than zero. Indeed, the statistical test indicated that the tactical adjustments recommended by TacticAI were constructive overall ( \({t}_{49}^{{{{{{{{\rm{avg}}}}}}}}}=9.20,\, p \, < \, 0.001\) ). Secondly, we verified that each of the five raters individually found TacticAI’s recommendations to be constructive, running a t -test on each of their ratings individually. For all of the five raters, their average ratings were found to be above zero with statistical significance ( \({t}_{49}^{A}=5.84,\, {p}^{A} \, < \, 0.001;{t}_{49}^{B}=7.88,\; {p}^{B} \, < \, 0.001;{t}_{49}^{C}=7.00,\; {p}^{C} \, < \, 0.001;{t}_{49}^{D}=6.04,\; {p}^{D} \, < \, 0.001;{t}_{49}^{E}=7.30,\, {p}^{E} \, < \, 0.001\) ). In addition, their ratings also shared a high level of inter-agreement ( F 1,4  = 0.45,  p  > 0.05) (Fig.  4 E), suggesting a level of practical usefulness that is generally recognised by human experts, even though they represent different backgrounds.

Taking all of these results together, we find TacticAI to possess strong components for prediction, retrieval, and tactical adjustments on corner kicks. To illustrate the kinds of salient recommendations by TacticAI, in Fig.  5 we present four examples with a high degree of inter-rater agreement.

These examples are selected from our case study with human experts, to illustrate the breadth of tactical adjustments that TacticAI suggests to teams defending a corner. The density of the yellow circles coincides with the number of times that the corresponding change is recognised as constructive by human experts. Instead of optimising the movement of one specific player, TacticAI can recommend improvements for multiple players in one generation step through suggesting better positions to block the opposing players, or better orientations to track them more efficiently. Some specific comments from expert raters follow. In A , according to raters, TacticAI suggests more favourable positions for several defenders, and improved tracking runs for several others—further, the goalkeeper is positioned more deeply, which is also beneficial. In B , TacticAI suggests that the defenders furthest away from the corner make improved covering runs, which was unanimously deemed useful, with several other defenders also positioned more favourably. In C , TacticAI recommends improved covering runs for a central group of defenders in the penalty box, which was unanimously considered salient by our raters. And in D , TacticAI suggests substantially better tracking runs for two central defenders, along with a better positioning for two other defenders in the goal area.

We have demonstrated an AI assistant for football tactics and provided statistical evidence of its efficacy through a comprehensive case study with expert human raters from Liverpool FC. First, TacticAI is able to accurately predict the first receiver after a corner kick is taken as well as the probability of a shot as the direct result of the corner. Second, TacticAI has been shown to produce plausible tactical variations that improve outcomes in a salient way, while being indistinguishable from real scenarios by domain experts. And finally, the system’s latent player representations are a powerful means to retrieve similar set-piece tactics, allowing coaches to analyse relevant tactics and counter-tactics that have been successful in the past.

The broader scope of strategy modelling in football has previously been addressed from various individual angles, such as pass prediction 14 , 15 , 16 , shot prediction 3 or corner kick tactical classification 7 . However, to the best of our knowledge, our work stands out by combining and evaluating predictive and generative modelling of corner kicks for tactic development. It also stands out in its method of applying geometric deep learning, allowing for efficiently incorporating various symmetries of the football pitch for improved data efficiency. Our method incorporates minimal domain knowledge and does not rely on intricate feature engineering—though its factorised design naturally allows for more intricate feature engineering approaches when such features are available.

Our methodology requires the position and velocity estimates of all players at the time of execution of the corner and subsequent events. Here, we derive these from high-quality tracking and event data, with data availability from tracking providers limited to top leagues. Player tracking based on broadcast video would increase the reach and training data substantially, but would also likely result in noisier model inputs. While the attention mechanism of GATs would allow us to perform introspection of the most salient factors contributing to the model outcome, our method does not explicitly model exogenous (aleatoric) uncertainty, which would be valuable context for the football analyst.

