two granddaughters when I get the chance!! I enjoy most
music except for Rap! I keep fit by jogging, walking, and bicycling(at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times and adventures together.
Profile No. | Data Item | Initial Codes |
---|---|---|
2 | I enjoy photography, lapidary & seeking collectables in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception. | HobbiesFuture plans Travel Unique Values Humour Music |
At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.
Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.
For better understanding, a mind-mapping example is given here:
You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation.
You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.
Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.
When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:
Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.
The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.
If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.
Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.
You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.
While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.
What is meant by thematic analysis.
Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.
Descriptive research is carried out to describe current issues, programs, and provides information about the issue through surveys and various fact-finding methods.
Experimental research refers to the experiments conducted in the laboratory or under observation in controlled conditions. Here is all you need to know about experimental research.
This post provides the key disadvantages of secondary research so you know the limitations of secondary research before making a decision.
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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.
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 ].
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.
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 ].
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 (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 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 ].
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 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.
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 ].
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
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 ].
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.
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 ].
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 ].
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, 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 ].
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.
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.
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.
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.
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.
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 ].
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.
Not applicable.
Endovascular treatment
Randomised Controlled Trial
Standard Operating Procedure
Standards for Reporting Qualitative Research
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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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If you’ve just completed a large surveying activity, forum conversation or just have a large amount of written feedback and comments to make sense of, then you might need to utilise qualitative analysis techniques.
Unpacking large amounts of qualitative data can be a daunting task but with a little preparation and some simple steps, drawing insights from your data can be made just that little bit easier.
In this article, we look at a simple process for organising and coding qualitative data.
The steps outlined below are especially useful if you have thoroughly planned your projects prior to engaging with your community.
Step 1: Gather your feedback Step 2: Coding your comments Step 3: Run your queries Step 4: Reporting
Step 1: Gather your feedback
The first step towards conducting qualitative analysis of your data is to gather all of the comments and feedback you want to analyze.
This data might be captured in different formats such as on paper or post-it notes or in online forums and surveys, so it’s important to get all of your content into a single place.
For this activity, you might consider a master spreadsheet as a place to collect all of your feedback or you might have other digital tools such as EngagementHQ to help you organise your content.
As part of organising your content you want to setup your analysis template. If you are using a spreadsheet you might consider using variables as seen below to help get you started.
Data Source | Stakeholder Type | Qualitative Data | Code | Question |
---|---|---|---|---|
Pop-up | Resident 1 | I want to see more bins… | Waste | 4 |
Pop-up | Resident 1 | I also want to see more lighting in the streets | Safety | 4 |
Pop-up | Worker 2 | I really do not like the new parking laws because… | Parking | 6 |
Above: Example of how to setup a qualitative analysis spreadsheet.
In the first column, you can see a field for data source. This variable will allow you to filter through your responses to compare views collected via different means.
Next, you can see a stakeholder type variable, which comes in handy for drilling down into different stakeholder groups which you might need to report on.
Obviously, you are going to need to have a field in your data for the actual feedback you collected and you might label this field “feedback” or “qualitative data”.
The most important variable required for your dataset is the code field which you will use to code and organise you data in the next step.
Finally, you can also include an identifier for the question the data was collected for to further help you drill down into your insights.
In your master data template you can also include multiple columns for collecting your coding and you might also be required to add any demographic fields you have captured.
You should allow a suitable amount of time for organising your data, especially if you are collecting it and entering it from a variety of sources.
Step 2: Coding your comments
The next step in this process is about coding your comments and most importantly reading and making a decision about how each one should be organised.
There are two ways to approach this;
The first way assumes that you are looking for a pre-defined set or list of issues or themes, whilst the other method is focused on unpacking themes without having any prior expectations about what they should be.
For the first method, it’s crucial you articulate your coding legend.
A good way to do this is to create a simple table outlining what each code is and what it covers.
This can be mapped to the areas you need to report on or the key components of your project.
Code | Description of what the code refers to |
---|---|
Safe | Discussion about improving the safety. |
Family-friendly | Comment is about being a good place for families. |
Connected | Discussion about improving transport, including active transport such as walking or cycling. |
Above is an example of a coding table used for qualitative analysis.
