qualitative research method definition pdf

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

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 – 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 – 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. ​ Fig.2, 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 – 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 – 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 – 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 ​ Table1. 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.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

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Availability of data and materials

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalizability

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

• Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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Qualitative Research, Second Edition: A Multi-Methods Approach to Projects for Doctor of Ministry Dissertations

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The second edition of Qualitative Research responds to the growing need in Doctor of Ministry programs for a textbook that guides students in Participatory Action Research, prospectus, and dissertation that reflect the recent trends in the discipline of practical theology. The Standards of Accreditation for the Commission on Accrediting of the Association of Theological Schools states, “The Doctor of Ministry is an advanced, professionally oriented degree that prepares people more deeply for religious leadership in congregations and other settings.” Standard 5.3 states, “The Doctor of Ministry degree has clearly articulated student learning outcomes that are consistent with the school’s mission and resources and address the following four areas: (a) advanced theological integration that helps graduates effectively engage their cultural context with theological acumen and critical thinking; (b) in-depth contextual competency that gives graduates the ability to identify, frame, and respond to crucial ministry issues; (c) leadership capacity that equips graduates to enhance their effectiveness as ministry leaders in their chosen settings; and (d) personal and spiritual maturity that enables graduates to reinvigorate and deepen their vocational calling.” In accordance with the standards, Qualitative Research guides students through appropriate research methods to satisfy the objectives of the degree in order to enhance ministerial leadership for the transformation of communities of practice.

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Key Features

• Responds to the growing need in Doctor of Ministry programs for a textbook that guides students.
• Helps graduates effectively engage their cultural context with theological acumen.
• Equips graduates to enhance their effectiveness as ministry leaders.
• Good Practice
• The Big Picture
• Tools of the Trade
• Other Toolboxes
• Taking Note
• Analyze This

Praise for Qualitive Research

Sensing’s plain-language guide for doing DMin research serves as a vital resource for DMin projects from conceptualization to reporting. Emphasizing the communal and transformative nature of DMin research clarifies that research is not just technical but can also testify to the actions and presence of God in the context and has the potential to catalyze lifelong learning and ministry development.

—Mark Chapman, Tyndale Seminary

As a DMin program director, I remain grateful for Tim Sensing’s indispensable book. Qualitative Research has become a proven reference guide to the research tools of practical theology that DMin students use to ask contextual questions about their ministry according to the academic standards of advanced ministerial education. This smartly updated second edition brings years of pastoral and teaching reflection to service in the church.

—Gregory Heille, OP, Aquinas Institute of Theology

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• Title : Qualitative Research: A Multi-Methods Approach to Projects for Doctor of Ministry Dissertations
• Author : Tim Sensing
• Edition: Second Edition
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• Print Publication Date: 2022
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• Language : English
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• Subjects : Doctor of ministry degree; Religion › Research; Theology › Research; Dissertations, Academic › Authorship; Research › Methodology; Pastoral theology › Research
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About Tim Sensing

Tim Sensing (DMin, PhD), is the director of academic services and professor of homiletics at the Graduate School of Theology located on the campus of Abilene Christian University. Tim has taught research methodologies for the Doctor of Ministry program since 1999.

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Risk and Influencing Factors for School Absenteeism among Students on the Autism Spectrum—A Systematic Review

• Review Paper
• Open access
• Published: 05 September 2024

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• Isabella Sasso   ORCID: orcid.org/0009-0007-0726-0939 1 &
• Teresa Sansour 1  

School plays an important role in the development of a child. The impact of school absenteeism extends beyond academic achievement, affecting one's ability to participate in life successfully. In particular, children with difficulties in communication and interaction are at risk of developing school absences. This systematic review therefore focused on school absenteeism among children on the autism spectrum and examined the risk and influencing factors contributing to school absences. Eighteen studies were included, thirteen of which used a quantitative design, two of which were mixed-method studies, and three of which had a qualitative design. Different studies had varying definitions of school absenteeism and employed diverse study designs, prompting the need for a narrative synthesis. We evaluated the data regarding the factors of individual, parental, and school based on the KiTes bioecological systems framework for school attendance and absence by Melvin et al. (2019). We identified the majority of risks and influences in relation to the school factor and identified interacting factors contributing to school absenteeism in all factors. We recognised research gaps and provided guidance for further research.

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Introduction

When children and youth attend school, they have access to education, a right proclaimed in Article 28 of the United Nations Convention on the Rights of the Child (The United Nations, 1989 ). School education fundamentally contributes to the cognitive, social, and emotional development of children and young people while simultaneously fulfilling essential social tasks (Pellegrini, 2007 ). Accordingly, absences from school have enormous consequences not only for educational success but also for emotional and social development and successful participation in life. School absenteeism increases the risk of all forms of mental illness (Lenzen et al., 2013 ; Melvin et al., 2019 ). Absences can cause distress in families (Gallé-Tessonneau & Heyne, 2020 ) and challenge professionals and resources (Wilson et al., 2008 ; Finning et al., 2019 ).

Defining School Absenteeism

In international research, scholars utilise a range of terms and criteria to assess school absenteeism. Consequently, it's imperative to interpret and compare research findings within this context. For the scope of this review, we employ the term 'school absenteeism' as an overarching concept. Absenteeism, broadly defined, refers to a student's absence from school for any reason, encompassing various forms of non-attendance (Kearney, 2016 ).