While the empirical study of our method’s efficacy has been focused on corner kicks in association football, it readily generalises to other set pieces (such as throw-ins, which similarly benefit from similarity retrieval, pass and/or shot prediction) and other team sports with suspended play situations. The learned representations and overall framing of TacticAI also lay the ground for future research to integrate a natural language interface that enables domain-grounded conversations with the assistant, with the aim to retrieve particular situations of interest, make predictions for a given tactical variant, compare and contrast, and guide through an interactive process to derive tactical suggestions. It is thus our belief that TacticAI lays the groundwork for the next-generation AI assistant for football.

We devised TacticAI as a geometric deep learning pipeline, further expanded in this section. We process labelled spatio-temporal football data into graph representations, and train and evaluate on benchmarking tasks cast as classification or regression. These steps are presented in sequence, followed by details on the employed computational architecture.

Raw corner kick data

The raw dataset consisted of 9693 corner kicks collected from the 2020–21, 2021–22, and 2022–23 (up to January 2023) Premier League seasons. The dataset was provided by Liverpool FC and comprises four separate data sources, described below.

Our primary data source is spatio-temporal trajectory frames (tracking data), which tracked all on-pitch players and the ball, for each match, at 25 frames per second. In addition to player positions, their velocities are derived from position data through filtering. For each corner kick, we only used the frame in which the kick is being taken as input information.

Secondly, we also leverage event stream data, which annotated the events or actions (e.g., passes, shots and goals) that have occurred in the corresponding tracking frames.

Thirdly, the line-up data for the corresponding games, which recorded the players’ profiles, including their heights, weights and roles, is also used.

Lastly, we have access to miscellaneous game data, which contains the game days, stadium information, and pitch length and width in meters.

Graph representation and construction

We assumed that we were provided with an input graph \({{{{{{{\mathcal{G}}}}}}}}=({{{{{{{\mathcal{V}}}}}}}},\,{{{{{{{\mathcal{E}}}}}}}})\) with a set of nodes \({{{{{{{\mathcal{V}}}}}}}}\) and edges \({{{{{{{\mathcal{E}}}}}}}}\subseteq {{{{{{{\mathcal{V}}}}}}}}\times {{{{{{{\mathcal{V}}}}}}}}\) . Within the context of football games, we took \({{{{{{{\mathcal{V}}}}}}}}\) to be the set of 22 players currently on the pitch for both teams, and we set \({{{{{{{\mathcal{E}}}}}}}}={{{{{{{\mathcal{V}}}}}}}}\times {{{{{{{\mathcal{V}}}}}}}}\) ; that is, we assumed all pairs of players have the potential to interact. Further analyses, leveraging more specific choices of \({{{{{{{\mathcal{E}}}}}}}}\) , would be an interesting avenue for future work.

Additionally, we assume that the graph is appropriately featurised. Specifically, we provide a node feature matrix, \({{{{{{{\bf{X}}}}}}}}\in {{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times k}\) , an edge feature tensor, \({{{{{{{\bf{E}}}}}}}}\in {{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times | {{{{{{{\mathcal{V}}}}}}}}| \times l}\) , and a graph feature vector, \({{{{{{{\bf{g}}}}}}}}\in {{\mathbb{R}}}^{m}\) . The appropriate entries of these objects provide us with the input features for each node, edge, and graph. For example, \({{{{{{{{\bf{x}}}}}}}}}_{u}\in {{\mathbb{R}}}^{k}\) would provide attributes of an individual player \(u\in {{{{{{{\mathcal{V}}}}}}}}\) , such as position, height and weight, and \({{{{{{{{\bf{e}}}}}}}}}_{uv}\in {{\mathbb{R}}}^{l}\) would provide the attributes of a particular pair of players \((u,\, v)\in {{{{{{{\mathcal{E}}}}}}}}\) , such as their distance, and whether they belong to the same team. The graph feature vector, g , can be used to store global attributes of interest to the corner kick, such as the game time, current score, or ball position. For a simplified visualisation of how a graph neural network would process such an input, refer to Fig.  1 A.