In this legend, you can outline your theme and description and if you want to take it a step further you might even add issues as a secondary tag within a theme.
If you decide to do the alternate method and unpack your qualitative data to try and derive themes for your code list, you are going to need to read a sample of your comments.
We recommend reading at least 25% of your comments and making a first pass judgement about where each piece of feedback might sit.
Once you have completed this you should ask a colleague to read through the same sample and check to see if they agree with your coding.
When this is complete, refer to the themes you have identified and complete a coding sheet as per above.
Regardless of which option you choose, you will be required to read through your comments and make some decisions about them.
Complete your coding by reading through each comment, using legend will be your guide.
If anything falls outside of your coding list, simply mark it with an identifier such as “unsure” and come back to it later.
Intimate knowledge of the feedback is often missed with automated tag clouds and sentiment analysis and it can encourage lazy practice and unintentionaly lead you to jump to incorrect conclusions.
This coding process can also be completed using EngagementHQ’s comment analysis tool, allowing you to digitally code comments and feedback across all nine engagement tools, including essay and single line text questions in surveys.
Step 3: Run your queries
Once you have coded all of your data, it is time to run your queries. In essence, this means looking for insights in your data. Your reporting requirements will determine the extent and type of the queries you run during this step.
Below are some recommended queries to run on your data:
Once you have run your queries and explored your data you should have a good foundation and enough insights to begin your reporting.
Step 4: Reporting
The final step is reporting on your findings.
This is a critical step as it’s your opportunity to tell the story of what you learnt from your consultation. If you fail to do this step well, your community will absolutely lose faith in your process and you might even face potential community outrage. Being transparent and timely is the best way to avoid this situation.
Use your insights to create a narrative about the issues and opportunities which your community have identified. When framing your insights you might consider using the following as a useful way of talking about and quantifying your findings;
“Participants frequently raised concerns around the flexibility in defining ‘sympathetic additions’ with some participants suggesting that altering heritage buildings in any way could impact the character of the neighbourhood.”
“Many participants commented that a three storey maximum height was preferred for the eastern precinct of the Futureville Village, rather than four.”
“Most participants suggested a three storey preferred height limit in Howitt Street, rather than four. The main concern was that this would affect the ‘village atmosphere’ of the street.”
You should also include relative charts and visuals to help your community further explore your data. Generate these in a spreadsheet or other data visualisation software application such as Microsoft Power BI, Google Studio or Qualtrics to name a few.
Once you have compiled and circulated your report, it’s good practice to again ask your community for final comments and input.
At this stage, you can test whether you have framed their concerns and issues correctly and allow yourself to make and final changes before you submit a final report and make your decisions.
As you can see, these four steps provide a simple process to follow for organising your data, determining your coding tables, running queries and reporting on your consultation.
Make these steps a part of your project planning process and ensure you always have an end to end picture of how you are going to collect and report on your data before you begin your consultation.
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Last Updated: October 26, 2022 Fact Checked
This article was co-authored by Jeremiah Kaplan . Jeremiah Kaplan is a Research and Training Specialist at the Center for Applied Behavioral Health Policy at Arizona State University. He has extensive knowledge and experience in motivational interviewing. In addition, Jeremiah has worked in the mental health, youth engagement, and trauma-informed care fields. Using his expertise, Jeremiah supervises Arizona State University’s Motivational Interviewing Coding Lab. Jeremiah has also been internationally selected to participate in the Motivational Interviewing International Network of Trainers sponsored Train the Trainer event. Jeremiah holds a BS in Human Services with a concentration in Family and Children from The University of Phoenix. There are 10 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 750,245 times.
Qualitative research is a broad field of inquiry that uses unstructured data collections methods, such as observations, interviews, surveys and documents, to find themes and meanings to inform our understanding of the world. [1] X Trustworthy Source PubMed Central Journal archive from the U.S. National Institutes of Health Go to source Qualitative research tends to try to cover the reasons for behaviors, attitudes and motivations, instead of just the details of what, where and when. Qualitative research can be done across many disciplines, such as social sciences, healthcare and businesses, and it is a common feature of nearly every single workplace and educational environment.
