qualitative research statements imply cause and effect

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• Published: 27 May 2020

How to use and assess qualitative research methods

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
• Wolfgang Wick 1 , 2 &
• Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 , 8 , 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 , 32 , 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Availability of data and materials

Not applicable.

Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

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What is qualitative research?

"Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1]  Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data."

"Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2]  Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2]  One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3]  Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest."

• Qualitative Study - Steven Tenny; Grace D. Brannan; Janelle M. Brannan; Nancy C. Sharts-Hopko. This article details what qualitative research is, and some of the methodologies used.

Examples of Qualitative Research

• Quantitative vs Qualitative Chart Chart showing examples of quantitative vs. qualitative research.

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9.1 Qualitative research: What is it and when should it be used?

Learning objectives.

• Define qualitative research
• Explain the differences between qualitative and quantitative research
• Identify the benefits and challenges of qualitative research

Qualitative versus quantitative research methods refers to data-oriented considerations about the type of data to collected and how they are analyzed. Qualitative research relies mostly on non-numeric data, such as interviews and observations to understand their meaning, in contrast to quantitative research which employs numeric data such as scores and metrics. Hence, qualitative research is not amenable to statistical procedures, but is coded using techniques like content analysis. Sometimes, coded qualitative data are tabulated quantitatively as frequencies of codes, but this data is not statistically analyzed.  Qualitative research has its roots in anthropology, sociology, psychology, linguistics, and semiotics, and has been available since the early 19th century, long before quantitative statistical techniques were employed.

Distinctions from Quantitative Research

In qualitative research, the role of the researcher receives critical attention.  In some methods such as ethnography, action research, and participant observation, the researcher is considered part of the social phenomenon, and her specific role and involvement in the research process must be made clear during data analysis. In other methods, such as case research, the researcher must take a “neutral” or unbiased stance during the data collection and analysis processes, and ensure that her personal biases or preconceptions does not taint the nature of subjective inferences derived from qualitative research.

Analysis in qualitative research is holistic and contextual, rather than being reductionist and isolationist. Qualitative interpretations tend to focus on language, signs, and meanings from the perspective of the participants involved in the social phenomenon, in contrast to statistical techniques that are employed heavily in positivist research. Rigor in qualitative research is viewed in terms of systematic and transparent approaches for data collection and analysis rather than statistical benchmarks for construct validity or significance testing.

Lastly, data collection and analysis can proceed simultaneously and iteratively in qualitative research. For instance, the researcher may conduct an interview and code it before proceeding to the next interview. Simultaneous analysis helps the researcher correct potential flaws in the interview protocol or adjust it to capture the phenomenon of interest better. The researcher may even change her original research question if she realizes that her original research questions are unlikely to generate new or useful insights. This is a valuable but often understated benefit of qualitative research, and is not available in quantitative research, where the research project cannot be modified or changed once the data collection has started without redoing the entire project from the start.

Benefits and Challenges of Qualitative Research

Qualitative research has several unique advantages. First, it is well-suited for exploring hidden reasons behind complex, interrelated, or multifaceted social processes, such as inter-firm relationships or inter-office politics, where quantitative evidence may be biased, inaccurate, or otherwise difficult to obtain. Second, it is often helpful for theory construction in areas with no or insufficient pre-existing theory. Third, qualitative research is also appropriate for studying context-specific, unique, or idiosyncratic events or processes. Fourth, it can help uncover interesting and relevant research questions and issues for follow-up research.

At the same time, qualitative research also has its own set of challenges. First, this type of research tends to be more time and resource intensive than quantitative research in data collection and analytic efforts. Too little data can lead to false or premature assumptions, while too much data may not be effectively processed by the researcher. Second, qualitative research requires well-trained researchers who are capable of seeing and interpreting complex social phenomenon from the perspectives of the embedded participants and reconciling the diverse perspectives of these participants, without injecting their personal biases or preconceptions into their inferences. Third, all participants or data sources may not be equally credible, unbiased, or knowledgeable about the phenomenon of interest, or may have undisclosed political agendas, which may lead to misleading or false impressions. Inadequate trust between participants and researcher may hinder full and honest self-representation by participants, and such trust building takes time. It is the job of the qualitative researcher to “see through the smoke” (hidden or biased agendas) and understand the true nature of the problem. Finally, given the heavily contextualized nature of inferences drawn from qualitative research, such inferences do not lend themselves well to replicability or generalizability.

Key Takeaways

• Qualitative research examines words and other non-numeric media
• Analysis in qualitative research is holistic and contextual
• Qualitative research offers unique benefits, while facing challenges to generalizability and replicability
• Qualitative methods – examine words or other media to understand their meaning

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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This chapter examines two approaches used in social science research: the “causes-of-effects” approach and the “effects-of-causes” approach. The quantitative and qualitative cultures differ in the extent to which and the ways in which they address causes-of-effects and effects-of-causes questions. Quantitative scholars, who favor the effects-of-causes approach, focus on estimating the average effects of particular variables within populations or samples. By contrast, qualitative scholars employ individual case analysis to explain outcomes as well as the effects of particular causal factors. The chapter first considers the type of research question addressed by both quantitative and qualitative researchers before discussing the use of within-case analysis by the latter to investigate individual cases versus cross-case analysis by the former to elucidate central tendencies in populations. It also describes the complementarities between qualitative and quantitative research that make mixed-method research possible.

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Market Research

Understanding the Purpose of a Qualitative Study: Methods and Examples

In a data-driven world that seems to worship the quantitative, it’s easy to overlook the profound contributions of qualitative study. Yet qualitative research, with its focus on exploring subjective human experiences, plays an indispensable role in our understanding of markets. 

In this blog post, we unravel the true purpose of a qualitative study, examining its various methods and citing real-world examples. Discover how this intricate blend of art and science can provide unparalleled views into consumer behavior, helping your brand connect with audiences on a deeply personal level. Let’s dive in and explore the depths from which real insights emerge.

Understanding Qualitative Research

Qualitative research is a methodological approach used to gain a deep understanding of phenomena or social issues through exploring participants’ experiences, behaviors, and perspectives in a specific context. It aims to provide rich descriptions and explanations of processes within identifiable local contexts. Unlike quantitative research, which focuses on numerical data analysis, qualitative research delves into the reasons behind people’s thoughts and feelings, complementing quantitative methods.

Imagine that you are researching patient satisfaction in healthcare settings. You might utilize qualitative research methods to capture how patients and healthcare professionals feel about care in a social, clinical, or interpersonal context. Through techniques such as interviews, focus groups, or observations, you can uncover valuable insights that provide a more nuanced understanding of patient experiences.

Qualitative vs Quantitative Research

When discussing research methodologies, it is important to distinguish between qualitative and quantitative approaches . While both aim to gather information, there are key differences in their objectives and methodologies.

Quantitative research focuses on collecting numerical data that can be analyzed statistically to identify patterns and generalize findings to a larger population or phenomenon. This type of research is concerned with measuring variables and testing hypotheses using tools like surveys or experiments. It aims to establish cause-and-effect relationships and draw conclusions based on statistical evidence.

In contrast, qualitative research collects non-numerical data to understand social life through targeted populations or places. It is framed in opposition to quantitative research, which uses numerical data for large-scale trends and causal relationships. Qualitative researchers use methods such as observation, interviews, open-ended surveys, focus groups, content analysis, or oral history to investigate the meanings that people attribute to their behavior and interactions. This approach provides an in-depth understanding of attitudes, behaviors, interactions, events, and social processes.

For instance, let’s consider a study on the experiences of individuals living with chronic pain. Through qualitative research methods like in-depth interviews or participant observation, researchers can uncover the lived experiences of individuals, exploring how pain impacts their daily lives, relationships, and overall well-being. This qualitative approach enables researchers to capture rich and contextual insights that quantitative methods alone cannot provide.

While quantitative research seeks to establish generalizability through statistical analysis, qualitative research delves into the subjective meanings and interpretations that individuals ascribe to their experiences. These two approaches complement each other in providing a more comprehensive understanding of complex phenomena.

• According to a review published in Nature in 2020, over 80% of all social sciences research employs some form of qualitative method.
• A 2019 study found that around 75% of health-related publications using qualitative methods were used for exploring patient experiences and perceptions.
• Another survey suggests that approximately 68% of qualitative research is utilized to develop hypotheses and theories in areas where available data is scant or nonexistent.

Conducting a Qualitative Study

Conducting a qualitative study requires methodological rigor and careful planning. Researchers must consider the appropriate approach, methods, and procedures to align with their research questions, objectives, and potential biases. Different methodologies can be employed based on the nature of the study.

One widely used methodology is ethnography, which involves observing participants in their natural environments over an extended period of time. This method allows researchers to understand how environmental constraints and context shape behaviors and outcomes.

Another approach is grounded theory, which suggests that theory should emerge from data rather than being driven by pre-existing hypotheses or theories. Grounded theory is particularly useful when little is known about a problem or a specific context.

Phenomenology aims to understand problems and situations through individuals’ subjective experiences. It explores how people make sense of their world and give meaning to their everyday lives.

Throughout the process of conducting qualitative research, it is crucial for researchers to practice reflexivity. Reflexivity acknowledges the researcher’s subjectivity and biases. Being transparent about one’s background and interests enables readers to draw their own conclusions about interpretations.

Now that we understand the key objectives of qualitative research and the importance of conducting it with methodological rigor, let’s delve into the foundational step of setting research questions.

Setting Research Questions

In any qualitative study, the starting point is setting research questions. These questions serve as a guide to uncovering insights and an in-depth understanding of the phenomenon under investigation. Rather than focusing on numerical data analysis like quantitative research, qualitative studies aim to explore the reasons behind people’s thoughts, feelings, and behaviors. The research questions bring clarity to what researchers seek to learn from participants. They also help ensure that the study remains focused and aligned with the objectives.

Let’s consider an example of a qualitative study exploring patients’ experiences with telehealth during the COVID-19 pandemic. The research questions could revolve around understanding how patients perceive the effectiveness of telehealth, uncovering barriers they face in accessing care, and exploring their satisfaction levels with virtual healthcare encounters.

By setting clear research questions at the outset, researchers can align their objectives with the topic of interest and design a robust methodology to collect relevant data.

Approach to Research Methodology

The methodology used in qualitative research plays a crucial role in shaping how data will be collected, analyzed, and interpreted. Scholars employ various approaches based on the nature of their research questions and desired outcomes. Some common methodologies include ethnography, grounded theory, and phenomenology.

Ethnography involves immersing oneself within a particular group or community to observe participants’ behavior in natural environments over an extended period of time. This approach provides insights into how environmental constraints and context influence behaviors and outcomes.

Grounded theory is valuable when little is known about a problem or context. It emphasizes deriving theories from data rather than relying on preconceived hypotheses or theories. This iterative process helps build a comprehensive understanding of the phenomenon being studied.

Phenomenology aims to understand problems and situations from the perspective of common understanding and subjective experience. It explores how individuals experience and give meaning to their world.

For instance, if researchers wanted to explore the experiences of nurses dealing with workplace burnout, they might choose a phenomenological approach. Through in-depth interviews and reflection on personal experiences, researchers can gain insights into the lived experiences of nurses coping with burnout and the meaning they attach to it.

By carefully selecting the appropriate research methodology, researchers lay a strong foundation for analyzing qualitative data effectively.

Analyzing Qualitative Data

Once the data from a qualitative study has been collected, the next step is to analyze it. Unlike quantitative research, where statistical analysis is employed, qualitative data analysis focuses on interpreting and making sense of the rich and diverse information gathered. This process involves several key steps.

Firstly, researchers must thoroughly familiarize themselves with the data by reading and re-reading transcripts, field notes, or other sources. This immersion allows them to identify recurring themes, patterns, or codes that emerge from the data. These themes may be based on specific words or phrases used by participants or broader concepts that arise organically.

Researchers then engage in coding, which involves systematically organizing and categorizing data based on identified themes or concepts. Codes act as labels that help in classifying different aspects of the data and enable researchers to compare and contrast findings across different sources.

Next, researchers engage in the process of data reduction, where they condense and summarize the vast amount of qualitative data collected. This often involves creating charts or matrices to visualize the relationships between themes or categories. Additionally, researchers may create narrative summaries or rich descriptions to communicate the essence of participants’ experiences effectively.

Finally, interpretations are made based on the analyzed data. These interpretations involve generating explanations or theories that offer insights into participants’ perspectives, behaviors, and interactions within their social context. Researchers need to remain reflexive throughout this process, acknowledging their own biases and subjectivity that may influence their interpretations.

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• Fuel innovation: Design products and services that resonate deeply with your target audience.
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Are you tired of staring at cold data, longing to understand the human truth behind the numbers? Discuss.io’s innovative qualitative research platform empowers you to go beyond the surface and delve into the rich tapestry of customer thoughts, feelings, and motivations. Schedule a demo and see how Discuss.io can transform your research process.

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• Correlation vs. Causation | Difference, Designs & Examples

Correlation vs. Causation | Difference, Designs & Examples

Published on July 12, 2021 by Pritha Bhandari . Revised on June 22, 2023.

Correlation means there is a statistical association between variables. Causation means that a change in one variable causes a change in another variable.

In research, you might have come across the phrase “correlation doesn’t imply causation.” Correlation and causation are two related ideas, but understanding their differences will help you critically evaluate sources and interpret scientific research.

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What’s the difference, why doesn’t correlation mean causation, correlational research, third variable problem, regression to the mean, spurious correlations, directionality problem, causal research, other interesting articles, frequently asked questions about correlation and causation.

Correlation describes an association between types of variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables. These variables change together: they covary. But this covariation isn’t necessarily due to a direct or indirect causal link.

Causation means that changes in one variable brings about changes in the other; there is a cause-and-effect relationship between variables. The two variables are correlated with each other and there is also a causal link between them.

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There are two main reasons why correlation isn’t causation. These problems are important to identify for drawing sound scientific conclusions from research.

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. For example, ice cream sales and violent crime rates are closely correlated, but they are not causally linked with each other. Instead, hot temperatures, a third variable, affects both variables separately. Failing to account for third variables can lead research biases to creep into your work.