To construct the input graphs, we first aligned the four data sources with respect to their game IDs and timestamps and filtered out 2517 invalid corner kicks, for which the alignment failed due to missing data, e.g., missing tracking frames or event labels. This filtering yielded 7176 valid corner kicks for training and evaluation. We summarised the exact information that was used to construct the input graphs in Table  2 . In particular, other than player heights (measured in centimeters (cm)) and weights (measured in kilograms (kg)), the players were anonymous in the model. For the cases in which the player profiles were missing, we set their heights and weights to 180 cm and 75 kg, respectively, as defaults. In total, we had 385 such occurrences out of a total of 213,246( = 22 × 9693) during data preprocessing. We downscaled the heights and weights by a factor of 100. Moreover, for each corner kick, we zero-centred the positions of on-pitch players and normalised them onto a 10 m × 10 m pitch, and their velocities were re-scaled accordingly. For the cases in which the pitch dimensions were missing, we used a standard pitch dimension of 110 m × 63 m as default.

We summarised the grouping of the features in Table  1 . The actual features used in different benchmark tasks may differ, and we will describe this in more detail in the next section. To focus on modelling the high-level tactics played by the attacking and defending teams, other than a binary indicator for ball possession—which is 1 for the corner kick taker and 0 for all other players—no information of ball movement, neither positions nor velocities, was used to construct the input graphs. Additionally, we do not have access to the player’s vertical movement, therefore only information on the two-dimensional movements of each player is provided in the data. We do however acknowledge that such information, when available, would be interesting to consider in a corner kick outcome predictor, considering the prevalence of aerial battles in corners.

Benchmark tasks construction

TacticAI consists of three predictive and generative models, which also correspond to three benchmark tasks implemented in this study. Specifically, (1) Receiver prediction, (2) Threatening shot prediction, and (3) Guided generation of team positions and velocities (Table  1 ). The graphs of all the benchmark tasks used the same feature space of nodes and edges, differing only in the global features.

For all three tasks, our models first transform the node features to a latent node feature matrix, \({{{{{{{\bf{H}}}}}}}}={f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) , from which we could answer queries: either about individual players—in which case we learned a relevant classifier or regressor over the h u vectors (the rows of H )—or about the occurrence of a global event (e.g. shot taken)—in which case we classified or regressed over the aggregated player vectors, ∑ u h u . In both cases, the classifiers were trained using stochastic gradient descent over an appropriately chosen loss function, such as categorical cross-entropy for classifiers, and mean squared error for regressors.

For different tasks, we extracted the corresponding ground-truth labels from either the event stream data or the tracking data. Specifically, (1) We modelled receiver prediction as a node classification task and labelled the first player to touch the ball after the corner was taken as the target node. This player could be either an attacking or defensive player. (2) Shot prediction was modelled as graph classification. In particular, we considered a next-ball-touch action by the attacking team as a shot if it was a direct corner, a goal, an aerial, hit on the goalposts, a shot attempt saved by the goalkeeper, or missing target. This yielded 1736 corners labelled as a shot being taken, and 5440 corners labelled as a shot not being taken. (3) For guided generation of player position and velocities, no additional label was needed, as this model relied on a self-supervised reconstruction objective.

The entire dataset was split into training and evaluation sets with an 80:20 ratio through random sampling, and the same splits were used for all tasks.

Graph neural networks

The central model of TacticAI is the graph neural network (GNN) 9 , which computes latent representations on a graph by repeatedly combining them within each node’s neighbourhood. Here we define a node’s neighbourhood, \({{{{{{{{\mathcal{N}}}}}}}}}_{u}\) , as the set of all first-order neighbours of node u , that is, \({{{{{{{{\mathcal{N}}}}}}}}}_{u}=\{v\,| \,(v,\, u)\in {{{{{{{\mathcal{E}}}}}}}}\}\) . A single GNN layer then transforms the node features by passing messages between neighbouring nodes 17 , following the notation of related work 10 , and the implementation of the CLRS-30 benchmark baselines 18 :

where \(\psi :{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{l}\times {{\mathbb{R}}}^{m}\to {{\mathbb{R}}}^{{k}^{{\prime} }}\) and \(\phi :{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{{k}^{{\prime} }}\to {{\mathbb{R}}}^{{k}^{{\prime} }}\) are two learnable functions (e.g. multilayer perceptrons), \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(t)}\) are the features of node u after t GNN layers, and ⨁ is any permutation-invariant aggregator, such as sum, max, or average. By definition, we set \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(0)}={{{{{{{{\bf{x}}}}}}}}}_{u}\) , and iterate Eq. ( 2 ) for T steps, where T is a hyperparameter. Then, we let \({{{{{{{\bf{H}}}}}}}}={f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})={{{{{{{{\bf{H}}}}}}}}}^{(T)}\) be the final node embeddings coming out of the GNN.