A distinction exists between problematic and non-problematic absences. Non-problematic absences may result from factors such as illness, bereavement, or other causes. However, even initially non-problematic or technically excused absences can transition into problematic ones if more than 10% of lessons are missed (Heyne et al., 2019 ; Lenzen et al., 2013 ), or if the child's development is compromised by the absence, leading to decreased grades or challenges in reintegrating into the academic environment (Kearney, 2016 ). Hence, Kearney ( 2003 ) defines non-problematic absenteeism as short- or long-term absences mutually agreed upon by parents and the school, with the possibility of compensatory measures. Additional terms for distinguishing between problematic and non-problematic absences include unexcused/excused, unauthorised/authorised (Gentle-Genitty et al., 2015 ), and illegitimate/legitimate (Kearney, 2003 ).

Heyne et al. ( 2019 ) differentiates four types of problematic absenteeism:

School refusal is defined as non-attendance at school due to emotional stress related to school attendance, where the parents are informed of absences and make reasonable efforts to ensure the child's attendance at school.

School withdrawal is defined as non-attendance with the knowledge of the parents or withholding by the parents.

Truancy includes absence without permission from the school and the parents. In addition, there are efforts to hide truancy from parents.

In the case of school exclusion , the school initiates the absence, for example, as a disciplinary action.

Kearney et al. ( 2019 ) intend to categorise heterogeneous concepts and provide general descriptions of common terms. School refusal must be distinguished from school refusal behaviour . While school refusal involves absence from school, school refusal behaviour is a broader term for various behaviour patterns based on the goal of avoiding school, whether anxiety-related or not (Kearney, 2016 ). School avoidance refers to an absence based on anxiety related to school. Most of these terms refer to an absence initiated by the individual, while school exclusion is initiated by the school, and school withdrawal is parent-initiated (Kearney et al., 2019 ).

Risk and Influencing Factors for School Absenteeism

To identify factors increasing the likelihood of experiencing school absenteeism, various system levels must be considered (Kearney, 2008 ). Melvin et al. ( 2019 ) propose a multilevel approach that applies Bronfenbrenner’s bio-ecological model to the factors associated with school absenteeism.

At the micro- and meso-system levels, factors such as the individual, parental/family, and school levels have been demonstrated to be associated with school attendance. Knowledge of these factors and their interactions can contribute to an understanding of school absenteeism (Melvin et al., 2019 ) (Fig.  1 ).

The KiTeS bioecological systems framework for school attendance and absence (Melvin et al., 2019 )

To categorise different types of absenteeism according to their initiation and relationship to the individual, the school, and the parental level, we constructed Fig.  2 . The categorisation is based on comprehensive research regarding different types of school absenteeism and their relation (Heyne et al., 2019 ; Kearney, 2008 ; Reissner et al., 2019 ; Tonge & Silverman, 2019 ).

Categorisation of absenteeism types based on individual, school, and parental levels

Defining Autism

The World Health Organisation (WHO, 2023 ) categorises “Autism Spectrum Disorder” (ASD) as a neurodevelopmental disorder characterised by “persistent deficits in the ability to initiate and to sustain reciprocal social interaction and social communication”. Another criterion implies “a range of restricted, repetitive, and inflexible patterns of behaviour, interests or activities that are clearly atypical or excessive for the individual’s age and sociocultural context”. The onset is typically in early childhood, but symptoms may manifest when social demands increase. Since there is a surge in social demands at school age, coping with developmental tasks becomes even more difficult, and special support is often needed (Kamp-Becker & Bölte, 2021 ). Overall, transitions from different developmental and life phases are important, as these are associated with a rise in vulnerability. As the term ‘spectrum’ suggests, symptoms and therefore needs for support vary among autistic individuals (Kamp-Becker & Bölte, 2021 ). Since autism is a lifelong condition, most individuals need support and services throughout their lifetime. Most services (such as therapy and social skills training) are used in early and middle childhood (Song et al., 2022 ). The US Center for Disease Control and Prevention (CDC, 2023 ) estimated that one in 36 children is diagnosed with ASD. ASD was 3,8 times more prevalent among boys than among girls (approximately 4% of boys and 1% of girls). In total, 37.9% of the autistic individuals had an intellectual disability (IQ < 70), 23.5% had an IQ between 71 and 85, and 38.6% had an IQ > 85. However, it should be noted that the prevalence varies between countries and studies. Autism is also often associated with psychiatric conditions. It is estimated that 70–72% of autistic youth have at least one psychiatric condition. Some of the most common disorders are anxiety, depression and attention deficit hyperactivity disorder (ADHD; Rosen et al., 2018 ).

School Absenteeism among Autistic Individuals

According to the Department for Education ( 2019 ) autistic students in England explore higher rates of school absences than non-autistic students with or without special educational needs. When educational needs align with challenges in social skills and communication, absenteeism rates tend to increase. On the other hand, schools comprise a variety of communication and interaction situations (Ashburner et al., 2010 ). Autistic students often find social interactions stressful, thereby facing an increased risk of limited participation and social exclusion (Roberts & Simpson, 2016 ). These circumstances elevate the risk of psychiatric conditions (Hebron & Humphrey, 2014 ). These co-occurring psychiatric conditions heighten the risk of school absenteeism (Finning et al., 2019 ).

To date no systematic review has investigated the risk factors of school absenteeism among autistic students. The aim of this study is to systematically review studies regarding the risk and influence factors for school absenteeism in autistic students. In particular, individual, educational, and parental factors of the micro- and mesosystems are considered. Another aim is to identify research gaps for further investigations.