The directionality problem occurs when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other. For example, vitamin D levels are correlated with depression, but it’s not clear whether low vitamin D causes depression, or whether depression causes reduced vitamin D intake.

You’ll need to use an appropriate research design to distinguish between correlational and causal relationships:

• Correlational research designs can only demonstrate correlational links between variables.
• Experimental designs can test causation.

In a correlational research design, you collect data on your variables without manipulating them.

Correlational research is usually high in external validity , so you can generalize your findings to real life settings. But these studies are low in internal validity , which makes it difficult to causally connect changes in one variable to changes in the other.

These research designs are commonly used when it’s unethical, too costly, or too difficult to perform controlled experiments. They are also used to study relationships that aren’t expected to be causal.

Without controlled experiments, it’s hard to say whether it was the variable you’re interested in that caused changes in another variable. Extraneous variables are any third variable or omitted variable other than your variables of interest that could affect your results.

Limited control in correlational research means that extraneous or confounding variables serve as alternative explanations for the results. Confounding variables can make it seem as though a correlational relationship is causal when it isn’t.

When two variables are correlated, all you can say is that changes in one variable occur alongside changes in the other.

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Regression to the mean is observed when variables that are extremely higher or extremely lower than average on the first measurement move closer to the average on the second measurement. Particularly in research that intentionally focuses on the most extreme cases or events, RTM should always be considered as a possible cause of an observed change.

Players or teams featured on the cover of SI have earned their place by performing exceptionally well. But athletic success is a mix of skill and luck, and even the best players don’t always win.

Chances are that good luck will not continue indefinitely, and neither can exceptional success.

A spurious correlation is when two variables appear to be related through hidden third variables or simply by coincidence.

The Theory of the Stork draws a simple causal link between the variables to argue that storks physically deliver babies. This satirical study shows why you can’t conclude causation from correlational research alone.

When you analyze correlations in a large dataset with many variables, the chances of finding at least one statistically significant result are high. In this case, you’re more likely to make a type I error . This means erroneously concluding there is a true correlation between variables in the population based on skewed sample data.

To demonstrate causation, you need to show a directional relationship with no alternative explanations. This relationship can be unidirectional, with one variable impacting the other, or bidirectional, where both variables impact each other.

A correlational design won’t be able to distinguish between any of these possibilities, but an experimental design can test each possible direction, one at a time.

• Physical activity may affect self esteem
• Self esteem may affect physical activity
• Physical activity and self esteem may both affect each other

In correlational research, the directionality of a relationship is unclear because there is limited researcher control. You might risk concluding reverse causality, the wrong direction of the relationship.

Causal links between variables can only be truly demonstrated with controlled experiments . Experiments test formal predictions, called hypotheses , to establish causality in one direction at a time.

Experiments are high in internal validity , so cause-and-effect relationships can be demonstrated with reasonable confidence.

You can establish directionality in one direction because you manipulate an independent variable before measuring the change in a dependent variable.

In a controlled experiment, you can also eliminate the influence of third variables by using random assignment and control groups.

Random assignment helps distribute participant characteristics evenly between groups so that they’re similar and comparable. A control group lets you compare the experimental manipulation to a similar treatment or no treatment (or a placebo, to control for the placebo effect ).

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 test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

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What is causal research design?

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14 May 2023

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Examining these relationships gives researchers valuable insights into the mechanisms that drive the phenomena they are investigating.

Organizations primarily use causal research design to identify, determine, and explore the impact of changes within an organization and the market. You can use a causal research design to evaluate the effects of certain changes on existing procedures, norms, and more.

This article explores causal research design, including its elements, advantages, and disadvantages.

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• Components of causal research

You can demonstrate the existence of cause-and-effect relationships between two factors or variables using specific causal information, allowing you to produce more meaningful results and research implications.

These are the key inputs for causal research:

The timeline of events

Ideally, the cause must occur before the effect. You should review the timeline of two or more separate events to determine the independent variables (cause) from the dependent variables (effect) before developing a hypothesis. 

If the cause occurs before the effect, you can link cause and effect and develop a hypothesis .

For instance, an organization may notice a sales increase. Determining the cause would help them reproduce these results. 

Upon review, the business realizes that the sales boost occurred right after an advertising campaign. The business can leverage this time-based data to determine whether the advertising campaign is the independent variable that caused a change in sales. 

Evaluation of confounding variables

In most cases, you need to pinpoint the variables that comprise a cause-and-effect relationship when using a causal research design. This uncovers a more accurate conclusion. 

Co-variations between a cause and effect must be accurate, and a third factor shouldn’t relate to cause and effect. 

Observing changes

Variation links between two variables must be clear. A quantitative change in effect must happen solely due to a quantitative change in the cause. 

You can test whether the independent variable changes the dependent variable to evaluate the validity of a cause-and-effect relationship. A steady change between the two variables must occur to back up your hypothesis of a genuine causal effect. 

• Why is causal research useful?

Causal research allows market researchers to predict hypothetical occurrences and outcomes while enhancing existing strategies. Organizations can use this concept to develop beneficial plans. 

Causal research is also useful as market researchers can immediately deduce the effect of the variables on each other under real-world conditions. 

Once researchers complete their first experiment, they can use their findings. Applying them to alternative scenarios or repeating the experiment to confirm its validity can produce further insights. 

Businesses widely use causal research to identify and comprehend the effect of strategic changes on their profits. 

• How does causal research compare and differ from other research types?

Other research types that identify relationships between variables include exploratory and descriptive research . 

Here’s how they compare and differ from causal research designs:

Exploratory research

An exploratory research design evaluates situations where a problem or opportunity's boundaries are unclear. You can use this research type to test various hypotheses and assumptions to establish facts and understand a situation more clearly.

You can also use exploratory research design to navigate a topic and discover the relevant variables. This research type allows flexibility and adaptability as the experiment progresses, particularly since no area is off-limits.

It’s worth noting that exploratory research is unstructured and typically involves collecting qualitative data . This provides the freedom to tweak and amend the research approach according to your ongoing thoughts and assessments. 

Unfortunately, this exposes the findings to the risk of bias and may limit the extent to which a researcher can explore a topic. 

This table compares the key characteristics of causal and exploratory research:

Descriptive research

This research design involves capturing and describing the traits of a population, situation, or phenomenon. Descriptive research focuses more on the " what " of the research subject and less on the " why ."

Since descriptive research typically happens in a real-world setting, variables can cross-contaminate others. This increases the challenge of isolating cause-and-effect relationships. 

You may require further research if you need more causal links. 

This table compares the key characteristics of causal and descriptive research.  

Causal research examines a research question’s variables and how they interact. It’s easier to pinpoint cause and effect since the experiment often happens in a controlled setting. 

Researchers can conduct causal research at any stage, but they typically use it once they know more about the topic.

In contrast, causal research tends to be more structured and can be combined with exploratory and descriptive research to help you attain your research goals. 

• How can you use causal research effectively?

Here are common ways that market researchers leverage causal research effectively:

Market and advertising research

Do you want to know if your new marketing campaign is affecting your organization positively? You can use causal research to determine the variables causing negative or positive impacts on your campaign. 

Improving customer experiences and loyalty levels

Consumers generally enjoy purchasing from brands aligned with their values. They’re more likely to purchase from such brands and positively represent them to others. 

You can use causal research to identify the variables contributing to increased or reduced customer acquisition and retention rates. 

Could the cause of increased customer retention rates be streamlined checkout? 

Perhaps you introduced a new solution geared towards directly solving their immediate problem. 

Whatever the reason, causal research can help you identify the cause-and-effect relationship. You can use this to enhance your customer experiences and loyalty levels.

Improving problematic employee turnover rates

Is your organization experiencing skyrocketing attrition rates? 

You can leverage the features and benefits of causal research to narrow down the possible explanations or variables with significant effects on employees quitting. 

This way, you can prioritize interventions, focusing on the highest priority causal influences, and begin to tackle high employee turnover rates. 

• Advantages of causal research

The main benefits of causal research include the following:

Effectively test new ideas

If causal research can pinpoint the precise outcome through combinations of different variables, researchers can test ideas in the same manner to form viable proof of concepts.

Achieve more objective results

Market researchers typically use random sampling techniques to choose experiment participants or subjects in causal research. This reduces the possibility of exterior, sample, or demography-based influences, generating more objective results. 

Improved business processes

Causal research helps businesses understand which variables positively impact target variables, such as customer loyalty or sales revenues. This helps them improve their processes, ROI, and customer and employee experiences.

Guarantee reliable and accurate results

Upon identifying the correct variables, researchers can replicate cause and effect effortlessly. This creates reliable data and results to draw insights from. 

Internal organization improvements

Businesses that conduct causal research can make informed decisions about improving their internal operations and enhancing employee experiences. 

• Disadvantages of causal research

Like any other research method, casual research has its set of drawbacks that include:

Extra research to ensure validity

Researchers can't simply rely on the outcomes of causal research since it isn't always accurate. There may be a need to conduct other research types alongside it to ensure accurate output.

Coincidence

Coincidence tends to be the most significant error in causal research. Researchers often misinterpret a coincidental link between a cause and effect as a direct causal link. 

Administration challenges

Causal research can be challenging to administer since it's impossible to control the impact of extraneous variables . 

Giving away your competitive advantage

If you intend to publish your research, it exposes your information to the competition. 

Competitors may use your research outcomes to identify your plans and strategies to enter the market before you. 

• Causal research examples

Multiple fields can use causal research, so it serves different purposes, such as. 

Customer loyalty research

Organizations and employees can use causal research to determine the best customer attraction and retention approaches. 

They monitor interactions between customers and employees to identify cause-and-effect patterns. That could be a product demonstration technique resulting in higher or lower sales from the same customers. 

Example: Business X introduces a new individual marketing strategy for a small customer group and notices a measurable increase in monthly subscriptions. 

Upon getting identical results from different groups, the business concludes that the individual marketing strategy resulted in the intended causal relationship.

Advertising research

Businesses can also use causal research to implement and assess advertising campaigns. 

Example: Business X notices a 7% increase in sales revenue a few months after a business introduces a new advertisement in a certain region. The business can run the same ad in random regions to compare sales data over the same period. 

This will help the company determine whether the ad caused the sales increase. If sales increase in these randomly selected regions, the business could conclude that advertising campaigns and sales share a cause-and-effect relationship. 

Educational research

Academics, teachers, and learners can use causal research to explore the impact of politics on learners and pinpoint learner behavior trends. 

Example: College X notices that more IT students drop out of their program in their second year, which is 8% higher than any other year. 

The college administration can interview a random group of IT students to identify factors leading to this situation, including personal factors and influences. 

With the help of in-depth statistical analysis, the institution's researchers can uncover the main factors causing dropout. They can create immediate solutions to address the problem.

Is a causal variable dependent or independent?

When two variables have a cause-and-effect relationship, the cause is often called the independent variable. As such, the effect variable is dependent, i.e., it depends on the independent causal variable. An independent variable is only causal under experimental conditions. 

What are the three criteria for causality?

The three conditions for causality are:

Temporality/temporal precedence: The cause must precede the effect.

Rationality: One event predicts the other with an explanation, and the effect must vary in proportion to changes in the cause.

Control for extraneous variables: The covariables must not result from other variables.  

Is causal research experimental?

Causal research is mostly explanatory. Causal studies focus on analyzing a situation to explore and explain the patterns of relationships between variables. 

Further, experiments are the primary data collection methods in studies with causal research design. However, as a research design, causal research isn't entirely experimental.

What is the difference between experimental and causal research design?

One of the main differences between causal and experimental research is that in causal research, the research subjects are already in groups since the event has already happened. 

On the other hand, researchers randomly choose subjects in experimental research before manipulating the variables.

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Causal research: definition, examples and how to use it.

16 min read Causal research enables market researchers to predict hypothetical occurrences & outcomes while improving existing strategies. Discover how this research can decrease employee retention & increase customer success for your business.

What is causal research?

Causal research, also known as explanatory research or causal-comparative research, identifies the extent and nature of cause-and-effect relationships between two or more variables.

It’s often used by companies to determine the impact of changes in products, features, or services process on critical company metrics. Some examples:

• How does rebranding of a product influence intent to purchase?
• How would expansion to a new market segment affect projected sales?
• What would be the impact of a price increase or decrease on customer loyalty?

To maintain the accuracy of causal research, ‘confounding variables’ or influences — e.g. those that could distort the results — are controlled. This is done either by keeping them constant in the creation of data, or by using statistical methods. These variables are identified before the start of the research experiment.

As well as the above, research teams will outline several other variables and principles in causal research:

• Independent variables

The variables that may cause direct changes in another variable. For example, the effect of truancy on a student’s grade point average. The independent variable is therefore class attendance.

• Control variables

These are the components that remain unchanged during the experiment so researchers can better understand what conditions create a cause-and-effect relationship.  

This describes the cause-and-effect relationship. When researchers find causation (or the cause), they’ve conducted all the processes necessary to prove it exists.

• Correlation

Any relationship between two variables in the experiment. It’s important to note that correlation doesn’t automatically mean causation. Researchers will typically establish correlation before proving cause-and-effect.

• Experimental design

Researchers use experimental design to define the parameters of the experiment — e.g. categorizing participants into different groups.

• Dependent variables

These are measurable variables that may change or are influenced by the independent variable. For example, in an experiment about whether or not terrain influences running speed, your dependent variable is the terrain.  

Why is causal research useful?

It’s useful because it enables market researchers to predict hypothetical occurrences and outcomes while improving existing strategies. This allows businesses to create plans that benefit the company. It’s also a great research method because researchers can immediately see how variables affect each other and under what circumstances.

Also, once the first experiment has been completed, researchers can use the learnings from the analysis to repeat the experiment or apply the findings to other scenarios. Because of this, it’s widely used to help understand the impact of changes in internal or commercial strategy to the business bottom line.

Some examples include:

• Understanding how overall training levels are improved by introducing new courses
• Examining which variations in wording make potential customers more interested in buying a product
• Testing a market’s response to a brand-new line of products and/or services

So, how does causal research compare and differ from other research types?

