The SRQR reporting guideline writing guide

For writing impactful qualitative health research articles that can be understood and used by a wide audience.

Note

If you have not used a writing guide before, read about our suggested writing process.

This guide is not a template. Don’t expect to fill it in and end up with a finished article. Instead, think of it as an exercise book.

  1. Collate information and make notes in this guide;
  2. Delete the prompts and headings, reorganise your notes into a narrative structure, moving content to tables, figures, or appendices when appropriate, thereby creating a writing outline.
  3. Draft, revise, and edit your text in a separate file, referring to your outline throughout.

Before you begin, double check that SRQR is the most applicable reporting guideline for your work. Other reporting guidelines have their own writing guide.

The UK EQUATOR Centre training helps researchers develop writing skills and to use reporting guidelines (like this one) to write research articles and applications that are complete, concise, and compelling. It covers many of the items of the SRQR reporting guideline, including how to prepare effective abstracts, titles, introduction and discussion sections, as well as how to use writing guides to create writing outlines, how to turn outlines into drafts, and drafts into polished text.

Introduction

Purpose or research question

What was the purpose of your study? Authors often frame this as one or more questions or statements, often asking “how” or “why”.

Consider the acronym SPIDER:

  • Sample (who did you speak to?),

  • Phenomena of Interest (the topic of the research, e.g., the behaviour or event you are interested in)

  • Design (your theoretical framework and methods)

  • Evaluation (the outcomes of your study e.g., experiences, attitudes, barriers)

  • Research type (e.g., qualitative, mixed-methods, case study, phenomenology, grounded theory)

NB. You’ll notice this guide asks questions in a different order to how articles are presented. There are more questions about your introduction later, and questions about your abstract and title appear later still. This is because we recommend beginning to write by first defining your research question, then describing your methods and results.

Methods

Qualitative approach and research paradigm

  • If you used a guiding theory, what was it?

  • What were your qualitative approach and paradigm? Describe them in your own words.

  • Why did you choose them?

  • What key references influenced your approach?

Context

  • Where was the research conducted (its setting/site)?

  • Why did you choose this setting/site?

  • Did any salient cultural, political, historical, economic or other external factors influence your study?

Although most of this context will fit best in your methods section, you can also consider placing additional context with your findings in the Results section to add evidence for interpretations and enhance discussion of transferability.

Sampling strategy

Describe how and why research participants, documents, or events were selected; criteria for deciding when no further sampling was necessary, and the rationale for those criteria.

Data collection methods

  • When did data collection begin and end?

  • Name your methods, and describe them step-by-step in sufficient detail to allow a reader to repeat them.

  • Why did you choose these methods? When justifying them, consider your research question, paradigm, approach, and other methods.

  • Who collected data? Describe any important characteristics of them, and any training they received for this study.

Data collection instruments and technologies

  • List all data collection instruments (e.g., interview schedules, questionnaires)

  • Who developed them, and how?

  • Where can readers access them? Consider sharing them in the article, as a supplement, or via a repository like the OSF.

  • Describe any equipment you used for audio or video recordings.

Units of study

Describe the number and relevant characteristics of participants, documents, or events included in the study. Describe the level of participation.

Data processing

  • If you processed data before analysing it, when and how did you do this? (e.g., transcribing, anonymisation, data entry).

  • How did you process data during analysis? (E.g. coding, organising)

  • If you used transcripts, did you check accuracy in any way?

  • How did you maintain data security and protect the privacy or participants?

Data analysis

What was your unit of analysis?

If you used an approach that has a well-defined process for data analysis (e.g., grounded theory, discourse analysis, phenomenography):

  • what literature guided you? (collate your references)

  • describe your processes in sufficient detail so readers can judge the extent to which your processes align with the guiding approach.

  • If you modified or deviated from the guiding approach, explain and justify these modifications.

Otherwise, describe your process step by step.

Who performed analysis steps?

Why did you choose this analysis process?

Ethical issues pertaining to human subjects

Did you receive ethical approval for this study? If so, what was the name of the review board, and what was the approval number?

If not, describe why.

What procedures did you use to protect participants? E.g.,

  • How did you collect informed consent? If you didn’t, why not?

  • How did you ensure data security and integrity, if at all?

  • Did you anonymize participants, if at all?

