qualitative research methods document analysis

Document Analysis

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• Benjamin Kutsyuruba 4  

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This chapter describes the document analysis approach. As a qualitative method, document analysis entails a systematic procedure for reviewing and evaluating documents through finding, selecting, appraising (making sense of), and synthesizing data contained within them. This chapter outlines the brief history, method and use of document analysis, provides an outline of its process, strengths and limitations, and application, and offers further readings, resources, and suggestions for student engagement activities.

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Additional Reading

Kutsyuruba, B. (2017). Examining education reforms through document analysis methodology. In I. Silova, A. Korzh, S. Kovalchuk, & N. Sobe (Eds.), Reimagining Utopias: Theory and method for educational research in post-socialist contexts (pp. 199–214). Sense.

Kutsyuruba, B., Christou, T., Heggie, L., Murray, J., & Deluca, C. (2015). Teacher collaborative inquiry in Ontario: An analysis of provincial and school board policies and support documents. Canadian Journal of Educational Administration and Policy, 172 , 1–38.

Kutsyuruba, B., Godden, L., & Tregunna, L. (2014). Curbing the early-career attrition: A pan-Canadian document analysis of teacher induction and mentorship programs. Canadian Journal of Educational Administration and Policy, 161 , 1–42.

Segeren, A., & Kutsyuruba, B. (2012). Twenty years and counting: An examination of the development of equity and inclusive education policy in Ontario (1990–2010). Canadian Journal of Educational Administration and Policy, 136 , 1–38.

Online Resources

Document Analysis: A How To Guide (12:27 min) https://www.youtube.com/watch?v=vOsE9saR_ck

Document Analysis with Philip Adu (1:16:40 min) https://youtu.be/bLKBffW5JPU

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Kutsyuruba, B. (2023). Document Analysis. In: Okoko, J.M., Tunison, S., Walker, K.D. (eds) Varieties of Qualitative Research Methods. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-031-04394-9_23

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The Basics of Document Analysis

Document analysis is the process of reviewing or evaluating documents both printed and electronic in a methodical manner. The document analysis method, like many other qualitative research methods, involves examining and interpreting data to uncover meaning, gain understanding, and come to a conclusion.

What is Meant by Document Analysis?

Document analysis pertains to the process of interpreting documents for an assessment topic by the researcher as a means of giving voice and meaning. In Document Analysis as a Qualitative Research Method by Glenn A. Bowen , document analysis is described as, “... a systematic procedure for reviewing or evaluating documents—both printed and electronic (computer-based and Internet-transmitted) material. Like other analytical methods in qualitative research, document analysis requires that data be examined and interpreted in order to elicit meaning, gain understanding, and develop empirical knowledge.”

During the analysis of documents, the content is categorized into distinct themes, similar to the way transcripts from interviews or focus groups are analyzed. The documents may also be graded or scored using a rubric.

Document analysis is a social research method of great value, and it plays a crucial role in most triangulation methods, combining various methods to study a particular phenomenon.

>> View Webinar: How-To’s for Data Analysis

Documents fall into three main categories:

• Personal Documents: A personal account of an individual's beliefs, actions, and experiences. The following are examples: e-mails, calendars, scrapbooks, Facebook posts, incident reports, blogs, duty logs, newspapers, and reflections or journals.
• Public Records: Records of an organization's activities that are maintained continuously over time. These include mission statements, student transcripts, annual reports, student handbooks, policy manuals, syllabus, and strategic plans.
• Physical Evidence: Artifacts or items found within a study setting, also referred to as artifacts. Among these are posters, flyers, agendas, training materials, and handbooks.

The qualitative researcher generally makes use of two or more resources, each using a different data source and methodology, to achieve convergence and corroboration. An important purpose of triangulating evidence is to establish credibility through a convergence of evidence. Corroboration of findings across data sets reduces the possibility of bias, by examining data gathered in different ways.

It is important to note that document analysis differs from content analysis as content analysis refers to more than documents. As part of their definition for content analysis, Columbia Mailman School of Public Health states that, “Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents).

How Do You Do Document Analysis?

In order for a researcher to obtain reliable results from document analysis, a detailed planning process must be undertaken. The following is an outline of an eight-step planning process that should be employed in all textual analysis including document analysis techniques.

• Identify the texts you want to analyze such as samples, population, participants, and respondents.
• You should consider how texts will be accessed, paying attention to any cultural or linguistic barriers.
• Acknowledge and resolve biases.
• Acquire appropriate research skills.
• Strategize for ensuring credibility.
• Identify the data that is being sought.
• Take into account ethical issues.
• Keep a backup plan handy.

Researchers can use a wide variety of texts as part of their research, but the most common source is likely to be written material. Researchers often ask how many documents they should collect. There is an opinion that a wide selection of documents is preferable, but the issue should probably revolve more around the quality of the document than its quantity.

Why is Document Analysis Useful?

Different types of documents serve different purposes. They provide background information, indicate potential interview questions, serve as a mechanism for monitoring progress and tracking changes within a project, and allow for verification of any claims or progress made.

You can triangulate your claims about the phenomenon being studied using document analysis by using multiple sources and other research gathering methods.

Below are the advantages and disadvantages of document analysis

• Document analysis may assist researchers in determining what questions to ask your interviewees, as well as provide insight into what to watch out for during your participant observation.
• It is particularly useful to researchers who wish to focus on specific case studies
• It is inexpensive and quick in cases where data is easily obtainable.
• Documents provide specific and reliable data, unaffected by researchers' presence unlike with other research methods like participant observation.

Disadvantages

• It is likely that the documents researchers obtain are not complete or written objectively, requiring researchers to adopt a critical approach and not assume their contents are reliable or unbiased.
• There may be a risk of information overload due to the number of documents involved. Researchers often have difficulties determining what parts of each document are relevant to the topic being studied.
• It may be necessary to anonymize documents and compare them with other documents.

How NVivo Can Help with Document Analysis

Analyzing copious amounts of data and information can be a daunting and time-consuming prospect. Luckily, qualitative data analysis tools like NVivo can help!

NVivo’s AI-powered autocoding text analysis tool can help you efficiently analyze data and perform thematic analysis . By automatically detecting, grouping, and tagging noun phrases, you can quickly identify key themes throughout your documents – aiding in your evaluation.

Additionally, once you start coding part of your data, NVivo’s smart coding can take care of the rest for you by using machine learning to match your coding style. After your initial coding, you can run queries and create visualizations to expand on initial findings and gain deeper insights.

These features allow you to conduct data analysis on large amounts of documents – improving the efficiency of this qualitative research method. Learn more about these features in the webinar, NVivo 14: Thematic Analysis Using NVivo.

>> Watch Webinar NVivo 14: Thematic Analysis Using NVivo

Learn More About Document Analysis

Watch Twenty-Five Qualitative Researchers Share How-To's for Data Analysis

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Qualitative Research Journal

ISSN : 1443-9883

Article publication date: 3 August 2009

This article examines the function of documents as a data source in qualitative research and discusses document analysis procedure in the context of actual research experiences. Targeted to research novices, the article takes a nuts‐and‐bolts approach to document analysis. It describes the nature and forms of documents, outlines the advantages and limitations of document analysis, and offers specific examples of the use of documents in the research process. The application of document analysis to a grounded theory study is illustrated.

• Content analysis
• Grounded theory
• Thematic analysis
• Triangulation

Bowen, G.A. (2009), "Document Analysis as a Qualitative Research Method", Qualitative Research Journal , Vol. 9 No. 2, pp. 27-40. https://doi.org/10.3316/QRJ0902027

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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

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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 – 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.

no external funding.

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

The authors declare no competing interests.

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Article Contents

Introduction, what is document analysis, the read approach, supplementary data, acknowledgements.

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Document analysis in health policy research: the READ approach

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Sarah L Dalglish, Hina Khalid, Shannon A McMahon, Document analysis in health policy research: the READ approach, Health Policy and Planning , Volume 35, Issue 10, December 2020, Pages 1424–1431, https://doi.org/10.1093/heapol/czaa064

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Document analysis is one of the most commonly used and powerful methods in health policy research. While existing qualitative research manuals offer direction for conducting document analysis, there has been little specific discussion about how to use this method to understand and analyse health policy. Drawing on guidance from other disciplines and our own research experience, we present a systematic approach for document analysis in health policy research called the READ approach: (1) ready your materials, (2) extract data, (3) analyse data and (4) distil your findings. We provide practical advice on each step, with consideration of epistemological and theoretical issues such as the socially constructed nature of documents and their role in modern bureaucracies. We provide examples of document analysis from two case studies from our work in Pakistan and Niger in which documents provided critical insight and advanced empirical and theoretical understanding of a health policy issue. Coding tools for each case study are included as Supplementary Files to inspire and guide future research. These case studies illustrate the value of rigorous document analysis to understand policy content and processes and discourse around policy, in ways that are either not possible using other methods, or greatly enrich other methods such as in-depth interviews and observation. Given the central nature of documents to health policy research and importance of reading them critically, the READ approach provides practical guidance on gaining the most out of documents and ensuring rigour in document analysis.

Rigour in qualitative research is judged partly by the use of deliberate, systematic procedures; however, little specific guidance is available for analysing documents, a nonetheless common method in health policy research.

Document analysis is useful for understanding policy content across time and geographies, documenting processes, triangulating with interviews and other sources of data, understanding how information and ideas are presented formally, and understanding issue framing, among other purposes.

The READ (Ready materials, Extract data, Analyse data, Distil) approach provides a step-by-step guide to conducting document analysis for qualitative policy research.

The READ approach can be adapted to different purposes and types of research, two examples of which are presented in this article, with sample tools in the Supplementary Materials .

Document analysis (also called document review) is one of the most commonly used methods in health policy research; it is nearly impossible to conduct policy research without it. Writing in early 20th century, Weber (2015) identified the importance of formal, written documents as a key characteristic of the bureaucracies by which modern societies function, including in public health. Accordingly, critical social research has a long tradition of documentary review: Marx analysed official reports, laws, statues, census reports and newspapers and periodicals over a nearly 50-year period to come to his world-altering conclusions ( Harvey, 1990 ). Yet in much of social science research, ‘documents are placed at the margins of consideration,’ with privilege given to the spoken word via methods such as interviews, possibly due to the fact that many qualitative methods were developed in the anthropological tradition to study mainly pre-literate societies ( Prior, 2003 ). To date, little specific guidance is available to help health policy researchers make the most of these wells of information.

The term ‘documents’ is defined here broadly, following Prior, as physical or virtual artefacts designed by creators, for users, to function within a particular setting ( Prior, 2003 ). Documents exist not as standalone objects of study but must be understood in the social web of meaning within which they are produced and consumed. For example, some analysts distinguish between public documents (produced in the context of public sector activities), private documents (from business and civil society) and personal documents (created by or for individuals, and generally not meant for public consumption) ( Mogalakwe, 2009 ). Documents can be used in a number of ways throughout the research process ( Bowen, 2009 ). In the planning or study design phase, they can be used to gather background information and help refine the research question. Documents can also be used to spark ideas for disseminating research once it is complete, by observing the ways those who will use the research speak to and communicate ideas with one another.

Documents can also be used during data collection and analysis to help answer research questions. Recent health policy research shows that this can be done in at least four ways. Frequently, policy documents are reviewed to describe the content or categorize the approaches to specific health problems in existing policies, as in reviews of the composition of drowning prevention resources in the United States or policy responses to foetal alcohol spectrum disorder in South Africa ( Katchmarchi et al. , 2018 ; Adebiyi et al. , 2019 ). In other cases, non-policy documents are used to examine the implementation of health policies in real-world settings, as in a review of web sources and newspapers analysing the functioning of community health councils in New Zealand ( Gurung et al. , 2020 ). Perhaps less frequently, document analysis is used to analyse policy processes, as in an assessment of multi-sectoral planning process for nutrition in Burkina Faso ( Ouedraogo et al. , 2020 ). Finally, and most broadly, document analysis can be used to inform new policies, as in one study that assessed cigarette sticks as communication and branding ‘documents,’ to suggest avenues for further regulation and tobacco control activities ( Smith et al. , 2017 ).

This practice paper provides an overarching method for conducting document analysis, which can be adapted to a multitude of research questions and topics. Document analysis is used in most or all policy studies; the aim of this article is to provide a systematized method that will enhance procedural rigour. We provide an overview of document analysis, drawing on guidance from disciplines adjacent to public health, introduce the ‘READ’ approach to document analysis and provide two short case studies demonstrating how document analysis can be applied.

Document analysis is a systematic procedure for reviewing or evaluating documents, which can be used to provide context, generate questions, supplement other types of research data, track change over time and corroborate other sources ( Bowen, 2009 ). In one commonly cited approach in social research, Bowen recommends first skimming the documents to get an overview, then reading to identify relevant categories of analysis for the overall set of documents and finally interpreting the body of documents ( Bowen, 2009 ). Document analysis can include both quantitative and qualitative components: the approach presented here can be used with either set of methods, but we emphasize qualitative ones, which are more adapted to the socially constructed meaning-making inherent to collaborative exercises such as policymaking.

The study of documents as a research method is common to a number of social science disciplines—yet in many of these fields, including sociology ( Mogalakwe, 2009 ), anthropology ( Prior, 2003 ) and political science ( Wesley, 2010 ), document-based research is described as ill-considered and underutilized. Unsurprisingly, textual analysis is perhaps most developed in fields such as media studies, cultural studies and literary theory, all disciplines that recognize documents as ‘social facts’ that are created, consumed, shared and utilized in socially organized ways ( Atkinson and Coffey, 1997 ). Documents exist within social ‘fields of action,’ a term used to designate the environments within which individuals and groups interact. Documents are therefore not mere records of social life, but integral parts of it—and indeed can become agents in their own right ( Prior, 2003 ). Powerful entities also manipulate the nature and content of knowledge; therefore, gaps in available information must be understood as reflecting and potentially reinforcing societal power relations ( Bryman and Burgess, 1994 ).

Document analysis, like any research method, can be subject to concerns regarding validity, reliability, authenticity, motivated authorship, lack of representativity and so on. However, these can be mitigated or avoided using standard techniques to enhance qualitative rigour, such as triangulation (within documents and across methods and theoretical perspectives), ensuring adequate sample size or ‘engagement’ with the documents, member checking, peer debriefing and so on ( Maxwell, 2005 ).

Document analysis can be used as a standalone method, e.g. to analyse the contents of specific types of policy as they evolve over time and differ across geographies, but document analysis can also be powerfully combined with other types of methods to cross-validate (i.e. triangulate) and deepen the value of concurrent methods. As one guide to public policy research puts it, ‘almost all likely sources of information, data, and ideas fall into two general types: documents and people’ ( Bardach and Patashnik, 2015 ). Thus, researchers can ask interviewees to address questions that arise from policy documents and point the way to useful new documents. Bardach and Patashnik suggest alternating between documents and interviews as sources as information, as one tends to lead to the other, such as by scanning interviewees’ bookshelves and papers for titles and author names ( Bardach and Patashnik, 2015 ). Depending on your research questions, document analysis can be used in combination with different types of interviews ( Berner-Rodoreda et al. , 2018 ), observation ( Harvey, 2018 ), and quantitative analyses, among other common methods in policy research.

The READ approach to document analysis is a systematic procedure for collecting documents and gaining information from them in the context of health policy studies at any level (global, national, local, etc.). The steps consist of: (1) ready your materials, (2) extract data, (3) analyse data and (4) distil your findings. We describe each of these steps in turn.

Step 1. Ready your materials

At the outset, researchers must set parameters in terms of the nature and number (approximately) of documents they plan to analyse, based on the research question. How much time will you allocate to the document analysis, and what is the scope of your research question? Depending on the answers to these questions, criteria should be established around (1) the topic (a particular policy, programme, or health issue, narrowly defined according to the research question); (2) dates of inclusion (whether taking the long view of several decades, or zooming in on a specific event or period in time); and (3) an indicative list of places to search for documents (possibilities include databases such as Ministry archives; LexisNexis or other databases; online searches; and particularly interview subjects). For difficult-to-obtain working documents or otherwise non-public items, bringing a flash drive to interviews is one of the best ways to gain access to valuable documents.

For research focusing on a single policy or programme, you may review only a handful of documents. However, if you are looking at multiple policies, health issues, or contexts, or reviewing shorter documents (such as newspaper articles), you may look at hundreds, or even thousands of documents. When considering the number of documents you will analyse, you should make notes on the type of information you plan to extract from documents—i.e. what it is you hope to learn, and how this will help answer your research question(s). The initial criteria—and the data you seek to extract from documents—will likely evolve over the course of the research, as it becomes clear whether they will yield too few documents and information (a rare outcome), far too many documents and too much information (a much more common outcome) or documents that fail to address the research question; however, it is important to have a starting point to guide the search. If you find that the documents you need are unavailable, you may need to reassess your research questions or consider other methods of inquiry. If you have too many documents, you can either analyse a subset of these ( Panel 1 ) or adopt more stringent inclusion criteria.