Well, there are a few research types that are used to find answers to some of the examples above:

1. Exploratory research

As its name suggests, exploratory research involves assessing a situation (or situations) where the problem isn’t clear. Through this approach, researchers can test different avenues and ideas to establish facts and gain a better understanding.

Researchers can also use it to first navigate a topic and identify which variables are important. Because no area is off-limits, the research is flexible and adapts to the investigations as it progresses.

Finally, this approach is unstructured and often involves gathering qualitative data, giving the researcher freedom to progress the research according to their thoughts and assessment. However, this may make results susceptible to researcher bias and may limit the extent to which a topic is explored.

2. Descriptive research

Descriptive research is all about describing the characteristics of the population, phenomenon or scenario studied. It focuses more on the “what” of the research subject than the “why”.

For example, a clothing brand wants to understand the fashion purchasing trends amongst buyers in California — so they conduct a demographic survey of the region, gather population data and then run descriptive research. The study will help them to uncover purchasing patterns amongst fashion buyers in California, but not necessarily why those patterns exist.

As the research happens in a natural setting, variables can cross-contaminate other variables, making it harder to isolate cause and effect relationships. Therefore, further research will be required if more causal information is needed.

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How is causal research different from the other two methods above?

Well, causal research looks at what variables are involved in a problem and ‘why’ they act a certain way. As the experiment takes place in a controlled setting (thanks to controlled variables) it’s easier to identify cause-and-effect amongst variables.

Furthermore, researchers can carry out causal research at any stage in the process, though it’s usually carried out in the later stages once more is known about a particular topic or situation.

Finally, compared to the other two methods, causal research is more structured, and researchers can combine it with exploratory and descriptive research to assist with research goals.

Summary of three research types

What are the advantages of causal research?

• Improve experiences

By understanding which variables have positive impacts on target variables (like sales revenue or customer loyalty), businesses can improve their processes, return on investment, and the experiences they offer customers and employees.

• Help companies improve internally

By conducting causal research, management can make informed decisions about improving their employee experience and internal operations. For example, understanding which variables led to an increase in staff turnover.

• Repeat experiments to enhance reliability and accuracy of results

When variables are identified, researchers can replicate cause-and-effect with ease, providing them with reliable data and results to draw insights from.

• Test out new theories or ideas

If causal research is able to pinpoint the exact outcome of mixing together different variables, research teams have the ability to test out ideas in the same way to create viable proof of concepts.

• Fix issues quickly

Once an undesirable effect’s cause is identified, researchers and management can take action to reduce the impact of it or remove it entirely, resulting in better outcomes.

What are the disadvantages of causal research?

• Provides information to competitors

If you plan to publish your research, it provides information about your plans to your competitors. For example, they might use your research outcomes to identify what you are up to and enter the market before you.

• Difficult to administer

Causal research is often difficult to administer because it’s not possible to control the effects of extraneous variables.

• Time and money constraints

Budgetary and time constraints can make this type of research expensive to conduct and repeat. Also, if an initial attempt doesn’t provide a cause and effect relationship, the ROI is wasted and could impact the appetite for future repeat experiments.

• Requires additional research to ensure validity

You can’t rely on just the outcomes of causal research as it’s inaccurate. It’s best to conduct other types of research alongside it to confirm its output.

• Trouble establishing cause and effect

Researchers might identify that two variables are connected, but struggle to determine which is the cause and which variable is the effect.

• Risk of contamination

There’s always the risk that people outside your market or area of study could affect the results of your research. For example, if you’re conducting a retail store study, shoppers outside your ‘test parameters’ shop at your store and skew the results.

How can you use causal research effectively?

To better highlight how you can use causal research across functions or markets, here are a few examples:

Market and advertising research

A company might want to know if their new advertising campaign or marketing campaign is having a positive impact. So, their research team can carry out a causal research project to see which variables cause a positive or negative effect on the campaign.

For example, a cold-weather apparel company in a winter ski-resort town may see an increase in sales generated after a targeted campaign to skiers. To see if one caused the other, the research team could set up a duplicate experiment to see if the same campaign would generate sales from non-skiers. If the results reduce or change, then it’s likely that the campaign had a direct effect on skiers to encourage them to purchase products.

Improving customer experiences and loyalty levels

Customers enjoy shopping with brands that align with their own values, and they’re more likely to buy and present the brand positively to other potential shoppers as a result. So, it’s in your best interest to deliver great experiences and retain your customers.

For example, the Harvard Business Review found that an increase in customer retention rates by 5% increased profits by 25% to 95%. But let’s say you want to increase your own, how can you identify which variables contribute to it?Using causal research, you can test hypotheses about which processes, strategies or changes influence customer retention. For example, is it the streamlined checkout? What about the personalized product suggestions? Or maybe it was a new solution that solved their problem? Causal research will help you find out.

Improving problematic employee turnover rates

If your company has a high attrition rate, causal research can help you narrow down the variables or reasons which have the greatest impact on people leaving. This allows you to prioritize your efforts on tackling the issues in the right order, for the best positive outcomes.

For example, through causal research, you might find that employee dissatisfaction due to a lack of communication and transparency from upper management leads to poor morale, which in turn influences employee retention.

To rectify the problem, you could implement a routine feedback loop or session that enables your people to talk to your company’s C-level executives so that they feel heard and understood.

How to conduct causal research first steps to getting started are:

1. Define the purpose of your research

What questions do you have? What do you expect to come out of your research? Think about which variables you need to test out the theory.

2. Pick a random sampling if participants are needed

Using a technology solution to support your sampling, like a database, can help you define who you want your target audience to be, and how random or representative they should be.

3. Set up the controlled experiment

Once you’ve defined which variables you’d like to measure to see if they interact, think about how best to set up the experiment. This could be in-person or in-house via interviews, or it could be done remotely using online surveys.

4. Carry out the experiment

Make sure to keep all irrelevant variables the same, and only change the causal variable (the one that causes the effect) to gather the correct data. Depending on your method, you could be collecting qualitative or quantitative data, so make sure you note your findings across each regularly.

5. Analyze your findings

Either manually or using technology, analyze your data to see if any trends, patterns or correlations emerge. By looking at the data, you’ll be able to see what changes you might need to do next time, or if there are questions that require further research.

6. Verify your findings

Your first attempt gives you the baseline figures to compare the new results to. You can then run another experiment to verify your findings.

7. Do follow-up or supplemental research

You can supplement your original findings by carrying out research that goes deeper into causes or explores the topic in more detail. One of the best ways to do this is to use a survey. See ‘Use surveys to help your experiment’.

Identifying causal relationships between variables

To verify if a causal relationship exists, you have to satisfy the following criteria:

• Nonspurious association

A clear correlation exists between one cause and the effect. In other words, no ‘third’ that relates to both (cause and effect) should exist.

• Temporal sequence

The cause occurs before the effect. For example, increased ad spend on product marketing would contribute to higher product sales.

• Concomitant variation

The variation between the two variables is systematic. For example, if a company doesn’t change its IT policies and technology stack, then changes in employee productivity were not caused by IT policies or technology.

How surveys help your causal research experiments?

There are some surveys that are perfect for assisting researchers with understanding cause and effect. These include:

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What is Qualitative in Qualitative Research

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• Published: 27 February 2019
• Volume 42 , pages 139–160, ( 2019 )

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• Patrik Aspers 1 , 2 &
• Ugo Corte 3  

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What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

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If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

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Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

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Causal Hypothesis

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In scientific research, understanding causality is key to unraveling the intricacies of various phenomena. A causal hypothesis is a statement that predicts a cause-and-effect relationship between variables in a study. It serves as a guide to study design, data collection, and interpretation of results. This thesis statement segment aims to provide you with clear examples of causal hypotheses across diverse fields, along with a step-by-step guide and useful tips for formulating your own. Let’s delve into the essential components of constructing a compelling causal hypothesis.

What is Causal Hypothesis?

A causal hypothesis is a predictive statement that suggests a potential cause-and-effect relationship between two or more variables. It posits that a change in one variable (the independent or cause variable) will result in a change in another variable (the dependent or effect variable). The primary goal of a causal hypothesis is to determine whether one event or factor directly influences another. This type of Simple hypothesis is commonly tested through experiments where one variable can be manipulated to observe the effect on another variable.

What is an example of a Causal Hypothesis Statement?

Example 1: If a person increases their physical activity (cause), then their overall health will improve (effect).

Explanation: Here, the independent variable is the “increase in physical activity,” while the dependent variable is the “improvement in overall health.” The hypothesis suggests that by manipulating the level of physical activity (e.g., by exercising more), there will be a direct effect on the individual’s health.

Other examples can range from the impact of a change in diet on weight loss, the influence of class size on student performance, or the effect of a new training method on employee productivity. The key element in all causal hypotheses is the proposed direct relationship between cause and effect.

100 Causal Hypothesis Statement Examples

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Causal hypotheses predict cause-and-effect relationships, aiming to understand the influence one variable has on another. Rooted in experimental setups, they’re essential for deriving actionable insights in many fields. Delve into these 100 illustrative examples to understand the essence of causal relationships.

• Dietary Sugar & Weight Gain: Increased sugar intake leads to weight gain.
• Exercise & Mental Health: Regular exercise improves mental well-being.
• Sleep & Productivity: Lack of adequate sleep reduces work productivity.
• Class Size & Learning: Smaller class sizes enhance student understanding.
• Smoking & Lung Disease: Regular smoking causes lung diseases.
• Pesticides & Bee Decline: Use of certain pesticides leads to bee population decline.
• Stress & Hair Loss: Chronic stress accelerates hair loss.
• Music & Plant Growth: Plants grow better when exposed to classical music.
• UV Rays & Skin Aging: Excessive exposure to UV rays speeds up skin aging.
• Reading & Vocabulary: Regular reading improves vocabulary breadth.
• Video Games & Reflexes: Playing video games frequently enhances reflex actions.
• Air Pollution & Respiratory Issues: High levels of air pollution increase respiratory diseases.
• Green Spaces & Happiness: Living near green spaces improves overall happiness.
• Yoga & Blood Pressure: Regular yoga practices lower blood pressure.
• Meditation & Stress Reduction: Daily meditation reduces stress levels.
• Social Media & Anxiety: Excessive social media use increases anxiety in teenagers.
• Alcohol & Liver Damage: Regular heavy drinking leads to liver damage.
• Training & Job Efficiency: Intensive training improves job performance.
• Seat Belts & Accident Survival: Using seat belts increases chances of surviving car accidents.
• Soft Drinks & Bone Density: High consumption of soft drinks decreases bone density.
• Homework & Academic Performance: Regular homework completion improves academic scores.
• Organic Food & Health Benefits: Consuming organic food improves overall health.
• Fiber Intake & Digestion: Increased dietary fiber enhances digestion.
• Therapy & Depression Recovery: Regular therapy sessions improve depression recovery rates.
• Financial Education & Savings: Financial literacy education increases personal saving rates.
• Brushing & Dental Health: Brushing teeth twice a day reduces dental issues.
• Carbon Emission & Global Warming: Higher carbon emissions accelerate global warming.
• Afforestation & Climate Stability: Planting trees stabilizes local climates.
• Ad Exposure & Sales: Increased product advertisement boosts sales.
• Parental Involvement & Academic Success: Higher parental involvement enhances student academic performance.
• Hydration & Skin Health: Regular water intake improves skin elasticity and health.
• Caffeine & Alertness: Consuming caffeine increases alertness levels.
• Antibiotics & Bacterial Resistance: Overuse of antibiotics leads to increased antibiotic-resistant bacteria.
• Pet Ownership & Loneliness: Having pets reduces feelings of loneliness.
• Fish Oil & Cognitive Function: Regular consumption of fish oil improves cognitive functions.
• Noise Pollution & Sleep Quality: High levels of noise pollution degrade sleep quality.
• Exercise & Bone Density: Weight-bearing exercises increase bone density.
• Vaccination & Disease Prevention: Proper vaccination reduces the incidence of related diseases.
• Laughter & Immune System: Regular laughter boosts the immune system.
• Gardening & Stress Reduction: Engaging in gardening activities reduces stress levels.
• Travel & Cultural Awareness: Frequent travel increases cultural awareness and tolerance.
• High Heels & Back Pain: Prolonged wearing of high heels leads to increased back pain.
• Junk Food & Heart Disease: Excessive junk food consumption increases the risk of heart diseases.
• Mindfulness & Anxiety Reduction: Practicing mindfulness lowers anxiety levels.
• Online Learning & Flexibility: Online education offers greater flexibility to learners.
• Urbanization & Wildlife Displacement: Rapid urbanization leads to displacement of local wildlife.
• Vitamin C & Cold Recovery: High doses of vitamin C speed up cold recovery.
• Team Building Activities & Work Cohesion: Regular team-building activities improve workplace cohesion.
• Multitasking & Productivity: Multitasking reduces individual task efficiency.
• Protein Intake & Muscle Growth: Increased protein consumption boosts muscle growth in individuals engaged in strength training.
• Mentoring & Career Progression: Having a mentor accelerates career progression.
• Fast Food & Obesity Rates: High consumption of fast food leads to increased obesity rates.
• Deforestation & Biodiversity Loss: Accelerated deforestation results in significant biodiversity loss.
• Language Learning & Cognitive Flexibility: Learning a second language enhances cognitive flexibility.
• Red Wine & Heart Health: Moderate red wine consumption may benefit heart health.
• Public Speaking Practice & Confidence: Regular public speaking practice boosts confidence.
• Fasting & Metabolism: Intermittent fasting can rev up metabolism.
• Plastic Usage & Ocean Pollution: Excessive use of plastics leads to increased ocean pollution.
• Peer Tutoring & Academic Retention: Peer tutoring improves academic retention rates.
• Mobile Usage & Sleep Patterns: Excessive mobile phone use before bed disrupts sleep patterns.
• Green Spaces & Mental Well-being: Living near green spaces enhances mental well-being.
• Organic Foods & Health Outcomes: Consuming organic foods leads to better health outcomes.
• Art Exposure & Creativity: Regular exposure to art boosts creativity.
• Gaming & Hand-Eye Coordination: Engaging in video games improves hand-eye coordination.
• Prenatal Music & Baby’s Development: Exposing babies to music in the womb enhances their auditory development.
• Dark Chocolate & Mood Enhancement: Consuming dark chocolate can elevate mood.
• Urban Farms & Community Engagement: Establishing urban farms promotes community engagement.
• Reading Fiction & Empathy Levels: Reading fiction regularly increases empathy.
• Aerobic Exercise & Memory: Engaging in aerobic exercises sharpens memory.
• Meditation & Blood Pressure: Regular meditation can reduce blood pressure.
• Classical Music & Plant Growth: Plants exposed to classical music show improved growth.
• Pollution & Respiratory Diseases: Higher pollution levels increase respiratory diseases’ incidence.
• Parental Involvement & Child’s Academic Success: Direct parental involvement in schooling enhances children’s academic success.
• Sugar Intake & Tooth Decay: High sugar intake is directly proportional to tooth decay.
• Physical Books & Reading Comprehension: Reading physical books improves comprehension better than digital mediums.
• Daily Journaling & Self-awareness: Maintaining a daily journal enhances self-awareness.
• Robotics Learning & Problem-solving Skills: Engaging in robotics learning fosters problem-solving skills in students.
• Forest Bathing & Stress Relief: Immersion in forest environments (forest bathing) reduces stress levels.
• Reusable Bags & Environmental Impact: Using reusable bags reduces environmental pollution.
• Affirmations & Self-esteem: Regularly reciting positive affirmations enhances self-esteem.
• Local Produce Consumption & Community Economy: Buying and consuming local produce boosts the local economy.
• Sunlight Exposure & Vitamin D Levels: Regular sunlight exposure enhances Vitamin D levels in the body.
• Group Study & Learning Enhancement: Group studies can enhance learning compared to individual studies.
• Active Commuting & Fitness Levels: Commuting by walking or cycling improves overall fitness.
• Foreign Film Watching & Cultural Understanding: Watching foreign films increases understanding and appreciation of different cultures.
• Craft Activities & Fine Motor Skills: Engaging in craft activities enhances fine motor skills.
• Listening to Podcasts & Knowledge Expansion: Regularly listening to educational podcasts broadens one’s knowledge base.
• Outdoor Play & Child’s Physical Development: Encouraging outdoor play accelerates physical development in children.
• Thrift Shopping & Sustainable Living: Choosing thrift shopping promotes sustainable consumption habits.
• Nature Retreats & Burnout Recovery: Taking nature retreats aids in burnout recovery.
• Virtual Reality Training & Skill Acquisition: Using virtual reality for training accelerates skill acquisition in medical students.
• Pet Ownership & Loneliness Reduction: Owning a pet significantly reduces feelings of loneliness among elderly individuals.
• Intermittent Fasting & Metabolism Boost: Practicing intermittent fasting can lead to an increase in metabolic rate.
• Bilingual Education & Cognitive Flexibility: Being educated in a bilingual environment improves cognitive flexibility in children.
• Urbanization & Loss of Biodiversity: Rapid urbanization contributes to a loss of biodiversity in the surrounding environment.
• Recycled Materials & Carbon Footprint Reduction: Utilizing recycled materials in production processes reduces a company’s overall carbon footprint.
• Artificial Sweeteners & Appetite Increase: Consuming artificial sweeteners might lead to an increase in appetite.
• Green Roofs & Urban Temperature Regulation: Implementing green roofs in urban buildings contributes to moderating city temperatures.
• Remote Work & Employee Productivity: Adopting a remote work model can boost employee productivity and job satisfaction.
• Sensory Play & Child Development: Incorporating sensory play in early childhood education supports holistic child development.

