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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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• Open access
• Published: 27 May 2020

How to use and assess qualitative research methods

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

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

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

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

What is qualitative research?

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

Why conduct qualitative research?

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

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

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

How to conduct qualitative research?

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

Iterative research process

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

Data collection

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

Document study

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

Observations

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

Semi-structured interviews

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

Focus groups

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

Choosing the “right” method

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

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

Possible combination of data collection methods

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

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

Data analysis

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

From data collection to data analysis

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

How to report qualitative research?

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

How to combine qualitative with quantitative research?

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

Three common mixed methods designs

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

How to assess qualitative research?

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

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

Reflexivity

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

Sampling and saturation

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

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

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

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

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

Member checking

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

Stakeholder involvement

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

How not to assess qualitative research

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

Protocol adherence

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

Sample size

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

Randomisation

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

Interrater reliability, variability and other “objectivity checks”

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

Not being quantitative research

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

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

Availability of data and materials

Not applicable.

Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

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Loraine Busetto, Wolfgang Wick & Christoph Gumbinger

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Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z

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Qualitative Experiments for Social Sciences

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Abstract: This paper presents the qualitative experiment as an alternative methodological solution that combines an open qualitative approach, and a structured and controlled experiment. Using three studies, including both a qualitative experiment and a traditional in-depth interviews approach, we compare the findings of both approaches to identify the benefits and risks of qualitative experiments. Our findings contribute by presenting a methodological framework and technical recommendations based on three validity criteria (internal, external, and interpretivist validity). The results thereby contribute methodologically by empirically investigating the usefulness of qualitative experiments based on a combination of quantitative and qualitative validity criteria identified in the literature.

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Whittemore, R., Chase, K.C., and Mandle, C.L. (2001). Validity in qualitative research. Qualitative Health Research, 11(4), 522-537. https://doi.org/10.1177/104973201129119299

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Qualitative Forschung

Du willst wissen, was eine qualitative Forschung ist und wie du dabei vorgehst? Das erfährst du in unserem Beitrag und im Video .

Was ist eine qualitative Forschung?

Ziele – qualitative forschung , methoden und beispiele – qualitative forschung, vorgehen – qualitative forschung, qualitative vs. quantitative forschung, vorteil – qualitative forschung, qualitative forschung – häufigste fragen, empirische forschung.

Die qualitative Forschung ist eine Methode , die du in deiner wissenschaftlichen Arbeit verwenden kannst.   Es geht dabei darum, subjektive Hintergründe zu einem Thema zu sammeln. Du musst bei dieser Methode also nichts messen. Hintergründe, die du untersuchen kannst, sind zum Beispiel Verhaltensweisen oder Meinungen .

Du wertest bei der qualitativen Forschung nicht-standardisierte Daten aus. Die gewinnst du aus Methoden, die keinen festen Rahmen haben. 

Qualitative Forschungsmethoden sind zum Beispiel folgende

• Gruppengespräche
• Beobachtungen
• Dokumente (z. B. Tagebücher, Videos oder Bilder)

Die qualitative Methode kannst du für deine Abschlussarbeit verwenden, um deine Forschungsfrage zu beantworten. Die Daten, die du bei der Forschungsmethode verwendest, wertest du dann interpretativ aus.

Die qualitative Forschung kannst du durchführen, um zum Beispiel Folgendes zu untersuchen:

• Verhaltensweisen oder
• Erwartungen 

Dabei nutzt du  offene Fragen , auf die deine Teilnehmer frei und ohne Vorgaben antworten können. Beispiele für qualitative Fragen sind:

„Arbeiten Sie gerne in Gruppen?“

„Soll es in der Mensa mehr vegane Gerichte geben?“

Bei der ersten qualitativen Frage wird nach einer Verhaltensweise gefragt. Mit der zweiten Frage nach den Meinungen der Befragten.

Beachte:   Deine qualitative Forschung muss folgende Gütekriterien erfüllen, damit die Forschung eine wissenschaftliche Gültigkeit hat. Achte besonders bei deiner Abschlussarbeit auf die Kriterien:

• Transparenz : Deine qualitative Studie muss nachvollziehbar sein.
• Intersubjektivität : Deine qualitative Studie muss von dir subjektiv reflektiert werden.
• Reichweite : Deine qualitative Studie muss, wenn du sie wiederholst, ähnliche Ergebnisse erzielen.