It is well known that Eq. ( 2 ) is remarkably general; it can be used to express popular models such as Transformers 19 as a special case, and it has been argued that all discrete deep learning models can be expressed in this form 20 , 21 . This makes GNNs a perfect framework for benchmarking various approaches to modelling player–player interactions in the context of football.

Different choices of ψ , ϕ and ⨁ yield different architectures. In our case, we utilise a message function that factorises into an attentional mechanism, \(a:{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{l}\times {{\mathbb{R}}}^{m}\to {\mathbb{R}}\) :

yielding the graph attention network (GAT) architecture 12 . In our work, specifically, we use a two-layer multilayer perceptron for the attentional mechanism, as proposed by GATv2 11 :

where \({{{{{{{{\bf{W}}}}}}}}}_{1},\, {{{{{{{{\bf{W}}}}}}}}}_{2}\in {{\mathbb{R}}}^{k\times h}\) , \({{{{{{{{\bf{W}}}}}}}}}_{e}\in {{\mathbb{R}}}^{l\times h}\) , \({{{{{{{{\bf{W}}}}}}}}}_{g}\in {{\mathbb{R}}}^{m\times h}\) and \({{{{{{{\bf{a}}}}}}}}\in {{\mathbb{R}}}^{h}\) are the learnable parameters of the attentional mechanism, and LeakyReLU is the leaky rectified linear activation function. This mechanism computes coefficients of interaction (a single scalar value) for each pair of connected nodes ( u ,  v ), which are then normalised across all neighbours of u using the \({{{{{{{\rm{softmax}}}}}}}}\) function.

Through early-stage experimentation, we have ascertained that GATs are capable of matching the performance of more generic choices of ψ (such as the MPNN 17 ) while being more scalable. Hence, we focus our study on the GAT model in this work. More details can be found in the subsection “Ablation study” section.

Geometric deep learning

In spite of the power of Eq. ( 2 ), using it in its full generality is often prone to overfitting, given the large number of parameters contained in ψ and ϕ . This problem is exacerbated in the football analytics domain, where gold-standard data is generally very scarce—for example, in the English Premier League, only a few hundred games are played every season.

In order to tackle this issue, we can exploit the immense regularity of data arising from football games. Strategically equivalent game states are also called transpositions, and symmetries such as arriving at the same chess position through different move sequences have been exploited computationally since the 1960s 22 . Similarly, game rotations and reflections may yield equivalent strategic situations 23 . Using the blueprint of geometric deep learning (GDL) 10 , we can design specialised GNN architectures that exploit this regularity.

That is, geometric deep learning is a generic methodology for deriving mathematical constraints on neural networks, such that they will behave predictably when inputs are transformed in certain ways. In several important cases, these constraints can be directly resolved, directly informing neural network architecture design. For a comprehensive example of point clouds under 3D rotational symmetry, see Fuchs et al. 24 .

To elucidate several aspects of the GDL framework on a high level, let us assume that there exists a group of input data transformations (symmetries), \({\mathfrak{G}}\) under which the ground-truth label remains unchanged. Specifically, if we let y ( X ,  E ,  g ) be the label given to the graph featurised with X ,  E ,  g , then for every transformation \({\mathfrak{g}}\in {\mathfrak{G}}\) , the following property holds:

This condition is also referred to as \({\mathfrak{G}}\) -invariance. Here, by \({\mathfrak{g}}({{{{{{{\bf{X}}}}}}}})\) we denote the result of transforming X by \({\mathfrak{g}}\) —a concept also known as a group action. More generally, it is a function of the form \({\mathfrak{G}}\times {{{{{{{\mathcal{S}}}}}}}}\to {{{{{{{\mathcal{S}}}}}}}}\) for some state set \({{{{{{{\mathcal{S}}}}}}}}\) . Note that a single group element, \({\mathfrak{g}}\in {\mathfrak{G}}\) can easily produce different actions on different \({{{{{{{\mathcal{S}}}}}}}}\) —in this case, \({{{{{{{\mathcal{S}}}}}}}}\) could be \({{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times k}\) ( X ), \({{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times | {{{{{{{\mathcal{V}}}}}}}}| \times l}\) ( E ) and \({{\mathbb{R}}}^{m}\) ( g ).