Causal Hypothesis Statement Examples in Research

Research hypothesis often delves into understanding the cause-and-effect relationships between different variables. These causal hypotheses attempt to predict a specific effect if a particular cause is present, making them vital for experimental designs.

• Artificial Intelligence & Job Market: Implementation of artificial intelligence in industries causes a decline in manual jobs.
• Online Learning Platforms & Traditional Classroom Efficiency: The introduction of online learning platforms reduces the efficacy of traditional classroom teaching methods.
• Nano-technology & Medical Treatment Efficacy: Using nano-technology in drug delivery enhances the effectiveness of medical treatments.
• Genetic Editing & Lifespan: Advancements in genetic editing techniques directly influence the lifespan of organisms.
• Quantum Computing & Data Security: The rise of quantum computing threatens the security of traditional encryption methods.
• Space Tourism & Aerospace Advancements: The demand for space tourism propels advancements in aerospace engineering.
• E-commerce & Retail Business Model: The surge in e-commerce platforms leads to a decline in the traditional retail business model.
• VR in Real Estate & Buyer Decisions: Using virtual reality in real estate presentations influences buyer decisions more than traditional methods.
• Biofuels & Greenhouse Gas Emissions: Increasing biofuel production directly reduces greenhouse gas emissions.
• Crowdfunding & Entrepreneurial Success: The availability of crowdfunding platforms boosts the success rate of start-up enterprises.

Causal Hypothesis Statement Examples in Epidemiology

Epidemiology is a study of how and why certain diseases occur in particular populations. Causal hypotheses in this field aim to uncover relationships between health interventions, behaviors, and health outcomes.

• Vaccine Introduction & Disease Eradication: The introduction of new vaccines directly leads to the reduction or eradication of specific diseases.
• Urbanization & Rise in Respiratory Diseases: Increased urbanization causes a surge in respiratory diseases due to pollution.
• Processed Foods & Obesity Epidemic: The consumption of processed foods is directly linked to the rising obesity epidemic.
• Sanitation Measures & Cholera Outbreaks: Implementing proper sanitation measures reduces the incidence of cholera outbreaks.
• Tobacco Consumption & Lung Cancer: Prolonged tobacco consumption is the primary cause of lung cancer among adults.
• Antibiotic Misuse & Antibiotic-Resistant Strains: Misuse of antibiotics leads to the evolution of antibiotic-resistant bacterial strains.
• Alcohol Consumption & Liver Diseases: Excessive and regular alcohol consumption is a leading cause of liver diseases.
• Vitamin D & Rickets in Children: A deficiency in vitamin D is the primary cause of rickets in children.
• Airborne Pollutants & Asthma Attacks: Exposure to airborne pollutants directly triggers asthma attacks in susceptible individuals.
• Sedentary Lifestyle & Cardiovascular Diseases: Leading a sedentary lifestyle is a significant risk factor for cardiovascular diseases.

Causal Hypothesis Statement Examples in Psychology

In psychology, causal hypotheses explore how certain behaviors, conditions, or interventions might influence mental and emotional outcomes. These hypotheses help in deciphering the intricate web of human behavior and cognition.

• Childhood Trauma & Personality Disorders: Experiencing trauma during childhood increases the risk of developing personality disorders in adulthood.
• Positive Reinforcement & Skill Acquisition: The use of positive reinforcement accelerates skill acquisition in children.
• Sleep Deprivation & Cognitive Performance: Lack of adequate sleep impairs cognitive performance in adults.
• Social Isolation & Depression: Prolonged social isolation is a significant cause of depression among teenagers.
• Mindfulness Meditation & Stress Reduction: Regular practice of mindfulness meditation reduces symptoms of stress and anxiety.
• Peer Pressure & Adolescent Risk Taking: Peer pressure significantly increases risk-taking behaviors among adolescents.
• Parenting Styles & Child’s Self-esteem: Authoritarian parenting styles negatively impact a child’s self-esteem.
• Multitasking & Attention Span: Engaging in multitasking frequently leads to a reduced attention span.
• Childhood Bullying & Adult PTSD: Individuals bullied during childhood have a higher likelihood of developing PTSD as adults.
• Digital Screen Time & Child Development: Excessive digital screen time impairs cognitive and social development in children.

Causal Inference Hypothesis Statement Examples

Causal inference is about deducing the cause-effect relationship between two variables after considering potential confounders. These hypotheses aim to find direct relationships even when other influencing factors are present.

• Dietary Habits & Chronic Illnesses: Even when considering genetic factors, unhealthy dietary habits increase the chances of chronic illnesses.
• Exercise & Mental Well-being: When accounting for daily stressors, regular exercise improves mental well-being.
• Job Satisfaction & Employee Turnover: Even when considering market conditions, job satisfaction inversely relates to employee turnover.
• Financial Literacy & Savings Behavior: When considering income levels, financial literacy is directly linked to better savings behavior.
• Online Reviews & Product Sales: Even accounting for advertising spends, positive online reviews boost product sales.
• Prenatal Care & Child Health Outcomes: When considering genetic factors, adequate prenatal care ensures better health outcomes for children.
• Teacher Qualifications & Student Performance: Accounting for socio-economic factors, teacher qualifications directly influence student performance.
• Community Engagement & Crime Rates: When considering economic conditions, higher community engagement leads to lower crime rates.
• Eco-friendly Practices & Brand Loyalty: Accounting for product quality, eco-friendly business practices boost brand loyalty.
• Mental Health Support & Workplace Productivity: Even when considering workload, providing mental health support enhances workplace productivity.

What are the Characteristics of Causal Hypothesis

Causal hypotheses are foundational in many research disciplines, as they predict a cause-and-effect relationship between variables. Their unique characteristics include:

• Cause-and-Effect Relationship: The core of a causal hypothesis is to establish a direct relationship, indicating that one variable (the cause) will bring about a change in another variable (the effect).
• Testability: They are formulated in a manner that allows them to be empirically tested using appropriate experimental or observational methods.
• Specificity: Causal hypotheses should be specific, delineating clear cause and effect variables.
• Directionality: They typically demonstrate a clear direction in which the cause leads to the effect.
• Operational Definitions: They often use operational definitions, which specify the procedures used to measure or manipulate variables.
• Temporal Precedence: The cause (independent variable) always precedes the effect (dependent variable) in time.

What is a causal hypothesis in research?

In research, a causal hypothesis is a statement about the expected relationship between variables, or explanation of an occurrence, that is clear, specific, testable, and falsifiable. It suggests a relationship in which a change in one variable is the direct cause of a change in another variable. For instance, “A higher intake of Vitamin C reduces the risk of common cold.” Here, Vitamin C intake is the independent variable, and the risk of common cold is the dependent variable.

What is the difference between causal and descriptive hypothesis?

• Causal Hypothesis: Predicts a cause-and-effect relationship between two or more variables.
• Descriptive Hypothesis: Describes an occurrence, detailing the characteristics or form of a particular phenomenon.
• Causal: Consuming too much sugar can lead to diabetes.
• Descriptive: 60% of adults in the city exercise at least thrice a week.
• Causal: To establish a causal connection between variables.
• Descriptive: To give an accurate portrayal of the situation or fact.
• Causal: Often involves experiments.
• Descriptive: Often involves surveys or observational studies.

How do you write a Causal Hypothesis? – A Step by Step Guide

• Identify Your Variables: Pinpoint the cause (independent variable) and the effect (dependent variable). For instance, in studying the relationship between smoking and lung health, smoking is the independent variable while lung health is the dependent variable.
• State the Relationship: Clearly define how one variable affects another. Does an increase in the independent variable lead to an increase or decrease in the dependent variable?
• Be Specific: Avoid vague terms. Instead of saying “improved health,” specify the type of improvement like “reduced risk of cardiovascular diseases.”
• Use Operational Definitions: Clearly define any terms or variables in your hypothesis. For instance, define what you mean by “regular exercise” or “high sugar intake.”
• Ensure It’s Testable: Your hypothesis should be structured so that it can be disproven or supported by data.
• Review Existing Literature: Check previous research to ensure that your hypothesis hasn’t already been tested, and to ensure it’s plausible based on existing knowledge.
• Draft Your Hypothesis: Combine all the above steps to write a clear, concise hypothesis. For instance: “Regular exercise (defined as 150 minutes of moderate exercise per week) decreases the risk of cardiovascular diseases.”

Tips for Writing Causal Hypothesis

• Simplicity is Key: The clearer and more concise your hypothesis, the easier it will be to test.
• Avoid Absolutes: Using words like “all” or “always” can be problematic. Few things are universally true.
• Seek Feedback: Before finalizing your hypothesis, get feedback from peers or mentors.
• Stay Objective: Base your hypothesis on existing literature and knowledge, not on personal beliefs or biases.
• Revise as Needed: As you delve deeper into your research, you may find the need to refine your hypothesis for clarity or specificity.
• Falsifiability: Always ensure your hypothesis can be proven wrong. If it can’t be disproven, it can’t be validated either.
• Avoid Circular Reasoning: Ensure that your hypothesis doesn’t assume what it’s trying to prove. For example, “People who are happy have a positive outlook on life” is a circular statement.
• Specify Direction: In causal hypotheses, indicating the direction of the relationship can be beneficial, such as “increases,” “decreases,” or “leads to.”

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A fuzzy-set qualitative comparative analysis for understanding the interactive effects of good governance practices and ceo profiles on esg performance.

1. Introduction

2. literature review, 2.1. attributes included in the ggc and esg performance, 2.2. csr committee and esg performance, 2.3. ceo profile and esg performance, 3. materials and methods, 3.2. variables, 3.3. methodology, 4.1. descriptive statistics and correlation analysis, 4.2. contrarian case analysis, 4.3. analysis of necessary conditions, 4.4. sufficiency analysis for high esg performance, 4.5. bundles of corporate governance practices, 4.6. sufficiency analysis for non-high esg performance, 4.7. robustness analysis for sufficiency, 5. discussion, 5.1. csr committee as the critical component, 5.2. the complementary relationship between the creation of a csr committee and high ggc compliance, 5.3. the substituted relationship between ceo duality and ceo tenure, 5.4. the relative importance of antecedent conditions configuring corporate governance bundles to improve e-s-g practices, 5.5. contributions and implications, 5.6. limitations and future directions, 6. conclusions, author contributions, data availability statement, conflicts of interest.