Hauptbestandteil der qualitativen Forschung ist die Interpretation von Ergebnissen . Dafür gibt es verschiedene qualitative Methoden, die du in einer Abschlussarbeit anwenden kannst:

Egal welche qualitative Methode du verwendest, das Vorgehen der Analyse sieht gleich aus. Bei der qualitativen Forschung gibt es fünf Schritte , um einen genauen Plan vor der Durchführung auszuarbeiten. Bei der qualitativen Forschung sehen diese wie folgt aus:

Datengewinnung

Datenanalyse, dokumentation.

Zunächst muss du herausarbeiten, warum die qualitative Methode angewandt wird und mit welchem Ziel . Du entwickelst also bereits hier deine Forschungsfrage . Mit diesem Schritt sollst du außerdem untersuchen, ob sich das weitere Vorgehen lohnt oder nicht.

Danach musst du entscheiden, wie die qualitative Studie  aussehen soll. Hier musst du zusätzlich zwischen der explorativen und deskriptiven Forschung unterscheiden. Zudem solltest du in diesem Schritt bei beiden Methoden erklären, wo deine Studie stattfinden soll und welche Forschungsmethode du verwendest.

In diesem Schritt suchst du dir eine der Forschungsmethoden und die Stichprobe aus. Bei einer qualitativen Forschung ist die Stichprobe eher klein. Es sollten also ungefähr 10 bis 100 Teilnehmer sein. Aus diesem Grund ist es besonders wichtig, dass die Auswahl sorgfältig getroffen wird.

Du solltest zudem schon festlegen, wie oft du die qualitative Methode durchführst. Die Daten können zum Beispiel telefonisch, persönlich oder auch schriftlich erhoben werden.

Bei der Datenanalyse untersuchst du die Daten der qualitativen Studie. Das bedeutet, dass du deine Ergebnisse interpretieren musst. Bei der qualitativen Forschung führst du eine offene Datenanalyse durch.

Du musst darauf achten, dass andere deine Analyse nachvollziehen können. Deswegen solltest du äußert präzise vorgehen und die Forschungsfrage nicht aus den Augen verlieren.

Zuletzt musst du alle deine Schritte übersichtlich und lückenlos  festhalten . Das hat einige Vorteile für die qualitative Forschung. Gehe dabei vor allem auf folgende Punkte ein:  

• die Forschungsfrage
• die verwendete qualitative Forschungsmethode
• die Ergebnisse

Verwendete Tabellen oder Charts musst du hier ebenfalls nennen. Dadurch ist deine qualitative Forschung verständlicher für Außenstehende.

Neben der qualitativen gibt es noch die quantitative Forschung . Beide Forschungsmethoden kannst du einzeln für deine wissenschaftliche Arbeit verwenden. Die quantitative und qualitative Forschung unterscheiden sich dabei deutlich.

Die qualitative und quantitativen Forschung kannst du aber auch zusammen  benutzen.

• Du kannst die qualitative Studie vor der quantitativen nutzen, um erste wichtige Kenntnisse zur erlangen. 
• Oder du nutzt die qualitative Forschung nach der quantitativen, um Ergebnisse zu vertiefen.

Unterschiede quantitative und qualitative Forschung:

Nutzt du die qualitative und quantitative Forschungsmethode zusammen, dann kannst du eine Studie durchführen, die alle Aspekte drin hat.

Eine qualitative Forschung solltest du aus folgenden Gründen durchführen.

✓  Du hast durch die qualitative Forschung die Möglichkeit, eine praxisnahe und transparente Datenerhebung und Auswertung durchzuführen.

✓  Durch die offene Forschungsmethode können die Befragten ehrlich und uneingeschränkt antworten .

✓  Die qualitative Methode hat zudem den Vorteil, dass sie die Hintergründe wie zum Beispiel Entscheidungen und Verhaltensweisen mit aufdeckt .

• Was sind qualitative Forschungsmethoden? Qualitative Methoden sind folgende: Interview, Gruppendiskussion, Inhaltsanalyse, Fallstudie, Nutzwertanalyse. Damit werden Einzelfälle und Stichproben untersucht, um subjektive Einblicke zu bekommen.
• Was ist der Unterschied zwischen der quantitativen und qualitativen Forschung? Bei der qualitativen Forschung geht es um die interpretative Auswertung von gesammelten Informationen. Die quantitative Forschung hingegen behandelt eine Analyse von Hypothesen und bestehenden Theorien.