The primary research questions for this systematic review include:

What types of school absenteeism have been identified in prior studies for autistic students?

What individual, school, and parental factors contribute to school absenteeism among autistic students?

Review Methods

The protocol for this systematic review has been registered online at PROSPERO, an international register for systematic reviews (Registration number: CRD42022343467). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) standards has been followed for all stages of this systematic review (Page et al., 2021 ). The following electronic databases covering all relevant disciplines have been searched for journal articles: ERIC (Ped), Web of Science and Scopus (Psych), and PubMed (Med) on 26th June 2022. Prior to this search, a preliminary search was performed and an updated search was carried out on 30th November 2023. The Cochrane and PROSPERO databases have been searched to confirm that there was no other existing or registered systematic review about the current topic. The search strategy included terms regarding autism and school absenteeism (see Table  1 ).

Inclusion Criteria

The studies included in this review have been selected based on the following predetermined inclusion criteria: (a) they focused on school-aged individuals with a formal diagnosis of autism; (b) they focused on individual, family or school factors having an influence on any form of school absenteeism; and (c) they were published in German or English. No restrictions were applied regarding the publication period of the included articles.

Exclusion Criteria

Studies were excluded from the review based on one or more of the following criteria: (a) they were published in languages other than English or German; (b) they were not empirical studies; (c) they focused on non-autism samples or mixed etiology groups and the data for autistic individuals were not reported separately; and (d) they did not scrutinise school absenteeism and influencing factors. This review did not include grey literature, but the search was not restricted to peer-reviewed articles.

Study Selection

Electronic searches identified 322 records. Following the removal of duplicates, each reviewer independently assessed 148 articles based on the title and abstract; each reviewer was blinded to the other’s ratings. Disagreements were solved by discussion.

After screening the full texts, 18 studies that met the inclusion criteria were identified and included in the current review.

Quality Assessment

The quality of each included study was assessed by both reviewers independently by using the Mixed Method Appraisal Tool (MMAT) described by Hong et al. ( 2018 ). Disagreements were resolved by both authors discussing the information presented. The MMAT is an appraisal tool for systematic reviews that include quantitative, qualitative and mixed methods studies. Hong et al. ( 2018 ) developed the tool based on a literature review of critical appraisal tools. By using this tool, the study quality was categorised as good, moderate or low. Sixteen studies were rated as ‘good’, and two studies were rated as ‘moderate’. One of these studies has limitations in its quality since the actual research question was not answered (Ochi et al., 2020 ). However, the authors identified these limitations. The other study did not formulate any explicit research questions (Kurita, 1991 ). No studies were excluded due to low quality.

Data Extraction

The first author (I.S.) extracted the data according to predefined criteria. The second author (T.S.) controlled the integrity and verified the accuracy of all the extracted data.

Data were extracted and coded for each study that met the inclusion criteria. The following descriptive data were extracted: study details, information about the sample, the definition of school absenteeism, the criteria for school absenteeism (e.g., 10% absence of school days), the data collection tool for school absenteeism, the risk and influencing factors, the absence rate, the intervention, and the effect of the intervention.

A narrative synthesis was provided due to the heterogeneity of the studies, especially regarding the terminology and measurement of school absenteeism as well as the criteria of different forms of school absenteeism.

Figure  3 shows the evaluation and screening process used to select the 18 studies included in this systematic review. Table 1 provides the details for each included study.

PRISMA 2020 flow diagram

Three studies were from the same research group (Bitsika et al., 2020 , 2021 , 2022 ). All three address the topic of bullying. Bitsika et al. ( 2022 ) studied a subsample of a larger cohort from Bitsika and Sharpley ( 2016 ). Bitsika et al. ( 2021 ) used a sample from Bitsika et al. ( 2020 ). Each article has a different research question.

Munkhaugen et al. ( 2019 ) based their research on a subsample from the first study (Munkhaugen et al., 2017 ).

Study Characteristics

All studies except Kurita ( 1991 ) are very recent (2017–2023). These studies were conducted in Australia (Adams, 2021 ; Bitsika et al., 2020 , 2021 , 2022 ), the United Kingdom (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ; Preece & Howley, 2018 ; Totsika et al., 2020 ; Truman et al., 2021 ), Sweden (Anderson, 2020 ), Japan (Kurita, 1991 ; Ochi et al., 2020 ), Norway (Munkhaugen et al., 2017 , 2019 ), the United States (Mattson et al., 2022 ; McClemont et al., 2021 ) and Denmark (Lassen et al., 2022 ).

Three of the 18 studies used qualitative designs, two used mixed method designs, and 13 used quantitative designs. Eleven studies employed cross-sectional designs, while there was also an evaluative case study that aimed to identify the impact of an intervention, a retrospective chart review study, a longitudinal study based on retrospective school datasets, a brief report of an observational study, a qualitative study with a multi-informant approach, a qualitative study that is based on case reports and a qualitative study consisting of an interpretative phenomenological approach.