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Share and Cite

Remo-Diez, N.; Mendaña-Cuervo, C.; Arenas-Parra, M. A Fuzzy-Set Qualitative Comparative Analysis for Understanding the Interactive Effects of Good Governance Practices and CEO Profiles on ESG Performance. Mathematics 2024 , 12 , 2726. https://doi.org/10.3390/math12172726

Remo-Diez N, Mendaña-Cuervo C, Arenas-Parra M. A Fuzzy-Set Qualitative Comparative Analysis for Understanding the Interactive Effects of Good Governance Practices and CEO Profiles on ESG Performance. Mathematics . 2024; 12(17):2726. https://doi.org/10.3390/math12172726

Remo-Diez, Nieves, Cristina Mendaña-Cuervo, and Mar Arenas-Parra. 2024. "A Fuzzy-Set Qualitative Comparative Analysis for Understanding the Interactive Effects of Good Governance Practices and CEO Profiles on ESG Performance" Mathematics 12, no. 17: 2726. https://doi.org/10.3390/math12172726

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Menstrual hygiene management interventions and their effects on schoolgirls’ menstrual hygiene experiences in low and middle countries: A systematic review

Balem Demtsu Betsu

1 Department of Midwifery, College of Health Sciences, Mekelle University, Mekelle, Ethiopia

Araya Abrha Medhanyie

2 School of Public Health, College of Health Sciences, Mekelle University, Mekelle, Ethiopia

Tesfay Gebregzabher Gebrehiwet

L. lewis wall.

3 Department of Anthropology, College of Arts and Sciences, Washington University in St. Louis, St. Louis, MO, United States of America

4 Department of Obstetrics & Gynecology, Washington University in St. Louis, St. Louis, MO, United States of America

5 Department of Obstetrics & Gynecology, Ayder Comprehensive Specialized Hospital, College of Health Sciences, Mekelle University, Mekelle, Ethiopia

Associated Data

data are all contained within the manuscript and/or Supporting Information files.

Inadequate menstrual hygiene management can result in physical, social, psychological, and educational challenges for schoolgirls. To address these issues, researchers have conducted intervention studies, but the impact on school attendance has varied. This review has systematically collected and evaluated evidence about the effects of menstrual hygiene interventions on schoolgirls.

A systematic search of the literature was done and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA statement). Both peer-reviewed journals and gray literature were searched using PubMed and Google Scholar. The search included individual, or cluster randomized controlled trials, and quasi-experimental studies, and covered the period from the date of indexing until January 3, 2023.

A review of sixteen trial studies showed that menstrual hygiene interventions have a positive effect on schoolgirls’ school attendance, performance, and dropout rates, as well as on their menstrual knowledge, attitudes, practices, and emotional well-being. There was a low to medium risk of bias in most of the studies. Additionally, the literature overlooked the impact of interventions that involve parental and male engagement, interventions correcting community misperceptions about menstruation, and the impact of infrastructure improvements on water, sanitation, and hygiene.

Interventions aimed at improving menstrual hygiene management can enhance schoolgirls’ educational outcomes, and can improve their menstrual knowledge, attitudes, and practices by helping them manage their periods more effectively. Most interventions have focused on the provision of menstrual products and menstrual education but have neglected improvements in the physical environment at home and school and the social norms surrounding menstruation. Trial studies should take a holistic approach that considers the total socio-cultural environment in which menstrual hygiene management takes place, thus enabling stakeholders and policymakers to develop sustainable, long-term solutions to these problems.

Introduction

Menstrual hygiene management (MHM) is defined as “women and adolescent girls using a clean menstrual management material to absorb or collect menstrual blood that can be changed in privacy as often as necessary for the duration of a menstrual period, using soap and water for washing the body as required, and having access to safe and convenient facilities to dispose of used menstrual management materials” [ 1 ]. Sommer et al. also added that menstruators should understand the menstrual cycle and be able to manage it comfortably and confidently [ 2 ].

Globally, over 50% of females are of reproductive age, and 500 million lack adequate menstrual hygiene facilities [ 3 – 5 ]. Many girls reach menarche without adequate knowledge or the skills to manage menstruation hygienically [ 6 – 9 ]. More than half (52%) of adolescent girls in Ethiopia have never received any information about menstrual hygiene [ 10 ], because of religious taboos, socio-cultural misinformation, and inadequate menstrual supplies and facilities, which leads to fear, confusion, and lack of confidence when menarche occurs [ 11 – 15 ].

Challenges related to menstrual hygiene have been found to contribute to absenteeism, poor academic performance, and school dropout among girls [ 16 – 19 ]. In Sub-Saharan Africa, a significant proportion of girls (50%-70%) miss school for 1.6–2.1 days per month due to menstruation, and more than half of girls in Ethiopia miss school during their periods [ 18 , 20 ]. Menstrual hygiene challenges also have negative impacts on health, psychosocial well-being, economic opportunities, and gender equality [ 21 – 26 ]. These challenges include insufficient knowledge about menstruation; inadequate access to water, sanitation, and hygiene services; lack of adequate hygiene materials; and social norms unsupportive of those who menstruate [ 27 – 29 ].

Several programs and global initiatives, such as the ‘MHM in Ten Agenda’, are being implemented to enhance menstrual hygiene management among schoolgirls [ 30 ]. These programs have utilized various interventions including the provision of menstrual hygiene products and supplies, improved water sanitation and hygiene facilities, as well as increased health education.

Studies assessing the effect of these interventions on girls’ school attendance, performance, physical and psychosocial wellbeing, and their knowledge and attitudes toward menstrual hygiene management indicated varying results. Some studies have shown positive effects on school attendance, whereas others have demonstrated no effect [ 31 – 34 ].

One of the largest intervention studies assessing the effects of improved menstrual hygiene management on school attendance in the Tigray Region of Ethiopia, conducted during the 2015–2016 school year, demonstrated 24% fewer school absences among girls compared to boys and showed that student sex was not a predictor of school absence during a similar time-period during the previous academic year [ 35 ].

Before the search date of January 3, 2023, various systematic reviews were conducted to assess the impact of menstrual hygiene management interventions on schoolgirls. However, these reviews had different population intervention control outcome and time (PICOT) criteria, and some included studies that used cross-sectional research methods [ 9 , 21 , 22 , 36 ]. This review specifically focuses on schoolgirls and includes up-to-date intervention studies, which distinguishes it from earlier reviews in terms of time, context, and population.

This review appraises and synthesizes the current evidence on the effects of menstrual hygiene management interventions on girls’ school attendance, school performance, and school dropout, as well as their effects on emotional well-being, menstrual knowledge, attitudes, and menstrual hygiene practices.

Materials and methods

Study design.

We conducted the review in accordance with the reporting guidelines described in the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement [ 37 ].

Author’s positionality

I (the first author) am a woman, a feminist, and an advocate for girls’ education. I am currently pursuing a PhD in public health. I am from Ethiopia, one of the countries studied in this paper. This gives me first-hand experience of what it is like to be a menstruating schoolgirl in an LMIC. However, I am an outsider to the other countries studied in this paper, which may leave room for bias or misunderstandings in the interpretation of results. In this systematic review, the researcher’s standpoint influences the research approach and findings. This study advocates accessible menstrual hygiene resources and aims to address the stigma surrounding menstruation. The conclusions are based on this perspective, and readers are encouraged to take this into account when interpreting the findings.

Inclusion criteria

We included studies in this review based on the following criteria: participants, interventions and comparators, outcomes of interest, and type of study (study design) (PICOT) [ 38 ].

Participants

The search was limited to studies that measured outcomes on schoolgirls because the objective of the review was to evaluate how menstrual hygiene management intervention programs impact schoolgirls’ attendance, academic performance, or dropout rates.

Interventions and setting

The menstrual hygiene management interventions can be categorized as interventions involving menstrual education and/or the provision of menstrual supplies.

• Menstrual education interventions include providing menstrual hygiene management information, puberty and reproductive health-related information, and training on the use of menstrual hygiene supplies.
• Menstrual supply interventions include providing menstrual hygiene materials and supplies as well as upgrading water and sanitation facilities. Menstrual hygiene materials are products used to absorb menstrual flow, such as pads, cloths, tampons, or cups. Menstrual supplies include soap and detergent, underwear, and analgesic medications for menstrual cramps. Menstrual facilities include toilets and water infrastructure, as well as private spaces for washing, changing, drying, and disposing of menstrual materials [ 27 , 39 ].

Schoolgirls who did not receive any of the interventions listed above.

School attendance, school performance, school dropout, emotional wellbeing, knowledge, attitudes, and menstrual hygiene practices.

Type of study design

Studies included individual, or cluster randomized controlled trials and quasi-experimental or non-randomized controlled trials.

Publication type

We included peer-reviewed journal articles and gray literature

Exclusion criteria

We excluded studies not available in the English language and conference abstracts.

Source of information and search strategy

We retrieved data from PubMed and Google Scholar databases using a mix of medical subject headings (MeSH) and relevant keywords from the date of indexing to January 3, 2023 ( Table 1 ). Citation lists and hand searches were done to locate additional citations. We limited the language to English. The search was re-run shortly before the final analysis.

Data management and selection process

We imported the identified, eligible studies into EndNote version X5, a specific software program for managing bibliographic data. Two independent researchers reviewed the data to double-check the title and abstracts against the eligibility criteria. Studies that the two reviewers agreed upon were subjected to full-text review. Any dispute was settled by a third reviewer and consensus was sought by discussion. Full-text articles of the potentially relevant studies were then screened for the final inclusion if they met eligibility criteria.

Data extraction process

A data extraction spreadsheet was prepared, and two reviewers extracted data manually. The spreadsheet was populated with the variables pertaining to the research question. From each study the following data were extracted; 1) Author name, 2) Year of publication, 3) Location, 4) Study design, 5) Population, 6) Sample size, 7) Duration of intervention, 8) Outcome measurement time, 9) Description of intervention, 10) Mode of intervention, and 11) Outcome of interest.

Risk of bias in individual studies

Two authors assessed the retrieved articles for quality and potential risk of bias using the Joanna Briggs Institute (JBI) critical appraisal assessment tools for Randomized Controlled Trials and Quasi-Experimental Studies/non-randomized experimental studies [ 40 ]. The studies that scored above half of the scored value of the tools were considered for minimum risk of bias and included in the review.

Data synthesis

We summarized data using tables, and narrative synthesis to include the type of intervention performed, characteristics of the target population, type of outcome, and a summary of the findings.

Sixteen trial studies that assessed the effect of menstrual hygiene management interventions on schoolgirls’ attendance, school performance, and school dropout, as well as emotional wellbeing and menstrual hygiene knowledge, attitudes, and practices were reviewed. This included 15 peer-reviewed articles and one article in the gray literature ( S1 Fig ). The studies represented a total of 17, 910 schoolgirls, 4,612 mothers, and 4,500 fathers/guardians from Ethiopia, Kenya, Nigeria, Ghana, Uganda, Iran, Nepal, Bangladesh, and Indonesia. The studies analyzed schoolgirls aged 9 to 25 years with a sample size that ranged from 60–8,839. The studies involved menstruating, pre-menarchal, and dysmenorrhea-affected schoolgirls, as well as parents ( Table 2 ).

The menstrual education components of the studies included puberty education, training on making reusable pads, distribution of books, magazines, posters, pamphlets, and menstrual calendars, as well as the incorporation of menstrual health topics into the school curriculum. Information was also shared through WhatsApp, face-to-face discussions, TV shows, festivals, essay competitions, storytelling, gamification, and question-and-answer sessions. These interventions were provided by trained individuals, including trained peer educators, teachers, research assistants, healthcare providers, and parents [ 31 , 32 , 34 , 35 , 41 , 42 , 44 – 46 , 49 – 52 ]. The menstrual supply interventions included the provision of disposable and reusable pads, underwear, menstrual cups, soap, or detergent to wash menstrual pads, and the installation or improvement of water, sanitation, and hygiene (WASH) facilities ( Table 2 ).

The reported trials evaluated several outcomes of interest, including school attendance, school dropout, and academic performance, as well as menstrual hygiene knowledge, attitudes, and practices, physical health, and emotional health including menstruation-related fear, shame, and stigma, as well as social attitudes regarding expected gender-related behavior (gender norms). Montgomery et al. suggested using school attendance and dropout rates as a proxy indicator of academic performance [ 32 ]. Some trials relied on self-reported/recorded attendance, which may have introduced recall bias, while others cross-checked attendance records [ 32 , 35 , 48 , 51 ] (Tables ​ (Tables3 3 and ​ and4). 4 ). The trial by Sol et al. cross-checked attendance of the school record with survey data using spot checks [ 50 ]. Official school attendance records were supplemented by individual diaries filled out by the schoolgirls in one of the trials [ 34 ]. Moreover, despite the presence of a standardized menstrual attitude questionnaire that can be validated contextually [ 53 ], some trials used non-standardized evaluation tools developed by specific researchers [ 31 , 42 , 45 , 52 ]. Causation is difficult to determine in some studies because the sample size was quite small [as few as 60 participants [ 46 ]].

Montgomery et al. carried out a study on peri-urban schools that were comparable, but they also incorporated a remote rural site lacking experience in using sanitary pads, with no access to electricity and unpaved roads. This may have affected baseline similarity and intervention fidelity, making it harder to determine the intervention’s actual effects [ 32 ]. In some studies, only a single-session educational intervention was provided, and only half of the girls attended the educational session in the study by Montgomery et al. [ 47 , 51 ]. Some studies did not provide information on the follow-up or dropout rates among the study groups [ 31 , 35 , 41 , 43 , 49 ], while others achieved a statistically significant improvement in school attendance and school performance using small sample sizes [ 32 , 46 ]. Blinding with respect to outcomes is crucial in reducing bias in experimental studies, but it is sometimes impractical to blind study participants, intervention providers, and assessors. This may lead to exaggerated intervention-effect estimates and performance bias [ 54 ]. Despite this, there are ongoing controversies concerning the advantage of blinding in clinical trials [ 55 – 57 ]. Outcome assessors, laboratory staff, and statisticians were blinded in only two trials [ 48 , 50 ].

The effect of menstrual hygiene management interventions on

Schoolgirls’ school attendance, performance, and dropout.

Of the trials that evaluated the effect of menstrual hygiene management interventions on school attendance, performance, and dropout, six studies reported that the intervention had a positive effect [ 32 , 35 , 46 , 47 , 50 , 51 ] while the remaining three studies found no significant effect [ 34 , 43 , 48 ].