Sehr gut! Jetzt weißt du, was eine qualitative Forschung ist und wie du sie anwendest. Was du bei einer empirischen Forschung noch alles beachten solltest, erfährst du in unserem Video dazu!

Beliebte Inhalte aus dem Bereich Wissenschaftliches Arbeiten

• Qualitative Inhaltsanalyse nach Mayring Dauer: 05:19
• Qualitative und quantitative Forschung Dauer: 05:51
• Gütekriterien qualitativer Forschung Dauer: 03:20

Weitere Inhalte: Wissenschaftliches Arbeiten

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Die Unterscheidung in qualitative und quantitative Untersuchungen ist nicht so trennscharf wie häufig angenommen. Sinnvoller wäre vermutlich eine Abgrenzung zwischen standardisierten und rekonstruktiven Verfahren oder zwischen hypothesenprüfenden und theoriebildenden Untersuchungen . Ganz allgemein gilt, dass auch quantitative Verfahren – also alle Methoden, die mit nötigen Mindestmengen, mit Messen, Zählen und Berechnen zu tun haben, auf qualitativen Füßen stehen und umgekehrt. Wer quantitativ arbeitet, bedient sich vieler Annahmen oder theoretischer Setzungen, die (zumeist) nicht mehr infrage gestellt, sondern „nur“ genutzt werden. Was etwa ein Mittelwert oder eine Korrelation aussagen, muss vorab theoretisch bestimmt worden sein. Qualitative Methoden wiederum arbeiten nie ohne Empirie … heißt eigentlich „Erfahrung“ oder „Erfahrungswissen“ und meint eine systematische und methodisch abgesicherte Sammlung von Daten. Die Empirie ist Grundlage aller Forschungen, egal ob hypothesenprüfend (quantitativ) oder theoriebildend (qualitativ). ">Empirie (selbst reine wissenschaftliche Theorie ... stammt vom griechischen <em>theorein</em>, was so viel bedeutet wie "anschauen", "beobachten" oder "betrachten". Mit Theorien sind üblicherweise durch Denkprozesse gewonnene Erkenntnisse oder Vermutungen über Zusammenhänge gemeint. Wissen dagegen basiert – sehr allgemein – auf Erfahrung, während Theorien dieses Erfahrungswissen in vermutete Zusammenhänge bringt. Jeder Methode gehen bestimmte Theorien voraus, die sie stabilisieren oder erst ermöglichen. Allerdings ist die Trennung in Wissen und Theorie unscharf, da jedes Wissen immer nur auf Grundlage bestimmter theoretischer Annahmen gewonnen werden kann (selbst wenn diese nicht transparent sind). Und Theorien selbst basieren immer schon auf bestimmten konkreten Beobachtungen, die auch als Anschauungswissen umschrieben werden können. ">Theorie kommt in Form des Signifikanten, also in Form der Sprache, nicht wirklich ohne Empirie aus), ohne die Deutung und den Vergleich vorgefundener Materialien.

Dennoch gibt es einige Dinge, die quantitative von qualitativen Methoden unterscheiden. Die Art und Weise, wie mit Untersuchungsmaterialien kommuniziert wird, ist in der quantitativen Forschung standardisiert (zum Beispiel über Fragebögen oder fixierte Messmethoden). Und nach der Datenerhebung muss genau definiert werden, welche Erfahrungs- oder Beobachtungskriterien überhaupt zugelassen sind. Bei qualitativen Zugängen ist es wichtig, dass die unterschiedlichen Relevanzsysteme von Forschenden und Erforschten systematisch und kontrolliert beachtet werden. Die Kommunikation ist nicht im eigentlichen Sinn „offen“, das ist missverständlich. Das heißt, in qualitativen Studien wird darauf geachtet, wie (auch unterschwellig) kommuniziert wird, was zwischen den Zeilen geschrieben steht oder gesagt wird und so weiter.

Standardisierte (quantitative) Verfahren untersuchen immer die statistische Verteilung bestimmter Merkmalskombinationen und deuten diese. Das wiederum können rekonstruktive (qualitative) Verfahren nicht leisten. Stattdessen erfassen diese Methoden Strukturen und liefern Interpretationen . Etwas salopp zugespitzt: Während quantitative Verfahren (vor dem Hintergrund bestimmter wissenschaftstheoretischer Annahmen) beschreiben, wie etwas ist, aber das Warum streng genommen nicht untersuchen können, nähern sich qualitative Perspektiven vorsichtig tastend solchen Fragen nach den Gründen an. Während also die einen Hypothesen (auch methodischer Art) empirisch prüfen, versuchen die anderen – ebenfalls empirisch – solche Hypothesen oder Theorien zu bilden.