The sample size ranged from N = 1799 in a quantitative cross-sectional study to N = 3 in qualitative case reports. The total sample included 3304 autistic students. The ages ranged between 3 and 21 years. Two studies examined absenteeism in preschoolers with autism. The sample was predominantly male except for the only qualitative study that explicitly focused on girls with autism (O’Hagan et al., 2022 ). Fourteen studies based their results on parental reports, and four studies considered additional school staff or other professionals involved. One qualitative study collected data by interviewing the autistic young people and one used a multi-informant approach by interviewing parents, professionals and school staff. Two studies focused on data bases: one used clinical data (Ochi et al., 2020 ) and the other used school datasets (Mattson et al., 2022 ). All studies collected data regarding mainstream schools. Six studies also collected data in a special school setting. The detailed information for each included study is summarised in Table  1 .

Types of School Absenteeism

In the included studies, school refusal was the most commonly used term for absenteeism. In total, 11 studies referred to this term (Adams, 2021 ; Bitsika et al., 2020 , 2021 , 2022 ; Kurita, 1991 ; McClemont et al., 2021 ; Munkhaugen et al., 2017 , 2019 ; Ochi et al., 2020 ; Preece & Howley, 2018 ; Totsika et al., 2020 ). The studies are based on the definition in which school refusal occurs due to emotional distress with knowledge of the parents (Heyne et al., 2019 ; Kearney, 2008 ). Totsika et al. ( 2020 ) referred to school withdrawal, truancy, school exclusion, and nonproblematic absence, as these are all categories included in the data collection tool they used (the School Non-Attendance Checklist [SNACK] by Heyne et al., 2019 ). Adams ( 2021 ) also used the SNACK and referred to the types defined by Heyne et al. ( 2019 ) but also described the difference between emerging and established school refusal . Furthermore, the author investigated full- and half-day absences. Bitsika et al., ( 2020 , 2021 , 2022 ) also followed the definition of school refusal established by Heyne et al. ( 2019 ). They argued that school refusal is often associated with absence from school, but it is not necessarily defined by absence; therefore, they used the term emerging school refusal (Bitsika et al., 2020 ).

Munkhaugen et al., ( 2017 , 2019 ) chose school refusal behaviour as the object of research. They referred to Kearney ( 2008 ) who defined school refusal behaviour as ‘child-motivated refusal to attend school and/or difficulties remaining in class’ (Munkhaugen et al., 2017 ).

Kurita ( 1991 ) operationalised school refusal according to Berg et al.’s ( 1969 ) definition as absence from school due to reluctance to attend with the knowledge of the parents, while no antisocial disorders occur with this absence.

O’Hagan et al. ( 2022 ) used the phrase ‘emotionally based’ school avoidance and referred to Munkhaugen et al. ( 2017 ). Hence, it can be assumed that O’Hagan et al. ( 2022 ) used school avoidance as a synonym for school refusal behaviour . Gray et al. ( 2023 ) also used the term school avoidance as a synonym for school refusal .

Truman et al. ( 2021 ) did not directly focus on school absenteeism. They evaluate school experiences in the context of extreme demand avoidance behaviour. One aspect relating to this group of autistic children is school exclusion due to challenging behaviour. They included both formal and informal exclusions. In contrast, Gray et al. ( 2023 ) and Martin-Denham ( 2022 ) had an explicit focus on school exclusion. Martin-Denham ( 2022 ) referred to the Education Act and the European Court, which stated that a decision to exclude has to be lawful, rational, proportionate and fair. A differentiation was made between a fixed period exclusion, where a student was excluded from school for a set period, and a permanent exclusion, when a student did not return to school. Gray et al. ( 2023 ) also referred to this differentiation between fixed-term and permanent exclusion.

Two other included studies addressed the differentiation between unexcused and excused absences (Mattson et al., 2022 ), school absences and nonproblematic absences, respectively (Anderson, 2020 ) (Table  2 ).

Criteria and Frequency of School Absenteeism

Criteria for school absenteeism.

The included studies used different criteria to operationalise absenteeism. Two studies used the criterion of 10% absence from school days (O’Hagan et al., 2022 ; Totsika et al., 2020 ). Adams ( 2021 ) also used this criterion but to discuss ‘persistent’ absence. Munkhaugen et al., ( 2017 , 2019 ) relied on the criteria described by Kearney and Silverman ( 1996 ), who differentiated between ‘self-corrective’ for < 2 weeks, ‘acute’ absence for 2–52 weeks, and ‘chronic’ absence for > 53 weeks. However, they did not provide information about how often the behaviour occurred during the period. Ochi et al. ( 2020 ) used more than 30 days per year as a criterion. Gray et al. ( 2023 ) utilised a broad definition of school exclusion “to ensure it captured the full range of experiences of autistic pupils who had persistent, problematic attendance and experience of leaving a mainstream setting due to unmet needs”. Martin-Denham ( 2022 ) refers to school exclusion as a legal term. Five studies did not explicitly determine a criterion for absence in terms of a number. Rather, they explained it with descriptions such as ‘prolonged’ (Preece & Howley, 2018 ). For the remaining five studies, it was not necessary to determine the criterion thematically or because of the study design.

Frequency of Absences

Despite the different study designs and terms, the results regarding the frequencies of absences are considered in the following.

Adams ( 2021 ) reported the highest rate of absenteeism: 72.6% of autistic children had shown ‘persistent absence’, defined as a 10% absence within the 20-day survey period. The average absenteeism rate in the study was 6.3 full days and 3.8 half days. In addition, 5.7% of the autistic students were absent for 4 weeks; all of them (partly among other reasons) did so due to school refusal.