If you offered compensation or incentives to facilitate participation, describe them.

Researcher characteristics and reflexivity

Introduce yourself and your team members so readers may understand your background, experience, and any other characteristics you feel may be relevant.

Upon beginning this study, did you or your team hold any perspectives, assumptions, prior knowledge or hypotheses (your “stance”)?

Describe how your characteristics or stance influenced choices you made when designing your study, and when collecting and analysing data.

Describe the researchers’ relationships to participants in the study and what decisions were made in light of these relationships.

Don’t be afraid of describing these – they’re not limitations! – reflexivity is a key strength in qualitative research.

Techniques to enhance trustworthiness

What techniques did you use to enhance trustworthiness and credibility of data collection and analysis?

Some authors prefer to present this as a table, especially if submitting to a quantitative journal.

See Lincoln and Guba’s Evaluative Criteria for trustworthiness.

Results

Synthesis and interpretation

What were your main findings? List them as bullet points. It’s often useful to begin your findings with a summary.

For each bullet point, make a note of variety and counter-examples.

If your findings include integration with prior literature or theory and/or the development of a theory, model or meta-narrative, consider using tables and figures and describe these as text placeholders (you’ll make real figures and tables after drafting).

Don’t worry about reporting exact frequency counts. Frequency counts play a limited role in qualitative research, and need not be reported unless they play a meaningful role in interpretation of the data. Instead, consider using words like “most”, “few”, “all”.

Introduction (again)

Problem Formulation

Beyond summarising what is already known about your topic, your introduction should identify what remains unknown and how your research question addresses this knowledge gap.

  • Bullet point what is already known with key references

  • Bullet point what is not known

  • Why is this gap important?

  • Look through your research question, and consider introducing your chosen sample, phenomena, design, evaluation, and research type. Why did you make these choices?

After you reorganise this document into a coherent narrative, this section will come directly before your research question. After this section, readers should be able to predict your research question and be familiar with the key terms you will use throughout your manuscript.

Think about how to structure your introduction to make the narrative clear and compelling.

Brainstorm ideas for a strong opening.

Discussion

Integration with prior work, implications, transferability, and contribution(s) to the field

  • How do your findings and conclusions relate to the literature you cited in your introduction? Do they challenge previous work, support it, or add new context?

  • How do your findings advance your field?

  • How might your findings transfer or generalize to other phenomena or fields?

Limitations

All research has limitations. The best researchers try hard to acknowledge them and discuss how they may have influenced findings.

  • Look back at the items above, and consider whether your chosen paradigm, approach, and methods may influence the situations to which the findings may reasonably apply

  • Look at the items above, and list any that you did not (or could not do). Consider how these gaps may have influenced your findings. If not, why not?

Other

Conflicts of interest

  • What conflicts of interest may have influenced the research? These could be financial, professional, or personal.

  • How might these have influenced the findings?

Some aspects may have been mentioned as part of Reflexivity.

Funding

  • List your sources of funding, including any grant codes.

  • Describe the role of your funders in data collection, analysis and reporting.

Title & Abstract

Abstract

We recommend writing your abstract and title last.

Your abstract needs to paint an accurate and interesting summary of your work.

  • If you have a target journal in mind, check their author instructions for limits on abstract length and structure.

  • It will be indexed by search engines, so look at your notes above and pick out the key phrases.

  • these key phrases should include:

    • your phenomena of interest

    • your methods, including the approach or perspective (e.g., general inductive, grounded theory),

    • context (setting, time period),

    • sample (number and key characteristics of participants, events, or documents),

    • data collection strategies (e.g., observation, interview, focus group),

    • and analysis techniques.

  • Craft these keywords into sentences that describe your research question and methods. Add a background sentence to justify why your research question is important.

  • Summarise your main findings (e.g., themes or inferences) and their implications.

Most journals invite authors to provide keywords in addition to the abstract, so if you struggle to include all key phrases within the abstract, consider providing them as keywords. Your abstract, title, and keywords will all be indexed, so to make your work as findable as possible, you should try to use as many different relevant terms as possible.

Title

Your title should include the nature and topic of your study, whilst also sparking readers’ interest.

List the keywords you would like to include, and brainstorm options to discuss with your colleagues.