Tip: Find the balance between a burning question and a researchable question. The former is something you really want to know about and is often quite broad. The latter is one that can be directly investigated using available research methods and tools.
For example, if your research question is "what is the meaning of teachers' work to second career teachers?" , that is not a question that can be answered with a 'yes' or 'no'. Nor is there likely to be a single overarching answer. This means that qualitative research is the best route.
To do qualitative research, start by deciding on a clear, specific question that you want to answer. Then, do a literature review to see what other experts are saying about the topic, and evaluate how you will best be able to answer your question. Choose an appropriate qualitative research method, such as action research, ethnology, phenomenology, grounded theory, or case study research. Collect and analyze data according to your chosen method, determine the answer to your question. For tips on performing a literature review and picking a method for collecting data, read on! Did this summary help you? Yes No
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What is qualitative research, about this guide, introduction.
The purpose of this guide is to provide a starting point for learning about qualitative research. In this guide, you'll find:
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data.
Qualitative researchers use multiple systems of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory, and phenomenology.
Watch the following video to learn more about Qualitative Research:
(Video best viewed in Edge and Chrome browsers, or click here to view in the Sage Research Methods Database )
The case study approach is useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context.
Ethnographies are an in-depth, holistic type of research used to capture cultural practices, beliefs, traditions, and so on. Here, the researcher observes and interviews members of a culture an ethnic group, a clique, members of a religion, etc. and then analyzes their findings.
Researchers will create and test a hypothesis using qualitative data. Often, researchers use grounded theory to understand decision-making, problem-solving, and other types of behavior.
Researchers use this type of framework to understand different aspects of the human experience and how their subjects assign meaning to their experiences. Researchers use interviews to collect data from a small group of subjects, then discuss those results in the form of a narrative or story.
This type of research attempts to understand the lived experiences of a group and/or how members of that group find meaning in their experiences. Researchers use interviews, observation, and other qualitative methods to collect data.
Watch the video "Choosing among the Five Qualitative Approaches" from Sage Research Methods database for more on these qualitative approaches:
Note: Video is best viewed using Chrome, Edge, or Safari browsers.
Poth, C. (Academic). (2023). Choosing among five qualitative approaches [Video]. Sage Research Methods. doi.org/10.4135/9781529629866
A comprehensive guide to quantitative data, how it differs from qualitative data, and why it's a valuable tool for solving problems.
Data is all around us, and every day it becomes increasingly important. Different types of data define more and more of our interactions with the world around us—from using the internet to buying a car, to the algorithms behind news feeds we see, and much more.
One of the most common and well-known categories of data is quantitative data or data that can be expressed in numbers or numerical values.
This guide takes a deep look at what quantitative data is , what it can be used for, how it’s collected, its advantages and disadvantages, and more.
Key takeaways:
Quantitative data is data that can be counted or measured in numerical values.
The two main types of quantitative data are discrete data and continuous data.
Height in feet, age in years, and weight in pounds are examples of quantitative data.
Qualitative data is descriptive data that is not expressed numerically.
Both quantitative research and qualitative research are often conducted through surveys and questionnaires.
Quantitative data is information that can be counted or measured—or, in other words, quantified—and given a numerical value.
Quantitative data is used when a researcher needs to quantify a problem, and answers questions like “what,” “how many,” and “how often.” This type of data is frequently used in math calculations, algorithms, or statistical analysis.
In product management, UX design , or software engineering, quantitative data can be the rate of product adoption (a percentage), conversions (a number), or page load speed (a unit of time), or other metrics. In the context of shopping, quantitative data could be how many customers bought a certain item. Regarding vehicles, quantitative data might be how much horsepower a car has.
Quantitative data is anything that can be counted in definite units and numbers . So, among many, many other things, some examples of quantitative data include:
Revenue in dollars
Weight in kilograms or pounds
Age in months or years
Distance in miles or kilometers
Time in days or weeks
Experiment results
Website conversion rates
Website page load speed
There are many differences between qualitative and quantitative data —each represents very different data sets and are used in different situations. Often, too, they’re used together to provide more comprehensive insights.
As we’ve described, quantitative data relates to numbers ; it can be definitively counted or measured. Qualitative data, on the other hand, is descriptive data that are expressed in words or visuals. So, where quantitative data is used for statistical analysis, qualitative data is categorized according to themes.