Implementing pad and education interventions together increased school attendance by almost 6 days per term, equivalent to 9% of a girls’ school year. The study also showed that puberty education alone improved school attendance levels five months after intervention [ 32 ].

Belay et al. carried out an intervention at 15 schools (5 rural and 10 urban) involving 8,839 students, both male and female. All students received an educational intervention concerning menstruation and the female students also received four reusable pads and two pairs of regular underwear. School attendance was analyzed before and after the intervention and compared with attendance data from the prior school year. After the intervention, girls had 24% fewer school absences than boys and student sex was not a predictor of school absence during a similar time period during the previous academic year [ 35 ]. In another study, providing reusable pads, soap, and puberty education; training on making reusable sanitary pads and providing the equipment necessary to make pads resulted in decreased school absenteeism compared to controls [ 47 ].

School and household-based menstrual hygiene management interventions implemented in 148 schools involving 2,127 schoolgirls showed reduced school absences and dropout rates. The interventions included the integration of menstrual health education into the school curriculum and building/maintaining water, sanitation, and hygiene (WASH) infrastructure at schools. The provision of household interventions to improve parents’ knowledge didn’t show significant differences between the household-based and school-based intervention arms [ 50 ] ( Table 2 ).

On the other hand, three trials showed that school-based interventions did not affect school attendance or dropout. According to Phillips-Howard et al. providing either menstrual pads or cups did not reduce school dropout (control = 8.0%, cups = 11.2%, pads = 10.2%), though the finding should be taken with caution because of a nearly 40% loss-to-follow-up. Likewise, in this study school absenteeism was not analyzed because it was rarely reported [ 48 ]. The trial by Oster et al. found that providing menstrual cups to girls did not significantly increase school attendance [ 34 ]. This is congruent with the study by Austrian et al. that found no significant effect on school attendance after the provision of disposable sanitary pads and reproductive health education [ 43 ] ( Table 2 ).

Menstrual knowledge, attitude, menstrual hygiene practice, and emotional wellbeing

Studies have shown that both menstrual education and menstrual hygiene supply interventions have a positive effect on menstrual hygiene-related knowledge, attitudes, and practices among schoolgirls ( Table 5 ). These interventions help reduce menstrual-related shame, stigma, fear and also increase self-efficacy and promote open discussion about menstruation [ 31 , 41 – 43 , 45 , 46 , 49 , 50 , 52 ]. In Ethiopia, a trial by Blake et al. found that distributing puberty books to schoolgirls improved their menstrual knowledge and attitudes towards menstruation and reduced menstrual shame and fear [ 45 ]. The interventions had a significant effect on menstrual hygiene management practices such as increasing bathing during menstruation [ 31 , 43 ] ( Table 2 ). The impact of menstrual hygiene interventions on lower genital tract infections was assessed in one trial. Though non-significant, there was a lower prevalence of lower genital tract infections among the pooled menstrual cup plus sanitary pad arms, as compared to non-intervention arms [ 48 ] ( Table 2 ).

Key : 0 = No Impact, + = Positive Impact

This review included trial studies that implemented different types of menstrual hygiene management interventions using diverse delivery strategies, different types of intervention providers, and different durations of intervention. School attendance, school performance, school dropout, menstrual hygiene knowledge, attitudes, and practices, and aspects of emotional health related to menstruation such as menstrual stigma and shame were outcomes of interest.

Six trial studies indicated a positive effect of interventions on school attendance, school dropout rates, and schoolgirls’ academic performance [ 32 , 35 , 46 , 47 , 50 , 51 ]. However, most of the studies had low to moderate levels of bias (Tables ​ (Tables3 3 and ​ and4). 4 ). School attendance is usually documented by schoolteachers or by using self-reported diaries, but this method may not provide data accurate enough to reach firm conclusions. In addition, school attendance alone may not be predictive of the academic performance of schoolgirls. Betsu et al. found that a girl’s physical presence in a classroom did not necessarily correlate with her mental presence or paying attention [ 58 ]. Moreover, mood swings and severe premenstrual symptoms resulting from hormonal changes during menstruation impact paying attention [ 59 ]. Additional indicators such as formal educational achievement, school participation, and enrollment in the succeeding grades of school might provide a more robust picture of schoolgirls’ academic achievement and continuing access to education. The trial by Nyadoy et al. showed improved academic scores after telling stories and playing games about menstrual hygiene management in the intervention arm; however, the outcome was measured only 6 weeks after the baseline assessment, and the small sample size makes it difficult to draw firm conclusions [ 46 ].

On the other hand, some studies have found interventions to have no effect, including the studies that evaluate menstrual cups [ 34 , 43 , 48 ]. Despite being a potentially viable intervention, many people falsely believe that menstrual cups can cause loss of virginity and reduced fertility [ 60 ]. Furthermore, menstrual cups may be difficult to clean effectively whenever water supplies are inadequate. This may influence the results of interventions using this device. Studies reporting positive effects, or no intervention effect may suffer from different forms of bias, posing challenges for policymakers and stakeholders looking for evidence-based menstrual hygiene interventions.

The systematic review by Chandra-Mouli et al. highlighted the importance of providing accurate biological information to menstruating girls. In most cases, girls receive most of their information from their mothers, but schoolgirls also seek menstrual information and support from older siblings, and their peers. All of these sources, and particularly their mothers, may be significant sources of menstrual misinformation [ 17 , 58 ]. There is a great need to improve community knowledge of the biology of menstruation, but despite this fact, mothers as a group are generally not targeted for improved education. In most of the studies we reviewed, premenarchal schoolgirls, in particular, receive insufficient education concerning menstruation, leaving them unprepared for the biological changes associated with menarche and contributing to their frustration, bewilderment, and anxiety when menstruation begins [ 61 ].

For schoolgirls to manage menstruation safely and comfortably, they need supportive social norms in the community as well as a welcoming environment both at home and at school. Interventions that improve parental involvement in menstrual hygiene management and that also target community sources of menstrual misinformation are generally lacking in the literature. The study by Sol et al. that engaged parents found a positive effect on menstrual hygiene management and school attendance [ 50 ]. Another study by Agbede et al. looking at the combined effect of peer and parent educational interventions had the highest mean score of menstrual hygiene management practice among its study arms [ 42 ].

Discussing menstruation with male family members (including, and perhaps especially, fathers) is another challenge faced by schoolgirls [ 62 ]. Approximately 13% of Tanzanian girls have encountered period teasing, while over 80% expressed fear of being teased, particularly by male classmates. This results in reduced school attendance, participation, and concentration in class [ 63 ]. Another article exploring the beliefs and attitudes boys and men hold about menstruation revealed that men generally have more negative attitudes towards menstruation and view it as debilitating and requiring secrecy. However, these attitudes may soften as men age and gain more knowledge and experience with menstruation [ 64 ]. Certain religious texts can be interpreted to associate menstruation with impurity and uncleanliness, which leads to menstrual restrictions, shame, and taboos in some cultures [ 65 ]. Many of these challenges are unaddressed by most of the menstrual intervention studies. A qualitative investigation by Betsu et al. indicated that many school teachers support attitudes promoting menstrual secrecy by their comments in class concerning menstrual hygiene, saying things like “drying reusable pads in a hidden place is helpful to prevent Michi , ” a folk-ailment believed by many locals in Ethiopia to be caused by the exposure of used or washed menstrual pads to sunlight [ 58 ]. Those who provide menstrual hygiene education and interventions should be aware of common cultural misperceptions regarding menstruation that exist in their communities. Better training of the teachers who provide classroom instruction concerning menstruation is also needed.

The review has certain limitations. Many of the studies that were included in this review relied on self-reported menstrual knowledge, attitude, and practices, as well as potentially inaccurate school attendance records, which could lead to over- or under-estimation of the findings due to poor recording of school attendance, social desirability bias and recall bias. Most of the studies reviewed did not address the impact of water, sanitation, and hygiene interventions or other community-based interventions on menstrual hygiene management. The review consisted of trials that used different interventions and methods of measuring outcomes and included a wide range of ages (9–25 years). These variations make it challenging to compare the studies with one another and to capture accurately the impact of menstrual hygiene interventions on the outcomes of interest. All relevant studies may not have been captured for this review due to limitations in the search strategies and limiting the studies reviewed to those in English. Most of the literature about menstrual hygiene management, especially in low- and middle-income countries, doesn’t adequately address the needs of people who identify as gender-nonconforming. Since gender-diverse persons make up such a small percentage of the population, it can be assumed that most of the academic literature on MHM is written from the perspective of cis women. This exclusion may affect menstrual hygiene needs and experiences of transgender, non-binary, and other gender-varied individuals [ 66 ]. There was a paucity of randomized controlled trials and quasi-randomized controlled trials, and this may have biased the resulting literature. As a result, conclusions concerning the effectiveness of menstrual hygiene interventions should be interpreted with caution.

The review also has several strengths, providing an extensive summary of English-language evidence. It offers valuable insights by presenting a comprehensive review of English-language trial studies that evaluate the effect of menstrual hygiene management interventions on various aspects of schoolgirls’ lives. With a large sample size from multiple countries, the study covers a broad range of interventions, including puberty education, distribution of menstrual supplies, and integration of menstrual health topics into school curriculums. The findings not only underscore the positive effects of these interventions, such as increased school attendance and enhanced menstrual hygiene knowledge and attitudes but also shed light on the challenges and limitations observed in certain studies. This review will also have a great contribution to identifying research gaps for further studies.

The results of this review have several implications for practice in the field of menstrual hygiene management. It highlights the need for comprehensive and accurate education about menstruation, not only for girls but also for their parents, teachers, and communities. This education should address misconceptions, and cultural taboos, and provide information on appropriate menstrual hygiene practices. This can be implemented by incorporating menstrual hygiene management into school curriculum and training; providing access to affordable and hygienic menstrual products; ensuring adequate water and sanitation facilities and creating supportive environments that reduce stigma and shame associated with menstruation.

The findings of this review have important policy implications. Governments and policymakers should prioritize menstrual hygiene as a public health issue and develop policies and guidelines to meet the needs of menstruating girls including affordable menstrual products, proper hygiene facilities, education about menstruation, and access to healthcare services for managing menstrual health. It is also crucial to address cultural beliefs and misconceptions surrounding menstruation through awareness campaigns and community engagement. Adequate funding should be allocated to ensure the effective implementation and monitoring of these policies to ensure that all girls have access to menstrual hygiene facilities and education.

While this review provides valuable insights into the effectiveness of menstrual hygiene management interventions, there are several areas that require further research. Future studies should aim to overcome the limitations identified in this review, such as biases and small sample sizes. Longitudinal studies with larger sample sizes are needed to assess the long-term impact of menstrual hygiene management interventions on school attendance, academic performance, and emotional well-being. Additionally, more research is needed to explore the effectiveness of different delivery strategies and intervention providers. It would also be beneficial to investigate the cultural and social factors that influence menstrual hygiene practices and develop interventions that address these specific contexts. Overall, future research should focus on generating robust evidence to inform the development of evidence-based interventions and policies in the field of menstrual hygiene management.

Menstrual hygiene management interventions can have a positive impact on schoolgirls’ attendance, reducing dropout rates, and improving their school performance and emotional health. Moreover, such interventions can improve knowledge, attitudes, and practices pertaining to menstruation and its management. A holistic approach that includes accurate menstrual education, better access to hygiene products, improved water, sanitation, and hygiene facilities; and greater engagement of parents, religious and community leaders is likely to make the greatest impact in this area. Of crucial importance is treating all who menstruate with the respect they deserve and reducing the stigmatization and shame that often surrounds this biological process.

It is also important to standardize the interventions used as well as the tools used to measure outcomes if such programs are to be evaluated properly. This will help to identify best practices and improve the overall effectiveness of the menstrual hygiene interventions used. Organizing large randomized clinical trials to address these issues in a well-structured manner would be extremely useful in moving our knowledge forward on how to improve menstrual hygiene among schoolgirls in low- and middle-income countries.

Supporting information

S1 checklist, s1 protocol, funding statement.

The author(s) received no specific funding for this work.

Data Availability

• PLoS One. 2024; 19(8): e0302523.

Decision Letter 0

PONE-D-23-35026Menstrual hygiene management interventions and their effects on schoolgirls’ menstrual hygiene experiences in low and middle countries: A systematic reviewPLOS ONE

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Reviewer #1: I would encourage the authors to tighten the piece prior to publication in two ways. First, the nature of the search seems to have translated into several important studies escaping capture as part of the dataset. This is not fatal; it is the nature of the searches the authors chose to run. But I think the authors will want to cabin their conclusions as being only as good as the data the searches yielded, acknowledging that other work that did not fit the authors' relatively narrow criteria were therefore excluded. Second, I think the authors would be well-served to address the linguistic challenges in talking about "women and girls" and "menstrual hygiene," when those terms have been made much more complicated by excellent work in this field. The authors gesture at this by the use of the term "menstruand" (which I found jarring and out of synch with most other scholarship in the field," but they never address the issue head-on.

Reviewer #2: General

Much is written in passive voice – active voice would be preferable. Occasionally you use ‘we’ – you should pick one narrative style and stick to it.

I don’t think it’s necessary to have the acronyms listed since there are only two (unless this is a requirement of the journal then please ignore me). You haven’t included PICOT.

There are times when you spell out MHM and others when you use the acronym – be consistent and use the acronym throughout

The paper should recognise that it isn’t just cisgender women who menstruate but potentially also transgender men, non-binary and other gender diverse persons. The term ‘menstruator’ could be used instead of women and girls to be inclusive.

I think it would be helpful if you stated the countries where studies present different evidence else you might fall into making sweeping generalising comments. For example lines 88-89.

You’ve used WASH as an acronym and spelled out but haven’t introduced it as an acronym – please revise

You could consider adding a positionality statement at the beginning of the paper.

Why were no studies in any Ethiopian languages selected?

‘intervention’ seems like a rather broad keyword

MHM definition – could point out that depending on the materials used somewhere to wash and dry reusable materials is also necessary. Could also mention the access to and ability to wash and dry underwear is also a necessity.

