qualitative research design paper

Qualitative research design (JARS–Qual)

JARS–Qual, developed in 2018, mark the first time APA Style has included qualitative standards. They outline what should be reported in qualitative research manuscripts to make the review process easier.

The seventh edition of the Publication Manual also includes content on qualitative studies, including standards for journal article reporting, considerations for sharing qualitative data, and a description of case studies as a type of qualitative article.

JARS–Qual include guidance for manuscripts that report

• Primary qualitative research
• Qualitative meta-analyses
• Qualitative Design Reporting Standards (JARS-Qual) (PDF, 141KB) Information recommended for inclusion in manuscripts that report primary qualitative research
• Qualitative Meta-Analysis Reporting Standards (PDF, 119KB) Information recommended for inclusion in manuscripts that report qualitative meta-analyses

For more information on how these standards were created, read Journal Article Reporting Standards for Qualitative Primary, Qualitative Meta-Analytic, and Mixed Methods Research in Psychology .

• Quantitative design standards
• Mixed methods standards
• Race, Ethnicity, and Culture standards

Return to Journal Article Reporting Standards homepage

Jars resources

• History of APA’s journal article reporting standards
• APA Style JARS supplemental glossary
• Supplemental resource on the ethic of transparency in JARS
• Frequently asked questions
• JARS-Quant Decision Flowchart (PDF, 98KB)
• JARS-Quant Participant Flowchart (PDF, 98KB)

Jars articles

• Jars –Quant article
• Jars –Qual / Mixed article
• Jars – rec executive summary

Questions / feedback

Email an APA Style Expert if you have questions, feedback, or suggestions for modules to be included in future JARS updates.

APA resources

• APA Databases and Electronic Resources
• APA Journals
• Journal Author Resource Center
• Education and Career
• Psychological Science
• Open Science at APA
• How to Review a Manuscript

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Neurol Res Pract

How to use and assess qualitative research methods

Loraine busetto.

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

Wolfgang Wick

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

Christoph Gumbinger

Associated data.

Not applicable.

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

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

What is qualitative research?

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

Why conduct qualitative research?

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

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

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

How to conduct qualitative research?

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

Iterative research process

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

Data collection

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

Document study

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

Observations

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

Semi-structured interviews

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

Focus groups

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

Choosing the “right” method

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

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

Possible combination of data collection methods

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

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

Data analysis

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

From data collection to data analysis

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

How to report qualitative research?

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

How to combine qualitative with quantitative research?

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

Three common mixed methods designs

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

How to assess qualitative research?

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

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

Reflexivity

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

Sampling and saturation

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

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

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

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

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

Member checking

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

Stakeholder involvement

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

How not to assess qualitative research

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

Protocol adherence

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

Sample size

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

Randomisation

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

Interrater reliability, variability and other “objectivity checks”

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

Not being quantitative research

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

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

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

no external funding.

Availability of data and materials

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

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

Prevent plagiarism. Run a free check.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). What Is a Research Design | Types, Guide & Examples. Scribbr. Retrieved August 18, 2024, from https://www.scribbr.com/methodology/research-design/

Is this article helpful?

Shona McCombes

Other students also liked, guide to experimental design | overview, steps, & examples, how to write a research proposal | examples & templates, ethical considerations in research | types & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Chapter 1. Introduction

“Science is in danger, and for that reason it is becoming dangerous” -Pierre Bourdieu, Science of Science and Reflexivity

Why an Open Access Textbook on Qualitative Research Methods?

I have been teaching qualitative research methods to both undergraduates and graduate students for many years.  Although there are some excellent textbooks out there, they are often costly, and none of them, to my mind, properly introduces qualitative research methods to the beginning student (whether undergraduate or graduate student).  In contrast, this open-access textbook is designed as a (free) true introduction to the subject, with helpful, practical pointers on how to conduct research and how to access more advanced instruction.  

Textbooks are typically arranged in one of two ways: (1) by technique (each chapter covers one method used in qualitative research); or (2) by process (chapters advance from research design through publication).  But both of these approaches are necessary for the beginner student.  This textbook will have sections dedicated to the process as well as the techniques of qualitative research.  This is a true “comprehensive” book for the beginning student.  In addition to covering techniques of data collection and data analysis, it provides a road map of how to get started and how to keep going and where to go for advanced instruction.  It covers aspects of research design and research communication as well as methods employed.  Along the way, it includes examples from many different disciplines in the social sciences.