How to cite

Describe how you used SRQR at the end of your Methods section, referencing the resources you used e.g.,

‘We used the SRQR(1) writing guide to draft this manuscript, and the SRQR reporting checklist(2) when editing, included in supplement A’

If you use a reporting checklist, remember to include it as a supplement when publishing so that readers can easily find information and see how you have interpreted the guidance.

References

1.
AUTHOR. The SRQR writing guide. In: #TODO, editor. The EQUATOR network reporting guideline platform [Internet]. The UK EQUATOR Centre; 2025. Available from: https:/jamesrharwood.github.io/equator-guidelines-website/guidelines/srqr/srqr-writing-guide.docx
2.
AUTHOR. The SRQR reporting checklist. In: #TODO, editor. The EQUATOR network reporting guideline platform [Internet]. The UK EQUATOR Centre; 2025. Available from: https:/jamesrharwood.github.io/equator-guidelines-website/guidelines/srqr/srqr-checklist.docx

Citation

For attribution, please cite this work as:
O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. The EQUATOR Network guideline dissemination platform. SRQR reporting guideline for writing qualitative research articles. Available from: https://jamesrharwood.github.io/equator-guidelines-website/guidelines/srqr/srqr-writing-guide.html

Reporting Guidelines are recommendations to help describe your work clearly

Your research will be used by people from different disciplines and backgrounds for decades to come. Reporting guidelines list the information you should describe so that everyone can understand, replicate, and synthesise your work.

Reporting guidelines do not prescribe how research should be designed or conducted. Rather, they help authors transparently describe what they did, why they did it, and what they found.

Reporting guidelines make writing research easier, and transparent research leads to better patient outcomes.

Easier writing

Following guidance makes writing easier and quicker.

Smoother publishing

Many journals require completed reporting checklists at submission.

Maximum impact

From nobel prizes to null results, articles have more impact when everyone can use them.

Who reads research?

You work will be read by different people, for different reasons, around the world, and for decades to come. Reporting guidelines help you consider all of your potential audiences. For example, your research may be read by researchers from different fields, by clinicians, patients, evidence synthesisers, peer reviewers, or editors. Your readers will need information to understand, to replicate, apply, appraise, synthesise, and use your work.

Cohort studies

A cohort study is an observational study in which a group of people with a particular exposure (e.g. a putative risk factor or protective factor) and a group of people without this exposure are followed over time. The outcomes of the people in the exposed group are compared to the outcomes of the people in the unexposed group to see if the exposure is associated with particular outcomes (e.g. getting cancer or length of life).

Source.

Case-control studies

A case-control study is a research method used in healthcare to investigate potential risk factors for a specific disease. It involves comparing individuals who have been diagnosed with the disease (cases) to those who have not (controls). By analysing the differences between the two groups, researchers can identify factors that may contribute to the development of the disease.

An example would be when researchers conducted a case-control study examining whether exposure to diesel exhaust particles increases the risk of respiratory disease in underground miners. Cases included miners diagnosed with respiratory disease, while controls were miners without respiratory disease. Participants' past occupational exposures to diesel exhaust particles were evaluated to compare exposure rates between cases and controls.

Source.

Cross-sectional studies

A cross-sectional study (also sometimes called a "cross-sectional survey") serves as an observational tool, where researchers capture data from a cohort of participants at a singular point. This approach provides a 'snapshot'— a brief glimpse into the characteristics or outcomes prevalent within a designated population at that precise point in time. The primary aim here is not to track changes or developments over an extended period but to assess and quantify the current situation regarding specific variables or conditions. Such a methodology is instrumental in identifying patterns or correlations among various factors within the population, providing a basis for further, more detailed investigation.

Source

Systematic reviews

A systematic review is a comprehensive approach designed to identify, evaluate, and synthesise all available evidence relevant to a specific research question. In essence, it collects all possible studies related to a given topic and design, and reviews and analyses their results.

The process involves a highly sensitive search strategy to ensure that as much pertinent information as possible is gathered. Once collected, this evidence is often critically appraised to assess its quality and relevance, ensuring that conclusions drawn are based on robust data. Systematic reviews often involve defining inclusion and exclusion criteria, which help to focus the analysis on the most relevant studies, ultimately synthesising the findings into a coherent narrative or statistical synthesis. Some systematic reviews will include a meta-analysis.