Lines 84-87 its clearer written as: More than half (52 %) of adolescent girls in Ethiopia

have never received any information about menstrual hygiene due to socio-cultural

87 misinformation, religious taboos, and inadequate menstrual supplies and facilities, which leads to fear, confusion, and lack of confidence when menarche occurs (11-15).

Line 93 – what is meant by gender empowerment exactly? Vague phrase it might be interesting to unpack it.

111-112 you use the word review a lot

Good justification for the work.

I wonder if it would produce, more results if you googled gender neutral terms like ‘menstruator’ – it could be written as a limitation if you didn’t do this.

The systematic review following PRISMA is clearly and transparently described. Having three reviewers to judge papers against the outlined eligibility criteria reduces bias.

Line 175 - I don’t think mode of intervention needs an explanation.

The search strategy is well documented and comprehensive

Data bases and other sources of information are specified

The process of study selection is clearly outlined.

The process of data extraction is well documented

The key characteristics of included studies is clearly presented.

The studies were rigorously assessed for risk of bias using the Joanna Briggs Institute critical appraisal assessment tools

Line 192-193 - I think you mean Saudi Arabia, not Saudi Riyadh

From like 251 – have you listed all of the education components because saying they ‘included things such as…’ makes it seem that the list is not exhaustive but it seems to be that way. And perhaps it should be if not too long to include each component.

Line 279 - Good and transparent analysis of intervention fidelity

Interesting discussion on blinding.

Line 305 – et al is repeated

Table 2 and 3 – could we have colour code for severity of bias?

Table 4 – reduce space between lines so table is less spread out – its currently across 13 pages

Table 4 – expand acronyms

Table 5 – I assume the 0 means no impact and + means positive impact but you need to dd this key somewhere. It would be clearer if you also did a colour i.e. green for positive effect , yellow for neutral. It would also look better if the symbols were in the centre of the square. Table 5 – could you add the type of intervention into the summary table and group similar types of intervention together?

370 – was the not paying attention due to hormonal fluctuations or worry and concern about not being able to manage their period?

An important narrative I feel is missing more on which there is a growing body of literature on is the role of men/boys/non-menstruators is upholding stigma/teasing etc. you touch upon this in lines 405-406 but feel some more literature on how non-menstruators act as a barrier could be added in.

407 – careful not to generalise all religious beliefs be specific.

Implications

447-449 – be more specific – state the unmet needs

Seems like the conclusion is the first instance apart from the title where you mention low and middle income countries… why would this not be an issue in high income countries? The United Kingdom is currently going through a cost of living crises where people are in hygiene poverty – unable to buy basic hygiene items like menstrual pads. Ref - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255001&utm_source=miragenews&utm_medium=miragenews&utm_campaign=news

Furthermore – Saudi Arabia is one of the countries studied, which is a high income country.

Is categorising countries by their economic status quite western and neoliberal?

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Reviewer #2:  Yes:  Georgia Hales

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Author response to Decision Letter 0

17 Jan 2024

To Reviewer 1

• Dear reviewer, the conclusion provided in the manuscript is yielded from the findings (literatures reviewed). The findings evidenced that the interventions can enhance schoolgirls' educational outcomes, and can improve their menstrual knowledge, attitudes, and practices by helping them manage their periods more effectively.

• We used the term “Menstruant” not “menstruand” may be it is editorial error. The use of women and girl is more dominant in WASH sectors. While menstruants is emerging form gender inclusive perspective, hence, in the write up we tried to balance the use of the terms. However our search clearly identified literatures that assessed the effect of menstrual interventions on “school girls”.

To Reviewer 2

Authors’ response

• We have converted passive voice to active voice in most of the relevant sections.

• Acronyms have been spelled out and made consistent in the current version.

• Although "Menstruants" is a gender inclusive term, the primary objective is to assess its effect on “school girls” and we had to be specific to these group of study participants. Moreover, the studies we have reviewed mostly used the term "Schoolgirls," "Girls," and "Women". For this reason, we maintained the terms. However, in the write up section we have also considered “menstruants”

• The findings are form different low and middle income countries, and citation is in place ( line 89)

• The academic and research language for Ethiopia is English. So, there is no study conducted using Ethiopian language and which is not included.

• We have included positionality statement under the method section on the current version (line 128)

“Intervention" is preceded by "menstrual hygiene management" and expressed as “menstrual hygiene management interventions” to be more specific, (lines 27 and 34)

Authors’ Response: Agreed, we utilized the standard definitions, and employing operational definitions may provide further assistance.

Lines 84-87 its clearer written as: More than half (52%) of adolescent girls in Ethiopia have never received any information about menstrual hygiene due to socio-cultural

The issue is fixed accordingly: (line 86-89)

• “More than half (52 %) of adolescent girls in Ethiopia have never received any information about menstrual hygiene (10), due to religious taboos, socio-cultural misinformation, and inadequate menstrual supplies and facilities, which leads to fear, confusion, and lack of confidence when menarche occurs (11-15).”

• “gender empowerment” modified to “gender equality” ( line 93)

• In fact, we purposefully limited our search terms to be gender-specific to highlight the impact the interventions on schoolgirls, but it is still valued concern because it may have limited searching relevant literature. We have included it on the limitation section of the manuscript

Authors’ Response

Written as “Mode of intervention” only (Line 200, in the modified version)

• Saudi Arabiya is excluded from the analysis as it is one of the high income countries. And modification is made on the manuscript accordingly ( Line 215)

From line 251 – have you listed all of the education components because saying they ‘included things such as…’ makes it seem that the list is not exhaustive, but it seems to be that way. And perhaps it should be if not too long to include each component.

Modified as “The menstrual education components of the studies included; puberty education, training on…” (line 239)

Authors’ response: Amendment made (the repeated et al is deleted/ line 295)

Table 2 and 3 – could we have color code for severity of bias?

Authors’ response: Color code is given accordingly

Authors’ response: Resolved (line space of the table is reduced)

Table 5 – I assume the 0 means no impact and + means positive impact but you need to add this key somewhere. It would be clearer if you also did a colour i.e. green for positive effect, yellow for neutral. It would also look better if the symbols were in the centre of the square. Table 5 – could you add the type of intervention into the summary table and group similar types of intervention together?

• Acronyms are expand on the heading section

• Key is provided for Table-4 on line 337

Authors’ response: lack of attention in education could be attributed by both the hormonal fluctuations and worry about not being able to manage their period. We have added additional literatures to illustrate it. (Line 353)

Authors’ Response: Noted and we have added more details as follows

“One article exploring the beliefs and attitudes boys and men hold about menstruation revealed that men generally have more negative attitudes towards menstruation and view it as debilitating and requiring secrecy. However, these attitudes may soften as men age and gain more knowledge and experience with menstruation” (line 390)

407 – careful not to generalize all religious beliefs be specific.

Authors’ response: Addressed in line 394 as “most religious beliefs”, to avoid generalization.

Authors’ Response: specified on the current version on line 435-439

“Governments and policymakers should prioritize menstrual hygiene as a public health issue and develop policies and guidelines to meet the needs of menstruating girls including affordable menstrual products, proper hygiene facilities, education about menstruation, and access to healthcare services for managing menstrual health.”

Seems like the conclusion is the first instance apart from the title where you mention low- and middle-income countries… why would this not be an issue in high income countries? The United Kingdom is currently going through a cost-of-living crises where people are in hygiene poverty – unable to buy basic hygiene items like menstrual pads. Ref - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255001&utm_source=miragenews&utm_medium=miragenews&utm_campaign=news

Is categorizing countries by their economic status quite western and neoliberal?

� Menstrual hygiene management is indeed an issue that affects individuals in both high-income and low-income countries. In high-income countries, access to menstrual products and facilities for proper hygiene may be more readily available (as compared to low income countries), but issues such as stigma and access to education about menstrual hygiene persist. In low-income countries, the challenges may include limited access to sanitary products, clean water, and sanitation facilities, as well as social stigma and inadequate education about menstrual health. Therefore, addressing menstrual hygiene management is an important aspect of promoting gender equality and ensuring the well-being of individuals across different socioeconomic contexts.

� We have used the World Bank classification of countries based on economic status and we have excluded the finding from Saudi Arabia.

� Categorization is made by income level ( world bank)

Submitted filename: Response to Reviewers .docx

Decision Letter 1

29 Jan 2024

PONE-D-23-35026R1Menstrual hygiene management interventions and their effects on schoolgirls’ menstrual hygiene experiences in low and middle countries: A systematic reviewPLOS ONE

Thanks for doing a good job with the first round of comments.   I'm afraid both reviewers have some further things for you to address but I think these should be pretty straightforward now. Please submit your revised manuscript by Mar 14 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at  gro.solp@enosolp . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

2. Is the manuscript technically sound, and do the data support the conclusions?

3. Has the statistical analysis been performed appropriately and rigorously?

4. Have the authors made all data underlying the findings in their manuscript fully available?

5. Is the manuscript presented in an intelligible fashion and written in standard English?

6. Review Comments to the Author

Reviewer #1: Line 24: uses passive voice

Line 32: “Accordingly” doesn’t flow from previous sentence

Line 35: “However” is clunky here.

Line 82: You use the word “menstruants” without explaining this word choice. This absolutely needs to be addressed. It is not widely accepted (by two different “camps” for different reasons). It is jarring and merits context. If you believe that this is the term that is emerging, you should at least cite to some authority for that, because I don’t think there is consensus/agreement that many readers will have heard it before (“menstruator” is much more common in significant literature).

Lines 84-89: You definitely need to explain your focus on women and girls. Your response to the reviewers makes clear that it is because that is what the studies you survey are studying…but this does not come through in your paper. The reader is still left wondering why you are focusing on *girls* (but then again, are you focused just on girls? In fact, later in the paper, such as at Lines 296-98, you do cite studies involving boys). This needs to be clarified.

Lines 95-96: I don’t think the average reader will understand what you mean by “weak enabling environments”’

Line 111: comma missing

Line 114: reviews “were conducted” (passive voice) by WHOM?

Lines 143-145: Here you talk about limiting your search to studies in which the participants were schoolgirls only. Is this accurate? See comment above re Lined 296-98. Perhaps I am misunderstanding.

Line 148: Punctuation missing?

Lines 149, 152, 160: Colon not necessary?

Line 165: Unnecessary comma

Line 210: Do you mean “findings” (plural)?

Line 212: Are “menstrual interventions” the same as “menstrual hygiene management interventions” discussed on Line 146?

Line 254: Passive voice

Line 258: Odd initial cap for “School”

Lines 272-73: Awkward and difficult to follow sentence.

Line 298: Were these period underwear or regular underwear?

Lines 300-302: Run-on sentence.

Line 464: Instead of talking about “menstruating girls and women,” might this be an appropriate place to talk about “all who menstruate,” notwithstanding your focus?

Line 466: Queer theory would ask us to look rigorously at the word “normal.” I think you mean “involuntary” or “inevitable” (or perhaps drop the adjective entirely).

Reviewer #2: Dear authors,

Thanks for the effort you’ve put in to addressing my comments. I feel like the paper has now avoided a couple of easy pitfalls. The information in the tables is also now much easier for the reader to digest – thanks for this. There are just a few things I’m not sure were fully addressed; perhaps I did not explain myself very well so here I elucidate:

I understand the want to just focus on one particular group (schoolgirls). However, I raised this point as it is often gender diverse persons who are left out and not represented in this type of data collection, which is something that needs to change if the research is to remain relevant going forwards. I appreciate the mention of menstruators – it’s important as a limitation if anything to explain the data set is solely focused on cisgender girls and women.

Largely you give the country where the reference is from but not all the time. It’s important to do this consistently in order to avoid making generalisation about all low-income countries.

I’m happy to see the inclusion of the positionality statement – it adds transparency. Something I was looking for was mention of the country/countries the authors are from. There adds incongruence if the authors were from a high-income country say but are commenting on low income countries. How does where the authors are from impact the direction of the study or interpretation of results?

That’s a shame that you’ve now had to exclude Saudi Arabia. I hope my point that menstrual health is a global issue that impacts high-income countries was taken not to diminish the lack of access in lower income countries but to highlight it’s not just an issue in low-income countries and that there are inequalities within countries as well as between them.

Great that you’ve added in some literature regarding men/boys/non-menstruators however the point that I wanted to get at was that it can be these groups that also act as a barrier to schoolgirls’ attendance through teasing or shaming.

I don’t think changing the line to ‘most religious beliefs’ avoids generalisation either… this is a sensitive point and I understand what you’re getting at… but I think what you’re saying could be misinterpreted. Religion is interpreted and enacted differently across the world. For example in the UK, I don’t think many Christians would feel stigma towards menstruation because of what is written in the Bible. But I understand that other cultures might. What you can say instead which is factually true is that in certain religious texts menstruation is presented as making the menstruator impure or unclean. I would be particular about using the exact wording – so much of religious text is down to interpretation.

I feel that for this work to be relevant, contemporary, and self-aware, the authors should take a little more time to contemplate these last points. It would also be good to have a statement from the authors on why this paper is important and what new contribution to the discussions on MHM it brings.

Many thanks,

7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

Author response to Decision Letter 1

Reviewer #1:

Line 24: uses passive voice

Authors ‘Response

Dear reviewers I agree with comments in line #24

The statement in line# 24 is replaced with active voice, “To address these issues, researchers have conducted intervention studies, but the impact on school attendance has varied”

I agree with comment of the reviewer, the statement in line 32 is paraphrased as

“Review of sixteen trial studies showed that menstrual hygiene interventions have a positive effect on schoolgirls' school attendance, performance, and dropout rates, as well as on their menstrual knowledge, attitudes, practices, and emotional well-being.”

The comment is well taken and “However” in line # 34 is omitted and the sentences is rephrased as “There was a low to medium risk of bias in the most of the studies.”

I agree with point raised and it is valued concern. Hence the word “menstruants” in line 82 is replaced with “menstruators” as per the recommendation

The comment is well accepted. The reason for focusing on schoolgirls is now explained in line 419-426 as follows, to make it clear to the reader.