Das (eigentlich nötige) Wechselspiel beider Perspektiven ( Triangulation ) funktioniert in der wissenschaftlichen Praxis leider oft nicht so gut wie nötig. Eine theoretische Kritik und Neuformulierung quantitativer Methoden ist schwierig, genauso wie eine empirische Sättigung theoretischer oder qualitativer Analysen. Aber das nur am Rande.

Hier die wichtigsten Unterschiede zwischen qualitativer und quantitativer Forschung.

Quantitative Forschungsansätze

• Vorrangiges Ziel: soziale Phänomene messbar machen und statistisch auswerten; Überprüfung von Hypothesen und Theorien
• Voraussetzungen: Vorliegen von Hypothesen und Theorien, die überprüft werden können; Wissen über statistische Verfahren und Methoden zur Datenerhebung, - auswertung und -interpretation
• Merkmale: Standardisierung
• Typische Fragestellungen: Liegen bei einer Gruppe von Personen bestimmte Einstellungen vor? Trifft auf Personen, die x haben, auch y zu? Sind die SchülerInnen im Sinne von PISA lese-/schreib- /rechenkompetent?
• Typische Verfahren der Datenerhebung: standardisierter Fragebogen; Experiment

Qualitative Forschungsansätze

• Vorrangiges Ziel: soziale Phänomene rekonstruieren; Hypothesen und Theorien generieren
• Voraussetzungen: offener, explorativer Zugriff auf das soziale Phänomen; Wissen über qualitative Verfahren und Methoden zur Datenerhebung, -auswertung und -interpretation
• Merkmale: keine Standardisierung
• Typische Fragestellungen: Welche Einstellungen liegen bei einer Gruppe von Personen vor? Gibt es weitere gemeinsame Merkmale von Personen, die x haben, und wenn ja, welche? Was heißt es, lese-/schreib-/rechenkompetent zu sein?
• Typische Verfahren der Datenerhebung: narratives Interview ; Gruppendiskussion; Beobachtung

Wer tiefer einsteigen will, bekommt hier weitere Details zu  beiden Blickrichtungen:

• Przyborski, Aglaja; Wohlrab-Sahr, Monika (2014): Qualitative Sozialforschung. Ein Arbeitsbuch. München: Oldenbourg, Kapitel 2: Methodologie und Standards qualitativer Sozialforschung.

Allgemeines

• Forschungsdesign
• Leitfadengestütztes Interview
• Narratives Interview
• Statistische Auswertung
• Diskursanalyse

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• Exploratory Research | Definition, Guide, & Examples

Exploratory Research | Definition, Guide, & Examples

Published on December 6, 2021 by Tegan George . Revised on November 20, 2023.

Exploratory research is a methodology approach that investigates research questions that have not previously been studied in depth.

Exploratory research is often qualitative and primary in nature. However, a study with a large sample conducted in an exploratory manner can be quantitative as well. It is also often referred to as interpretive research or a grounded theory approach due to its flexible and open-ended nature.

Table of contents

When to use exploratory research, exploratory research questions, exploratory research data collection, step-by-step example of exploratory research, exploratory vs. explanatory research, advantages and disadvantages of exploratory research, other interesting articles, frequently asked questions about exploratory research.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use this type of research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

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Exploratory research questions are designed to help you understand more about a particular topic of interest. They can help you connect ideas to understand the groundwork of your analysis without adding any preconceived notions or assumptions yet.

Here are some examples:

• What effect does using a digital notebook have on the attention span of middle schoolers?
• What factors influence mental health in undergraduates?
• What outcomes are associated with an authoritative parenting style?
• In what ways does the presence of a non-native accent affect intelligibility?
• How can the use of a grocery delivery service reduce food waste in single-person households?

Collecting information on a previously unexplored topic can be challenging. Exploratory research can help you narrow down your topic and formulate a clear hypothesis and problem statement , as well as giving you the “lay of the land” on your topic.

Data collection using exploratory research is often divided into primary and secondary research methods, with data analysis following the same model.

