Funding

What to write

Describe any sources of funding and other support for the study.

Describe the role of funders in data collection, data analysis, and reporting if applicable.

Training

The UK EQUATOR Centre runs training on how to write using reporting guidelines.

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Citation

For attribution, please cite this work as:
O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. SRQR reporting guideline for writing qualitative research articles. The EQUATOR Network guideline dissemination platform. doi:10.1234/equator/1010101

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.

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Smoother publishing

Many journals require completed reporting checklists at submission.

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Maximum impact

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

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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).

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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.

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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.

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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.

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Systematic review protocols

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Meta analyses of Observational Studies

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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.

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Randomised Trial Protocols

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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.

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Case Reports

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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.

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Animal Research

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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.

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Economic Evaluations in Healthcare

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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.

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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.

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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.