“Most of the literature about menstrual hygiene management, especially in low- and middle-income counties, don’t adequately address the needs of people who identify as gender-nonconforming. Menstrual discourses, are frequently written from the perspective of a cis woman, highlighting only the menstrual experiences of adolescent girls and cis women. This exclusion may affect menstrual hygiene needs and experiences of transgender, non-binary, and other gender varied individuals”

This review investigated how involving males (boys, fathers, or parents) in interventions might improve schoolgirls' experiences. We included intervention studies targeting these male groups alongside traditional MHM interventions. This broadens the range of interventions studied, but the outcome/impact must be measured specifically on schoolgirls.

The comment is well accepted. In the old version we explained the word “weak enabling environments”’ using the list of challenges that came after it . i.e. “Insufficient knowledge about menstruation, inadequate access to water, sanitation and hygiene services, lack of adequate hygiene materials, and social norms unsupportive of those who menstruate”

But if it creates confusion for readers, the already listed challenges are more descriptive and we have omitted the phrase “weak enabling environment” to avoid ambiguity to the reader

Authors ‘Response .

This is well taken and Comma is used in line #110 “2015-2016 school year, demonstrated”

Dear reviewer, we have included the list of 4 citations which helps to address the potential question about “who conducted the review?”. The 4 citations in line 116 can address this concern than listing the name of the authors who did the review.

Dear reviewer, your understanding is correct. The statement is rephrased to “

The search was limited to studies that measured outcomes on schoolgirls because the objective of the review was to evaluate how menstrual hygiene management intervention programs impact schoolgirls' attendance, academic performance, or dropout rates. ” The study aimed to evaluate the effects of intervention programs on school attendance, performance, and dropout rates of schoolgirls, as clearly stated in lines 144-145. Therefore, while the intervention may involve parents, the community, and males, the outcomes must be assessed specifically for schoolgirls. We have included studies that involved parents or males in the intervention, with the actual outcomes measured for the schoolgirls. Additionally, we used terms such as fathers, mothers, community, and parents as search terms to encompass various types of interventions.

This is well noted punctuation is in place now, “supplies.”

Well noted and accepted, the colon in line 149 and 152 is removed and we kept the colon in line 159 and we deleted the word “includes” to make appropriate use of colon

Well accepted, comma in line 165 is removed the statement is written as “we excluded studies not available in the English language and conference abstracts.”

This is noted, line 208 in this version, is changed into plural “a summary of the findings”

Yes, it is the same and line 210 is corrected as “Sixteen trial studies that assessed the effect of menstrual hygiene management interventions…”

Dear reviewer the comment is accepted, the statement in line 252 is changed into active voice as follows

“Montgomery et al. suggested that using school attendance and dropout rates as a proxy indicator of academic performance”

This is accepted. Line 256 Changed from Official School attendance� Official school attendance

The comment is well accepted. The statement in line 270-272 which was “Montgomery et al. conducted study on comparable peri-urban schools but included one remote rural site without experience in using sanitary pads that had no electricity, and no paved roads.”is rephrased as follows:

“Montgomery et al. carried out study on peri-urban schools that were comparable, but they also incorporated a remote rural site lacking experience in using sanitary pads, with no access to electricity and unpaved roads.”

Dear reviewer, fortunately I physically knew the underwear’s provided in this specific intervention and they are normal/regular underwear’s and the term “ regular” is added ( line 297-298)

Well noted and accepted. The statement in line 297-299 “School attendance (data collected prospectively on-site by team researchers) was analyzed before and after the intervention and also compared with attendance data from the prior school year”

Is amended as

“School attendance was analyzed before and after the intervention and also compared with attendance data from the prior school year”

Line 464: Instead of talking about “menstruating girls and women,” might this be an appropriate place to talk about “all who menstruate,” notwithstanding your focus

Dear reviewer this comment is accepted. “Menstruating girls and women “is replaced with “all who menstruate” on the current version ( line # 470)

Line 466: Queer theory would ask us to look rigorously at the word “normal.” I think you mean “involuntary” or “inevitable” (or perhaps drop the adjective entirely) (line # 474).

Well Noted, the word “Normal” is dropped from line # 471

Reviewer #2: Dear authors,

The following paragraph would explain the limitation, which is placed in line # 416-421

“Most of the literature about menstrual hygiene management, especially in low- and middle-income counties, don’t adequately address the needs of people who identify as gender-nonconforming. Menstrual hygiene issues, are frequently written from the perspective of a cis woman, highlighting only the menstrual experiences of adolescent girls and cis women. This exclusion may affect menstrual hygiene needs and experiences of transgender, non-binary, and other gender varied individuals.”

Largely you give the country where the reference is from but not all the time. It’s important to do this consistently in order to avoid making generalization about all low-income countries.

The input is considered and included the countries where the citation exists, but country was not included mainly on table 5

Dear reviewer this is well accepted. The following statement is added the statement “I am from Ethiopia one of the low-income countries” in line 130 to indicate the principal investigator is from low income country.

Dear reviewer, issues of menstrual poverty and inequity are global challenges. It is widely recognized that individuals who menstruate in high-income countries encounter various challenges, although the severity and nature of these challenges may vary from LMIC. Our initial focus for the review was on addressing issues in low and middle-income countries. And we wrongly classified Saudi Arabia as LMIC and include it in the review process. However, this does not imply that menstrual hygiene management is not an issue in high income countries. Your feedback in the previous review highlighted the inclusion of one high-income country, Saudi Arabia, in the review. This was an important observation. It would not be appropriate to include a high-income country when the review is intended to focus on LMICs. This was the sole reason for its exclusion.

Dear reviewer the comment is well received. The following statement is included in line #389-392 on the current version which may address the concern about the effect of teasing by boys on school attendance and participation.

“Approximately 13% of Tanzanian girls have encountered period teasing, while over 80% expressed fear of being teased, particularly by male classmates. This results in reduced school attendance, participation, and concertation in class.”

I don’t think changing the line to ‘most religious beliefs’ avoids generalization either… this is a sensitive point or I understand what you’re getting at… but I think what you’re saying could be misinterpreted. Religion is interpreted and enacted differently across the world. For example in the UK, I don’t think many Christians would feel stigma towards menstruation because of what is written in the Bible. But I understand that other cultures might. What you can say instead which is factually true is that in certain religious texts menstruation is presented as making the menstruator impure or unclean. I would be particular about using the exact wording – so much of religious text is down to interpretation.

This is well accepted and the statement “Menstrual restrictions and cultural taboos are often rooted in most religious beliefs and untrue cultural assumptions” Is replaced with following statement on the current version (line #395-397)

“In certain religious texts, menstruation is often framed as making the menstruator impure or unclean, leading to menstrual restrictions, shame, and taboos”

The contribution of the study to the discussion on MHM is highlighted as follows in the strength section (line #425-434)

The review also has several strengths, providing an extensive summary of English-language evidence. It offers valuable insights by presenting a comprehensive review of English-language trial studies that evaluate the effect of menstrual hygiene management interventions on various aspects of schoolgirls' lives. With a large sample size from multiple countries, the study covers a broad range of interventions, including puberty education, distribution of menstrual supplies, and integration of menstrual health topics into school curriculums. The findings not only underscore the positive effects of these interventions, such as increased school attendance and enhanced menstrual hygiene knowledge and attitudes, but also shed light on the challenges and limitations observed in certain studies. This review will also have great contribution in identifying research gaps for further studies.

Submitted filename: Response to reviweres.docx

Decision Letter 2

18 Mar 2024

PONE-D-23-35026R2Menstrual hygiene management interventions and their effects on schoolgirls’ menstrual hygiene experiences in low and middle countries: A systematic reviewPLOS ONE

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.  There are just a few more minor points to address, as well as the need to check the grammar further.

Please submit your revised manuscript by May 02 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at  gro.solp@enosolp . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Reviewer #1: All comments have been addressed

Reviewer #1: N/A

Reviewer #1: I implore the authors to run their manuscript through a grammar-checking program (Grammarly, Paperpal, anything) or Chat GPT to improve the grammar and punctuation, especially in the first three pages. The authors have nicely addressed the substantive comments and have responded to specific grammatical errors pointed out by the reviewers. The manuscript still needs to be gone through line by line because parts of it are not well-edited. I do not consider it a good use of my professional time to provide a third round of input pointing out grammatical errors that could be addressed readily by the authors.

Reviewer #2: Thanks for addressing the comments. There are just a couple more tiny things to address and then I’m happy for the work to be published.

1. ‘frequently written from the perspective of a cis woman’ – although I completely agree with this perhaps it requires a reference? Maybe you could explain why you know this to be true as I’m not sure you’d actually be able to find a reference. You could say ‘Since gender-diverse persons make up such a small percentage of the population, it can be assumed that the vast majority of academic literature on MHM is written from the perspective of cis women’. Maybe I’m being unnecessarily pedantic here.

2. I think you would do well to read around the purpose of positionality statements and this would help to inform how to write one. I wasn’t looking for you just to state that you are Ethiopian but how that background and identity shapes and influences the research. For example something like: I am a woman (?) who comes from Ethiopia, which is one of the countries studied in this paper. This gives me first-hand experience of what it’s like to be a menstruating schoolgirl in an LMIC. However, I am an outsider to the other countries studied in this paper, which may leave room for bias or misunderstandings in the interpretation of results’. Something like this.

3. Perhaps I’m being too cautious but I still believe this could be interpreted as offensive to some readers ‘In certain religious texts, menstruation is often framed as making the menstruator impure or unclean, leading to menstrual restrictions, shame, and taboos’. To reiterate, so much religious text is down to interpretation and this is what can cause the issues of negative views towards menstruation, not necessarily the words in the text itself. I don’t know if you’re religious or not but this would be an instance where your positionality affects how you interpret data. For example, I’m not religious and therefore don’t understand or have association to any religious texts. I gave a lecture on MHM making the exact same point as you that religious texts e.g. The Quran says that menstruation is impure. This was of course from a translation from Arabic into English where meaning can get lost anyway. A Muslim student put their hand up to explain that this was a very negative interpretation of the text and that it doesn’t mean impure in a dirty way as was interpreted. I think it would be inoffensive but still make the same point to say ‘certain religious texts can be interpreted to associate menstruation with impurity and uncleanliness, which leads to menstrual restrictions, shame, and taboos in some cultures’ you need a reference for this as well.

Author response to Decision Letter 2

31 Mar 2024

Response to Reviewers

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict-of-interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

________________________________________

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #1: N/A

Reviewer #1: I implore the authors to run their manuscript through a grammar-checking program (Grammarly, Paperpal, anything) or Chat GPT to improve the grammar and punctuation, especially in the first three pages. The authors have nicely addressed the substantive comments and have responded to specific grammatical errors pointed out by the reviewers. The manuscript still needs to be gone through line by line because parts of it are not well-edited. I do not consider it a good use of my professional time to provide a third round of input pointing out grammatical errors that could be addressed readily by the authors.

Authors’ Response:

Dear reviewer, this is valued concern. The necessary grammar and spelling errors are fixed throughout the document on the current version manuscript.

Reviewer #2: Thanks for addressing the comments. There are just a couple tinier things to address and then I’m happy for the work to be published.

Dear reviewer this comment is valued and is addressed as follows in line # 415 and the statement is replaced with the recommended way of rephrasing.

‘Since gender-diverse persons make up such a small percentage of the population, it can be assumed that most of the academic literature on MHM is written from the perspective of cis women’.

2. I think you would do well to read around the purpose of positionality statements and this would help to inform how to write one. I wasn’t looking for you just to state that you are Ethiopian but how that background and identity shapes and influences the research. For example, something like: I am a woman (?) who comes from Ethiopia, which is one of the countries studied in this paper. This gives me first-hand experience of what it’s like to be a menstruating schoolgirl in an LMIC. However, I am an outsider to the other countries studied in this paper, which may leave room for bias or misunderstandings in the interpretation of results. Something like this.

Dear reviewer the comment is well noted and the positionality statement in line #128-136, is rephrased as;

“I (the first author) am a woman, a feminist, and an advocate for girls' education. I am currently pursuing a PhD in public health. I am from Ethiopia, which is one of the countries studied in this paper. This gives me first-hand experience of what it’s like to be a menstruating schoolgirl in an LMIC. However, I am an outsider to the other countries studied in this paper, which may leave room for bias or misunderstandings in the interpretation of results. In this systematic review, the researcher's standpoint influences the research approach and findings. This study advocates for accessible menstrual hygiene resources and aims to address the stigma surrounding menstruation. The conclusions are based on this perspective, and readers are encouraged to take this into account when interpreting the findings.”

3. Perhaps I’m being too cautious, but I still believe this could be interpreted as offensive to some readers ‘In certain religious texts, menstruation is often framed as making the menstruator impure or unclean, leading to menstrual restrictions, shame, and taboos’. To reiterate, so much religious text is down to interpretation, and this is what can cause the issues of negative views towards menstruation, not necessarily the words in the text itself. I don’t know if you’re religious or not, but this would be an instance where your positionality affects how you interpret data. For example, I’m not religious and therefore don’t understand or have association to any religious texts. I gave a lecture on MHM making the exact same point as you that religious texts e.g. The Quran says that menstruation is impure. This was of course from a translation from Arabic into English where meaning can get lost anyway. A Muslim student put their hand up to explain that this was a very negative interpretation of the text and that it doesn’t mean impure in a dirty way as was interpreted. I think it would be inoffensive but still make the same point to say ‘Certain religious texts can be interpreted to associate menstruation with impurity and uncleanliness, which leads to menstrual restrictions, shame, and taboos in some cultures’ you need a reference for this as well.

Dear reviewers I agree with comments. The statement in line Line #392-94: “In certain religious texts, menstruation is often framed as making the menstruator impure or unclean, leading to menstrual restrictions, shame, and taboos”

Is replaced with

‘Certain religious texts can be interpreted to associate menstruation with impurity and uncleanliness, which leads to menstrual restrictions, shame, and taboos in some cultures. And reference is cited.

Submitted filename: Response to Reviewers.docx

Decision Letter 3

PONE-D-23-35026R3

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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14 Jun 2024

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