Primary research

In primary research, your data is collected directly from primary sources : your participants. There is a variety of ways to collect primary data.

Some examples include:

• Survey methodology: Sending a survey out to the student body asking them if they would eat vegan meals
• Focus groups: Compiling groups of 8–10 students and discussing what they think of vegan options for dining hall food
• Interviews: Interviewing students entering and exiting the dining hall, asking if they would eat vegan meals

Secondary research

In secondary research, your data is collected from preexisting primary research, such as experiments or surveys.

Some other examples include:

• Case studies : Health of an all-vegan diet
• Literature reviews : Preexisting research about students’ eating habits and how they have changed over time
• Online polls, surveys, blog posts, or interviews; social media: Have other schools done something similar?

For some subjects, it’s possible to use large- n government data, such as the decennial census or yearly American Community Survey (ACS) open-source data.

How you proceed with your exploratory research design depends on the research method you choose to collect your data. In most cases, you will follow five steps.

We’ll walk you through the steps using the following example.

Therefore, you would like to focus on improving intelligibility instead of reducing the learner’s accent.

Step 1: Identify your problem

The first step in conducting exploratory research is identifying what the problem is and whether this type of research is the right avenue for you to pursue. Remember that exploratory research is most advantageous when you are investigating a previously unexplored problem.

Step 2: Hypothesize a solution

The next step is to come up with a solution to the problem you’re investigating. Formulate a hypothetical statement to guide your research.

Step 3. Design your methodology

Next, conceptualize your data collection and data analysis methods and write them up in a research design.

Step 4: Collect and analyze data

Next, you proceed with collecting and analyzing your data so you can determine whether your preliminary results are in line with your hypothesis.

In most types of research, you should formulate your hypotheses a priori and refrain from changing them due to the increased risk of Type I errors and data integrity issues. However, in exploratory research, you are allowed to change your hypothesis based on your findings, since you are exploring a previously unexplained phenomenon that could have many explanations.

Step 5: Avenues for future research

Decide if you would like to continue studying your topic. If so, it is likely that you will need to change to another type of research. As exploratory research is often qualitative in nature, you may need to conduct quantitative research with a larger sample size to achieve more generalizable results.

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It can be easy to confuse exploratory research with explanatory research. To understand the relationship, it can help to remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research investigates research questions that have not been studied in depth. The preliminary results often lay the groundwork for future analysis.

Explanatory research questions tend to start with “why” or “how”, and the goal is to explain why or how a previously studied phenomenon takes place.

Like any other research design , exploratory studies have their trade-offs: they provide a unique set of benefits but also come with downsides.

• It can be very helpful in narrowing down a challenging or nebulous problem that has not been previously studied.
• It can serve as a great guide for future research, whether your own or another researcher’s. With new and challenging research problems, adding to the body of research in the early stages can be very fulfilling.
• It is very flexible, cost-effective, and open-ended. You are free to proceed however you think is best.

Disadvantages

• It usually lacks conclusive results, and results can be biased or subjective due to a lack of preexisting knowledge on your topic.
• It’s typically not externally valid and generalizable, and it suffers from many of the challenges of qualitative research .
• Since you are not operating within an existing research paradigm, this type of research can be very labor-intensive.

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.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

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

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

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.

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Experimentelle Studie | Definition und 5-Schritt-Beispiel

Übersetzt am 3. September 2022 von Tobias Solis. Ursprünglich veröffentlicht von Rebecca Bevans

Experimente werden verwendet, um kausale Zusammenhänge zu untersuchen. Bei einem Experiment manipulierst du eine oder mehrere unabhängige Variablen und misst die Auswirkung auf eine oder mehrere abhängige Variablen.

Experimentelle Studien sind Forschungsdesigns, bei denen anhand eines Experiments systematisch eine Hypothese getestet wird.

Um ein Experiment zu entwickeln, sind fünf Schritte nötig:

• Betrachte die Variablen und wie sie zusammenhängen
• Schreibe eine spezifische, überprüfbare Hypothese
• Entwirf ein Experiment, durch das du deine unabhängige Variable manipulieren kannst
• Teile die Teilnehmenden Gruppen zu, entweder between subjects oder within subjects
• Plane, wie du deine abhängige Variable messen wirst

Um gültige Schlussfolgerungen zu erhalten, musst du außerdem eine repräsentative Stichprobe auswählen und alle Fremdvariablen kontrollieren, die deine Ergebnisse beeinflussen könnten.