It is worth noting that GNNs may also be derived using a GDL perspective if we set the symmetry group \({\mathfrak{G}}\) to \({S}_{| {{{{{{{\mathcal{V}}}}}}}}}|\) , the permutation group of \(| {{{{{{{\mathcal{V}}}}}}}}|\) objects. Owing to the design of Eq. ( 2 ), its outputs will not be dependent on the exact permutation of nodes in the input graph.

Frame averaging

A simple mechanism to enforce \({\mathfrak{G}}\) -invariance, given any predictor \({f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) , performs frame averaging across all \({\mathfrak{G}}\) -transformed inputs:

This ensures that all \({\mathfrak{G}}\) -transformed versions of a particular input (also known as that input’s orbit) will have exactly the same output, satisfying Eq. ( 5 ). A variant of this approach has also been applied in the AlphaGo architecture 25 to encode symmetries of a Go board.

In our specific implementation, we set \({\mathfrak{G}}={D}_{2}=\{{{{{{{{\rm{id}}}}}}}},\leftrightarrow,\updownarrow,\leftrightarrow \updownarrow \}\) , the dihedral group. Exploiting D 2 -invariance allows us to encode quadrant symmetries. Each element of the D 2 group encodes the presence of vertical or horizontal reflections of the input football pitch. Under these transformations, the pitch is assumed completely symmetric, and hence many predictions, such as which player receives the corner kick, or takes a shot from it, can be safely assumed unchanged. As an example of how to compute transformed features in Eq. ( 6 ), ↔( X ) horizontally reflects all positional features of players in X (e.g. the coordinates of the player), and negates the x -axis component of their velocity.

Group convolutions

While the frame averaging approach of Eq. ( 6 ) is a powerful way to restrict GNNs to respect input symmetries, it arguably misses an opportunity for the different \({\mathfrak{G}}\) -transformed views to interact while their computations are being performed. For small groups such as D 2 , a more fine-grained approach can be assumed, operating over a single GNN layer in Eq. ( 2 ), which we will write shortly as \({{{{{{{{\bf{H}}}}}}}}}^{(t)}={g}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{{\bf{H}}}}}}}}}^{(t-1)},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) . The condition that we need a symmetry-respecting GNN layer to satisfy is as follows, for all transformations \({\mathfrak{g}}\in {\mathfrak{G}}\) :

that is, it does not matter if we apply \({\mathfrak{g}}\) it to the input or the output of the function \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) —the final answer is the same. This condition is also referred to as \({\mathfrak{G}}\) -equivariance, and it has recently proved to be a potent paradigm for developing powerful GNNs over biochemical data 24 , 26 .

To satisfy D 2 -equivariance, we apply the group convolution approach 13 . Therein, views of the input are allowed to directly interact with their \({\mathfrak{G}}\) -transformed variants, in a manner very similar to grid convolutions (which is, indeed, a special case of group convolutions, setting \({\mathfrak{G}}\) to be the translation group). We use \({{{{{{{{\bf{H}}}}}}}}}_{{\mathfrak{g}}}^{(t)}\) to denote the \({\mathfrak{g}}\) -transformed view of the latent node features at layer t . Omitting E and g inputs for brevity, and using our previously designed layer \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) as a building block, we can perform a group convolution as follows:

Here, ∥ is the concatenation operation, joining the two node feature matrices column-wise; \({{\mathfrak{g}}}^{-1}\) is the inverse transformation to \({\mathfrak{g}}\) (which must exist as \({\mathfrak{G}}\) is a group); and \({{\mathfrak{g}}}^{-1}{\mathfrak{h}}\) is the composition of the two transformations.