As mentioned above, examples of quantitative data include distance in miles or age in years.
Qualitative data , however, is expressed by describing or labeling certain attributes, such as “chocolate milk,” “blue eyes,” and “red flowers.” In these examples, the adjectives chocolate, blue, and red are qualitative data because they tell us something about the objects that cannot be quantified.
Further reading: The differences between categorical and quantitative Data and examples of qualitative data
Quantitative data is made up of numerical values has numerical properties, and can easily undergo math operations like addition and subtraction. The nature of quantitative data means that its validity can be verified and evaluated using math techniques.
All quantitative data can be measured numerically, as shown above. But these data types can be broken down into more specific categories, too.
There are two types of quantitative data: discrete and continuous . Continuous data can be further divided into interval data and ratio data .
In reference to quantitative data, discrete data is information that can only take certain fixed values. While discrete data doesn’t have to be represented by whole numbers, there are limitations to how it can be expressed.
The number of players on a team
The number of employees at a company
The number of items eggs broken when you drop the carton
The number of outs a hitter makes in a baseball game
The number of right and wrong questions on a test
A website's bounce rate (percentages can be no less than 0 or greater than 100)
Discrete data is typically most appropriately visualized with a tally chart, pie chart, or bar graph, as shown below.
Continuous data , on the other hand, can take any value and varies over time. This type of data can be infinitely and meaningfully broken down into smaller and smaller parts.
Website traffic
Water temperature
The time it takes to complete a task
Because continuous data changes over time, its insights are best expressed with a line graph or grouped into categories, as shown below.
Continuous data can be further broken down into two categories: interval data and ratio data.
Interval data is information that can be measured along a continuum, where there is equal, meaningful distance between each point on a scale. Interval data is always expressed in numbers where the distance between two points is standardized and equal. These numbers can also be called integers.
Examples of interval data include temperature since it can move below and above 0.
Ratio data has all the properties of interval data, but unlike interval data, ratio data also has a true zero. For example, weight in grams is a type of ratio data because it is measured along a continuous scale with equal space between each value, and the scale starts at 0.0.
Other examples of ratio data are weight, length, height, and concentration.
Ratio data gets its name because the ratio of two measurements can be interpreted meaningfully, whereas two measurements cannot be directly compared with intervals.
For example, something that weighs six pounds is twice as heavy as something that weighs three pounds. However, this rule does not apply to interval data, which has no zero value. An SAT score of 700, for instance, is not twice as good as an SAT score of 350, because the scale does not begin at zero.
Similarly, 40º is not twice as hot as 20º. Saying uses 0º as a reference point to compare the two temperatures, which is incorrect.
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Quantitative and qualitative research can both yield valuable findings, but it’s important to choose which type of data to collect based on the nature and objectives of your research.
Quantitative research is likely most appropriate if the thing you are trying to study or measure can be counted and expressed in numbers. For example, quantitative methods are used to calculate a city’s demographics—how many people live there, their ages, their ethnicities, their incomes, and so on.
Qualitative data is defined as non-numerical data such as language, text, video, audio recordings, and photographs. This data can be collected through qualitative methods and research such as interviews, survey questions, observations, focus groups, or diary accounts.
Conducting qualitative research involves collecting, analyzing, and interpreting qualitative non-numerical data (like color, flavor, or some other describable aspect). Methods of qualitative analysis include thematic analysis, coding, and content analysis.
If the thing you want to understand is subjective or measured along a scale, you will need to conduct qualitative research and qualitative analysis.
To use our city example from above, determining why a city's population is happy or unhappy—something you would need to ask them to describe—requires qualitative data.
In short: The goal of qualitative research is to understand how individuals perceive their own social realities. It's commonly used in fields like psychology, social sciences and sociology, educational research, anthropology, political science, and more.
In some instances, like when trying to understand why users are abandoning your website, it’s helpful to assess both quantitative and qualitative data . Understanding what users are doing on your website—as well as why they’re doing it (or how they feel when they’re doing it)—gives you the information you need to make your website’s experience better.
Learn how the best-of-the-best are connecting quantitative data and experience to accelerate growth.