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Inhaltsverzeichnis

Schritt 1: definiere deine variablen, schritt 2: schreibe deine hypothese auf, schritt 3: gestalte dein experiment, schritt 4: ordne die teilnehmenden den behandlungsgruppen zu, schritt 5: miss deine abhängige variable, häufig gestellte fragen.

Du solltest mit einer konkreten Forschungsfrage beginnen. Wir werden mit zwei Beispielforschungsfragen arbeiten. Eine stammt aus der Psychologie und eine aus der Ökologie.

Du möchtest wissen, wie sich die Smartphone-Nutzung vor dem Schlafengehen auf das Schlafverhalten auswirkt.

Insbesondere fragst du, wie sich die Anzahl der Minuten, die eine Person ihr Smartphone vor dem Schlafengehen verwendet, auf die Anzahl der Stunden auswirkt, die sie schläft.

Ökologie: Temperatur und Bodenatmung

Du möchtest wissen, wie sich die Temperatur auf die Bodenatmung auswirkt.

Um deine Forschungsfrage in eine experimentelle Hypothese zu übersetzen, musst du die Hauptvariablen definieren und Vorhersagen darüber treffen, wie diese zusammenhängen.

Beginne damit, die unabhängigen und abhängigen Variablen aufzulisten.

Anschließend musst du über mögliche Fremd- und Störvariablen nachdenken und überlegen, wie du diese in deinem Experiment kontrollieren kannst.

Schließlich kannst du diese Variablen in einem Diagramm zusammenfassen. Verwende Pfeile, um die möglichen Beziehungen zwischen Variablen anzuzeigen, und füge Zeichen hinzu, um die erwartete Richtung der Beziehungen anzuzeigen.

Hier prognostizieren wir, dass sich die Dauer der Smartphone-Nutzung negativ auf die Schlafdauer auswirkt, und prognostizieren einen unbekannten Einfluss der natürlichen Schlafschwankungen auf die Schlafdauer.

Hier sagen wir voraus, dass eine steigende Temperatur die Bodenatmung erhöht und die Bodenfeuchtigkeit verringert, während eine abnehmende Bodenfeuchtigkeit zu einer verringerten Bodenatmung führt.

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Zu deiner Korrektur

Nachdem du nun ein starkes konzeptionelles Verständnis des Phänomens hast, das du untersuchst, solltest du spezifische, überprüfbare Hypothesen formulieren können, die deine Forschungsfrage beantworten.

In den nächsten Schritten wird beschrieben, wie ein kontrolliertes Experiment entworfen wird. Bei einem kontrollierten Experiment musst du

• die unabhängige(n) Variable(n) systematisch und präzise manipulieren,
• die abhängige(n) Variable(n) genau messen und
• alle potenziellen Störvariablen kontrollieren.

Wenn dein Studiendesign diese Kriterien nicht erfüllt, gibt es andere Forschungsmethoden, die du verwenden kannst, um deine Forschungsfrage zu beantworten.

Wie du die unabhängige Variable manipulierst, kann die externe Validität des Experiments beeinflussen – das heißt, inwieweit die Ergebnisse verallgemeinerbar und außerhalb deiner Forschung anwendbar sind.

Zunächst musst du entscheiden, wie stark du deine unabhängige Variable variieren möchtest.

• Knapp über den für deine Studienregion natürlichen Temperaturbereich
• Auf einen höheren Temperaturbereich, um die zukünftige Erderwärmung nachzuahmen
• Auf einen extremen Temperaturbereich, der jenseits jeder zukünftigen Erderwärmung liegt

Zweitens musst du möglicherweise entscheiden, wie fein du deine unabhängige Variable variieren möchtest. Die Entscheidung wirkt sich darauf aus, wie viel du aus deinen Ergebnissen schließen kannst.

Manchmal ist diese Entscheidung bereits durch dein experimentelles Design festgelegt.

• als kategoriale Variable: entweder binär (ja/nein) oder in Stufen (keine Smartphone-Nutzung, geringe Smartphone-Nutzung, hohe Smartphone-Nutzung)
• als kontinuierliche Variable (Dauer der Smartphone-Nutzung in Minuten)

Wie du die Teilnehmenden auf dein Experiment verteilst, ist entscheidend, um gültige und zuverlässige Ergebnisse zu erhalten.