Exploring the framing of diseases in Pakistani media

In Table 1 , we present a non-exhaustive list of the types of documents that can be included in document analyses of health policy issues. In most cases, this will mean written sources (policies, reports, articles). The types of documents to be analysed will vary by study and according to the research question, although in many cases, it will be useful to consult a mix of formal documents (such as official policies, laws or strategies), ‘gray literature’ (organizational materials such as reports, evaluations and white papers produced outside formal publication channels) and, whenever possible, informal or working documents (such as meeting notes, PowerPoint presentations and memoranda). These latter in particular can provide rich veins of insight into how policy actors are thinking through the issues under study, particularly for the lucky researcher who obtains working documents with ‘Track Changes.’ How you prioritize documents will depend on your research question: you may prioritize official policy documents if you are studying policy content, or you may prioritize informal documents if you are studying policy process.

Types of documents that can be consulted in studies of health policy

During this initial preparatory phase, we also recommend devising a file-naming system for your documents (e.g. Author.Date.Topic.Institution.PDF), so that documents can be easily retrieved throughout the research process. After extracting data and processing your documents the first time around, you will likely have additional ‘questions’ to ask your documents and need to consult them again. For this reason, it is important to clearly name source files and link filenames to the data that you are extracting (see sample naming conventions in the Supplementary Materials ).

Step 2. Extract data

Data can be extracted in a number of ways, and the method you select for doing so will depend on your research question and the nature of your documents. One simple way is to use an Excel spreadsheet where each row is a document and each column is a category of information you are seeking to extract, from more basic data such as the document title, author and date, to theoretical or conceptual categories deriving from your research question, operating theory or analytical framework (Panel 2). Documents can also be imported into thematic coding software such as Atlas.ti or NVivo, and data extracted that way. Alternatively, if the research question focuses on process, documents can be used to compile a timeline of events, to trace processes across time. Ask yourself, how can I organize these data in the most coherent manner? What are my priority categories? We have included two different examples of data extraction tools in the Supplementary Materials to this article to spark ideas.

Case study Documents tell part of the story in Niger

Document analyses are first and foremost exercises in close reading: documents should be read thoroughly, from start to finish, including annexes, which may seem tedious but which sometimes produce golden nuggets of information. Read for overall meaning as you extract specific data related to your research question. As you go along, you will begin to have ideas or build working theories about what you are learning and observing in the data. We suggest capturing these emerging theories in extended notes or ‘memos,’ as used in Grounded Theory methodology ( Charmaz, 2006 ); these can be useful analytical units in themselves and can also provide a basis for later report and article writing.

As you read more documents, you may find that your data extraction tool needs to be modified to capture all the relevant information (or to avoid wasting time capturing irrelevant information). This may require you to go back and seek information in documents you have already read and processed, which will be greatly facilitated by a coherent file-naming system. It is also useful to keep notes on other documents that are mentioned that should be tracked down (sometimes you can write the author for help). As a general rule, we suggest being parsimonious when selecting initial categories to extract from data. Simply reading the documents takes significant time in and of itself—make sure you think about how, exactly, the specific data you are extracting will be used and how it goes towards answering your research questions.

Step 3. Analyse data

As in all types of qualitative research, data collection and analysis are iterative and characterized by emergent design, meaning that developing findings continually inform whether and how to obtain and interpret data ( Creswell, 2013 ). In practice, this means that during the data extraction phase, the researcher is already analysing data and forming initial theories—as well as potentially modifying document selection criteria. However, only when data extraction is complete can one see the full picture. For example, are there any documents that you would have expected to find, but did not? Why do you think they might be missing? Are there temporal trends (i.e. similarities, differences or evolutions that stand out when documents are ordered chronologically)? What else do you notice? We provide a list of overarching questions you should think about when viewing your body of document as a whole ( Table 2 ).

Questions to ask your overall body of documents

HIV and viral hepatitis articles by main frames (%). Note: The percentage of articles is calculated by dividing the number of articles appearing in each frame for viral hepatitis and HIV by the respectivenumber of sampled articles for each disease (N = 137 for HIV; N = 117 for hepatitis). Time frame: 1 January 2006 to 30 September 2016

Representations of progress toward Millennium Development Goal 4 in Nigerien policy documents. Sources: clockwise from upper left: ( WHO 2006 ); ( Institut National de la Statistique 2010 ); ( Ministè re de la Santé Publique 2010 ); ( Unicef 2010 )

In addition to the meaning-making processes you are already engaged in during the data extraction process, in most cases, it will be useful to apply specific analysis methodologies to the overall corpus of your documents, such as policy analysis ( Buse et al. , 2005 ). An array of analysis methodologies can be used, both quantitative and qualitative, including case study methodology, thematic content analysis, discourse analysis, framework analysis and process tracing, which may require differing levels of familiarity and skills to apply (we highlight a few of these in the case studies below). Analysis can also be structured according to theoretical approaches. When it comes to analysing policies, process tracing can be particularly useful to combine multiple sources of information, establish a chronicle of events and reveal political and social processes, so as to create a narrative of the policy cycle ( Yin, 1994 ; Shiffman et al. , 2004 ). Practically, you will also want to take a holistic view of the documents’ ‘answers’ to the questions or analysis categories you applied during the data extraction phase. Overall, what did the documents ‘say’ about these thematic categories? What variation did you find within and between documents, and along which axes? Answers to these questions are best recorded by developing notes or memos, which again will come in handy as you write up your results.

As with all qualitative research, you will want to consider your own positionality towards the documents (and their sources and authors); it may be helpful to keep a ‘reflexivity’ memo documenting how your personal characteristics or pre-standing views might influence your analysis ( Watt, 2007 ).

Step 4. Distil your findings

You will know when you have completed your document review when one of the three things happens: (1) completeness (you feel satisfied you have obtained every document fitting your criteria—this is rare), (2) out of time (this means you should have used more specific criteria), and (3) saturation (you fully or sufficiently understand the phenomenon you are studying). In all cases, you should strive to make the third situation the reason for ending your document review, though this will not always mean you will have read and analysed every document fitting your criteria—just enough documents to feel confident you have found good answers to your research questions.

Now it is time to refine your findings. During the extraction phase, you did the equivalent of walking along the beach, noticing the beautiful shells, driftwood and sea glass, and picking them up along the way. During the analysis phase, you started sorting these items into different buckets (your analysis categories) and building increasingly detailed collections. Now you have returned home from the beach, and it is time to clean your objects, rinse them of sand and preserve only the best specimens for presentation. To do this, you can return to your memos, refine them, illustrate them with graphics and quotes and fill in any incomplete areas. It can also be illuminating to look across different strands of work: e.g. how did the content, style, authorship, or tone of arguments evolve over time? Can you illustrate which words, concepts or phrases were used by authors or author groups?

Results will often first be grouped by theoretical or analytic category, or presented as a policy narrative, interweaving strands from other methods you may have used (interviews, observation, etc.). It can also be helpful to create conceptual charts and graphs, especially as this corresponds to your analytical framework (Panels 1 and 2). If you have been keeping a timeline of events, you can seek out any missing information from other sources. Finally, ask yourself how the validity of your findings checks against what you have learned using other methods. The final products of the distillation process will vary by research study, but they will invariably allow you to state your findings relative to your research questions and to draw policy-relevant conclusions.

Document analysis is an essential component of health policy research—it is also relatively convenient and can be low cost. Using an organized system of analysis enhances the document analysis’s procedural rigour, allows for a fuller understanding of policy process and content and enhances the effectiveness of other methods such as interviews and non-participant observation. We propose the READ approach as a systematic method for interrogating documents and extracting study-relevant data that is flexible enough to accommodate many types of research questions. We hope that this article encourages discussion about how to make best use of data from documents when researching health policy questions.

Supplementary data are available at Health Policy and Planning online.

The data extraction tool in the Supplementary Materials for the iCCM case study (Panel 2) was conceived of by the research team for the multi-country study ‘Policy Analysis of Community Case Management for Childhood and Newborn Illnesses’. The authors thank Sara Bennett and Daniela Rodriguez for granting permission to publish this tool. S.M. was supported by The Olympia-Morata-Programme of Heidelberg University. The funders had no role in the decision to publish, or preparation of the manuscript. The content is the responsibility of the authors and does not necessarily represent the views of any funder.

Conflict of interest statement . None declared.

Ethical approval. No ethical approval was required for this study.

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Document Analysis - How to Analyze Text Data for Research

Introduction

What is document analysis, where is document analysis used, how to perform document analysis, what is text analysis, atlas.ti as text analysis software.

In qualitative research , you can collect primary data through surveys , observations , or interviews , to name a few examples. In addition, you can rely on document analysis when the data already exists in secondary sources like books, public reports, or other archival records that are relevant to your research inquiry.

In this article, we will look at the role of document analysis, the relationship between document analysis and text analysis, and how text analysis software like ATLAS.ti can help you conduct qualitative research.

Document analysis is a systematic procedure used in qualitative research to review and interpret the information embedded in written materials. These materials, often referred to as “documents,” can encompass a wide range of physical and digital sources, such as newspapers, diaries, letters, policy documents, contracts, reports, transcripts, and many others.

At its core, document analysis involves critically examining these sources to gather insightful data and understand the context in which they were created. Research can perform sentiment analysis , text mining, and text categorization, to name a few methods. The goal is not just to derive facts from the documents, but also to understand the underlying nuances, motivations, and perspectives that they represent. For instance, a historical researcher may examine old letters not just to get a chronological account of events, but also to understand the emotions, beliefs, and values of people during that era.

Benefits of document analysis

There are several advantages to using document analysis in research:

• Authenticity : Since documents are typically created for purposes other than research, they can offer an unobtrusive and genuine insight into the topic at hand, without the potential biases introduced by direct observation or interviews.
• Availability : Documents, especially those in the public domain, are widely accessible, making it easier for researchers to source information.
• Cost-effectiveness : As these documents already exist, researchers can save time and resources compared to other data collection methods.

However, document analysis is not without challenges. One must ensure the documents are authentic and reliable. Furthermore, the researcher must be adept at discerning between objective facts and subjective interpretations present in the document.

Document analysis is a versatile method in qualitative research that offers a lens into the intricate layers of meaning, context, and perspective found within textual materials. Through careful and systematic examination, it unveils the richness and depth of the information housed in documents, providing a unique dimension to research findings.

Document analysis is employed in a myriad of sectors, serving various purposes to generate actionable insights. Whether it's understanding customer sentiments or gleaning insights from historical records, this method offers valuable information. Here are some examples of how document analysis is applied.

Analyzing surveys and their responses

A common use of document analysis in the business world revolves around customer surveys . These surveys are designed to collect data on the customer experience, seeking to understand how products or services meet or fall short of customer expectations.

By analyzing customer survey responses , companies can identify areas of improvement, gauge satisfaction levels, and make informed decisions to enhance the customer experience. Even if customer service teams designed a survey for a specific purpose, text analytics of the responses can focus on different angles to gather insights for new research questions.

Examining customer feedback through social media posts

In today's digital age, social media is a goldmine of customer feedback. Customers frequently share their experiences, both positive and negative, on platforms like Twitter, Facebook, and Instagram.

Through document analysis of social media posts, companies can get a real-time pulse of their customer sentiments. This not only helps in immediate issue resolution but also in shaping product or service strategies to align with customer preferences.

Interpreting customer support tickets

Another rich source of data is customer support tickets. These tickets often contain detailed descriptions of issues faced by customers, their frustrations, or sometimes their appreciation for assistance received.

By employing document analysis on these tickets, businesses can detect patterns, identify recurring issues, and work towards streamlining their support processes. This ensures a smoother and more satisfying customer experience.

Historical research and social studies

Beyond the world of business, document analysis plays a pivotal role in historical and social research. Scholars analyze old manuscripts, letters, and other archival materials to construct a narrative of past events, cultures, and civilizations.

As a result, document analysis is an ideal method for historical research since generating new data is less feasible than turning to existing sources for analysis. Researchers can not only examine historical narratives but also how those narratives were constructed in their own time.

Turn to ATLAS.ti for your data analysis needs

Try out our powerful data analysis tools with a free trial to make the most out of your data today.

Performing document analysis is a structured process that ensures researchers can derive meaningful, qualitative insights by organizing source material into structured data . Here's a brief outline of the process:

• Define the research question
• Choose relevant documents
• Prepare and organize the documents
• Begin initial review and coding
• Analyze and interpret the data
• Present findings and draw conclusions

The process in detail

Before diving into the documents, it's crucial to have a clear research question or objective. This serves as the foundation for the entire analysis and guides the selection and review of documents. A well-defined question will focus the research, ensuring that the document analysis is targeted and relevant.

The next step is to identify and select documents that align with the research question. It's vital to ensure that these documents are credible, reliable, and pertinent to the research inquiry. The chosen materials can vary from official reports, personal diaries, to digital resources like social media data , depending on the nature of the research.

Once the documents are selected, they need to be organized in a manner that facilitates smooth analysis. This could mean categorizing documents by themes, chronology, or source types. Digital tools and data analysis software , such as ATLAS.ti, can assist in this phase, making the organization more efficient and helping researchers locate specific data when needed.

With everything in place, the researcher starts an initial review of the documents. During this phase, the emphasis is on identifying patterns, themes, or specific information relevant to the research question.

Coding involves assigning labels or tags to sections of the text to categorize the information. This step is iterative, and codes can be refined as the researcher delves deeper.

After coding, interesting patterns across codes can be analyzed. Here, researchers seek to draw meaningful connections between codes, identify overarching themes, and interpret the data in the context of the research question .

This is where the hidden insights and deeper understanding emerge, as researchers juxtapose various pieces of information and infer meaning from them.

Finally, after the intensive process of document analysis, the researcher consolidates their findings, crafting a narrative or report that presents the results. This might also involve visual representations like charts or graphs, especially when demonstrating patterns or trends.

Drawing conclusions involves synthesizing the insights gained from the analysis and offering answers or perspectives in relation to the original research question.

Ultimately, document analysis is a meticulous and iterative procedure. But with a clear plan and systematic approach, it becomes a potent tool in the researcher's arsenal, allowing them to uncover profound insights from textual data.

Text analysis, often referenced alongside document analysis, is a method that focuses on extracting meaningful information from textual data. While document analysis revolves around reviewing and interpreting data from various sources, text analysis hones in on the intricate details within these documents, enabling a deeper understanding. Both these methods are vital in fields such as linguistics, literature, social sciences, and business analytics.

In the context of document analysis, text analysis emerges as a nuanced exploration of the textual content. After documents have been sourced, be it from books, articles, social networks, or any other medium, they undergo a preprocessing phase. Here, irrelevant information is eliminated, errors are rectified, and the text may be translated or converted to ensure uniformity.

This cleaned text is then tokenized into smaller units like words or phrases, facilitating a granular review. Techniques specific to text analysis, such as topic modeling to determine discussed subjects or pattern recognition to identify trends, are applied.

The derived insights can be visualized using tools like graphs or charts, offering a clearer understanding of the content's depth. Interpretation follows, allowing researchers to draw actionable insights or theoretical conclusions based on both the broader document context and the specific text analysis.

Merging text analysis with document analysis presents unique challenges. With the proliferation of digital content, managing vast data sets becomes a significant hurdle. The inherent variability of language, laden with cultural nuances, idioms, and sometimes sarcasm, can make precise interpretation elusive.

Many text analysis tools exist that can facilitate the analytical process. ATLAS.ti offers a well-rounded, useful solution as a text analytics software . In this section, we'll highlight some of the tools that can help you conduct document analysis.

Word Frequencies

A word cloud can be a powerful text analytics tool to understand the nature of human language as it pertains to a particular context. Researchers can perform text mining on their unstructured text data to get a sense of what is being discussed. The Word Frequencies tool can also parse out specific parts of speech, facilitating more granular text extraction.

Sentiment Analysis

The Sentiment Analysis tool employs natural language processing (NLP) and machine learning to analyze text based on sentiment and facilitate natural language understanding. This is important for tasks such as, for example, analyzing customer reviews and assessing customer satisfaction, because you can quickly categorize large numbers of customer data records by their positive or negative sentiment.

AI Coding relies on massive amounts of training data to interpret text and automatically code large amounts of qualitative data. Rather than read each and every document line by line, you can turn to AI Coding to process your data and devote time to the more essential tasks of analysis such as critical reflection and interpretation.

These text analytics tools can be a powerful complement to research. When you're conducting document analysis to understand the meaning of text, AI Coding can help with providing a code structure or organization of data that helps to identify deeper insights.

AI Summaries

Dealing with large numbers of discrete documents can be a daunting task if done manually, especially if each document in your data set is lengthy and complicated. Simplifying the meaning of documents down to their essential insights can help researchers identify patterns in the data.

AI Summaries fills this role by using natural language processing algorithms to simplify data to its salient points. Text generated by AI Summaries are stored in memos attached to documents to illustrate pathways to coding and analysis or to highlight how the data conveys meaning.