Totsika et al. ( 2020 ) reported that 43% of autistic children showed persistent absence during a 23-day period. The average absence rate was 5 days. The median number of days missed was 2. Moreover, 64% of the autistic children missed at least 1 day, and 7% did not attend school on any of the 23 days. Similarly, Munkhaugen et al. ( 2017 ) reported that 42.6% of autistic students exhibited school refusal behaviour. Bitsika et al. ( 2020 ) reported that 56.1% of autistic boys who reported being bullied experienced emerging school refusal, but as seen in the definition stated above, this is not a clear indication of actual absence from school.

Kurita ( 1991 ) reported the lowest frequencies: 23.7% of autistic students experienced school refusal (as defined above). In addition, 28.1% were reported to have shown an unwillingness to go to school that did not result in absenteeism. According to the data provided by parents of autistic children, 35% indicated that their child had already refused to go to school (McClemont et al., 2021 ). Regarding school exclusion, Truman et al. ( 2021 ) reported that 50% of autistic children were informally excluded from school.

Anderson ( 2020 ) reported the frequency of absences between different school types. The rates of absences for reasons other than illness (unexcused absence) did not significantly differ between primary (51.3%) and secondary (57.6%) schools. In primary schools for students with learning disabilities absences due to illness (excused absences) were the main cause (83.8%) among autistic students. The rate of absenteeism for reasons other than illness (unexcused absence) increased in secondary schools for students with learning disabilities (36.3%). In elementary schools, the median percentage of school day absences was 9.1% in the study by Mattson et al. ( 2022 ). On 39.1% of all missed school days analysed, students had excused absences, while 60.9% of absences were unexcused. Lassen et al. ( 2022 ) reported more absences among autistic children than among the control group.

Data Collection Tool for School Absenteeism

As stated above, two studies used the SNACK conducted by Heyne et al. ( 2019 ). Adams ( 2021 ) modified the SNACK by also asking about half-day absences.

The majority of studies used nonvalidated scales. Six studies used self-constructed questionnaires (Anderson, 2020 ; Bitsika et al., 2020 , 2021 , 2022 ; Kurita, 1991 ; McClemont et al., 2021 ; Munkhaugen et al., 2017 , 2019 ; Truman et al., 2021 ). Lassen et al. ( 2022 ) asked about frequency via a 5-point Likert scale with descriptive ratings (never, rarely, sometimes, often, very often).

Qualitative studies (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ) as well as a mixed-method study (Preece & Howley, 2018 ) have used interviews for data collection.

Two studies used existing datasets. One of them used clinical data (Ochi et al., 2020 ), whereas the other used school datasets (Mattson et al., 2022 ).

Risk and Influencing Factors

According to the Kids and Teens at School Framework (KiTeS) by Melvin et al. ( 2019 ), the extracted risk and influencing factors for school absenteeism among autistic students were divided into individual, school and parental factors.

Individual Factors

Age, gender, diagnosis, intellectual level and psychiatric conditions were identified as factors at the individual level.

Mattson et al. ( 2022 ) reported that age was weakly and negatively correlated with the median percentage of days absent. They demonstrated that younger participants exhibited more frequent absences on average than older students. Another study reported that the mean age at the onset of school refusal was 12.6 ± 2.2 years in autistic students, which was significantly younger than in those without autism (13.8 ± 2.1 years; Ochi et al., 2020 ). In contrast, Totsika et al. ( 2020 ) reported slightly increased rates of not attending school with increasing age. Among children who missed any school days, refusal was more likely among older children.

Anderson ( 2020 ) revealed a gender difference in the disadvantage of girls on the autism spectrum. They exhibited higher rates of absence (54.6%) for reasons other than illness than autistic boys (43.9%). Compared with boys, girls exhibited significantly more short absences for reasons other than illness. For continuous periods of absence longer than four weeks, there was no significant difference between boys and girls.

In the study of O’Hagan et al. ( 2022 ), two mothers of autistic children with school avoidance indicated that feeling different from others without an explanation of a diagnosis led to low confidence and self-esteem. Families and professionals, in the study of Preece and Howley ( 2018 ), identified a late diagnosis as contributing to non-attendance since the special needs of autistic students were therefore not recognised and addressed. The findings of Martin-Denham ( 2022 ) indicate “barriers to gaining prompt assessment and identification of special educational needs and disability (SEND)”. However, students who already have a diagnosis may struggle with feeling different (Martin-Denham, 2022 ) and having a “desire to fit in” (Gray et al., 2023 ).

Intellectual Level

While most of the related studies have focused on autistic students without intellectual disabilities, Kurita ( 1991 ) found that autistic students who experienced school refusal tended to be more intelligent than those who did not. The intellectual level was significantly greater for autistic children who refused school than for those who did not.

Psychiatric Conditions

Truman et al. ( 2021 ) focused on a group of autistic children with extreme demand avoidance behaviour. These children showed more specific behavioural difficulties. They were able to mask their difficulties at school and then experienced a meltdown after. Fifty percent of parents informally excluded their children from school so that they could be home-educated, reducing their anxiety and stress.

On the other hand, the parents in the study of Gray et al. ( 2023 ) reported that they did not notice the anxiety of their children because they could not communicate their feelings. Anxiety was also demonstrated by aggression, which in turn led to school exclusions and, in some cases, led to symptoms of depression, including self-harm and suicide attempts.

Adams ( 2021 ) reported a 3% increased risk for half-day absences when the child experienced anxiety.