Berücksichtige zunächst die Studiengröße : Wie viele Personen werden in das Experiment einbezogen? Allgemein gilt: Je mehr Teilnehmende, desto größer ist die statistische Aussagekraft des Experiments.

Teile deine Teilnehmenden anschließend nach dem Zufallsprinzip verschiedenen Behandlungsgruppen zu. Jede Gruppe erhält eine andere Behandlung (z. B. keine Smartphone-Nutzung, geringe Smartphone-Nutzung, hohe Smartphone-Nutzung).

Du solltest auch eine Kontrollgruppe einbeziehen, die keine Behandlung erhält. Die Kontrollgruppe sagt uns, was mit deinen Testpersonen ohne experimentelle Intervention passiert wäre.

Um deinen Teilnehmenden Gruppen zuweisen, musst du zwei Hauptentscheidungen treffen:

• Vollständig randomisiertes Design vs. randomisiertes Blockdesign.
• Between Subjects Design vs. Within Subjects Design.

Vollständig randomisiertes Design vs. randomisiertes Blockdesign

Ein Experiment kann vollständig randomisiert oder innerhalb von Blöcken (auch Strata genannt) randomisiert werden:

• Bei einem vollständig randomisierten Design wird jede teilnehmende Person zufällig einer Behandlungsgruppe zugeordnet.
• Bei einem randomisierten Blockdesign (auch stratifizierte Randomisierung genannt) werden die teilnehmenden Personen zunächst nach gemeinsamen Merkmalen gruppiert. Anschließend werden sie nach dem Zufallsprinzip verschiedenen Behandlungsgruppen zugewiesen.

In manchen Fällen ist Randomisierung nicht praktisch oder sogar unethisch, sodass forschende Personen teilweise zufällige oder sogar nicht zufällige Forschungsdesigns erstellen.

Ein experimentelles Design, bei dem Behandlungen nicht zufällig zugewiesen werden, wird Quasi-Experiment genannt.

Between-Subjects-Design vs. Within-Subjects-Design

Bei einem Between-Subjects-Design (auch bekannt als Independent-Measures-Design oder klassisches ANOVA-Design) erhalten die teilnehmenden Personen nur eine der möglichen Ebenen einer experimentellen Behandlung.

In der medizinischen oder sozialen Forschung kannst du die Matched-Pairs-Technik verwenden. Bei dieser Technik wird sichergestellt, dass jede Behandlungsgruppe zu gleichen Anteilen Teilnehmende mit bestimmten Merkmalen enthält (z. B. jung, gebildet etc.)

Bei einem Within-Subjects-Design (auch Messwiederholungsdesign genannt) erhält jede teilnehmende Person nacheinander jede der experimentellen Behandlungen.

Der Begriff Within-Subjects-Design kann sich auch auf ein experimentelles Design beziehen, bei dem ein Effekt im Laufe der Zeit auftritt und individuelle Reaktionen im Laufe der Zeit gemessen werden, um diesen Effekt zu messen, sobald er auftritt.

Oft wird bei Within-Subjects-Designs die Reihenfolge der Behandlung der einzelnen teilnehmenden Personen randomisiert oder umgekehrt. So wird sichergestellt, dass die Reihenfolge der Behandlung die Ergebnisse des Experiments nicht beeinflusst.

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Schließlich musst du entscheiden, wie du die Datenerhebung deiner abhängigen Variablen durchführst. Dabei solltest du Reliabilität und Validität gewährleisten und Verzerrungen minimieren.

Einige Variablen, wie z. B. Temperatur, können mit wissenschaftlichen Instrumenten objektiv gemessen werden. Andere Variablen müssen möglicherweise operationalisiert werden, um sie in messbare Beobachtungen umzuwandeln.

• Bitte die teilnehmenden Personen, aufzuschreiben, wann sie jeden Tag schlafen gehen und aufstehen.
• Bitte die teilnehmenden Personen, einen Schlaftracker zu tragen.

Wie genau du deine abhängige Variable misst, wirkt sich auch auf die Art der statistischen Analyse aus, die du auf deine Daten anwenden kannst.

Experimente sind immer kontextabhängig. Für ein gutes Experiment berücksichtigst du alle einzigartigen Gegebenheiten des von dir untersuchten Phänomens. So erhältst du Informationen, die sowohl gültig als auch relevant für deine Forschungsfrage sind.