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The SAGE Handbook of Qualitative Data Analysis

• Edited by: Uwe Flick
• Publisher: SAGE Publications Ltd
• Publication year: 2014
• Online pub date: November 21, 2013
• Discipline: Anthropology
• Methods: Case study research , Coding , Narrative research
• DOI: https:// doi. org/10.4135/9781446282243
• Keywords: coding , content analysis , grounded theory , interviews , qualitative data analysis , qualitative research , social research Show all Show less
• Print ISBN: 9781446208984
• Online ISBN: 9781446282243
• Buy the book icon link

Subject index

The wide range of approaches to data analysis in qualitative research can seem daunting even for experienced researchers. This handbook is the first to provide a state-of-the art overview of the whole field of QDA; from general analytic strategies used in qualitative research, to approaches specific to particular types of qualitative data, including talk, text, sounds, images and virtual data.

The handbook includes chapters on traditional analytic strategies such as grounded theory, content analysis, hermeneutics, phenomenology and narrative analysis, as well as coverage of newer trends like mixed methods, reanalysis and meta-analysis. Practical aspects such as sampling, transcription, working collaboratively, writing and implementation are given close attention, as are theory and theorization, reflexivity, and ethics.

Written by a team of experts in qualitative research from around the world, this handbook is an essential compendium for all qualitative researchers and students across the social sciences.

Front Matter

• International Advisory Editorial Board
• Endorsements
• List of Tables and Figures
• About the Editor
• Notes on Contributors
• Acknowledgements
• Chapter 1 | Mapping the Field Mapping the Field
• Chapter 2 | Notes Toward a Theory of Qualitative Data Analysis
• Chapter 3 | Analytic Inspiration in Ethnographic Fieldwork
• Chapter 4 | Sampling Strategies in Qualitative Research
• Chapter 5 | Transcription as a Crucial Step of Data Analysis
• Chapter 6 | Collaborative Analysis of Qualitative Data
• Chapter 7 | Qualitative Comparative Practices: Dimensions, Cases and Strategies
• Chapter 8 | Reflexivity and the Practice of Qualitative Research
• Chapter 9 | Induction, Deduction, Abduction
• Chapter 10 | Interpretation and Analysis1
• Chapter 11 | Grounded Theory and Theoretical Coding
• Chapter 12 | Qualitative Content Analysis
• Chapter 13 | Phenomenology as a Research Method
• Chapter 14 | Narrative Analysis: The Constructionist Approach
• Chapter 15 | Documentary Method
• Chapter 16 | Hermeneutics and Objective Hermeneutics
• Chapter 17 | Cultural Studies
• Chapter 18 | Netnographic Analysis: Understanding Culture Through Social Media Data
• Chapter 19 | Using Software in Qualitative Analysis
• Chapter 20 | Analysing Interviews
• Chapter 21 | Analysing Focus Groups
• Chapter 22 | Conversations and Conversation Analysis
• Chapter 23 | Discourses and Discourse Analysis
• Chapter 24 | Analysing Observations
• Chapter 25 | Analysing Documents
• Chapter 26 | Analysing News Media
• Chapter 27 | Analysing Images
• Chapter 28 | Analysis of Film
• Chapter 29 | Analysing Sounds
• Chapter 30 | Video Analysis and Videography
• Chapter 31 | Analysing Virtual Data
• Chapter 32 | Reanalysis of Qualitative Data
• Chapter 33 | Qualitative Meta-Analysis
• Chapter 34 | Quality of Data Analysis
• Chapter 35 | Ethical Use of Qualitative Data and Findings
• Chapter 36 | Analytic Integration in Qualitatively Driven (QUAL) Mixed and Multiple Methods Designs
• Chapter 37 | Generalization in and from Qualitative Analysis
• Chapter 38 | Theorization from Data
• Chapter 39 | Writing and/as Analysis or Performing the World1
• Chapter 40 | Implementation: Putting Analyses into Practice

Back Matter

• Author Index

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Monday, January 20, 2020

A QDA recipe? A ten-step approach for qualitative document analysis using MAXQDA

Guest post by Professional MAXQDA Trainer Dr. Daniel Rasch .

Introduction

Qualitative text or document analysis has evolved into one of the most used qualitative methods across several disciplines ( Kuckartz, 2014 & Mayring, 2010). Its straightforward structure and procedure enable the researcher to adapt the method to his or her special case – nearly to every need.

This article proposes a recipe of ten simple steps for conducting qualitative document analyses (QDA) using MAXQDA (see table 1 for an overview).

Table 1: Overview of the “QDA recipe”

The ten steps for conducting qualitative document analyses using MAXQDA

Step 1: the research question(s).

As always, research begins with the question(s). Three aspects should be covered when dealing with the research question(s):

• What do you want to find out exactly,
• what relevance does your research on this exact question have, and
• what contribution is your research going to make to your discipline?

Highlight these questions in your introduction and make your research stand out.

Step 2: Data collection and data sampling

After you have decided on the questions, you should think about how to answer them. What kind of qualitative data will best answer your question? Interviews – how many and with whom? Documents – which ones and where to collect them from?

At this point, you can already start thinking about validity: are you going to use a representative or a biased sample? Check the different options for sampling and its effects on validity ( Krippendorff, 2019 ).

Step 3: Select and prepare the data

For this step, MAXQDA 2020 is an excellent tool to help you prepare the selected data for any further steps . Whatever type of qualitative data you choose, you can import it into MAXQDA and then you can have MAXQDA assist in transcribing it. In the end, qualitative document analysis is all about written forms of communication (Kuckartz, 2014).

Figure 1: Import the data you have chosen or selected

Step 4: Codebook development

It takes time to develop a solid codebook. Working deductively, the process is a little easier with codes deriving from the theoretical considerations in the context of your research. Inductively, there are various steps you can use, ranging from creative coding to in-vivo-codes.

Content-wise, you can apply all sorts of codes, such as themes or evaluations, two of the most commonly used styles of content analysis (see thematic and evaluative content analysis in Kuckartz, 2014).

Figure 2: coding options in MAXQDA

• a brief definition,
• a long definition,
• criteria for when to use the code, 
• criteria for when not to use the code, and
• an example.

Using MAXQDA’s code memos simplify the process of creating and maintaining a good codebook . First, you can always go back to the codes and view and review your codebook within your project, and second, you can simply export the codebook as an attachment or appendix for publication purposes (use: Reports > Codebook ).

Figure 3: Creating a new code with code memo

Step 5: Unitizing and coding instructions

Before the process of coding starts, it is necessary to decide on the units of, as well as the rules for, coding. It is especially important to decide on your unit of coding (sentences, paragraphs, quasi-sentences, etc.). Coding rules help to keep this choice consistent and support you to stick to your research question(s) because every passage you code and every memo you write should be done in order to answer your research question(s). Decision rules should be added: what are you going to do if a passage does not fit in your subcodes but should be coded because it is important for your research question?

Step 6: Trial, training, reliability

Trial runs are of major importance. Not only do they show you, which codes work and which do not, but they also help you to rethink your choices in terms of the unit of coding, the content of the codebook, and reliability. Since there are different options for the latter, stick to what works best for you: either a qualitative comparison of what you have coded or quantitative indicators like Krippendorff’s alpha if need be .

You can test yourself or a team you work with and there might even be some situations, where a reliability test is not helpful or needed. When testing the codebook, be sure to test the variability of your collected documents and be sure that the entire codebook is tested. 

MAXQDA helps you compare different forms of agreement for more an unlimited number of texts, divided into two different document groups (one document group coded by coder 1, a second document group coded by coder 2 – be aware, that you can also test yourself and be coder 2 yourself).

Figure 4: Intercoder agreement

Step 7: Revision and modification

After checking, which codes work and which do not, you can revise the codebook and modify it. As Schreier puts it: “No coding frame (codebook – DR) is perfect” (Schreier, 2012: 147).

Step 8: Coding

There are many different coding strategies, but one thing is for sure: qualitative work needs time and reading, as well as working with the material over and over again.

One coding strategy might be to first make yourself comfortable with the documents and start coding after second or third reading only. Another strategy is to concentrate on some of your codes first and do a second round of coding with the other codes later.

Step 9: Analyze and compare

Analyze and compare – these two words are the essence of the qualitative analysis at this step. At the core of each qualitative document analysis is the description of the content and the comparison of these contents between the documents you analyze.

After everything has been coded, you can make use of different analysis strategies: paraphrase, write summaries, look for intersections of codes, patterns of likeliness between the documents using simple or complex queries.

Figure 5: different analysis strategies in MAXQDA

Step 10: Interpretation and presentation

Reporting and summarizing qualitative findings is difficult. Most often, we find simple descriptions of the content with the use of quotations, paraphrases or other references to the text. However, MAXQDA makes it fast and easier with many options to choose from . The easiest way is to generate a table to sum up your findings – if your data or the findings allow for this.

MAXQDA offers several options: either map relations of codes, documents or memos with the MAXMaps , create matrices between codes and documents ( Code Matrix Browser ) or codes and codes ( Code Relations Browser ) to display the distribution of codes inside your data or even using different colors to map the distribution of codes or single documents.

Figure 6: Visual Tools for presentation

The Code Matrix Browser also enables you to quantify the qualitative data using two clicks. You can export these numbers for further analysis with statistical packages, to run causal relation and effect calculations, such as regressions or correlations ( Rasch, 2018 ).

Summary and adoption

Qualitative document analysis is one of the most popular techniques and adaptable to nearly every field. MAXQDA is a software tool that offers many options to make your analysis and therefore your research easier .

The recipe works best for theory-driven, deductive coding. However, it can be also used for inductive, explorative work by switching some of these steps around: for example, your codebook development might be one step to do during or after the trial and testing, since codes are developed inductively during the coding process. Still, it is important to define these codes properly.

The above-mentioned recipe has been used as a basis for several publications by the author. Starting with simple comparison of qualitative and quantitative text analysis ( Boräng et al., 2014 ), to the usage of the qualitative data as a basis for regression models ( Eising et al., 2015 ; Eising et al., 2017 ) to a book using mixed methods and therefore both qualitative and quantitative data analysis ( Rasch, 2018 ).

About the author

Daniel Rasch is a post-doctoral researcher in political science at the German University of Administrative Sciences, Speyer. He received his Ph.D. with a mixed methods analysis of lobbyists‘ success in the European Union. He focuses on the quantification of qualitative data. He is an experienced MAXQDA lecturer and has been a Professional MAXQDA Trainer since 2012.

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Document Analysis as a Qualitative Research Method

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Cites methods from "Document Analysis as a Qualitative ..."

... Once we generated a corpus of exemplars, we used the constant comparative method [33] and a document analysis approach [14] to describe the breadth of artifacts and nature of the dark patterns these artifacts included. ...

318  citations

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... Furthermore, these journal articles are a cost-effective means of information collection, as well as providing a method that remains unaffected and unaltered by the process of research, or presence of the researcher (Bowen, 2009). ...

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"Document Analysis as a Qualitative ..." refers background or methods in this paper

... Although data in most grounded theory studies come from interviews and observations, entire studies can be conducted with only documents (Glaser & Strauss, 1967). ...

... The constant comparative method (Glaser & Strauss, 1967) guided the data analysis, which was based on an inductive approach geared to identifying patterns and discovering theoretical properties in the data. ...

... Glaser and Strauss (1967) called attention to the usefulness of documents for theory building—a process that ‘begs for comparative analysis [with the library offering] a fantastic range of comparison groups, if only the researcher has the ingenuity to discover them’ (p. 179). ...

... My study employed a multimethod approach, encompassing semistructured interviews, nonparticipant observation, and document analysis, adhering to the principles of the grounded theory methodology (Glaser & Strauss, 1967; Strauss & Corbin, 1998). ...

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalizability

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

• Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

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.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

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9 methodologies for a successful qualitative research assignment

Qualitative research is important in the educational and scientific domains. It enables a deeper understanding of phenomena, experiences, and context. Many researchers employ such research activities in the fields of history, sociology, and anthropology. For such researchers, learning quality analysis insights is crucial. This way, they can perform well throughout their research journey. Writing a qualitative research assignment is one such way to practice qualitative interpretations. When students address various qualitative questions in these projects, they become efficient in conducting these activities at a higher level, such as for a master’s or Ph.D. thesis.

The FormPlus highlights why researchers prefer qualitative research over quantitative research. It is faster, scientific, objective, focused, and acceptable. Researchers who don’t know what to expect from the research outcomes usually choose qualitative research. In this guide, we will discuss the top methodologies that students can employ while writing their qualitative research assignments. This way, you can write an appealing document that perfectly demonstrates your qualitative research skills.

However, being stressed with academic and daily life commitments, if you find it challenging to manage time exclusively for such projects, availing of assignment writing services can make it manageable. Instead of doing anything wrong in the hustle, get it done by the professionals specifically working to handle these academic write-ups. Now, let’s define quality research before we discuss the actual topic.

What is meant by qualitative research?

Quality research is a market research method that gathers data from conversational and open-ended communication. In simple words, it is about what people think and why they think so. It relates to the nature or standard of something rather than dealing with its quantity. Such researchers collect nonnumerical data to understand opinions, concepts, and ideas.

How do you write a qualitative research assignment? Top 9 methodologies

Writing an assignment requires your command of various tasks. Qualitative research assignment design involves research, writing, structuring, and providing citations of the resources used. Assignment writing plays a crucial role in upgrading your grades.

So, you must make it accurate and authentic. Write it with the utmost care without skipping any important aspects. Sometimes, it can be hard, but it becomes easy if you correctly use effective methodologies. This is why we have brought together some of the common methodologies you can use to write your qualitative research assignments.

1. Interviews

A qualitative interview is mostly used in projects that involve market research. In this study personal interaction is required to collect in-depth information of the participants. In qualitative research for assignment, consider the interview as a personal form of research agenda rather than a focused group study. A qualitative interview requires careful planning so that you can gather meaningful data.

Here are the simple steps to consider for its implementation in a qualitative research assignment:

• Define research objectives.
• Identify the target population.
• Obtain informed consent of participants.
• Make an interview guideline.
• Select a suitable location.
• Conduct the interview.
• Show respect for participant’s perspectives.
• Analyse the data.

2. Observation

In qualitative observation, the researcher gathers data from five senses: sight, hearing, touch, smell, and taste. It is a subject approach that depends on the sensory organ of the researcher. This method allows you to better understand the culture, process, and people under study. Some of its characteristics to consider for writing a qualitative research assignment include,

• It is a naturalistic inquiry of the participants in a natural environment.
• This approach is subjective and depends on the researcher’s observation.
• It does not seek a definite answer to a query.
• The researcher can recognise their own biases when compiling findings.

3. Questionnaires

In this type of survey, the researcher asks open-ended questions to participants. This way, they price the long written or typed document. In writing qualitative research assignments, these questions aim to reveal the participants’ narratives and experiences. Once you know what type of information you need, you can start curating your questionnaire form. The questions must be specific and clear enough that the participants can comprehend them.

Below are the main points that must be considered when creating qualitative research questionnaires.

• Avoid jargon and ambiguity in the questions.
• Each question should contribute to the research objectives.
• Use simple language.
• The questions should be neutral and unbiased.
• Be precise, as the complex questions can overwhelm the respondents.
• Always conduct a pilot test.
• Put yourself in the respondent’s shoes while asking questions.

4. Case Study

A case study is a detailed analysis of a person, place, thing, organisation, or phenomenon. This method is appropriate when you want to gain a contextual, concrete, and in-depth understanding of the real-world problem for writing your qualitative research assignment. This method is especially helpful when you need more time to conduct large-scale research activities.

The four crucial steps below can be followed up with this methodology.

• Select a case that has the potential to provide new and unexpected insights into the subject.
• Make a theoretical framework.
• Collect your data from various primary and secondary resources.
• Describe and analyse the case to provide a clear picture of the subject.

5. Focus Groups

Focused group research has some interesting properties. In this method, a planned interview is conducted within a small group. For this purpose, some of the participants are sampled from the study population to record data for writing a qualitative research assignment. Typically, a focused group has features like,

• At least four to ten participants must meet for up to two hours.
• There must be a facilitator who can guide the discussion by asking open-ended questions.
• The emphasis must be put on the group discussion rather than the discussion of the group members with the facilitator.
• The discussion should be recorded and transcribed by the researchers.

6. Ethnographic Research

It is the most in-depth research method that involves studying people in their natural environment. It requires the researcher to adopt the target audience environment. The environment can be anything from an organisation to a city or any remote location.

However, the geographical constraints can be a problem in this study. For students who are writing their qualitative research assignment, some of the features of ethnographic research to write in their document include,

• The researcher can get a more realistic picture of the study.
• It uncovers extremely valuable insights.
• Provides accurate predictions.
• You can extend the observation to create more in-depth data.
• You can interact with people within a particular context.

7. Record Keeping

This method is similar to going to the library to collect data from books. You consult various relayed books, note the important points, and take note of the referencing. So, the researcher uses already existing data rather than introducing new things in the field.