Munkhaugen et al. ( 2019 ) showed that autistic students with school refusal behaviour were more socially impaired than those without such behaviour. Nonetheless, low social motivation had the strongest association with school refusal behaviour. Parents commented that negative thoughts about relationships with peers and teachers, as well as about school subjects, were frequent reasons for their children’s school refusal behaviour. Lassen et al. ( 2022 ) reported that school absence is accompanied by internalising symptoms such as anxiety. This association was even stronger than that with autistic or externalising symptoms and was not unique to the autism group.

School Factors

School factors had the greatest influence on school absenteeism. Five studies focused their research on bullying. Other factors related to the school setting are the school type, the school environment and negative experiences.

The significance of school factors can even be seen in the oldest study. Two-thirds of parents of autistic children who refused school indicated that school refusal behaviour was a precipitating factor. The majority were school-related, with “teasing by schoolmates” being the most common factor (Kurita, 1991 ).

McClemont et al. ( 2021 ) reported that autistic children with ADHD were more likely to refuse school due to bullying (68%) than autistic children without ADHD (28%) or no diagnosis (18%). In this study, an autism diagnosis or another diagnosis did not impact the frequency of school refusal due to bullying compared to children with no diagnosis. In contrast, Ochi et al. ( 2020 ) reported that bullying was significantly associated with school refusal in autistic boys and girls. In the sample of Bitsika et al. ( 2020 ), which consisted only of autistic boys, “being bullied explained more of the variance in emerging school refusal than did age, ASD-related difficulties (judged by their mothers), and self-reported anxiety and depression”. Eighty-five percent of the surveyed boys reported that they had been bullied at school, and 56% of them asked their parents if they could stay at home as a result of the bullying. Bitsika et al. ( 2021 ) identified in another sample from a previous study (Bitsika & Sharpley, 2016 ) the most common bullying experiences: being called mean names or being sworn at (experienced by 75.9% of the sample); being joked about or laughed at (67.2%); being hit, pushed, or kicked (63.8%); having had something taken from them (55.1%); being “ganged up on” (56.9%); and having been reported to teachers when they had not done things that were reportable (51.3%). A participant of Gray et al. ( 2023 ) talked about being bullied because he “didn’t know what they were going on about”.

McClemont et al. ( 2021 ) described another aspect of bullying: autistic youth with a behaviour support plan (BSP) were more likely to refuse school due to bullying than were those without. Anderson ( 2020 ) cited bullying as a factor that had a limited influence. The remaining factors outlined below exerted a more pronounced influence on absences.

School Type

Totsika et al. ( 2020 ) highlighted the significance of school type. The risk for persistent non-attendance increased by 104% when the autistic child attended a mainstream school, by 100% for total days absent, and by 79% for total number of days missed. Additionally, school exclusions were slightly more frequent in mainstream schools. Anderson ( 2020 ) also revealed a significant difference between school type and absence from school. School absence due to illness was the main cause of absence in primary schools for students with learning disabilities (83.8%), but the rate of absenteeism for reasons other than illness increased when students attended secondary schools for students with learning disabilities (36.3%). The results indicate that the rate of school absenteeism among autistic students in primary school is relatively high and increases when pupils start secondary school. Gray et al. ( 2023 ) revealed that the amount of support in schools varied “depending on knowledge, willingness to accommodate needs and carrying out advice and implementing statutory guidelines”.

In a study by Martin-Denham ( 2022 ), caregivers noted a lack of knowledge, skills, understanding, and funding in mainstream secondary schools. All participants noted that barriers to mainstream education occurred because the school staff was not adequately trained in supporting children with SEND. Similar factors regarding the school staff were mentioned by participants in the study by Gray et al. ( 2023 ): lack of understanding of autism, negative attitudes and problematic responses and interactions. Additional factors included a lack of flexibility regarding rules and homework on the one hand and unstructured times on the other hand. Not knowing the needs and not understanding the reactions of the autistic children led to school exclusions or exclusions from school events.

Anderson ( 2020 ) asserted that a lack of autism competence among school staff was the most common reason for children’s school absence.

School Environment

The second most common reason in the study by Anderson ( 2020 ) was the lack of adaptation of the school environment (24.3%), followed by a lack of support in learning (23.5%) and social situations (23.8%). Factors identified by Preece and Howley ( 2018 ) regarding the school environment include a lack of understanding and appropriate support, the size of the school, and the number of students because of sensory issues such as noise. Gray et al. ( 2023 ) also listed the sensory issues of participants. The number of people, large classrooms with bright light and unstructured times led to feelings of overwhelm or sensory overload. Contrary to reports recommending the use of safe spaces to support emotional regulation, Martin-Denham ( 2022 ) noted that schools were unable to implement them due to a lack of space. A different perspective regarding the learning environment was found in the study by Gray et al. ( 2023 ). Many young people described meltdowns when doing homework because of their need for “straight separation between school and home”.