Bei einer experimentellen Studie wird ein Experiment durchgeführt, um die Beziehung zwischen Variablen zu untersuchen. Um ein kontrolliertes Experiment zu entwerfen, benötigst du:

• Eine überprüfbare Hypothese
• Mindestens eine unabhängige Variable, die genau manipuliert werden kann
• Mindestens eine abhängige Variable, die genau gemessen werden kann

Um das Experiment vorzubereiten, entscheidest du:

• Wie du die Variable(n) manipulierst
• Wie du potenzielle Störvariablen kontrollierst
• Wie viele Teilnehmenden bzw. Stichproben für das Experiment benötigt werden
• Wie die Teilnehmenden auf die Behandlungsgruppen verteilt werden

Bei einer experimentellen Studie mit Between-Subjects-Design werden alle Teilnehmenden während des Experiments nur mit je einer Behandlung untersucht. Die Forschenden bewerten Gruppenunterschiede zwischen Teilnehmenden mit unterschiedlichen Behandlungen.

Bei einem Within-Subjects-Design werden alle Teilnehmenden während des Experiments mit allen Behandlungen untersucht. Die Forschenden testen dieselben Teilnehmenden wiederholt auf Unterschiede in der Reaktion auf unterschiedliche Behandlungen.

Das Wort ‚between‘ (zwischen) bedeutet, dass du verschiedene Bedingungen anhand verschiedener Teilnehmender vergleichst, während das Wort ‚within‘ (innerhalb) bedeutet, dass du verschiedene Bedingungen anhand derselben Person testest.

Bei einer experimentellen Studie erhält die Behandlungsgruppe die experimentelle Behandlung, deren Wirkung die Forschenden untersuchen möchten.

Die Kontrollgruppe erhält die experimentelle Behandlung nicht.

Beide Gruppen sollten ansonsten identisch sein.

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Das hat anderen studierenden noch gefallen, deskriptive forschung: definition, methoden und beispiele, explanative studie: definition, beispiel, anleitung, explorative forschung: definition, anleitung und beispiele, aus versehen plagiiert finde kostenlos heraus.

Welche qualitativen Forschungsmethoden gibt es?

(Einteilungsgesichtspunkte unterschiedlicher Methoden)

• First Online: 21 January 2022

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Zusammenfassung

In der qualitativen Forschung gibt es direkte und indirekte Methoden. Bei den indirekten Methoden wird nicht direkt nach dem gefragt, was man wissen will, sondern indirekt über einen Umweg. Dies hat zur Folge, dass das eigentliche Erkenntnisziel verschleiert wird und die befragte Person nicht weiß, was der Fragende in Wahrheit herausfinden will. Auf diese Weise kann die Antwort vom Befragten nicht beeinflusst oder verfälscht werden, beinhaltet aber trotzdem Informationen, die auf das Erkenntnisziel hinführen. Sie hat sogar noch den Vorteil, dass in ihr die impliziten unbewussten Einstellungen der befragten Person zutage treten. Erläutert werden im Überblick neben unterschiedlichen Befragungsmethoden die weiteren qualitativen Ermittlungsmethoden Beobachtung, Experiment sowie apparative Verfahren wie zum Beispiel neueste psychophysiologische und neurophysiologische Messungen.

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Kirchmair, R. (2022). Welche qualitativen Forschungsmethoden gibt es?. In: Qualitative Forschungsmethoden. Angewandte Psychologie Kompakt. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62761-7_2

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Physics > Physics Education

Title: low-cost demonstration of the zeeman effect: from qualitative observation to quantitative experiments.

Abstract: The Zeeman effect, a fundamental quantum phenomenon, demonstrates the interaction between magnetic fields and atomic systems. While precise spectroscopic measurements of this effect have advanced significantly, there remains a lack of simple, visually accessible demonstrations for educational purposes. Here, we present a low-cost experiment that allows for direct visual observation of the Zeeman effect. Our setup involves a flame containing sodium (from table salt) placed in front of a sodium vapor lamp. When a magnetic field is applied to the flame, the shadow cast by the flame noticeably lightens, providing a clear, naked-eye demonstration of the Zeeman effect. Furthermore, we conduct two quantitative experiments using this setup, examining the effects of varying magnetic field strength and sodium concentration. This innovative approach not only enriches the experimental demonstration for teaching atomic physics at undergraduate and high school levels but also provides an open platform for students to explore the Zeeman effect through hands-on experience.

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