Later on, this data can be used to conduct new research. Yet, when faced with the vast resources available in your institution’s library, seeking assistance from UK-based assignment writing services is an excellent solution if you need help pinpointing the most relevant information for your topic. Proficient in data gathering and adept at structuring qualitative research assignments, these professionals can significantly elevate your academic results.

This method is mostly used by companies to understand a group of customers’ behaviour, characteristics, and motivation. It allows respondents to ask in-depth questions about their experience. In a business market, it helps you understand how your customers make decisions. The intent is to understand them at their level and make related changes in your setup. The researcher must ask generic and precise questions that have a clear purpose.

Consider the below examples of qualitative survey questions. It can be useful in recording data and writing qualitative research assignments.

• Why did you buy this skin care product?
• What is the overall narrative of this brand?
• How do you feel after buying this product?
• What sets this brand apart from others?
• How will this product fulfil your needs?
• What are the things that you expect from this brand to grant you?

9. Action Research

This method involves collaboration and empowerment of the participants. It is mostly appropriate for marginalised groups where there is no flexibility.

The primary characteristics of the action research that can be quoted in your qualitative research assignment include,

• It is action-oriented, and participants are actively involved in the research.
• There is a collaborative process between participants and researchers.
• The nature of action research is flexible to the changing situation.

However, the survey also accompanies some of the limitations, including,

• The researcher can misinterpret the open-ended questions.
• The data ownership between the researcher and participants needs to be negotiated.
• The ethical considerations must be kept.
• It is not considered a scientific method as it is fluid in data collection. Consequently, it may not attract the finding.

What is the difference between quantitative and qualitative research?

Both research types share the common aim of knowledge acquisition. In quantitative research, the use of numbers and objective measures is used. It seeks answers to questions like when and where.

On the other hand, in qualitative research, the researcher is concerned with subjective phenomena. Such data can’t be numerically measured. For example, you might conduct a survey to analyse how different people experience grief.

What are the 4 types of qualitative research?

There are various types of qualitative research. It may include,

● Phenomenological studies:

It examines the human experience via description provided by the people involved. These are the lived experiences of the people. It is usually used in research areas where little knowledge is known.

● Ethnographic studies:

It involves the analysis of data about cultural groups. In such analysis, the researcher mostly lives with different communities and becomes part of their culture to provide solid interpretations.

● Grounded theory studies:

In this qualitative approach, the researcher collects and analyses the data. Later on, a theory is developed that is grounded in the data. It used both inductive and deductive approaches for theory development.

● Historical studies:

It is concerned with the location, identification, evaluation, and synthesis of data from the past. These researchers are not concerned with discovering past events but with relating these events to the present happenings.

The Research Gate provides a flow chart illustrating various qualitative research methods.

What are The 7 characteristics of qualitative research?

The following are some of the distinct features of qualitative research. You can write about them in your qualitative research assignment, as they are collected from reliable sources.

• It can even capture the changing attitude within the target group.
• It is beyond the limitations associated with quantitative research
• It explains something that numbers alone can’t describe.
• It is a flexible approach to improve the outcomes.
• A researcher is not supposed to become more speculative about the results.
• This approach is more targeted.
• It keeps the cost of data collection down.

What are the advantages and disadvantages of qualitative research?

The pros of qualitative research can’t be denied. However, some cons are also associated with this research.

• Explore attitudes and behaviours in depth.
• It encourages discussions for better results.
• Generate descriptive data that can formulate new theories.
• The small sample size can be a problem.
• Bias in the sample collection.
• Lack of privacy if you are covering a sensitive topic.

Qualitative research assignment examples

The Afe Babalola University ePortal provides an example of a qualitative assignment. Here is the description of quality questions and related answers. You can get an idea about how to handle your quality research assignment project with this sample.

The questions asked in the paper are displayed below.

The Slide Team presents a template for further compressing other details, such as the qualitative research assignment template. You can use it to make your presentation look professional.

Writing a qualitative research assignment is crucial, especially if you want to engage in research activities for your master’s thesis. Most researchers choose this method because of the associated credibility and reliability of the results. In the above guide, we have discussed some of the prominent features of this method. All of the given data can help you in writing your assignments. We have discussed the benefits of each methodology and a brief account of how you can carry it.

However, even after going through this whole guideline, if the concepts of the Qualitative Research methods assignment seem ambiguous and you think you can’t write a good project, then ask professional to “ write my assignment .” These experts can consult the best sources for the data collection of your project. Consequently, they will deliver you the winning document that can stand out among other write-ups.

• Open access
• Published: 01 April 2024

Midwives’ lived experiences of caring for women with mobility disabilities during pregnancy, labour and puerperium in Eswatini: a qualitative study

• Annie M. Temane 1 ,
• Fortunate N. Magagula 2 &
• Anna G. W. Nolte 1  

BMC Women's Health volume  24 , Article number:  207 ( 2024 ) Cite this article

Metrics details

Midwives encounter various difficulties while aiming to achieve excellence in providing maternity care to women with mobility disabilities. The study aimed to explore and describe midwives’ experiences of caring for women with mobility disabilities during pregnancy, labour and puerperium in Eswatini.

A qualitative, exploratory, descriptive, contextual research design with a phenomenological approach was followed. Twelve midwives working in maternal health facilities in the Hhohho and Manzini regions in Eswatini were interviewed. Purposive sampling was used to select midwives to participate in the research. In-depth phenomenological interviews were conducted, and Giorgi’s descriptive phenomenological method was used for data analysis.

Three themes emerged from the data analysis: midwives experienced physical and emotional strain in providing maternity care to women with mobility disabilities, they experienced frustration due to the lack of equipment to meet the needs of women with mobility disabilities, and they faced challenges in providing support and holistic care to women with mobility disabilities during pregnancy, labour and puerperium.

Conclusions

Midwives experienced challenges caring for women with mobility disabilities during pregnancy, labour and the puerperium in Eswatini. There is a need to develop and empower midwives with the knowledge and skill to implement guidelines and enact protocols. Moreover, equipment and infrastructure are required to facilitate support and holistic maternity care for women with mobility disabilities.

Peer Review reports

Globally, few studies have focused on midwives’ views of providing maternity care to women with mobility disabilities during pregnancy, labour and the puerperium [ 1 ]. In The Disabled World [ 2 ], the World Health Organisation (WHO) defines ‘disability’ as an umbrella term covering impairments, activity limitations, and participation restrictions. Furthermore, the WHO defines an ‘impairment’ as a problem in bodily function or structure; an ‘activity limitation’ as a difficulty encountered by an individual in executing a task or action; and ‘participation restriction’ as a problem experienced by an individual in various life situations [ 2 ]. In this study, mobility disabilities refer to an impairment in the functioning of the upper and lower extremities as experienced by women during pregnancy, labour and the puerperium.

Midwives, as frontline workers in the delivery of maternity care [ 3 ] responsible for the lives of the mother and the baby, are accountable for providing competent and holistic care for women during pregnancy, labour and puerperium. As part of healthcare provision, midwives play an important role in ensuring that every woman, including women with mobility disabilities, receives the best maternity care during pregnancy, labour and puerperium. Moridi et al. [ 4 ] state that women with mobility disabilities are entitled to feel safe, respected and well cared for by midwives, who must be sufficiently prepared to care for these women.

According to the Global Population Report, [ 5 ] more than one billion people have some form of disability. Eswatini is classified as a middle-income setting in the southern African region, measuring 17 000 square kilometres with a population of 1 093 238. Of the population, 76.2% reside in rural areas (833 472), and 23.8% (259 766) reside in urban areas [ 6 ]. The economy is largely agricultural as most industries manufacture agricultural products [ 7 ]. Of the Eswatini population, 146 554 (13%) live with disabilities, with most being women (87 258; 16%), 22,871 (14.1%) and 26,270 (14.3%) of them reside in the Hhohho and Manzini regions respectively [ 8 ]. 15% (125 545) of people with disabilities live in rural areas, and 85% of the disabled population is unemployed [ 8 ], which means most of these individuals are economically disadvantaged. Furthermore, according to the Eswatini Central Statistics Office, 8 26.5% of people with disabilities have a mobility (walking) disability, with 63.5% of these being women.

Midwives may encounter difficulties while aiming to achieve excellence in providing maternity care to women with mobility disabilities in what may be challenging circumstances [ 9 ]. The WHO [ 10 ] claims people with disabilities do not receive the health services they need and are thus likely to find healthcare providers have inadequate skills. Lawler et al. [ 11 ] argue that ineffective interactions and poor communication with women needing care, particularly among health professionals engaged in providing maternity services, limit these women’s opportunities to participate in decision-making processes during pregnancy, childbirth, and postpartum care. According to the University of Johannesburg, [ 12 ] the midwife, together with the mother, have to engage collaboratively in order to come up with opportunities to promote health while removing any challenges that could impede the achievement thereof.

Walsh-Gallagher et al. [ 13 ] postulate that healthcare professionals tend to view women with disabilities as liabilities and regard them as high risk; they often exclude them from the individualised plan of care, which leads to an increase in these women’s fears about their maternity care. These challenges frequently result in health disparities and prevent women with mobility disabilities from receiving optimal maternity care. By exploring midwives’ experiences of this phenomenon, guidelines for support can be developed to extend available knowledge on maternity care for women with mobility disabilities during pregnancy, labour and puerperium.

Study design

The aim of the study was to explore and describe midwives’ experiences of caring for women with mobility disabilities during pregnancy, labour and puerperium in the Hhohho and Manzini regions of Eswatini. A qualitative, [ 14 ] exploratory, [ 15 ] descriptive, [ 16 ] contextual [ 17 ] research design with a phenomenological approach [ 18 ] was applied for this study to gain insight and understanding of the research phenomenon [ 19 ]. The phenomenon under study was midwives’ lived experiences caring for women with mobility disabilities during pregnancy, labour and puerperium. The participants were approached face-to-face to participate in the study. The researchers followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) to report on this qualitative study [ 20 ].

The setting for the study was the Hhohho and Manzini regions of Eswatini. The researcher collected data at the site where participants experienced the phenomenon, as emphasised by Yildiz, [ 21 ] within the context in which they were comfortable to be interviewed [ 22 ]. This setting included maternal health facilities in hospitals and public health units.

Population and sampling

The study’s population comprised midwives working in maternal health facilities in hospitals and public health units, that is, one referral hospital and one public health unit in the Hhohho region and two referral hospitals and one public health unit in the Manzini region of Eswatini. Purposive sampling was used to select midwives to participate in the study; [ 16 ] 12 midwives from both regions were included. The midwives were between the ages of 35 and 55, and all midwives were black in race and identified as females. The years of experience in the field ranged between 5 and 15 years. The criteria for inclusion were midwives who had provided maternity care to women with mobility disabilities during pregnancy, labour and puerperium for a period of not more than two to three years, willing to participate in the study. The sample size was determined by repetitions of key statements about the research phenomenon during data collection, termed data saturation [ 23 ]. None of the participants refused to participate in the study.

Table  1 summarises the participants’ demographic characteristics.

Data collection

In-depth phenomenological, face-to-face, individual interviews were conducted to collect data [ 17 ]. The researcher who was a Midwifery lecturer held a Master’s Degree in Maternal and Neonatal science at the time of the study requested approval from the Unit manager to seek permission from the midwives to take part in the study. The midwives were given an information letter which included objectives of the study and the reasons for conducting the study. After recruiting midwives and obtaining their written consent to participate in the study and permission to audio-record the interviews, the researcher set up appointments with them for the interviews, and the data collection process commenced. The central question posed to participants was: How was it for you to care for a woman with a mobility disability during pregnancy, labour and puerperium? A pilot of the tool was performed on the first participant who met the inclusion criteria and possessed the same characteristics as those of the study sample. The pre-testing question yielded positive results, the participant responded to the question asked and there was no need to rephrase it or further test it.

The interviews were conducted from March 2019 to July 2019 and lasted 30–45 min. The researcher conducted interviews until the data became redundant and repetitive, reflecting that saturation had been reached, in congruence with Fouché et al. [ 25 ] In addition, field notes were recorded in a notebook after each in-depth phenomenological interview. No repeat interviews were held. The researcher ensured bracketing by omitting any perceptions from her past experiences that were likely to influence her interpretation of the research findings.

Data analysis

Before data analysis commenced, data were organised in computer files after being transcribed and translated into narrative form. Data from each participant were coded and stored in the relevant file and kept in a safe place; only the researcher could access the information. Back-up copies were made of all the data, and the master copies were stored in a safe to which only the researcher had access.

Data collection and analysis occurred concurrently. The researcher was guided by Giorgi et al.’s [ 26 ] five-step method of data analysis. This entailed the researcher reading all the transcribed data and the entire ‘naïve description’ provided by the participants during the interviews. The demarcation of ‘meaning units’ within narratives followed. In addition, the researcher marked where meaning shifts occurred and transformed meaning units into descriptive expressions. The researcher laid out the general structure of midwives’ experiences. Moreover, an independent coder was provided with the raw data (after signing a confidentiality agreement) to analyse the findings. The researcher and independent coder analysed the data separately and met for a consensus discussion. Both agreed on all the units of analysis, with an inter-coder reliability of 100%.

Measures of trustworthiness

The research was informed by Guba and Lincoln’s [ 27 ] model in relation to credibility, transferability, dependability and confirmability. For credibility, the researcher ensured prolonged engagement in the field [ 28 ], peer debriefing, [ 29 ] member checking, and an external auditor was used [ 25 ]. The study was also presented at a national conference. Transferability refers to the ability to extend the findings of one’s study to comparable environments or participants, as stated by Pitney et al. [ 30 ] The researcher ensured the study’s transferability by providing a richly documented account and in-depth description of all aspects and processes of the study protocol. Data saturation also confirmed transferability [ 23 ]. Dependability is evident in a study when other researchers are able to follow the researcher’s decision trail [ 31 ]. The researcher ensured dependability by densely describing the research process in congruence with Fouché et al.’s [ 25 ] guidelines, so that other researchers can follow similar steps of the same research methodology. Confirmability occurs when the research is judged by the way in which the findings and conclusions achieve their aim and are not the result of the researcher’s prior assumptions and preconceptions [ 32 ]. The researcher ensured this by remaining true to the research process through reflexivity and not compromising the research process in any way [ 28 ]. In addition, the researcher engaged an independent coder and provided a chain of evidence of the entire research process to enable an audit. Therefore, all forms of collected data, including raw data, reflexive journals, [ 29 ] notes and transcriptions, were recorded.

Ethical clearance to conduct this study was obtained from the University of Johannesburg Faculty of Health Sciences Higher Degrees Committee (ref. no. HDC-01-50-2018), University of Johannesburg Faculty of Health Research Ethics Committee (ref. no. REC-01-82-2018), and the Eswatini National Health Research Review Board (ref. no. NHRRB982/2018). The researcher applied and adhered to the four principles to be considered when conducting research: autonomy, beneficence, non-maleficence and justice [ 33 ]. Autonomy was adhered to by affording the participants the right to choose to participate in the study and by signing a written informed consent form a week after it was given to them before the interviews commenced. Beneficence was ensured through doing good and doing no harm to participants by prioritising the participants’ interests above those of the researcher, and did not engage in any practice that jeopardised their rights. Non-maleficence was observed by eradicating any possible harmful risks in the study; the researcher ensured the safety of the participants by conducting interviews in a familiar, private environment where they felt free and safe from harm. Furthermore, justice was observed by treating all participants equally regardless of their biographical, social and economic status.

Three themes and categories emerged from the data analysis. Table  2 summarises the themes and categories of midwives’ lived experiences caring for women with mobility disabilities during pregnancy, labour and puerperium in Eswatini.

Theme 1: physical and emotional efforts required from midwives to provide maternity care to women with mobility disabilities

Category 1.1: midwives experienced that woman with mobility disabilities needed assistance getting onto the bed during labour and delivery.

According to the participants, caring for women with mobility disabilities weighed heavily on them physically as they were required to assist the women onto delivery beds, which were too high for the women to climb up on their own:

“The beds are too high, they need to be adjustable…unless you change her to another room, we only have one in the other room…but to be honest she delivered on the same high bed with the help…It’s uncomfortable even with me who is normal, how about someone who has a disability? Getting the woman onto the bed is also uncomfortable for us we end up having pain on our backs.” (M3) . “The challenge is that I couldn’t help her to climb on to the bed, because I needed someone to assist when she came for postnatal care as she was even carrying 3 babies, I didn’t know what to do…I eventually went out and asked for assistance from my colleague…” (M10) . “I believe that the equipment should accommodate the women with disability, however, ours is not accommodative to the women…there are no special delivery beds, specifically designed for them because in my opinion the beds have to be shorter so they can be able to get on to them easily…yes so that they can be able to climb on the beds” (M1) .

Category 1.2: midwives experienced challenges in manoeuvring women with mobility disabilities during labour

Midwives reported it was difficult to perform some procedures while progressing these women during labour and delivery. This situation called for some adjustment and improvisation on their part, and they were unsure if it was the right thing to do.