Negative Experiences

The factors described above led in the study by Gray et al. ( 2023 ) to a feeling of being treated unfairly, “which made me just feel stressed and I just refused to engage [in school]”. The results of a parent and teacher questionnaire survey of autistic students by Munkhaugen et al. ( 2017 ) suggested avoiding specific subjects, conflicts with peers or teachers, and insufficient information concerning the subjects or activities in school as possible reasons for school refusal behaviour. In the study of Truman et al. ( 2021 ), parents described negative school experiences to be at least partly caused by a lack of understanding of autism. Some of these parents considered the reason for the misunderstanding in their child’s ability to mask their difficulties. The parents also indicated that ‘masking’ may be the reason why their children’s special needs were not adequately addressed. Others reported that home-education can reduce the anxiety of their autistic children: “All the stress of having to deal with the situations gone. Can now concentrate on learning and living” (Truman et al., 2021 ). A mother in the study by Martin-Denham ( 2022 ) described that anxiety due to a focus on negative aspects in school led to a desire to die: “You can see the anxiety, and when your son says he wants to die that is hard to listen to. So, every day he would come home with this planner and […] there would be no positives, […]. So, he felt down all the time”.

Parental Factors

Parental factors that exert an influence are parental unemployment and illness. In addition, demographic characteristics are closely linked to parental factors, such as living in a two-parent household or having educational qualifications. In most studies, demographic characteristics had no influence on school absenteeism (Kurita, 1991 ; McClemont et al., 2021 ; Munkhaugen et al., 2017 , 2019 ). However, Totsika et al. ( 2020 ) reported an association between school exclusions and not living in a two-parent household: the risk increased by 37% to 75%.

Parental Unemployment

The risk of non-attendance increased by 52% to 78% if parents were unemployed. Fifty-two percent had persistent absence, 57% had total days missed, and 78% had days absent (Totsika et al., 2020 ). Adams ( 2021 ) even reported an increase of 85% when parents reported not having paid employment. On the other hand, parents in two studies (Gray et al., 2023 ; Martin-Denham, 2022 ) reported “having to give up” their jobs as a result of school absenteeism.

Illness of family members

In the study by Munkhaugen et al. ( 2017 ), illness of other family members was the only sociodemographic factor that showed a significant association with school refusal behaviour in autistic students. Similarly, Adams ( 2021 ) reported that the risk of school refusal increased by 20% as parental depression scores increased.

Supporting Factors

Mainly, four included studies (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ; Preece & Howley, 2018 ) identified several aspects to support re-engagement. The most mentioned aspects were the quality of interactions between teachers and autistic students, as well as between parents and teachers. The development of a flexible learning approach was identified as supporting, as well as incorporating the voice of the young person into their support plan and the opportunity to ask questions (Martin-Denham, 2022 ; O’Hagan et al., 2022 ). Autistic students valued a flexible and structured approach in support, as well as the opportunity to control their own learning and feel respected and listened to (Gray et al., 2023 ). Additionally, regarding the overall school environment, smaller group sizes and structures in the classroom and learning were mentioned, as well as being part of the school community and relationships with peers (Gray et al., 2023 ; O’Hagan et al., 2022 ; Preece & Howley, 2018 ). The consistent and effective collaboration and communication between parents and teachers were affirmed by both parents and teachers (Gray et al., 2023 ; Martin-Denham, 2022 ). Parents also claim that an earlier diagnosis contributes to school engagement (Martin-Denham, 2022 ; O’Hagan et al., 2022 ).

The risk and influencing factors of school absenteeism among students on the autism spectrum were systematically reviewed across 18 studies. Nine studies solely included parents as participants, one study additionally included teachers, and one other study additionally included autistic children. Three studies based their data on the children, parents and professional staff. Two studies used existing records.

The most common term for school absenteeism was school refusal, while studies have used different criteria for determining absences. Based on the frequencies of school absenteeism shown among different study designs, it is clear that this is a serious phenomenon that occurs among autistic students internationally. Several identified factors also showed similarities and complementarities within the included studies.

The school level predominantly exhibited the most significant factors influencing absenteeism. Bullying was the most frequently studied influence and the factor with the greatest impact.

Other factors at the school level that significantly influenced absenteeism included school type, school environment, and negative experiences. In five studies, bullying was found to be a risk factor for school absenteeism (McClemont et al., 2021 ; Ochi et al., 2020 ; Bitsika et al., 2020 , 2021 , 2022 ). All studies revealed significant associations between bullying and absenteeism. Being bullied can also lead to anxiety and depression up to suicidal attempts or ideation (Martin-Denham, 2022 ). Autistic students with school refusal had higher scores for major depression, general anxiety, and separation anxiety, as well as significantly greater levels of somatic symptoms and sleeping difficulties (Bitsika et al., 2022 ). Conversely, individual factors, including externalizing symptoms and reduced social motivation, also increase the risk for bullying (Karande, 2018 ). However, other studies that examined bullying, among other factors, found that these factors had greater influences on absences (Anderson, 2020 ; Gray et al., 2023 ). The influence of school type was mostly related to a lack of knowledge of school staff in mainstream schools (Anderson, 2020 ; Gray et al., 2023 ; Martin-Denham, 2022 ). This factor was accompanied by a lack of adapting to the school environment due to a lack of resources or support (Anderson, 2020 ; Gray et al., 2023 ; Martin-Denham, 2022 ; Preece & Howley, 2018 ). A lack of support and understanding in schools increases the risk of anxiety and depression (Martin-Denham, 2022 ). The results may suggest that these conditions are contributing factors because individual needs are not addressed. Therefore, it is necessary to explore in more detail what leads to bullying and which interactional processes take place. Similarly, more research focusing on interactive processes in schools is needed.