Quantitative data is most helpful when trying to understand something that can be counted and expressed in numbers.
Pros of quantitative data:
Quantitative data is less susceptible to selection bias than qualitative data.
It can be tested and checked, and anyone can replicate both an experiment and its results.
Quantitative data is relatively quick and easy to collect.
Cons of quantitative data:
Quantitative data typically lacks context. In other words, it tells you what something is but not why it is.
Conclusions drawn from quantitative research are only applicable to the particular case studied, and any generalized conclusions are only hypotheses.
There are many ways to collect quantitative data , with common methods including surveys and questionnaires. These can generate both quantitative data and qualitative data, depending on the questions asked.
Once the data is collected and analyzed, it can be used to examine patterns, make predictions about the future, and draw inferences.
For example, a survey of 100 consumers about where they plan to shop during the holidays might show that 45 of them plan to shop online, while the other 55 plan to shop in stores.
Surveys and questionnaires are commonly used in quantitative research and qualitative research because they are both effective and relatively easy to create and distribute. With a wide array of simple-to-use tools, conducting surveys online is a quick and convenient research method.
These research types are useful for gathering in-depth feedback from users and customers, particularly for finding out how people feel about a certain product, service, or experience. For example, many e-commerce companies send post-purchase surveys to find out how a customer felt about the transaction — and if any areas could be improved.
Another common way to collect quantitative data is through a consumer survey, which retailers and other businesses can use to get customer feedback, understand intent, and predict shopper behavior .
There are many public datasets online that are free to access and analyze. In some instances, rather than conducting original research through the methods mentioned above, researchers analyze and interpret this previously collected data in the way that suits their own research project. Examples of public datasets include:
The Bureau of Labor Statistics Data
The Census Bureau Data
World Bank Open Data
The CIA World Factbook
An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive.
Controlled experiments, A/B tests , blind experiments , and many others fall under this category.
With large data pools, a survey of each individual person or data point may be infeasible. In this instance, sampling is used to conduct quantitative research. Sampling is the process of selecting a representative sample of data , which can save time and resources. There are two types of sampling : random sampling (also known as probability sampling) and non-random sampling (also known as non-probability sampling).
Probability sampling allows for the randomization of the sample selection, meaning that each sample has the same probability of being selected for survey as any other sample.
In non-random sampling, each sample unit does not have the same probability of being included in the sample. This type of sampling relies on factors other than random chance to select sample units, such as the researcher’s own subjective judgment. Non-random sampling is most commonly used in qualitative research.
Typically, data analysts and data scientists use a variety of special tools to gather and analyze quantitative data from different sources.
For example, many web analysts and marketing professionals use Google Analytics (pictured below) to gather data about their website’s traffic and performance. This tool can reveal how many visitors come to your site in a day or week, the length of an average session, where traffic comes from, and more. In this example, the goal of this quantitative analysis is to understand and optimize your site’s performance.
Google Analytics is just one example of the many quantitative analytics tools available for different research professionals.
Other quantitative data tools include…
Microsoft Excel
Microsoft Power BI
Apache Spark
A perfect digital customer experience is often the difference between company growth and failure. And the first step toward building that experience is quantifying who your customers are, what they want, and how to provide them what they need.
Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.
But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.
Fullstory's DXI platform combines the quantitative insights of product analytics with picture-perfect session replay for complete context that helps you answer questions, understand issues, and uncover customer opportunities.
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Is quantitative data objective.
Quantitative researchers do everything they can to ensure data’s objectivity by eliminating bias in the collection and analysis process. However, there are factors that can cause quantitative data to be biased.
For example, selection bias can occur when certain individuals are more likely to be selected for study than others. Other types of bias include reporting bias , attrition bias , recall bias , observer bias , and others.
Quantitative research is used in many fields of study, including psychology, digital experience intelligence , economics, demography, marketing, political science, sociology, epidemiology, gender studies, health, and human development. Quantitative research is used less commonly in fields such as history and anthropology.
Many people who are seeking advanced degrees in a scientific field use quantitative research as part of their studies.