Effectively, Eq. ( 8 ) implies our D 2 -equivariant GNN needs to maintain a node feature matrix \({{{{{{{{\bf{H}}}}}}}}}_{{\mathfrak{g}}}^{(t)}\) for every \({\mathfrak{G}}\) -transformation of the current input, and these views are recombined by invoking \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) on all pairs related together by applying a transformation \({\mathfrak{h}}\) . Note that all reflections are self-inverses, hence, in D 2 , \({\mathfrak{g}}={{\mathfrak{g}}}^{-1}\) .

It is worth noting that both the frame averaging in Eq. ( 6 ) and group convolution in Eq. ( 8 ) are similar in spirit to data augmentation. However, whereas standard data augmentation would only show one view at a time to the model, a frame averaging/group convolution architecture exhaustively generates all views and feeds them to the model all at once. Further, group convolutions allow these views to explicitly interact in a way that does not break symmetries. Here lies the key difference between the two approaches: frame averaging and group convolutions rigorously enforce the symmetries in \({\mathfrak{G}}\) , whereas data augmentation only provides implicit hints to the model about satisfying them. As a consequence of the exhaustive generation, Eqs. ( 6 ) and ( 8 ) are only feasible for small groups like D 2 . For larger groups, approaches like Steerable CNNs 27 may be employed.

Network architectures

While the three benchmark tasks we are performing have minor differences in the global features available to the model, the neural network models designed for them all have the same encoder–decoder architecture. The encoder has the same structure in all tasks, while the decoder model is tailored to produce appropriately shaped outputs for each benchmark task.

Given an input graph, TacticAI’s model first generates all relevant D 2 -transformed versions of it, by appropriately reflecting the player coordinates and velocities. We refer to the original input graph as the identity view, and the remaining three D 2 -transformed graphs as reflected views.

Once the views are prepared, we apply four group convolutional layers (Eq. ( 8 )) with a GATv2 base model (Eqs. ( 3 ) and ( 4 )) as the \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) function. Specifically, this means that, in Eqs. ( 3 ) and ( 4 ), every instance of \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(t-1)}\) is replaced by the concatenation of \({({{{{{{{{\bf{h}}}}}}}}}_{{\mathfrak{h}}}^{(t-1)})}_{u}\parallel {({{{{{{{{\bf{h}}}}}}}}}_{{{\mathfrak{g}}}^{-1}{\mathfrak{h}}}^{(t-1)})}_{u}\) . Each GATv2 layer has eight attention heads and computes four latent features overall per player. Accordingly, once the four group convolutions are performed, we have a representation of \({{{{{{{\bf{H}}}}}}}}\in {{\mathbb{R}}}^{4\times 22\times 4}\) , where the first dimension corresponds to the four views ( \({{{{{{{{\bf{H}}}}}}}}}_{{{{{{{{\rm{id}}}}}}}}},\, {{{{{{{{\bf{H}}}}}}}}}_{\leftrightarrow },\, {{{{{{{{\bf{H}}}}}}}}}_{\updownarrow },\, {{{{{{{{\bf{H}}}}}}}}}_{\leftrightarrow \updownarrow }\in {{\mathbb{R}}}^{22\times 4}\) ), the second dimension corresponds to the players (eleven on each team), and the third corresponds to the 4-dimensional latent vector for each player node in this particular view. How this representation is used by the decoder depends on the specific downstream task, as we detail below.

For receiver prediction, which is a fully invariant function (i.e. reflections do not change the receiver), we perform simple frame averaging across all views, arriving at

and then learn a node-wise classifier over the rows of \({{{{{{{{\bf{H}}}}}}}}}^{{{{{{{{\rm{node}}}}}}}}}\in {{\mathbb{R}}}^{22\times 4}\) . We further decode H node into a logit vector \({{{{{{{\bf{O}}}}}}}}\in {{\mathbb{R}}}^{22}\) with a linear layer before computing the corresponding softmax cross entropy loss.

For shot prediction, which is once again fully invariant (i.e. reflections do not change the probability of a shot), we can further average the frame-averaged features across all players to get a global graph representation:

and then learn a binary classifier over \({{{{{{{{\bf{h}}}}}}}}}^{{{{{{{{\rm{graph}}}}}}}}}\in {{\mathbb{R}}}^{4}\) . Specifically, we decode the hidden vector into a single logit with a linear layer and compute the sigmoid binary cross-entropy loss with the corresponding label.