“Even though she was a bit uncomfortable and anxious because the leg was just straight and could not bend, I reassured her…She had to remove the artificial leg and remain with the stump. I placed her on the lithotomy position. With the other hand she had to hold on to the ankle of the normal foot, even though it was awkward and difficult to manoeuvre, she managed to deliver the baby.” (M1) . “Luckily for us, she didn’t sustain a tear and we were saved from suturing her cause we foresaw difficulties as how we could have done it as she couldn’t open her thighs well due to the disability…yes I had to get a partner to assist, since she couldn’t even open her thighs. She also couldn’t cooperate possibly because of the pain that is also more reason I asked for my colleague to assist.” (M6) . “…yes…let me make an example, in my case she had a fracture, even if the pelvis was gynaecoid, there were problems of finding the right position for her during delivery, when she had to push the baby out…” (M8) . “The one that I saw did not have one leg. She had come for her postnatal care. We assisted and her on the couch, with my colleague. Since she couldn’t keep her legs open, I asked my colleague to keep one of her legs open whilst I examined her.” (M12) .

Category 1.3: midwives experienced anxiety and the need to exercise patience when caring for women with mobility disabilities

The participants experienced an emotional and psychological burden when caring for women with mobility disabilities. They felt unqualified and foresaw difficulties that triggered anxiety, which led to them not knowing what to do and how to handle these women.

“It was during labour…the woman was limping the woman she was on crutches. The moment she came into the ward I am a human being I just felt sorry for her kutsi (as to) how is she going to take care of the baby, and the hand was somehow deformed.” (M3) . “At first its emotionally draining as an individual you cause you start sympathising…(other midwife chips in)…yes you even find yourself saying things just because you pity her, and in the process they get hurt.” (M6) . “It came as a shock and it was my first experience, it came as a shock as to how I was going to help her as even my experience was limited in that area.” (M7) . “As I was taking care of her it became necessary for me to put myself into her shoes and to bear with her considering her situation….When you see her for the first time you would pity her yet she is now used to it.” (M1) .

Theme 2: lack of equipment to meet the needs of women with mobility disabilities

Category 2.1: midwives reported a lack of special beds and infrastructure to meet the needs of women with mobility disabilities.

Midwives reported their frustration at the lack of sufficient equipment like special beds and examination tables, tailored for women with mobility disabilities. It was a challenge to provide maternity care for women without this equipment.

“I believe that the infrastructure and equipment should accommodate the women with mobility disability, however, ours is not accommodative to the women…Usually we don’t have the prenatal ward in the maternity, most women who come in the latent phase have to ambulate, or go to the waiting huts and come back when the labour pains are stronger…There are no special delivery beds, specifically designed for them because in my opinion the beds have to be shorter so they can be able to get on to them easily. We do not even have toilets meant for them.” (M1) . “I was anxious as to how was she going to push how to push cause we do not have the right beds when it was time for pushing I asked for assistance…” (M2) . “The challenge is that I couldn’t help her to climb on to the bed, because I needed someone to assist when she came for postnatal care…the beds need to be adjustable so that they are able to be pushed lower for the mother to move from wheel chair to the bed and we pull the bed up again to examine her.” (M11) .

Theme 3: challenges in providing holistic care to women with mobility disabilities during pregnancy, labour, and puerperium

Category 3.1: midwives reported a lack of guidelines and protocols in caring holistically for women with mobility disabilities.

Midwives emphasised a lack of guidelines, protocols and knowledge about caring holistically for women with mobility disabilities. This resulted in everyone making their own decisions and doing as they saw fit in caring for these women:

“I think during antenatal care they (the women with mobility disabilities) need to be prepared for labour cause for others the pain is extraordinary, apart from the pain threshold, they also face self-esteem issues, they are looked down upon…I only saw that she was disabled during assessment cause nothing was recorded on the antenatal care card.” (M2) . “I was not aware of the disability at first, I only discovered when she was pushing…she was admitted and progressed by another midwife, I only attended to her when she was pushing… there was nothing written on the nurse’s notes/ handover notes about her disability.” (M5) . “There is no normal practice for a woman with mobility disability when they come and they are in labour, I usually admit regardless of the stage of labour or dilatation…It is not a protocol, it’s a midwife’s prerogative.” (M1) . “We assess and come up with our own discretion even in terms of admitting them (women with mobility disability). Some midwives will admit them regardless of the stage of labour and disregard the protocol that women who come into labour have to ambulate if they are in the latent phase.” (M8) . “There is one that came the past 3 days she has 3 children now and we just scheduled her for c/section because we know that she has been having c/section since she started. Just from looking at the way she walked, we could tell that she couldn’t deliver normally.” (M9) .

Category 3.2: midwives experienced challenges in allowing significant others to support women with mobility disabilities during labour and delivery

Consequent to the challenges in providing holistic care to women with mobility disabilities, midwives experienced challenges in allowing significant others to support these women during labour and delivery.

“It can depend on the patients themselves, they should decide and we need to be flexible for it to happen…as you can see our labour room also has the issue of privacy…we would need to restructure cause we have beds for 5 or more women in labour room…and then bringing someone from outside could be tricky” (M6) . “Maybe…not sure though, that they can bring their relatives, but maybe, considering staffing limitation…also the issue of discrimination and privacy, they (the women with disabilities) might feel we discriminate against them because they are disabled we now treat them differently.” (M7) . “Maybe if she can (bring her relative) but that’s not necessary, because I can always ask my colleague to assist, unless there is no one…” (M12) .

Childbirth is a special experience that requires a personal connection between the midwife and the woman giving birth, characterised by successful communication and respect [ 34 ]. However, the themes identified in the study indicated that midwives experienced challenges caring for women with mobility disabilities during pregnancy, labour and puerperium based on their limited capacity and preparedness, and lack of protocols to care for these women. They also reported a lack of supportive equipment for women with mobility disabilities. This posed a challenge for them in attending to these women’s specific needs, and they did not always know how to handle the situation appropriately.

One of the themes centred on midwives’ experiences of the physical and emotional efforts required of them to provide maternity care to women with mobility disabilities. They explained women with mobility disabilities required assistance getting onto the bed during labour and delivery, and more manoeuvring was expected of them (as midwives) as they had to adjust their performance and some procedures. The midwives also reported challenges in providing holistic care to women with mobility disabilities during pregnancy, labour and puerperium. Konig-Bachmann et al. [ 35 ] reiterate that caring for women with disabilities requires a level of flexibility, adaptation beyond routine procedures, and demands a high degree of improvisation from healthcare providers to ensure high-quality care. Morrison et al. [ 36 ] also found that healthcare providers reported difficulties with equipment when providing healthcare for women with physical disabilities; particularly the beds being too high for them to access. Smeltzer et al. [ 37 ] similarly allude to the importance of educating and training clinicians to equip them with knowledge and technical skills to provide more effective care to women with physical disabilities.

The midwives also shared that labour and deliveries were further complicated by some women with mobility disabilities not being able to cooperate due to the pain they experienced; others could not change position due to their disability. In a study by Sonalkar et al., [ 38 ] healthcare providers described the gynaecologic examination as challenging to complete as it required patience and the ability to be adaptable to different methods and positioning. Similarly, Konig-Bachmann et al. [ 35 ] indicate that in order to provide high-quality care for women with disabilities, healthcare providers need to exercise strong flexibility, adapt beyond routine procedures, and engage in a high degree of improvisation. Byrnes and Hickey [ 39 ] concur with this study’s findings and state that due to mobility restrictions, it may be difficult to assess the fundal height and foetal growth in women with physical disabilities.

Some midwives reported their caregiving role was emotionally draining as they felt sorry and pitied the women with mobility disabilities; thus, they needed to show compassion and reassure them. According to Mgwili et al., [ 40 ] psychoanalytic thinkers associate pity among staff members upon first contact with a physically disabled person as being instigated by personal feelings, stimulated by the disability. The midwives in this study stated they needed to be more patient and adjust their approach to caring for these women. Tarasoff [ 41 ] and Schildberger et al. [ 42 ] reiterated that healthcare providers seemed uncomfortable with women’s disability, consequently failing to offer needed support. According to Sonalkar et al., [ 38 ] healthcare providers reported there would be less fear and concern about hurting women with disabilities if midwives had increased training. Similarly, Mitra et al. [ 43 ] mentioned that healthcare providers had a general lack of confidence in their ability to provide adequate maternity care for women with physical disabilities.

Another theme was midwives’ challenges in providing competent and quality care for women with mobility disabilities due to a lack of equipment, including special beds and examination tables to meet these women’s needs. The examination, labour and delivery beds were too high and could not be adjusted for the women to get on by themselves, or even with the assistance of a midwife. In addition, the midwives reported there was no prenatal ward or waiting huts where they could place these women during the latent phase of labour. The midwives further emphasised there were no special toilets for women with mobility disabilities, which made it hazardous and difficult for them. Mitra et al. [ 43 ] concur on the barriers to providing maternity care to women with physical disabilities presented from health professionals’ perspectives. The authors indicated that participants from their study reported inaccessible equipment, including examination tables, as a barrier, making it more difficult and time-consuming to care for women with physical disabilities. In addition, Sonalkar et al. [ 38 ] said healthcare providers shared their concern about the lack of adjustable examination tables and transfer equipment, thus presenting a barrier to equitable care for women with disabilities.

Midwives further reported a lack of guidelines and protocols. This resulted in everyone making their own decisions and doing as they saw fit in caring for these women, and, in most instances, not recording the disability at all during antenatal care and admission into labour records. They often only discovered that the woman had a mobility disability at a later stage, when they were in labour. Sonalkar et al. [ 38 ] reported that healthcare providers felt frustrated and overwhelmed by the uncertainty of whether they made the correct decisions when caring for women with physical disabilities due to the lack of guidelines forcing them to use their own judgement. Mitra et al. [ 43 ] determined that most healthcare providers reported a lack of maternity practice guidelines for women with physical disabilities. Also, healthcare providers highlighted the importance of learning about disabilities and having a better understanding of a condition, particularly if it is likely to be exacerbated during pregnancy [ 44 ]. The need to make and read the notes on these women’s antenatal care cards or reports was emphasised.

Due to the lack of clear guidelines and protocols in caring for women with mobility disabilities, the midwives reported they sometimes admitted the woman into the labour ward regardless of the stage of labour, while other midwives did not and wanted them to walk around and come back for admission once they are in the active phase of labour. Furthermore, the midwives explained they often referred these women for caesarean sections right away, regardless of whether the woman could deliver normally due to mere panic from just seeing the disability or based on a previous record of surgery. Smeltzer et al. [ 45 ] researched obstetric clinicians’ experiences and educational preparation in caring for pregnant women with physical disabilities, and they agree on the lack of knowledge among health professionals caring for women with mobility disability.

Devkota et al. [ 46 ] also agree regarding midwives’ inefficiency in providing quality care for women with mobility disabilities. They claim healthcare providers often struggle to understand women with disabilities’ needs as they are not formally trained to provide services to this population. These healthcare providers were found to be undertrained in specific skills that would equip them to provide better and more targeted services for women with disabilities.

Consequent to the challenges in providing holistic care to women with mobility disabilities during pregnancy, labour and puerperium, midwives experienced challenges in allowing significant others to support these women. They reported that as much as they needed assistance caring for these women, and as much as the women would prefer to have their family members or significant others assisting them, this is not possible due to the lack of privacy, especially in public health facilities. Walsh-Gallager et al.’s [ 13 ] study on the ambiguity of disabled women’s experiences of pregnancy, childbirth and motherhood resonate with this study’s findings. The authors reported that women with disabilities’ partners were denied access or had their visits curtailed on several occasions due to inflexible hospital visiting policies. Redshaw et al. [ 47 ] reiterated the same in their study; disabled women were less likely to say their companion or partner was welcome to visit, let alone provide any form of assistance. In addition, a study by Bassoumah and Mohammad [ 48 ] reported that women with disabilities were denied their spouses’ support while receiving maternity care. Byrnes and Hickey [ 39 ] also concur that every effort should be made to allow women with disabilities who are in labour to receive support from significant others, and they should be active partners in the labour process.

Limitations

The study was limited to two of the four regions of Eswatini, namely Hhohho and Manzini; hence, the results could not be generalised for the whole country. The study also only focused on mobility disabilities due to time constraints and limited funds. Future research could be conducted to cover all other forms of disabilities.

This study focused on midwives’ lived experiences caring for women with mobility disabilities during pregnancy, labour and puerperium in Eswatini. In-depth phenomenological interviews were conducted, the findings were analysed, and themes were established. The findings illustrate that midwives experienced challenges caring for women with mobility disabilities during pregnancy, labour and puerperium in Eswatini. There is a need to develop and implement guidelines to empower midwives with knowledge and skill to provide support and holistic maternity care, and enact protocols. They should also have access to appropriate equipment and infrastructure specifically tailored towards promoting optimal health for women with mobility disabilities.

Data availability

The data analysed is available from the corresponding author upon reasonable request.

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Acknowledgements

The authors would like to acknowledge the midwives in the Hhohho and Manzini regions of Eswatini who participated in the study and provided their own experiences of providing maternity care to women with mobility disabilities during pregnancy, labour and puerperium.

The research received funding from the University of Johannesburg Postgraduate Supervisor-linked Bursary.

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F.N.M conducted the research and wrote the manuscript. A.M.T supervised, reviewed, and finalised the manuscript. A.G.W.N co-supervised the study and edited the manuscript for final submission.

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Ethical clearance to conduct this study was obtained from the University of Johannesburg Faculty of Health Sciences Higher Degrees Committee (ref. no. HDC-01-50-2018), University of Johannesburg Faculty of Health Research Ethics Committee (ref. no. REC-01-82-2018) and the Eswatini National Health Research Review Board (ref. no. NHRRB982/2018). Participation in this study was voluntary, and informed consent was obtained from participants before the interviews commenced.

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Temane, A.M., Magagula, F.N. & Nolte, A.G.W. Midwives’ lived experiences of caring for women with mobility disabilities during pregnancy, labour and puerperium in Eswatini: a qualitative study. BMC Women's Health 24 , 207 (2024). https://doi.org/10.1186/s12905-024-03032-z

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The effect of a midwifery continuity of care program on clinical competence of midwifery students and delivery outcomes: a mixed-methods protocol

• Fatemeh Razavinia   ORCID: orcid.org/0000-0002-6827-509X 1 , 2 ,
• Parvin Abedi   ORCID: orcid.org/0000-0002-6980-0693 3 ,
• Mina Iravani   ORCID: orcid.org/0000-0002-8854-1738 4 ,
• Eesa Mohammadi   ORCID: orcid.org/0000-0001-6169-9829 5 ,
• Bahman Cheraghian   ORCID: orcid.org/0000-0001-5446-6998 6 ,
• Shayesteh Jahanfar   ORCID: orcid.org/0000-0001-6149-1067 7 &
• Mahin Najafian   ORCID: orcid.org/0000-0002-6649-3931 8  

BMC Medical Education volume  24 , Article number:  338 ( 2024 ) Cite this article

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The midwifery continuity of care model is one of the care models that have not been evaluated well in some countries including Iran. We aimed to assess the effect of a program based on this model on the clinical competence of midwifery students and delivery outcomes in Ahvaz, Iran.

This sequential embedded mixed-methods study will include a quantitative and a qualitative phase. In the first stage, based on the Iranian midwifery curriculum and review of seminal midwifery texts, a questionnaire will be developed to assess midwifery students’ clinical competence. Then, in the second stage, the quantitative phase (randomized clinical trial) will be conducted to see the effect of continuity of care provided by students on maternal and neonatal outcomes. In the third stage, a qualitative study (conventional content analysis) will be carried out to investigate the students’ and mothers’ perception of continuity of care. Finally, the results of the quantitative and qualitative phases will be integrated.

According to the nature of the study, the findings of this research can be effectively used in providing conventional midwifery services in public centers and in midwifery education.

Trial registration

This study was approved by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences (IR.AJUMS.REC.1401.460). Also, the study protocol was registered in the Iranian Registry for Randomized Controlled Trials (IRCT20221227056938N1).

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Providing quality services to pregnant women has been recommended to all countries to achieve the Millennium Development Goals (MDGs) (Goals 3, 4 and 5) [ 1 ]. There are different care methods to maintain maternal and neonatal health during pregnancy and postpartum [ 1 ]. One of these care models is continuity of care that can be provided by a midwife or an obstetrician.

Midwifery continuity of care is a relationship-based care provided by a midwife who can be supported by one to three more midwives. They provide planned care for a woman during pregnancy, labor, birth, and the early postpartum period up to 6 weeks after delivery [ 2 ].

Continuity of midwifery care has become a global effort to enable women to have access to high-quality maternity care and delivery services [ 3 ]. As a result, many service providers today are transitioning to a continuous care model [ 4 ], and they have considered continuous care to be necessary for realizing women's rights [ 5 ]. Also, continuous midwifery care is known as the gold standard in maternity care to achieve excellent results for women [ 5 , 6 ]. In order to strengthen midwifery services to achieve global health goals in 2015, the World Health Organization (WHO) proposed a midwife-led continuous care model [ 7 ].