Statistics is a branch of mathematics that is commonly used in quantitative research. To conduct quantitative research with statistical methods, a researcher would collect data based on a hypothesis, and then that data is manipulated and studied as part of hypothesis testing, proving the accuracy or reliability of the hypothesis.
It depends on the researcher’s goal. If the researcher wants to measure something—for example, to understand “how many” or “how often,”—quantitative data is appropriate. However, if a researcher wants to learn the reason behind something—to understand “why” something is—qualitative research methods will better answer these questions.
Further reading: Qualitative vs. quantitative data — what's the difference?
Qualitative and quantitative data differ on what they emphasize—qualitative focuses on meaning, and quantitative emphasizes statistical analysis.
Categorical & quantitative variables both provide vital info about a data set. But each is important for different reasons and has its own pros/cons.
Quantitative data is used for calculations or obtaining numerical results. Learn about the different types of quantitative data uses cases and more.
Discover how just-in-time data, explained by Lane Greer, enhances customer insights and decision-making beyond real-time analytics.
Jordan Morrow shares how AI-driven decision-making can revolutionize your business by harnessing data and enhancing your decision-making processes.
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Updated 23 Jul 2024
Students in social sciences frequently seek to understand how people feel, think, and behave in specific situations or relationships that evolve over time. To achieve this, they employ various techniques and data collection methods in qualitative research allowing for a deeper exploration of human experiences. Participant observation, in-depth interviews, and other qualitative methods are commonly used to gather rich, detailed data to uncover key aspects of social behavior and relationships. What is qualitative research? This article will answer this question and guide you through the essentials of this methodology, including data collection techniques and analytical approaches.
This inquiry method is helpful for learners interested in how to conduct research . It focuses on understanding human behavior, experiences, and social phenomena from the perspective of those involved. What does qualitative mean? It uses non-numerical data, such as interviews, observations, and textual analysis, to understand people’s feelings, thoughts, and actions.
Qualitative analysis is crucial in education, healthcare, social sciences, marketing, and business. It helps gain detailed insights into behaviors, experiences, and cultural phenomena. This approach is fundamental during exploratory phases, for understanding complex issues, and when context-specific insights are required. By focusing on depth over breadth, this approach is often employed when researchers seek to explore complex issues, understand the context of a phenomenon, or investigate things that are not easily quantifiable. It uncovers rich, nuanced data essential for developing theories and evaluating programs.
Now that you know the answer to “Why is qualitative data important?”, let’s consider how this method differs from quantitative. Both studies represent two main types of research methods. The qualitative approach focuses on understanding behaviors, experiences, and perspectives using interviews, observations, and analyzing texts. These studies are based on reflexivity and aim to explore complexities and contexts, often generating new ideas or theories. Researchers analyze data to find patterns and themes, clarifying the details. However, findings demonstrated in the results section of a research paper may not apply broadly because they often use small, specific groups rather than large, random samples.
Quantitative studies, on the other hand, emphasize numerical data and statistical analysis to measure variables and relationships. They use methods such as surveys, experiments, or analyzing existing data to collect structured information. The goal is quantifying phenomena, testing hypotheses, and determining correlations or causes. Statistical methods are used to analyze data, identifying patterns and significance. Quantitative studies produce results that can be applied to larger populations, providing generalizable findings. However, they may lack the detailed context that qualitative methods offer.
To better understand the answer to “What is qualitative research?”, it’s necessary to consider various approaches within this methodology, each with its unique focus, implications, and functions.
This theory aims to understand and describe the lived experiences of individuals regarding a particular phenomenon.
Peculiarities:
Example: Studying the experiences of people living with chronic illness to understand how it affects their daily lives.
The approach involves immersive, long-term observation and participation in particular cultural or social contexts.
Example: Observing and participating in the daily life of a rural village to understand its social structure and cultural practices.
This approach seeks to develop a research paper problem statement and theories based on participant data.
Example: Developing a theory on how people cope with job loss by interviewing and analyzing the experiences of unemployed individuals.
Case studies involve an in-depth examination of a single case or a small number of cases.
Example: One of the qualitative research examples is analyzing a specific company’s approach to innovation to understand its success factors.
This methodology focuses on the stories and personal interpretations of individuals.
Example: Collecting and analyzing the life stories of veterans to understand their experiences during and after military service.