At the individual level, age, gender, diagnosis, intellectual level and psychiatric conditions were identified. Three studies reported on the influence of age (Mattson et al., 2022 ; Ochi et al., 2020 ; Totsika et al., 2020 ). Autistic students were younger at the onset of school absences, and the frequency of absences increased with age. These results are consistent with the increasing social demands at school age (Kamp-Becker & Bölte, 2021 ). However, there is a need for further investigation of the influence of age. Mediating variables associated with age must also be considered. Anderson ( 2020 ) showed that girls had greater rates of short absences than boys. There was no other statement regarding gender since most participants were boys. Recent surveys continue to show that autism is four times more prevalent in boys than in girls (CDC, 2020 ). Nevertheless, there is clearly a lack of research regarding autism in female students. Three studies showed that feeling different from others contributed to school absences (Gray et al., 2023 ; Martin-Denham, 2022 ; O’Hagan et al., 2022 ). This shows the need for education on neurodiversity in schools (Honeybourne, 2018 ). Since Kurita ( 1991 ) reported that autistic students who experienced school refusal tended to be more intelligent, studies have focused on autistic students without intellectual disability. Nevertheless, more current research on the school attendance of autistic children with intellectual disabilities would be desirable. Five studies acknowledged the contribution of psychiatric conditions to school absences (Adams, 2021 ; Gray et al., 2023 ; Lassen et al., 2022 ; Munkhaugen et al., 2019 ; Truman et al., 2021 ). All five studies found anxiety to be a contributing factor. Lassen et al. ( 2022 ) even found a stronger association with internalising symptoms such as anxiety than with externalising or autistic symptoms. As anxiety is one of the most common psychiatric conditions in autistic individuals, it can influence school outcomes (for more information, see the review of Adams et al., 2019 ). The influence of other psychiatric disorders should be investigated in more detail in further research.

The parental factors that influence school absenteeism are parental unemployment and illness.

Two studies (Adams, 2021 ; Totsika et al., 2020 ) revealed an increased risk when parents were unemployed.

On the other hand, parents reported having to quit their jobs due to the school absenteeism of their children (Gray et al., 2023 ; Martin-Denham, 2022 ). Due to the effects on the socioeconomic status of a family, a further link between these two aspects should be explored in further research.

The effect of the illness of a family member was also reported by two studies. Munkhaugen et al. ( 2017 ) found this to be the only sociodemographic factor with a significant association. However, Adams ( 2021 ) reported an increased risk when parents had high depression scores. Mental health issues in parents due to stress and guilt were also shown to result from school absenteeism in two studies (Gray et al., 2023 ; Martin-Denham, 2022 ). Parents mentioned that they also need support in regard to school absenteeism in their autistic children (Martin-Denham, 2022 ). Families benefit from organisations that support the family as well as the school (Martin-Denham, 2022 ). In particular, children with special needs are dependent on the support of their parents or other caregivers (Romero & Lee, 2007 ), emphasising the need to support them in dealing with their children and school. Research that involves further system levels is needed. Support for parents can also be provided through interaction between parents and schools. Successful collaboration between parents and schools has a positive impact on the school experience of autistic students (Lilley, 2019 ) (Fig.  4 ).

Interactions between factors

Missing school is claimed to be going hand in hand with missing important developmental steps for life in society (Pellegrini, 2007 ). However, as seen in the results, for children on the autism spectrum, there are also risks for development and mental health in schools, which need to be fixed to enable the development of autistic children in schools. Parents affirmed home-education as a possibility for their autistic children to “concentrate on learning and living” (Truman et al., 2021 ). The advantages and disadvantages of home-education could be further investigated, as school is an important area in the lives of children and adolescents. The results show that the interaction between parental and individual factors is necessary but has not been adequately investigated. None of the included studies investigated parental withholding. Nevertheless, this sensitive and complex phenomenon should be examined in future research.

The results show that the school situation of autistic children should be investigated further. There is a lack of longitudinal studies regarding the education and school situation of autistic students, as well as a lack of validated scales for data collection. Nevertheless, the actuality of the included studies indicates that there is a growing research base on this topic.

Limitations

The results of this systematic review must be classified within its limitations. Given the overall scarcity of research in this area, a research question was developed that yielded the broadest possible results while allowing us to draw consistent conclusions. Therefore, studies with different terms of school absenteeism and different study designs were included. In addition, school absenteeism must be viewed in the context of school and health care systems in each country. This diversity makes comparability difficult and was carried out by the researchers on the basis of a narrative synthesis. The synthesis might reflect the researcher’s interpretation of the data. Given the heterogeneity, it is possible that other studies included risk and influencing factors that were not identified. The specific influences of the COVID-19 pandemic were not considered. Due to limited resources, this review was conducted by only two researchers, which may have introduced limitations in the search strategy. Finally, only studies published in English or German were considered.

This systematic review provides a comprehensive summary of mainly recently published studies on the factors influencing school absenteeism among autistic students. Eighteen studies were included, each with a different research focus and study design. Taken together, the results provide a picture of the different influences at the individual, school and parental levels. Future research should incorporate other system levels as well as self-reports of autistic students and validated scales to draw conclusions for the inclusion of neurodivergent students.

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Sasso, I., Sansour, T. Risk and Influencing Factors for School Absenteeism among Students on the Autism Spectrum—A Systematic Review. Rev J Autism Dev Disord (2024). https://doi.org/10.1007/s40489-024-00474-x

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Received : 06 May 2024

Accepted : 20 August 2024

Published : 05 September 2024

DOI : https://doi.org/10.1007/s40489-024-00474-x

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