For guided generation (position/velocity adjustments), we generate the player positions and velocities with respect to a particular outcome of interest for the human coaches, predicted over the rows of the hidden feature matrix. For example, the model may adjust the defensive setup to decrease the shot probability by the attacking team. The model output is now equivariant rather than invariant—reflecting the pitch appropriately reflects the predicted positions and velocity vectors. As such, we cannot perform frame averaging, and take only the identity view’s features, \({{{{{{{{\bf{H}}}}}}}}}_{{{{{{{{\rm{id}}}}}}}}}\in {{\mathbb{R}}}^{22\times 4}\) . From this latent feature matrix, we can then learn a conditional distribution from each row, which models the positions or velocities of the corresponding player. To do this, we extend the backbone encoder with conditional variational autoencoder (CVAE 28 , 29 ). Specifically, for the u -th row of H id , h u , we first map its latent embedding to the parameters of a two-dimensional Gaussian distribution \({{{{{{{\mathcal{N}}}}}}}}({\mu }_{u}| {\sigma }_{u})\) , and then sample the coordinates and velocities from this distribution. At training time, we can efficiently propagate gradients through this sampling operation using the reparameterisation trick 28 : sample a random value \({\epsilon }_{u} \sim {{{{{{{\mathcal{N}}}}}}}}(0,1)\) for each player from the unit Gaussian distribution, and then treat μ u  +  σ u ϵ u as the sample for this player. In what follows, we omit edge features for brevity. For each corner kick sample X with the corresponding outcome o (e.g. a binary value indicating a shot event), we extend the standard VAE loss 28 , 29 to our case of outcome-conditional guided generation as

where h u is the player embedding corresponding to the u th row of H id , and \({\mathbb{KL}}\) is Kullback–Leibler (KL) divergence. Specifically, the first term is the generation loss between the real player input x u and the reconstructed sample decoded from h u with the decoder p ϕ . Using the KL term, the distribution of the latent embedding h u is regularised towards p ( h u ∣ o ), which is a multivariate Gaussian in our case.

A complete high-level summary of the generic encoder–decoder equivariant architecture employed by TacticAI can be summarised in Supplementary Fig.  2 . In the following section, we will provide empirical evidence for justifying these architectural decisions. This will be done through targeted ablation studies on our predictive benchmarks (receiver prediction and shot prediction).

Ablation study

We leveraged the receiver prediction task as a way to evaluate various base model architectures, and directly quantitatively assess the contributions of geometric deep learning in this context. We already see that the raw corner kick data can be better represented through geometric deep learning, yielding separable clusters in the latent space that could correspond to different attacking or defending tactics (Fig.  2 ). In addition, we hypothesise that these representations can also yield better performance on the task of receiver prediction. Accordingly, we ablate several design choices using deep learning on this task, as illustrated by the following four questions:

Does a factorised graph representation help? To assess this, we compare it against a convolutional neural network (CNN 30 ) baseline, which does not leverage a graph representation.

Does a graph structure help? To assess this, we compare against a Deep Sets 31 baseline, which only models each node in isolation without considering adjacency information—equivalently, setting each neighbourhood \({{{{{{{{\mathcal{N}}}}}}}}}_{u}\) to a singleton set { u }.

Are attentional GNNs a good strategy? To assess this, we compare against a message passing neural network 32 , MPNN baseline, which uses the fully potent GNN layer from Eq. ( 2 ) instead of the GATv2.

Does accounting for symmetries help? To assess this, we compare our geometric GATv2 baseline against one which does not utilise D 2 group convolutions but utilises D 2 frame averaging, and one which does not explicitly utilise any aspect of D 2 symmetries at all.

Each of these models has been trained for a fixed budget of 50,000 training steps. The test top- k receiver prediction accuracies of the trained models are provided in Supplementary Table  2 . As already discussed in the section “Results”, there is a clear advantage to using a full graph structure, as well as directly accounting for reflection symmetry. Further, the usage of the MPNN layer leads to slight overfitting compared to the GATv2, illustrating how attentional GNNs strike a good balance of expressivity and data efficiency for this task. Our analysis highlights the quantitative benefits of both graph representation learning and geometric deep learning for football analytics from tracking data. We also provide a brief ablation study for the shot prediction task in Supplementary Table  3 .