Countries use different midwifery care models. In Iran, for example, primary health services that are specific to pregnant mothers are provided in public health centers by midwives working in the network system and in compliance with the level of services and the referral system [ 8 ].

In general, midwifery continuous care not only has an important impact on a wide range of health and clinical outcomes for mothers and neonates but also brings about economic consequences for the health system [ 2 , 9 ]. This care model is useful for healthcare professionals as well [ 10 ], and it has improved the job satisfaction of midwives [ 11 ]. The midwife is the main guide in planning, organizing and providing care to a woman from the beginning of pregnancy to the postpartum period [ 12 ]. In 2011, in order to increase job motivation and satisfaction, promote retention of the midwifery workforce [ 13 ], and alleviate the shortage of workforce at the international level [ 14 ], the Nursing and Midwifery Advisory Center recommended using midwifery students (at the bedside and to perform midwifery work) to overcome this problem.

Providing high quality care requires enhancing the clinical competence of the professionals [ 4 ]. There is a close relationship between the concept of patient care quality and clinical competence. Therefore, clinical competence is of unique importance in midwifery practice [ 15 ]. As a result, in order to achieve quality patient care, midwifery professionals need to train students to become workforce with clinical competence in order to provide quality care in the health system. WHO defined clinical competence as a level of performance that demonstrates the effective application of knowledge, skills, and judgment [ 16 ].

A previous study showed that clinical competence of midwives plays an important role in managing the process of providing care, achieving care goals, and improving the quality of midwifery services [ 17 ]. In other words, the graduates of this field must have an acceptable level of clinical and professional skills in performing midwifery duties so that the health of mothers, children, and ultimately the community can be improved.

In Iran, prenatal care and the care during labor, delivery and postpartum are not continuous, and a new health provider may take the responsibility of care at any stage. This fragmented care may negatively affect the pregnancy outcomes and increase the rate of cesarean section [ 18 ]. Furthermore, the results of some studies in Iran indicate that the clinical competence obtained by midwifery students is far from optimal and that they do not acquire the necessary skills and abilities at the end of their studies [ 19 ]. Farrokhi et al. showed that the performance quality of 70% of midwives is average, and only 18.5% of them have good quality performance [ 20 ]. Several factors play a role in acquiring, maintaining and improving clinical competence [ 21 ]. There are a number of solutions that can increase the clinical competence of midwifery students, and one is the use of different care models such as the continuity of care model. The continuity of care model allows students to develop their midwifery knowledge, skills, and values individually [ 22 ]. Despite the strong foundation of midwifery in Iran, midwifery care models have not yet been tested. Some studies have reported that the quality of services provided during pregnancy, delivery and after delivery in Iran is poor to moderate. Also, these studies emphasize the necessity of a paradigm shift for better quality care and greater satisfaction of mothers, and they consider lack of continuity of care as the reason for the increase in unnecessary cesarean sections [ 23 , 24 , 25 ]. Moreover, the lack of qualified and experienced workforce has led to low quality health services, including midwifery care, and an increase in the economic burden of health. In Iran, no study has yet been conducted to investigate the effect of the midwifery continuity of care model on the students’ clinical competence and pregnancy outcomes. Given the importance of this topic, using a mixed-methods study design, we aimed to assess the effect of a midwifery continuity of care program on the clinical competence of midwifery students and pregnancy outcomes in Ahvaz, Iran.

Specific objectives

To determine the effect of midwifery continuity of care program on the clinical competence of midwifery students.

To determine the effect of a midwifery continuity of care program provided by midwifery students on pregnancy outcomes.

To explain the perception of midwifery students and mothers about the use of the midwifery continuity of care program provided by midwifery students.

Methods/design

Study design.

This sequential embedded mixed-methods study will include a quantitative phase and a qualitative one. A mixed (embedded) experimental design involves the collection and analysis of quantitative and qualitative data by the researcher and the integration of the information into an experimental study or intervention trial. This design adds qualitative data to an experiment or intervention to integrate the personal experience of research participants. Therefore, the qualitative data are converted into a secondary source of data embedded before and after the test. Qualitative data is added to the experiment in differrent ways, including: before the experiment, during the experiment, or after the experiment [ 26 , 27 ]. Embedded mixed-methods studies that are qualitative followed by quantitative are used to understand the rationale for the results and receive feedback from participants (to confirm and support the findings of the quantitative studies) [ 27 ]. In the first stage of this study, a questionnaire for assessing midwifery students’ clinical competence will be created based on the midwifery curriculum of Iran and a review of seminal texts of midwifery. Then, the effect of continuity of care provided by midwifery students on maternal and neonatal outcomes will be assessed in a randomized clinical trial. In the third stage, a qualitative study will be carried out to investigate the perception of students and mothers. Finally, the results of the quantitative and qualitative phases will be integrated (Fig.  1 ).

Sequential and embedded mixed-methods design

First stage: questionnaire development

This questionnaire will be developed based on midwifery curriculum and a comprehensive and systematic search (with no time limit) in English and Persian databases (Web of Science, Embase, Scopus, ProQuest, Google scholar, Magiran, SID).

Tool design

There are four steps in tool development:

Choosing a conceptual model to show aspects of clinical competence in the measurement process

Explaining the purpose of the tool

Designing the route map

Developing the tool (use of methods, classification of objects, rules and procedures for scoring tools) [ 28 ].

Answer to the objects

A 1 to 4-point Likert scale will be used for scoring [ 29 ].

Content validity

To ensure the selection of the most important and correct content (necessity of the case), the content validity will be assessed. Also, to ensure that the instrument items are designed in the best way to measure the content, the content validity index will be calculated [ 30 ].

Reliability

Reliability will be evaluated using internal consistency (Cronbach's alpha coefficient ≥ 0.7) and stability (test-re-test ≥ 0.74) by piloting the questionnaire on 20 midwifery students [ 31 ].

Second stage: quantitative phase

A randomized controlled clinical trial will be conducted in this phase of research to examine the effect of the continuous care program of midwifery students on their clinical competence and pregnancy outcomes.

Sample size

According to the study objective and previous study results [ 32 ] with α = 0.01, β = 0.1, p 1  = 0.51 and p 2  = 0.021, the sample size will be n  = 23. Considering a 20% dropout rate, the final sample size will be 58 women (29 women in each group).

Data collection

This phase of the randomized clinical trial will be conducted with the participation of 58 undergraduate midwifery students at their 7th and 8th semesters. The students will be divided randomly to intervention (continuous care) and control (routine care) groups providing care to 58 pregnant women in six health centers and two hospitals (Sina and Razi) in Ahvaz city, southwest of Iran.

The study will begin after receiving the approval of the Ethics Committee of Ahvaz University of Medical Sciences and registering the study in the Iranian Registry for Randomized Clinical Trials. Inclusion criteria will be willingness to participate in the study.

Randomization

To implement the intervention, the students will be divided into two intervention (providing continuous care for pregnant women) and control (providing standard care for pregnant women) groups. Allocating students will be done using permuted block randomization technique with a block size of four and an allocation ratio of 1:1. Five blocks of 4 pieces and 3 blocks of 3 pieces will be extracted randomly using WIN PEPI software. In each block of 4, 2 students will be in control and 2 will be in intervention group. Also, in each block of 3 students, 1 student will be in control and 2 will be in intervention group, and the arrangement of each person is random. To prevent contamination, first the control group will provide routine care, and then the intervention group will conduct continuity of care for pregnant women. Mothers are randomly selected based on the hospital where they will give birth. As a result, Razi Hospital will be the control group and Sina Hospital will be the intervention group.

Intervention

Women who meet the inclusion criteria will be recruited in the study using a non-probability convenience sampling method. Women in the intervention group will be included in the study after their first pregnancy visit (6–10 weeks of gestation) and will receive continuous care by midwifery students. Women in the control group will receive the usual and routine care, and will be included in the study at the time of delivery. They will have a gestational age of more than 37 weeks based on the inclusion criteria of the study. Their delivery will be performed by midwifery students who will follow them up until six weeks after delivery.

At first, the necessary training will be given by the lead researcher (FR) to the students in orientation sessions held for both groups separately. In the intervention group, each midwifery student as the main midwife will be responsible for taking care of two or three pregnant women and will be the back-up midwife for two other pregnant women (under the supervision of other students). The lead researcher will create a group in WhatsApp with the participation of students in the intervention group, and they can communicate with each other and the researcher. Also, the midwifery students will be directly and indirectly under the supervision of a qualified person (lead researcher). Another WhatsApp group will be created for the women of the intervention and control groups (to facilitate communication between the researcher and the women). Two midwifery students will be introduced to each pregnant woman in the intervention group (as a main midwife and a backup midwife). If the main midwife is not available, the woman will be in contact with the backup midwife. The backup student will meet the woman at least once and will be introduced to her.

Instruments

All students and pregnant women participating in this study will complete a demographic questionnaire. A checklist will be provided for collecting data during prenatal care, labor, and delivery.

Also, the midwifery students will complete the clinical competency questionnaire at the beginning and end of the study.

Care will be provided and recorded by the main student according to the pregnancy care protocol. Also, danger signs will be taught to the students according to the national protocol, and emergencies will be handled by the midwifery student under the supervision of the lead researcher. Admission to hospital will be arranged by the student, and all information will be recorded. Pregnancy, labour and delivery, postpartum, and newborn checklist will be completed. Students will complete a demographic and obstetric questionnaire that includes questions about age, education, occupation, gravidity, parity, abortions, live and dead children, last contraceptive method, intended and unintended pregnancies, last menstrual period (LMP), gestational age, date of birth, body mass index (BMI), previous pregnancy and childbirth records, high-risk behavior of the mother and father, current history of special care, test and ultrasound results, and participation in childbirth preparation class. Also, the following data will be recorded in the labor and delivery and post-partum checklist: checking the conditions of labor according to the partograph, length of labor, need for induction and the method used type of delivery, examination of perineal trauma, postpartum bleeding, and examination of the condition of the mother up to 6 weeks after delivery. In addition, the amount of bleeding will be checked visually and by measuring the level of hemoglobin and hematocrit. Apgar score of the newborn will be recorded (in infant checklist) in minutes 1 and 5. Also, the newborn’s hospitalization status, breastfeeding and anthropometric indices will be recorded.

The students in the intervention group will start prenatal care < 20 weeks of gestation. At least five round of prenatal care will be provided by each student according to national guidelines for each pregnant woman. Pregnant women can communicate with their in-charge students in non-emergency cases from 8:00 a.m. to 23:00 p.m. and in emergency cases 24 h a day, all days a week. All reports will be recorded by the students. During labor and delivery, the student and the lead researcher will be present at the mother's bedside. In case of natural vaginal delivery (NVD), delivery will be done by a student midwife under the supervision of the researcher. In case of cesarean delivery (CS), a student will be present at the patient's bedside. Postpartum care will be provided by midwifery students in both groups (intervention and control). Each student will be at the mother's bedside for two hours after delivery. The conditions of labor, delivery, and the neonate will be recorded by the student in the relevant form. Also, the mother will be followed up by telephone for up to 6 weeks after delivery (postpartum). The clinical competency questionnaire will be completed by students before and after the intervention.

Inclusion criteria

Inclusion criteria for midwifery students will be: studying at the seventh and eighth semester and willingness to participate in the study.

Inclusion criteria for service recipients (pregnant women) will be: age 18 – 40 years, Iranian nationality, singleton pregnancy, low risk pregnancy, and gestational age < 20 wks.

Exclusion criteria

Exclusion criteria will be: history of psychiatric disorders, previous caesarean section, use of alcohol and tobacco, or having a disease that requires prenatal care by a specialist.

Primary outcome

Clinical competence of midwifery students.

Secondary outcome

Mode of delivery, length of labor stages, the need to induction, postpartum bleeding first and fifth minute Apgar score, admission of neonate to the neonatal intensive care unit, breastfeeding initiation, and exclusive breastfeeding up to 6 weeks postpartum.

Data analysis

Statistical analyses will be done using SPSS version 26.0 (SPSS, Inc., Chicago, IL, USA). The independent t-test and Chi-square tests will be used for continuous data and categorical data, respectively. ANCOVA test will be used to eliminate the influence of confounding variables. The effect size will be calculated. A 95% confidence interval (CI) and p values will be reported. P -values less than 0.5 will be considered statistically significant.

Third step of research: qualitative study

This phase will be a qualitative study using conventional content analysis.

Purposeful sampling will be used in this study [ 33 ]. Sampling will continue until data saturation [ 34 ], i.e., no new information or data about a class or relationships between classes is revealed.

This phase of the study is a conventional qualitative content analysis [ 35 ] aimed at examining the perceptions of midwifery students and mothers receiving continuous care. The researcher will conduct in-depth, semi-structured interviews with open-ended questions with students and mothers in the group of the continuous care program. All interviews will be done by the lead researcher who is qualified in qualitative research method. The interview will start with a general and open question such as: “Please tell me about your experiences or feelings about participating in the continuous midwifery care program. How did you feel about participating in this program?” Then, in-depth exploratory questions will be asked based on their answers (e.g., what do you mean? Why? Can you elaborate on that? Can you give me an example so I can understand what you mean?). All interviews will be recorded with the participants' consent. Paralinguistic features, such as mood and features of the participants, including tone of voice, facial expressions, and their posture, will be recorded by the researcher during the interview [ 35 ].

The data will be analyzed based on Granheim and Lundman's 2004 content analysis approach [ 36 ].

Interviews will be transcribed at the end of each interview. Data analysis begins with a careful study of all data so that the researcher can immerse herself in the data and gain an overview. Interviews will be transcribed verbatim. Key concepts will be highlighted and codes will be extracted. Then the first interpretations will be made and analyzed. Labels emerge for codes that represent more than one key concept and are usually taken directly from the text and become the initial coding map. Then the codes are placed in the category based on their similarity. Then, definitions will be created for each category, subcategory and code. When reporting findings, examples of each code and data category will be provided [ 35 ].

Inclusion criteria for midwifery students will be: studying at the seventh or eighth semester, willingness to participate in the study.

Inclusion criteria for service recipients (pregnant women) will be: receiving continuous care provided by the student, willingness to participate in the study, and being able to communicate.

The qualitative study and interview data will be analyzed based on the content analysis approach of Granheim and Lundman 2004 [ 36 ] as follows:

Reading and re-reading the interviews after completion of each interview

Selection of the unit of analysis

Determination of semantic units

Classification

Extraction of information content

In the first step, the data is converted into text format. As soon as possible after the interview, the interview will be typed verbatim. Then the whole text will be read several times to get a general understanding of the content of interview. Each meaning unit will be converted into condensed meaning units and then coded. The Codes will be classified into subcategories and categories based on their common characteristics. Finally, the content of the categories will be revealed, taking into account their hidden meaning [ 36 ].

Trustworthiness

Five criteria of will be used to increase data trustworthiness according to Lincoln & Guba [ 37 ]. These include: 1. Credibility, 2. Dependability, 3. Confirmability, 4. Transferability, 5. Authenticity.

Credibility of the data will be ensured by continuous engagement of the researchers with the subject, member checks, and external checks. Dependability will be ensured by relying on the insight of external observers. In order to increase the confirmability, data will be accurately recorded and reported. Also, transferability will be ensured by presenting the research process accurately, clearly and purposefully, which includes purposive sampling and presenting the research results to a number of people with the same profile of the participants who did not participate in the research. Finally, authenticity will be guaranteed by continuous reflection on information, long-term presence of the researcher, interview recording, writing, and reporting of findings.

Combining qualitative and quantitative phases

Data combination will be done using data integration strategies. The integration or combination of data starts from quantitative data analysis. Then qualitative data is collected by interview. In fact, the qualitative study is a secondary source of embedded data in the collection of experimental test data (continuous care) after the quantitative study. In this research, in order to understand the results of the RCT, the views of the participants will be unified in order to get a correct understanding of the intervention (implementation of the continuity of care model by the students) from the mothers' and students' point of view (Fig.  2 ).

Study diagram

Study status

The development of the evaluation tools was made. Also, sampling the quantitative phase of the study and the basic of the program are in process (Table 1 ).

This is the first mixed-methods study to be conducted in Iran investigating the effect of a midwifery continuity of care program on clinical competence of midwifery students and pregnancy outcomes. According to the recommendations of the WHO, midwifery continuity of care should be adopted in order to increase the quality of pregnancy care as well as the satisfaction of pregnant women and service providers [ 7 ]. Contrary to the recommendation of WHO, the continuous care program is neither implemented in Iran's health system nor included in the midwifery curriculum. The results of this study can help health planners and policy makers to implement high quality midwifery care program based on global recommendations.

The study has several strengths. The use of a mixed-methods study design (combination of quantitative and qualitative approaches) in contrast to the separate use of quantitative and qualitative studies provides a better understanding of the research questions [ 38 ]. In embedded design, one type of data collection (quantitative or qualitative) plays a supporting and essential role for another type. As a result, the embedded mixed-methods technique in the qualitative phase after designing the intervention will be used to receive feedback from the participants to confirm and support the findings of quantitative phase [ 39 ]. Also, interviews with mothers and midwifery students in the intervention group can reflect their positive and negative experiences of this program. Considering that Iran's healthcare system lacks continuous midwifery care, the findings of this research can be effectively used in providing conventional midwifery services in public centers and in midwifery education.