This theoretical model involves a collaborative approach in which researchers and participants work together to solve a problem or improve a situation.
Example: Teachers collaborating with researchers to develop and test new teaching approaches to improve student engagement.
It examines language use in texts, conversations, and other forms of communication.
Example: Analyzing political speeches to understand how leaders construct and convey their messages to the public.
Each of these examples of qualitative research offers unique tools and perspectives, enabling researchers to delve deeply into complex issues and gain a rich understanding of the issue they study.
Various techniques exist to explore phenomena in depth and understand the complexities of human behavior, experiences, and social interactions. Some key methodologies that are commonly used in different sciences include several approaches.
These are informal and open-ended, designed to capture detailed narratives without imposing preconceived notions. Researchers typically start with a broad question and encourage interviewees to share their stories freely.
They involve a core set of questions that allow researchers to explore topics deeply, adapting their inquiries based on responses received. This method of qualitative research design aims to gather rich, descriptive information, such as understanding what qualities make a good teacher.
They differ from closed-ended surveys in that they seek opinions and descriptions through open-ended questions. They allow for gathering diverse viewpoints from a larger group than one-on-one interviews would permit.
It relies on researchers' skills to observe and interpret unbiased behaviors or activities. For instance, in education research, observation might track how students stay focused and manage distractions, recorded through field notes taken during or shortly after the observation.
This involves participants or researchers documenting daily activities or study contexts. Participants might record their social interactions or exercise routines, giving detailed data for later analysis. Researchers may also maintain diaries to document study contexts, helping to explain findings and other information sources.
All types of qualitative research have their strengths for gathering detailed information and exploring the social, cultural, and psychological aspects of exploration topics. Learners often use several methods (triangulation) to confirm their findings and deepen their understanding of complex subjects. If you need assistance choosing the most appropriate method to explore, feel free to contact our website, as we offer essays for sale and support with academic papers.
This approach has unique strengths, making it valuable in many sciences. One of the primary advantages of qualitative research is its ability to capture participants' voices and perspectives accurately. It is highly adaptable, allowing researchers to modify the technique as new questions and ideas arise. This flexibility allows researchers to investigate new ideas and trends without being limited to set methods from the start. While this approach has many strengths, it also has significant drawbacks. A research paper writer faces practical and theoretical limitations when analyzing and interpreting data. Let’s consider all the pros and cons of this methodology in detail.
So, qualitative methodology offers significant benefits, such as adaptability, real-world context, rich insights, and fostering innovation. However, it also presents challenges like unpredictability, bias, limited applicability, or time- and labor-intensive. Understanding these pros and cons helps researchers make informed decisions about when and how to effectively utilize various types of qualitative research designs in their studies.
Qualitative research provides a valuable understanding of complicated human experiences and social situations, making it a strong tool in various areas of study. Despite its challenges, such as unreliability, subjectivity, and limited generalizability, its strengths in flexibility, natural settings, and generating meaningful insights make it an essential approach. If you are one of the students looking to incorporate qualitative methodology into their academic papers, EduBirdie is here to help. Our experts can guide you through the process, ensuring your work is thorough, credible, and impactful.
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Step 1: Gather your qualitative data and conduct research (Conduct qualitative research) The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.
Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you're ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration. LEARN MORE FREE TRIAL
This question is particularly relevant to researchers new to the field and practice of qualitative research and instructors and mentors who regularly introduce students to qualitative research practices. In this article, we seek to offer what we view as a useful starting point for learning how to do qualitative analysis. We begin by discussing ...
Step 3: Exploratory data analysis. You can run a few simple exploratory analyses to get to know your data. For instance, you can create a word list or word cloud of all your text data or compare and contrast the words in different documents. You can also let ATLAS.ti find relevant concepts for you.
While many books and articles guide various qualitative research methods and analyses, there is currently no concise resource that explains and differentiates among the most common qualitative approaches. We believe novice qualitative researchers, students planning the design of a qualitative study or taking an introductory qualitative research course, and faculty teaching such courses can ...
Qualitative research methods. Each of the research approaches involve using one or more data collection methods.These are some of the most common qualitative methods: Observations: recording what you have seen, heard, or encountered in detailed field notes. Interviews: personally asking people questions in one-on-one conversations. Focus groups: asking questions and generating discussion among ...