Training details

We train each of TacticAI’s models in isolation, using NVIDIA Tesla P100 GPUs. To minimise overfitting, each model’s learning objective is regularised with an L 2 norm penalty with respect to the network parameters. During training, we use the Adam stochastic gradient descent optimiser 33 over the regularised loss.

All models, including baselines, have been given an equal hyperparameter tuning budget, spanning the number of message passing steps ({1, 2, 4}), initial learning rate ({0.0001, 0.00005}), batch size ({128, 256}) and L 2 regularisation coefficient ({0.01, 0.005, 0.001, 0.0001, 0}). We summarise the chosen hyperparameters of each TacticAI model in Supplementary Table  1 .

Data availability

The data collected in the human experiments in this study have been deposited in the Zenodo database under accession code https://zenodo.org/records/10557063 , and the processed data which is used in the statistical analysis and to generate the relevant figures in the main text are available under the same accession code. The input and output data generated and/or analysed during the current study are protected and are not available due to data privacy laws and licensing restrictions. However, contact details of the input data providers are available from the corresponding authors on reasonable request.

Code availability

All the core models described in this research were built with the Graph Neural Network processors provided by the CLRS Algorithmic Reasoning Benchmark 18 , and their source code is available at https://github.com/google-deepmind/clrs . We are unable to release our code for this work as it was developed in a proprietary context; however, the corresponding authors are open to answer specific questions concerning re-implementations on request. For general data analysis, we used the following freely available packages: numpy v1.25.2 , pandas v1.5.3 , matplotlib v3.6.1 , seaborn v0.12.2 and scipy v1.9.3 . Specifically, the code of the statistical analysis conducted in this study is available at https://zenodo.org/records/10557063 .

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Acknowledgements

We gratefully acknowledge the support of James French, Timothy Waskett, Hans Leitert and Benjamin Hervey for their extensive efforts in analysing TacticAI’s outputs. Further, we are thankful to Kevin McKee, Sherjil Ozair and Beatrice Bevilacqua for useful technical discussions, and Marc Lanctôt and Satinder Singh for reviewing the paper prior to submission.

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These authors contributed equally: Zhe Wang, Petar Veličković, Daniel Hennes.

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Google DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK

Zhe Wang, Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, Jerome Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess & Demis Hassabis

Liverpool FC, AXA Training Centre, Simonswood Lane, Kirkby, Liverpool, L33 5XB, UK

William Spearman

Liverpool FC, Kirkby, UK

University of Alberta, Amii, Edmonton, AB, T6G 2E8, Canada

Michael Bowling

Google DeepMind, London, UK

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Contributions

Z.W., D. Hennes, L.P. and K.T. coordinated and organised the research effort leading to this paper. P.V. and Z.W. developed the core TacticAI models. Z.W., W.S. and I.G. prepared the Premier League corner kick dataset used for training and evaluating these models. P.V., Z.W., D. Hennes and N.T. designed the case study with human experts and Z.W. and P.V. performed the qualitative evaluation and statistical analysis of its outcomes. Z.W., P.V., D. Hennes, N.T., L.P., M. Kaisers, Y.B., R.E., L.K.W., F.P., W.S., I.G., N.H., M.B., D. Hassabis and K.T. contributed to writing the paper and providing feedback on the final manuscript. J.C., Y.Y., A.R., M. Khan, N.B., P.S. and P.M. contributed valuable technical and implementation discussions throughout the work’s development.

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Correspondence to Zhe Wang , Petar Veličković or Karl Tuyls .

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The authors declare no competing interests but the following competing interests: TacticAI was developed during the course of the Authors’ employment at Google DeepMind and Liverpool Football Club, as applicable to each Author.

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Wang, Z., Veličković, P., Hennes, D. et al. TacticAI: an AI assistant for football tactics. Nat Commun 15 , 1906 (2024). https://doi.org/10.1038/s41467-024-45965-x

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