Considering that this care model will be implemented for the first time in Iran's midwifery education and healthcare system, there may be two possible limitations in this study: lack of infrastructure and interference with other educational programs.

Availability of data and materials

All the data that will be obtained will be published in the next article after the implementation of the study.

Abbreviations

Body mass index

Cesarean section

Last menstrual period

Millennium Development Goals

Natural vaginal delivery

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The study was funded by Ahvaz Jundishapur University of Medical Sciences.

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Midwifery Department, Reproductive Health Promotion Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Fatemeh Razavinia

Midwifery Department, Menopause Andropause Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Midwifery Department, Menopause Andropause Research Center, Ahvaz Jundisahpur University of Medical Sciences, Golestan BLvd, Ahvaz, Iran

Parvin Abedi

Reproductive Health Promotion Research Center, Midwifery Department, Nursing and Midwifery School, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Mina Iravani

Department of Nursing, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran

Eesa Mohammadi

Alimentary Tract Research Center, Clinical Sciences Research Institute, Department of Biostatistics and Epidemiology, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Bahman Cheraghian

MPH Program, Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, USA

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FR, PA, MI, EM, BCh, ShJ and MN conceptualized the study. FR will collect the data. FR drafted the protocol. PA revised the manuscript. The authors read and approved the final manuscript.

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Razavinia, F., Abedi, P., Iravani, M. et al. The effect of a midwifery continuity of care program on clinical competence of midwifery students and delivery outcomes: a mixed-methods protocol. BMC Med Educ 24 , 338 (2024). https://doi.org/10.1186/s12909-024-05321-5

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Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences

Authors of this article:

Original Paper

• Masaru Kamba, PhD   ; 
• Wan Jou She, PhD   ; 
• Kiki Ferawati, MStat   ; 
• Shoko Wakamiya, PhD   ; 
• Eiji Aramaki, PhD  

Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan

Corresponding Author:

Eiji Aramaki, PhD

Division of Information Science

Graduate School of Science and Technology

Nara Institute of Science and Technology

8916-5 Takayama-cho

Ikoma, 630-0192

Phone: 81 0743 72 5250

Email: [email protected]

Background: Despite being a pandemic, the impact of the spread of COVID-19 extends beyond public health, influencing areas such as the economy, education, work style, and social relationships. Research studies that document public opinions and estimate the long-term potential impact after the pandemic can be of value to the field.

Objective: This study aims to uncover and track concerns in Japan throughout the COVID-19 pandemic by analyzing Japanese individuals’ self-disclosure of disruptions to their life plans on social media. This approach offers alternative evidence for identifying concerns that may require further attention for individuals living in Japan.

Methods: We extracted 300,778 tweets using the query phrase Corona-no-sei (“due to COVID-19,” “because of COVID-19,” or “considering COVID-19”), enabling us to identify the activities and life plans disrupted by the pandemic. The correlation between the number of tweets and COVID-19 cases was analyzed, along with an examination of frequently co-occurring words.

Results: The top 20 nouns, verbs, and noun plus verb pairs co-occurring with Corona no-sei were extracted. The top 5 keywords were graduation ceremony , cancel , school , work , and event . The top 5 verbs were disappear , go , rest , can go , and end . Our findings indicate that education emerged as the top concern when the Japanese government announced the first state of emergency. We also observed a sudden surge in anxiety about material shortages such as toilet paper. As the pandemic persisted and more states of emergency were declared, we noticed a shift toward long-term concerns, including careers, social relationships, and education.

Conclusions: Our study incorporated machine learning techniques for disease monitoring through the use of tweet data, allowing the identification of underlying concerns (eg, disrupted education and work conditions) throughout the 3 stages of Japanese government emergency announcements. The comparison with COVID-19 case numbers provides valuable insights into the short- and long-term societal impacts, emphasizing the importance of considering citizens’ perspectives in policy-making and supporting those affected by the pandemic, particularly in the context of Japanese government decision-making.

Introduction

The spread of COVID-19 has become a global pandemic, significantly affecting social and economic sectors worldwide [ 1 ]. In the early stages of the pandemic, health authorities recommended social distancing to control the spread of the virus, reduce cases, and avoid overwhelming health care facilities [ 2 - 4 ]. Each country had its own strategy for dealing with COVID-19. A survey conducted across 6 countries illustrated the public’s perception of measures taken in response to COVID-19 [ 5 ]. Other surveys have been conducted in the United Kingdom and European countries to aid interdisciplinary research on public health, particularly regarding COVID-19 [ 6 ]. Different results were observed owing to social distancing policies, which affected several aspects of life, including economic activities [ 7 ] and consumer behavior, such as drops in mobility [ 8 ]. Concerns about cybersecurity risks were also raised, as companies might not have been prepared for adequate work-from-home options for employees [ 9 ]. The association between implementing some mitigation policies in response to COVID-19 and the outcomes regarding public mobility were noted [ 10 ], one of which was also observed in Japan.

After the government confirmed the first COVID-19 case in Japan on January 16, 2020, the number of cases quickly escalated within 3 months, leading to the declaration of a state of emergency to prevent the further spread of the infection. This measure significantly impacted the daily routines and social lives of Japanese residents, forcing them to refrain from going out, close schools, work from home, and be restricted from visiting crowded locations such as department stores and movie theaters. The first state of emergency effectively reduced the number of COVID-19 cases [ 11 ], albeit at a high cost to public mental well-being, education quality, and the economy. Furthermore, the number of cases quickly bounced back, peaking at 1762 new daily cases after the state of emergency was lifted, an increase from the peak of 701 new daily cases during the first wave [ 11 ]. These numbers suggest that the government was confronted with the dilemma of mitigating the social and economic impact of the lockdown and stopping the spread of COVID-19 [ 12 ]. Due to the fluctuations in COVID-19 cases, the government declared other states of emergency, recognizing the profound and deeply rooted impact the COVID-19 lockdown could have on societal and economic levels.

There have been various investigations into the states of emergency. For instance, studies have predicted SARS-CoV-2 infections using state-space models [ 13 ] and examined their impact on mental health [ 14 ]. In the aspect of mobility, studies have shown the suppression of social activities of the masses [ 15 ]. The tourism industry was among the hardest hit sectors, and the arrival of visitors decreased by 93% by March 2020 [ 16 ]. Statistics also show that Japan’s gross domestic product in 2020 decreased by 4.28%, indicating a substantial impact on the economy [ 17 ]. Interestingly, the unemployment rate only slightly increased to 2.8% in 2020, but started declining by 2022 (2.64%), following the gradual recovery of the gross domestic product (2.14% growth by 2021 and 1.03% by 2022) [ 17 , 18 ]. This trend of recovery indicates the strong resilience of the Japanese economy.

Furthermore, it also changed people’s behavior, such as following the advisory to stay at home, as confirmed by cell phone location data [ 19 - 21 ]. Such large-scale societal and behavioral changes warrant further investigation through various means to offer a chance to monitor and reflect the short- and long-term impacts of COVID-19 in the future.

Literature Review

The disruption caused by pandemic-related restrictions may be seen as a failure to perform planned activities, but detecting such disruptions was challenging. For example, it is difficult to obtain behavioral data on trips that individuals could not take or events they could not attend owing to the restriction. Social media, which people use to share their activities, proved to be a great source of information in such cases. Twitter (currently X) is a widely used social media platform in many countries and has a sufficiently large population for social data analysis in health care contexts [ 22 , 23 ]. Japan has a particularly high population density of Twitter users, even when compared to the major countries that use Twitter, such as the United States. Furthermore, owing to language exclusivity, it is easier to filter comments related to Japanese society using Japanese keywords [ 24 ]. Twitter has also been frequently used to help summarize peoples’ responses about the pandemic and its measures, showing the challenges experienced throughout [ 25 ]. Prior studies in Korea and Japan used Twitter to determine public opinion, showing popular words during the pandemic [ 26 ]. Because people actively share their daily lives on Twitter, the site has the potential to be a data source for investigating the impact of restrictions on the public. Using Twitter as a resource, this study aims to explore and visualize plans disrupted in Japan due to COVID-19 pandemic measures.

There are many studies on COVID-19 that investigate social media platforms, such as Twitter. Chen et al [ 27 ] investigated the levels of anxiety during the COVID-19 pandemic. The adverse effects on the mental health of the public was also one of the impacts of the pandemic, as explained in the research by Li et al [ 28 ], who analyzed COVID-19–related tweets into different emotions and investigated the mental health aspects and how they recovered from the COVID-19 crisis. Lyu et al [ 29 ] investigated the topics and sentiments in public COVID-19 vaccine–related discussions, whereas Krittanawong et al [ 30 ] investigated misinformation dissemination related to COVID-19 on Twitter. Aside from studies focused on the pandemic itself, COVID-19 vaccines have also been highly researched topics on Twitter. Ansari and Khan assessed public responses through sentiment analysis of COVID-19 vaccines using Twitter, revealing an overall negative tone in the tweets [ 31 ]. Ferawati et al [ 32 ] explored how Twitter reported vaccine-related side effects by comparing the side effects of 2 types of messenger RNA vaccines developed by Pfizer and Moderna in Japan and Indonesia, respectively. Gao et al [ 33 ] examined COVID-19 concerns in each Japanese prefecture, and Uehara et al [ 34 ] investigated the attitudes toward vaccines or vaccination during the COVID-19 pandemic in different Japanese prefectures using search queries from Yahoo! JAPAN. Our study adopts a unique approach to examine how the COVID-19 pandemic has disrupted everyday activities. Our main focus is on understanding the direct impact of the pandemic on society through the observation of expressions, life disruptions, and plans.

For research on citizen feedback, Ishida et al [ 35 ] proposed a method that uses social media data. They implemented a multitask learning framework to estimate the associated viewpoints using bidirectional encoder representations from the transformer model. However, this method requires considerable effort to label the data. This study uses search queries and validates word co-occurrence to infer the themes of topics discussed during the COVID-19 pandemic in Japan, proposing an efficient and low-resource method for social media analysis.

Objectives and Approach

We aimed to report on the impact of COVID-19 on Japanese society by analyzing public opinions extracted from social media data. Specifically, we focused on the popular term Corona no-sei (in Japanese コロナのせい, meaning “due to COVID-19,” “because of COVID-19,” or “considering COVID-19”), which clearly conveyed complaints or concerns about life event disruptions caused by the COVID-19 pandemic. Our study used 2 types of data: the daily COVID-19 case count and Japanese tweets containing the Japanese phrase Corona no-sei posted on Twitter between February 1, 2020, and November 30, 2021. We analyzed the trends in the number of tweets and COVID-19 cases to quantitatively explore their relationship and the words frequently used in the tweets to qualitatively explore social needs in the first 2 years of the COVID-19 pandemic.

In conclusion, we critically compared our findings with those identified in other similar studies to provide an alternative evidence base for the impact of COVID-19 on Japanese society.

COVID-19 Cases

To track the daily rise in COVID-19 cases, we gathered the number of new positive cases in Japan by manually downloading data from a dedicated COVID-19 site maintained by the NHK, Japan’s national broadcaster [ 36 ]. Our aim was to investigate the correlation between the number of positive cases and the volume of tweets. A total of 1,726,943 COVID-19–positive cases were recorded between February 1, 2020, and November 30, 2021.

Tweets and Keywords Extraction

Another data set for this study includes 300,778 tweets containing the Japanese phrase Corona no-sei during the same period as the recorded COVID-19 cases (between February 1, 2020, and November 30, 2021). We chose this period because by the end of January 2020, the Japanese government had officially established the Japan Anti-Coronavirus National Task Force to actively address the pandemic. In addition, we aimed to include the maximum possible data until the initiation of this study in mid-November 2021. Furthermore, this period also included 3 emergency announcements by the Japanese government, making it a representative period for studying the impact of COVID-19 on Japanese society.

We counted the number of tweets per month and found that the maximum number of tweets was 517,688 in April 2020; the minimum number of tweets was 24,625 in November 2021; and the average number of tweets was 136,717.6. The Corona no-sei phrase is frequently used by the public in social media and everyday conversation to express the (often negative) feelings when Japanese individuals’ activities or life plans were interrupted by the COVID-19 outbreak. Although there are several expressions synonymous with Corona no-sei (eg, “because of the new coronavirus” and “because of COVID-19”), we chose Corona no-sei as a casual expression used by the public in social media and colloquial speech. The tweet data were provided by the NTT DATA Corporation, which has a real-time backup of Japanese firehose data from X Corporation (formerly known as Twitter). Data access was granted to a few collaborative research institutes, including the University of Tokyo, and one of the authors was granted permission to use the self-adaptive classification system to extract the data and keywords [ 37 ].

Although applying a clustering approach for topic modeling can be useful in grasping the topics discussed in the tweets, it does not apply to our context wherein we were targeting COVID-19 as the main subject and aiming to identify the co-occurrence of events. Instead, we extracted co-occurrence nouns and verbs from the obtained Corona no-sei tweets by applying dependency analysis implemented in the system developed by Yoshinaga et al [ 37 - 39 ]. We used the base-phrase chunker to extract all tweets containing the Corona no-sei keyword (“keyword” is bunsetsu in Japanese). The built-in classifier then extracted the relevant verbs, nouns, and verb-noun-pairs for users based on the nonstack dependency parser, which achieved 99.01% accuracy in base-phrase chunking and 92.23% accuracy in dependency parsing [ 37 ]. Researchers who did not use the system and database maintained by the University of Tokyo could use the same tool published by the laboratory Pecco and DepP [ 37 - 40 ]. To avoid overinterpretation, we omitted tweets that described a disruption of plans but did not include COVID-19–related keywords.

Analysis of the Keywords and its Correlation to the COVID-19 Pandemic Trends

The contexts following Corona no-sei , which indicate a high level of negative concern about COVID-19, frequently contain verbs in the negative form and nouns associated with them. By aggregating these nouns and verbs, we extracted information on the restrictions imposed and the events or plans canceled owing to the COVID-19 pandemic. This information enabled us to capture the potential social and psychological impact of disrupted life plans. Note that, by events or plans, we refer to the specific type of occurrences (eg, university entrance exam) rather than a certain event (eg, a pop singer’s concert in 2019). The frequency of nouns and verbs in tweets containing Corona no-sei was counted to identify the restrictions placed on people’s lives.

To investigate the correlation between tweet volumes and COVID-19 cases, we constructed transition diagrams for each. In addition, Pearson correlation coefficients were also calculated. Next, we examined the nouns and verbs co-occurring with Corona no-sei over the entire study period and specifically on the day with the highest tweet activity.

The cross-validation of the keywords and tweet contents was performed by randomly extracting 20 tweets from the top 5 verb and noun pairs and other keyword pairs that were deemed worthy of discussion by the researchers. The tweet contents were further annotated to ensure that they were aligned with the researchers’ interpretations of keywords. We then discussed the themes extracted by analyzing and cross-validating the themes and noteworthy keywords.

Ethical Considerations

This study used publicly available data and did not handle identifiable private information, meaning that it was exempt from Institutional Review Board approval according to the Ethical Guidelines for Research of the Japanese National Government [ 41 ]. The NTT DATA Corporation obtained tweets according to Twitter terms of service and approved the use of the data for this study.

Figure 1 shows the time trend of Corona no-sei tweets (blue line) compared to the trend of positive cases (red line). There were 3 states of emergency announcements within our targeted period between February 1, 2020, and November 30, 2021, which are highlighted in gray in Figure 1 . The number of areas under the state of emergency is indicated by the bar graph in the upper part of the figure because the target areas were changed during each state of emergency. The periods during which the states of emergency were imposed roughly corresponded to an increase in case numbers. Interestingly, the announcement of a state of emergency was highly effective in suppressing the number of cases. Regarding the spike caused by the Tokyo Olympics (which took place between July 23, 2021, and August 8, 2021), the case number quickly dropped to below 5000 per day within 3 months.

As the blue line indicates, the Corona no-sei tweets peaked in March 2020, roughly before the first state of emergency was announced and reached the second highest number when the first state of emergency was imposed. After the first announcement of the state of emergency, the number of tweets using Corona no-sei showed a downward trend until the end of our data collection period. There were a few instances of small increases in Corona no-sei tweets before the second and third states of emergency announcements, but overall, the number of reported plan disruptions never reached the level observed before the first state of emergency announcement. The scatter plot for case numbers and the numbers of Corona no-sei tweets is shown in Figure 2 , with Pearson correlation coefficients of 0.86, 0.93, and 0.61, respectively, for the first, second, and third states of emergency.