Abstract. 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 ...
When to use thematic analysis. Thematic analysis is a good approach to research where you're trying to find out something about people's views, opinions, knowledge, experiences or values from a set of qualitative data - for example, interview transcripts, social media profiles, or survey responses. Some types of research questions you might use thematic analysis to answer:
The SAGE Handbook of. tive Data AnalysisUwe FlickMapping the FieldData analys. s is the central step in qualitative research. Whatever the data are, it is their analysis that, in a de. isive way, forms the outcomes of the research. Sometimes, data collection is limited to recording and docu-menting naturally occurring ph.
Abstract. Qualitative data is often subjective, rich, and consists of in-depth information normally presented in the form of words. Analysing qualitative data entails reading a large amount of transcripts looking for similarities or differences, and subsequently finding themes and developing categories. Traditionally, researchers 'cut and ...
Qualitative data analysis is. concerned with transforming raw data by searching, evaluating, recogni sing, cod ing, mapping, exploring and describing patterns, trends, themes an d categories in ...
Qualitative Research. Qualitative research is a type of research methodology that focuses on exploring and understanding people's beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.
QDA Method #1: Qualitative Content Analysis. Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.
Let's recap. In this post, we've explored the basics of narrative analysis in qualitative research. The key takeaways are: Narrative analysis is a qualitative analysis method focused on interpreting human experience in the form of stories or narratives.; There are two overarching approaches to narrative analysis: the inductive (exploratory) approach and the deductive (confirmatory) approach.
Step 5: Report on your data and tell the story. Once you have analyzed your qualitative data, the next step is to report on it. Qualitative data analysis reports provide a way to convey the insights you have gained from your data in an easily understandable format.
Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management.
practice of qualitative research and instructors and mentors who regularly introduce students to qualitative research practices. In this article, we seek to offer what we view as a useful starting point for learning how to do qualitative analysis. We begin by discussing briefly the general landscape of qualitative research methodologies and ...
Thematic Analysis - A Guide with Examples. Thematic analysis is one of the most important types of analysis used for qualitative data. When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the ...
Quality assessment. 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 ...
1967: 244). When conducting qualitative analysis, we generally identify categories in our data. These categories are generally described as codes or groupings of codes, such as the first- and. second-order codes and overarching categories often described in classical grounded theory. (Strauss & Corbin, 1990).
Abstract. This paper describes the research process - from planning to presentation, with the emphasis on credibility throughout the whole process - when the methodology of qualitative content analysis is chosen in a qualitative study. The groundwork for the credibility initiates when the planning of the study begins.
Step 3: Run your queries. Step 4: Reporting. Step 1: Gather your feedback. The first step towards conducting qualitative analysis of your data is to gather all of the comments and feedback you want to analyze. This data might be captured in different formats such as on paper or post-it notes or in online forums and surveys, so it's important ...
1. Collect your data. Each of the research methodologies has uses one or more techniques to collect empirical data, including interviews, participant observation, fieldwork, archival research, documentary materials, etc. The form of data collection will depend on the research methodology.
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. ... historical analysis, discourse analysis, ethnography, grounded theory, and phenomenology. Watch the following video to learn more about Qualitative Research:
Rigor in Qualitative Research . Evaluators should take active steps to ensure their data and findings are trustworthy and thorough. Best practices for rigor should be incorporated at every stage of the research process, from design to reporting. The four primary components of rigor in qualitative research follow (Williams & Kimmons,
Qualitative research is a type of research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation. This type of research typically involves in-depth interviews, focus groups, or observations in order to collect data that is rich in detail and context.
Methods of qualitative analysis include thematic analysis, coding, and content analysis. If the thing you want to understand is subjective or measured along a scale, you will need to conduct qualitative research and qualitative analysis. To use our city example from above, determining why a city's population is happy or unhappy—something you ...
Qualitative analysis is crucial in education, healthcare, social sciences, marketing, and business. It helps gain detailed insights into behaviors, experiences, and cultural phenomena. This approach is fundamental during exploratory phases, for understanding complex issues, and when context-specific insights are required.