Source

Systematic review protocols

TODO

Meta analyses of Observational Studies

TODO

Randomised Trials

A randomised controlled trial (RCT) is a trial in which participants are randomly assigned to one of two or more groups: the experimental group or groups receive the intervention or interventions being tested; the comparison group (control group) receive usual care or no treatment or a placebo. The groups are then followed up to see if there are any differences between the results. This helps in assessing the effectiveness of the intervention.

Source

Randomised Trial Protocols

TODO

Qualitative research

Research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation. This type of research typically involves in-depth interviews, focus groups, or field observations in order to collect data that is rich in detail and context. Qualitative research is often used to explore complex phenomena or to gain insight into people's experiences and perspectives on a particular topic. It is particularly useful when researchers want to understand the meaning that people attach to their experiences or when they want to uncover the underlying reasons for people's behavior. Qualitative methods include ethnography, grounded theory, discourse analysis, and interpretative phenomenological analysis.

Source

Case Reports

TODO

Diagnostic Test Accuracy Studies

Diagnostic accuracy studies focus on estimating the ability of the test(s) to correctly identify subjects with a predefined target condition, or the condition of interest (sensitivity) as well as to clearly identify those without the condition (specificity).

Prediction Models

Prediction model research is used to test the accurarcy of a model or test in estimating an outcome value or risk. Most models estimate the probability of the presence of a particular health condition (diagnostic) or whether a particular outcome will occur in the future (prognostic). Prediction models are used to support clinical decision making, such as whether to refer patients for further testing, monitor disease deterioration or treatment effects, or initiate treatment or lifestyle changes. Examples of well known prediction models include EuroSCORE II for cardiac surgery, the Gail model for breast cancer, the Framingham risk score for cardiovascular disease, IMPACT for traumatic brain injury, and FRAX for osteoporotic and hip fractures.

Source

Animal Research

TODO

Quality Improvement in Healthcare

Quality improvement research is about finding out how to improve and make changes in the most effective way. It is about systematically and rigourously exploring "what works" to improve quality in healthcare and the best ways to measure and disseminate this to ensure positive change. Most quality improvement effectiveness research is conducted in hospital settings, is focused on multiple quality improvement interventions, and uses process measures as outcomes. There is a great deal of variation in the research designs used to examine quality improvement effectiveness.

Source

Economic Evaluations in Healthcare

TODO

Meta Analyses

A meta-analysis is a statistical technique that amalgamates data from multiple studies to yield a single estimate of the effect size. This approach enhances precision and offers a more comprehensive understanding by integrating quantitative findings. Central to a meta-analysis is the evaluation of heterogeneity, which examines variations in study outcomes to ensure that differences in populations, interventions, or methodologies do not skew results. Techniques such as meta-regression or subgroup analysis are frequently employed to explore how various factors might influence the outcomes. This method is particularly effective when aiming to quantify the effect size, odds ratio, or risk ratio, providing a clearer numerical estimate that can significantly inform clinical or policy decisions.

How Meta-analyses and Systematic Reviews Work Together

Systematic reviews and meta-analyses function together, each complementing the other to provide a more robust understanding of research evidence. A systematic review meticulously gathers and evaluates all pertinent studies, establishing a solid foundation of qualitative and quantitative data. Within this framework, if the collected data exhibit sufficient homogeneity, a meta-analysis can be performed. This statistical synthesis allows for the integration of quantitative results from individual studies, producing a unified estimate of effect size. Techniques such as meta-regression or subgroup analysis may further refine these findings, elucidating how different variables impact the overall outcome. By combining these methodologies, researchers can achieve both a comprehensive narrative synthesis and a precise quantitative measure, enhancing the reliability and applicability of their conclusions. This integrated approach ensures that the findings are not only well-rounded but also statistically robust, providing greater confidence in the evidence base.

Why Don't All Systematic Reviews Use a Meta-Analysis?

Systematic reviews do not always have meta-analyses, due to variations in the data. For a meta-analysis to be viable, the data from different studies must be sufficiently similar, or homogeneous, in terms of design, population, and interventions. When the data shows significant heterogeneity, meaning there are considerable differences among the studies, combining them could lead to skewed or misleading conclusions. Furthermore, the quality of the included studies is critical; if the studies are of low methodological quality, merging their results could obscure true effects rather than explain them.