When compared with the number of the Corona no-sei tweets during the entire period, the correlation between COVID-19 daily cases and the Corona no-sei was not very evident. We were able to observe a slight increase of Corona no-sei tweets before the case number started rising, but the extent of increase in case numbers was disproportional to the extent of increase in Corona no-sei tweets. Even though the number of cases peaked in September 2021 during the third state of emergency, there was only a slight increase in Corona no-sei tweets compared to the high number of complaints at the very beginning of the COVID-19 pandemic. This indicates that Japanese residents might have adapted to the restrictions or disruptions caused by the COVID-19 pandemic lockdown.

We further investigated the nouns and verbs in the tweets that we sampled. Tables 1 and 2 show the number of tweets for the top 20 nouns and verbs tweeted on February 28, 2020, when the tweet number reached the highest level. Tables 3 and 4 show the top 20 words (nouns and verbs) that co-occurred with Corona no-sei tweets in descending order to highlight the most disrupted activities or plans during our data aggregation period. For nouns, here, Corona was excluded because it was a word included in the query and was clearly the most frequently detected. For nouns, the top 5 most frequently mentioned words were work , abort , home , live , and friends after excluding the words that indicate the grammatical tense. These keywords indicated that, over a longer period, Japanese individuals started developing concerns over their disrupted work and social life. For verbs, go was the most frequent, but in the actual tweets, it was sometimes used in the negative, and in the context, the verb was unlikely to be used in the affirmative, so the verb was likely used to indicate they cannot go even if it is in the affirmative in this paper ( Textbox 1 ). Hence, the top 5 mean go , can go <negation>, look , meet <negation>, and get out . The results show that there are restrictions on the actions of going, seeing, and meeting as verbs. Compared with the single-day result on February 28, 2020, the concern about work appeared as the top 5 in Tables 1 and 2 , suggesting that Japanese individuals placed clear emphasis on their work routines. In addition, the desire for live concerts increased over the long run, making live concert the fourth most frequently mentioned keyword in Table 1 . Coincidentally, concerns related to friends and missing opportunities to meet up were also observed in both tables, showing the disruption of social relationships and recreational occasions. Both studies indicated that people regarded the COVID-19 pandemic as the main cause of their disrupted plans to hang out with friends or attend large public events. In addition to activities, the keyword finding also reflected the concern of resource shortage, such as toilet paper, masks, and even money, which were critical in supporting daily lives or normal health care practices.

Because the keywords indicated both long-term and short-term concerns, we cross-validated the tweet contents by selecting keyword pairs based on the top 5 keywords related to long-term concerns and those related to short-term anxiety on material shortage. A total of 160 tweets were randomly sampled based on the following keyword pairs (20 tweets each): ライブ＋行く／行けない ( live concert + go / go <negation>); 家＋行く／行けない (home+go/go<negation>); 友達＋行く／行けない ( friends + go / go <negation>); 中止＋行く／行けない ( cancel + go / go <negation>); 友達＋会う／会える (friends+meet/can meet); 仕事＋行く／行けない ( work + go / go <negation>); トイレットペーパー＋なくなる (toilet paper+vanish); and マスク＋なくなる ( mask + vanish ). One of the authors annotated the tweets according to the themes reflected by the keywords. Key findings are discussed in the following section.

Verb and example

• Go: “Due to COVID-19, the day I've been looking forward to going out with the guy I love has been postponed... I can't help it now and I'll accept it, but I was looking forward to it.”
• Meet: “It doesn't feel like April at all due to COVID-19, but I can't wait for it to end so that we can all meet, eat, and shop together comfortably. Six years already... I want to quit my job lol.”

Principal Findings

Our findings revealed that the COVID-19 pandemic significantly disrupted daily routines in Japan, particularly in terms of work, education, social activities, and material shortages (with regard to the temporary spike of anxiety). The findings from our study correspond with numerous studies conducted in diverse countries, highlighting the extensive impact of the COVID-19 pandemic on social life, economy, public mental health, and education [ 5 ]. In this section, we discuss key findings across a temporal spectrum, focusing on 4 crucial aspects: disruption of work routines, public anxiety stemming from perceived resource shortages, concerns regarding social relationships, and interference with the curriculum.

Top Concerns

The impact of the COVID-19 pandemic on the labor market in Japan is unequivocal, mirroring the challenges faced by numerous countries. The pandemic necessitated a shift in work dynamics with the unintended pilot of remote collaboration. Notably, certain categories of Japanese workers, contingent on their employment contracts, exhibited heightened susceptibility to these alterations in work patterns. In our findings, the keyword work demonstrated associations with part-time , abort , and money , indicating that individuals expressing concerns about their work conditions may grapple with job uncertainty, stemming either from the part-time nature of their employment or an abrupt reduction in income. This discovery aligns seamlessly with prior research examining the repercussions of the COVID-19 pandemic on Japan’s labor sector. As described by Kikuchi et al [ 42 ] in their study, individuals in contingent employment, along with women and those with lower income, were notably susceptible. The shift toward teleworking and the accompanying uncertainty about long-term income during the COVID-19 pandemic had a disproportionately adverse impact on these specific demographic groups [ 42 ]. Fukai et al [ 43 ] endorsed these findings through extensive government statistical analysis. According to their research, Japanese individuals employed part-time in service industries or compelled to take leave or face job loss following the declaration of a state of emergency were identified as particularly vulnerable groups significantly affected by the COVID-19 pandemic [ 43 ]. Although the use of part-time or contingent workers has traditionally been a standard practice for Japanese companies seeking to optimize budget and resource allocation, the advent of the COVID-19 pandemic has pushed issues related to work to the forefront of public concern. Researchers caution that this could potentially exacerbate inequality for susceptible individuals unless actively addressed by government support [ 44 ].

In summary, our findings provide substantial evidence for concerns among Japanese internet users regarding job disruption, employment disparities, and inadequate financial resilience. Failing to address these issues during multiple states of emergency, the Japanese government risks compromising the equality within Japan’s labor market significantly. Interestingly, a study conducted by Chen et al [ 27 ], who sampled 6535 Reddit posts, identified strikingly identical subjects that propelled nationwide anxiety in the United States. Notably, concerns about career, finance, and the future were prevalent. However, our research suggests that health and death concerns were not as prominent in Japan, as observed in the study by Chen et al [ 27 ]. We hypothesized that the emphasis on collectivism and harmony in Japanese society could shape individuals’ concerns during crises (particularly in the case of a national crisis). For example, apprehensions about not being perceived as “useful” or causing “inconvenience” to others, possibly even relying on government subsidies, were more pronounced than concerns related to health and mortality.

Sudden and Perhaps Excessive Anxiety About Material Shortage

The scarcity of certain items, including toilet paper, masks, and tissues, as outlined in Table 1 , emerged as a significant issue in Japan. Our findings closely parallel earlier Twitter studies investigating hoarding behaviors, particularly concerning toilet paper [ 45 ]. Although initially observed in the United States, panic buying for household goods rapidly became a global phenomenon. Notably, toilet paper has emerged as a frequently hoarded item, often signaling a surge in demand during natural disasters [ 46 , 47 ]. Although the act of stockpiling toilet paper may seem irrational and has been widely ridiculed on social media, the adverse effects of bulk purchasing have not been as severe. Social scientists may view this behavior as a coping mechanism during a natural disaster [ 48 ]. Contrary to the commonly perceived overhoarding of toilet paper, the mask shortage was deemed a more severe public health crisis and a direct threat to well-being. A 2020 agent-based simulation conducted by Tatapudi et al [ 48 ] illustrated that universal mask use could potentially reduce infections by 20% [ 49 ]. At the time of the study, the total number of people infected by COVID-19 was 541 million, indicating that implementing universal mask use could potentially spare 108 million cases. Numerous studies have indicated a negative correlation between mask use and the COVID-19 infection rate [ 44 , 50 ].

However, the situation in Japan presents a slightly different scenario. The Japanese government faced criticism for a perceived slow response to the awareness of mask shortages, as the pandemic was considered relatively “under control” in its early stages. As the mask crisis unfolded, many Japanese citizens became concerned about their reliance on masks manufactured abroad, prompting the government to take actions to boost domestic mask production. Unfortunately, heightened anxiety also led to the “Abenomasks” incident, wherein the government faced backlash for stockpiling over 82 million unused masks [ 51 ]. A crucial lesson learned from this incident is that although social media serves as a critical channel for the dissemination of news and raising public awareness, the emotional contagion and overpromotion of a particular disaster can backfire, impeding the rational coping mechanisms of citizens and the decision-making processes of the government. Our findings, along with those of numerous other studies, indicate that further efforts are needed to develop effective protocols for addressing the widely contagious anxiety stemming from the dissemination of information about natural disasters on social media.

Concerns About Social Relationships

Keywords pertaining to relationships, social life, and collective events were prevalent in our analysis. For instance, the top 20 frequently occurring nouns associated with Corona no-sei included friends, family, live, events, and one person. The most frequent verbs in the context of Corona no-sei were go , can go <negation>, meet <negation>, buy , meet , can go , and play . The example in Table 4 illustrates how Japanese individuals linked go and meet to their social events. While it may appear that many tweets express concerns about social relationships, these keywords actually reflect people venting their frustration about being unable to meet and engage in activities together, rather than indicating an actual loss of relationships. Interestingly, a study by Goodwin and Takahashi [ 52 ] also yielded similar findings. Most Japanese respondents in their survey gauging perceptions of relationship quality during the COVID-19 pandemic indicated that there were no discernible changes in their perceived relationship quality. Only a few reported that their trust and relationship with communities had declined compared to the prepandemic era [ 52 ]. There was also a report indicating that students, due to reduced communication with friends, face an increased risk of mental health problems [ 53 ].

These findings suggest that events, such as the COVID-19 pandemic, may lead individuals to experience heightened anxiety and stress. While this emotional response could temporarily disrupt their social activities and coping mechanisms against trauma, it may not have a lasting impact on their perceived relationship quality. In fact, the example tweets we analyzed illustrated how individuals, despite feeling frustrated, expressed eagerness to resume their social activities after the pandemic. Hence, we argue that concerns about relationship disruption may be transient and serve as a positive signal prompting individuals in Japan to actively nurture their relationships. As suggested by the study conducted by Goodwin and Takahashi [ 52 ], dedicating additional time to communication, particularly in the context of romantic relationships, could further enhance the quality of these connections [ 42 ].

Concern for Education Discontinuation

The peak volume of tweets was recorded on February 28, 2020, coinciding with the government’s announcement of the simultaneous closure of all elementary, junior high, and senior high schools in Japan. In fact, in the most frequent nouns and verbs shown in Tables 1 and 2 , the top words related to the simultaneous closure of schools were graduation ceremony , cancel , lose , rest , and go <negation>, all of which reflected Japanese citizens’ concerns about the discontinuation of education, the cancelation of the graduation ceremony, and missing school classes. It is essential to note that in Japan, the graduation ceremony typically takes place in March and the new school and work year commences in April. Despite the Japanese government’s earnest efforts to mitigate the spread of COVID-19, as scrutinized by scientists, the decision to close schools in Japan did not yield a substantial impact on preventing the spread of COVID-19. Instead, it deprived children of valuable learning and developmental opportunities [ 54 ]. Moreover, with the closure of schools, there was a surge in the demand for digital education or internet-based learning platforms. However, many schools and student households were ill-equipped to handle this impromptu shift to an internet-based education system. As discussed in detail by Iwabuchi et al [ 55 ], the unequal distribution of resources among schools in Japan further intensified the digital learning disparities brought about by the COVID-19 pandemic–induced school closures. The more well-funded private and prefecture-sponsored schools had often already implemented or could quickly set up the necessary e-learning system to cope with the lack of face-to-face lecturing. However, most public schools were forced to send learning materials to students by mail, risking a huge learning gap between students in private and public schools. The long-term impact on students’ physical and mental development remains uncertain, given that most schools were able to resume normalcy after the lifting of the state of emergency. A study conducted by Nishimura et al [ 56 ] on medical students clearly indicated a deterioration in subjective mental well-being.

Concerns were also observed regarding web-based alternatives, with growing apprehensions that they fail to adequately substitute the essential in-person learning and hands-on field practice integral to medical education. The diverse concerns reflected in education-related keywords in Table 1 suggest that many Japanese individuals transitioned their focus from one-time events, such as graduation ceremony and school holiday , to longer-term mental and societal impacts, such as opportunity , stress , and university . This shift implies that the long-term effects would take time to manifest compared to short-term disruptions of specific incidents, such as a graduation ceremony. Further studies are crucial to monitor and unveil a complete picture of this disruption.

Long- and Short-Term Concerns and the Impact on the Society

Following the World Health Organization’s official declaration that COVID-19 was no longer considered a global health emergency on May 4, 2023, individuals who survived now faced a familiar daily life with some changes that were difficult to imagine in the prepandemic era. However, there is still an impact on society that can be challenging to trace and monitor. The economic repercussion, such as inflation and tumbling currency values in Japan, are gradually occurring. Schoolchildren who have lost education for almost 1 year are bracing for their future growth. An increasing number of companies are eager to get talent to opt in for remote working styles to attract employees who were reluctant to return to city offices. Individuals are probably no longer worried about toilet paper but will gradually sense the subtle shifting of their workstyles, social styles, and even learning styles. However, due to the limitations of our data, we were not able to speculate about the postpandemic future. Our discussion offers possible clues to further trace the causes of societal changes. The profound effects of the COVID-19 pandemic on society and public health require further investigation and monitoring.

Limitations and Future Work

It should be noted that our study had some limitations in extracting data from social platforms such as Twitter. One limitation is the lack of geolocation metadata. Although we capitalized on the language exclusivity of Japanese tweets and the domestic majority to extract representative samples of Japanese citizens, it is important to note that there may be some minor contributions from Japanese speakers residing outside Japan. This limitation should be considered when interpreting the findings of this study. Another limitation arises from the bias present on Twitter, as its use is lower among older adults compared to the younger population. To mitigate this bias, stratified analysis is necessary to account for the effects of age. However, the current system lacks age data. Consequently, the results should be interpreted with the awareness that the perspectives of the older adults are underrepresented.

Because the purpose of this study was to derive an interruption schedule, we specifically targeted verbs and nouns to better represent social connections (families and friends), locations, events, subjects, and actions, rather than using adjectives or phrases that might focus on emotional descriptions or concrete situations. This approach limited our options for sentiment-related analysis methods or topic modeling, which could reveal emotional reactions instead of generic events and the involvement of close connections. Although people’s sentiments were deemed beyond the scope of this study, in future studies, we would like to analyze how people’s sentiments have changed through sentiment analysis [ 57 ]. With the introduction of transformer-based large language models, such as bidirectional encoder representations from transformers and text-to-text transfer transformers, more contextual and in-depth understanding and analysis might be made available for researchers in social media data. This should be considered in future studies.

We also did not address concerns regarding the safety of cybersecurity during the work-from-home period during the pandemic. We noticed that in the United States, data breaches and the security of the work environment were one of the top concerns [ 58 ]; however, based on our current results, there was no direct implication on this aspect in Japan during the COVID-19 pandemic. This will be considered in our future work.

Conclusions

Overall, by adding the analysis on Corona no-sei to the conventional symptom-based monitoring, we were able to identify the underlying concerns at the peak of the disruption and across the whole-time span of the 3 announcements of state of emergency. Our findings and a comparison of the tweets against COVID-19 case numbers yielded rich insights into people’s short- and long-term concerns and potential aspects of societal impact caused by the announcements of the state of emergency. Although more studies from different fields would help to reveal the whole landscape of social and psychological impact caused by COVID-19, we believed that the keywords reflected in Corona no-sei tweets provided more nuanced descriptions of real-life problems Japanese individuals faced during the COVID-19 pandemic and revealed the development of different concerns in response to the change of policies.

Timely communication of analysis results is crucial, especially when dealing with issues of significant social impact, such as a global pandemic. A delay in delivering results can hinder decision-making processes and require substantial resources to recover from the initial losses caused by poor decisions. For policy makers, especially the Japanese government, this study reflects the opinions of citizens and should be considered when reviewing the effectiveness and suitability of a policy as well as assessing further measures to support those impacted during the pandemic.

Acknowledgments

This research was conducted as part of the COVID-19 AI & Simulation Project run by Mitsubishi Research Institute commissioned by the Cabinet Secretariat. The methodology used in this study was developed through the support from Japan Science and Technology Agency (JST) Strategic International Collaborative Research Program (SICORP) (grant JPMJSC2107) and Japan Society for the Promotion of Science (JSPS) KAKENHI (grant JP 22K12041) in Japan. The authors acknowledge Dr Naoki Yoshinaga, Dr Masashi Toyoda, and Dr Masaru Kitsuregawa for providing the tweet-analysis system.

Conflicts of Interest

None declared.

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Edited by T Mackey; submitted 06.06.23; peer-reviewed by L Chen, Y Pachankis, G Sebastian, M Elbattah; comments to author 29.06.23; revised version received 11.08.23; accepted 06.03.24; published 01.04.24.

©Masaru Kamba, Wan Jou She, Kiki Ferawati, Shoko Wakamiya, Eiji Aramaki. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 01.04.2024.

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