Protocol

A plan or set of steps that defines how something will be done. Before carrying out a research study, for example, the research protocol sets out what question is to be answered and how information will be collected and analysed.

Source

Research Paradigm

The set of beliefs and assumptions that guide the research process. These commonly include positivist, post-positivist, constructivist or interpretivist, and critical theory. Qualitative research generally draws from a post-positivist or constructivist/interpretivist paradigm."

Instruments

Data collection instruments include (but are not limited to) interview or focus group guides, observational protocols and prompts for field notes, and data extraction or coding protocols for selection and analysis of documents, photographs, videos, or other artifacts"

Bias

A term drawn from quantitative research, bias technically means a systematic error, where a particular research finding deviates from a 'true' finding. This might come about through errors in the manner of interviewing, or by errors in sampling. In qualitative research this is a problematic concept, since by definition the qualitative researcher is part of the process, and all researchers are different. This human factor has been said to be both the greatest strength and the greatest weakness of qualitative method. What can be done in commercial qualitative research, however, is to minimise obvious and avoidable sources of bias, for example by not confining all the fieldwork to one social group or geographic location, by taking steps to recognise the personal views of the researcher, (using techniques such as bracketing), and by working in teams.

Sampling strategy

Several sampling strategies are commonly used in qualitative research, although most fall under the umbrella of purposeful (or purposive) sampling.

Purposeful sampling means that participants, documents, or events are selected for their relevance to the research question, based on guiding theory or experiences and assumptions of the researchers. Over the course of the research process, researchers may determine that additional or different participants, documents, or events should be included to address the research question.

Other sampling techniques, such as theoretical sampling (seeking examples of theoretical constructs), snowball sampling (using study participants to identify additional participants who meet study criteria), and convenience sampling (including any volunteers with no or minimal criteria for inclusion) may be appropriate depending on the question and approach, so long as the authors provide explanation and justification.

Qualitative Approach

A qualitative “approach” is a general way of thinking about conducting qualitative research. It describes, either explicitly or implicitly, the purpose of the qualitative research, the role of the researcher(s), the stages of research, and the method of data analysis. Commonly used approaches include ethnography, grounded theory, case study, phenomenology, and narrative research.

Data Collection Methods

Researchers may choose to use information from multiple sources, contexts, and/or time points depending on their approach and research question(s). Data collection methods include (but are not limited to) interviews, focus groups, observations (direct or indirect via video), and review of written text, photographs, and other documents or materials. See {{< meta items.data-collection-instruments.title >}} for triangulation.

Iterative

Qualitative research often occurs as an iterative process, meaning that researchers begin data analysis before they complete data collection. The data collection and analysis process may occur in phases or stages. As part of an iterative collection-analysis process, researchers will often alter their data collection methods to explore their preliminary impressions in greater depth and/or actively pursue confirming and disconfirming perspectives.

Study period

The start and end dates for data collection and analysis.

Ethnography

The scientific description of peoples and cultures with their customs, habits, and mutual differences.

Read more

Grounded theory

A method consisting of a set of systematic, but flexible, guidelines for conducting inductive qualitative inquiry aimed toward theory construction. This method focuses squarely on the analytic phases of research, although both data collection and analysis inform and shape each other and are conducted in tandem.

Read more

Degree of participation

For example, if some participants were observed and interviewed and others only interviewed, or if some participants completed multiple interviews and others completed a single interview, these variations should be explained.

Unit of analysis

In qualitative research, the unit of analysis is not necessarily the same as the unit of sampling (e.g., individual participants or events). Instead, some approaches use specific events as the unit of analysis, such as mentions of a particular topic or experience, or observations of a particular behavior or phenomenon, while others use groups rather than individual group participants. This specification has implications for how the data are organized and analyzed as well as the inferences drawn from the data.

Reflexivity

Reflexivity refers to intentional, systematic consideration of the potential or actual effects of the researcher(s) on all aspects of the study process.

Transferability

The transferability of a research finding is the extent to which it can be applied in other contexts and studies. It is thus equivalent to or a replacement for the terms generalizability and external validity.

Generalizability

The appropriate scope for generalization of the findings beyond the study (e.g., to other settings, populations, time periods, circumstances)

Analytic findings

Analytic findings may include interpretations, inferences, narratives, themes, and models.

Frequency counts

The frequency of specific themes or codes.