The primary goal has been to create a useful, accessible, engaging textbook for use across many disciplines.  And, let’s face it.  Textbooks can be boring.  I hope readers find this to be a little different.  I have tried to write in a practical and forthright manner, with many lively examples and references to good and intellectually creative qualitative research.  Woven throughout the text are short textual asides (in colored textboxes) by professional (academic) qualitative researchers in various disciplines.  These short accounts by practitioners should help inspire students.  So, let’s begin!

What is Research?

When we use the word research , what exactly do we mean by that?  This is one of those words that everyone thinks they understand, but it is worth beginning this textbook with a short explanation.  We use the term to refer to “empirical research,” which is actually a historically specific approach to understanding the world around us.  Think about how you know things about the world. [1] You might know your mother loves you because she’s told you she does.  Or because that is what “mothers” do by tradition.  Or you might know because you’ve looked for evidence that she does, like taking care of you when you are sick or reading to you in bed or working two jobs so you can have the things you need to do OK in life.  Maybe it seems churlish to look for evidence; you just take it “on faith” that you are loved.

Only one of the above comes close to what we mean by research.  Empirical research is research (investigation) based on evidence.  Conclusions can then be drawn from observable data.  This observable data can also be “tested” or checked.  If the data cannot be tested, that is a good indication that we are not doing research.  Note that we can never “prove” conclusively, through observable data, that our mothers love us.  We might have some “disconfirming evidence” (that time she didn’t show up to your graduation, for example) that could push you to question an original hypothesis , but no amount of “confirming evidence” will ever allow us to say with 100% certainty, “my mother loves me.”  Faith and tradition and authority work differently.  Our knowledge can be 100% certain using each of those alternative methods of knowledge, but our certainty in those cases will not be based on facts or evidence.

For many periods of history, those in power have been nervous about “science” because it uses evidence and facts as the primary source of understanding the world, and facts can be at odds with what power or authority or tradition want you to believe.  That is why I say that scientific empirical research is a historically specific approach to understand the world.  You are in college or university now partly to learn how to engage in this historically specific approach.

In the sixteenth and seventeenth centuries in Europe, there was a newfound respect for empirical research, some of which was seriously challenging to the established church.  Using observations and testing them, scientists found that the earth was not at the center of the universe, for example, but rather that it was but one planet of many which circled the sun. [2]   For the next two centuries, the science of astronomy, physics, biology, and chemistry emerged and became disciplines taught in universities.  All used the scientific method of observation and testing to advance knowledge.  Knowledge about people , however, and social institutions, however, was still left to faith, tradition, and authority.  Historians and philosophers and poets wrote about the human condition, but none of them used research to do so. [3]

It was not until the nineteenth century that “social science” really emerged, using the scientific method (empirical observation) to understand people and social institutions.  New fields of sociology, economics, political science, and anthropology emerged.  The first sociologists, people like Auguste Comte and Karl Marx, sought specifically to apply the scientific method of research to understand society, Engels famously claiming that Marx had done for the social world what Darwin did for the natural world, tracings its laws of development.  Today we tend to take for granted the naturalness of science here, but it is actually a pretty recent and radical development.

To return to the question, “does your mother love you?”  Well, this is actually not really how a researcher would frame the question, as it is too specific to your case.  It doesn’t tell us much about the world at large, even if it does tell us something about you and your relationship with your mother.  A social science researcher might ask, “do mothers love their children?”  Or maybe they would be more interested in how this loving relationship might change over time (e.g., “do mothers love their children more now than they did in the 18th century when so many children died before reaching adulthood?”) or perhaps they might be interested in measuring quality of love across cultures or time periods, or even establishing “what love looks like” using the mother/child relationship as a site of exploration.  All of these make good research questions because we can use observable data to answer them.

What is Qualitative Research?

“All we know is how to learn. How to study, how to listen, how to talk, how to tell.  If we don’t tell the world, we don’t know the world.  We’re lost in it, we die.” -Ursula LeGuin, The Telling

At its simplest, qualitative research is research about the social world that does not use numbers in its analyses.  All those who fear statistics can breathe a sigh of relief – there are no mathematical formulae or regression models in this book! But this definition is less about what qualitative research can be and more about what it is not.  To be honest, any simple statement will fail to capture the power and depth of qualitative research.  One way of contrasting qualitative research to quantitative research is to note that the focus of qualitative research is less about explaining and predicting relationships between variables and more about understanding the social world.  To use our mother love example, the question about “what love looks like” is a good question for the qualitative researcher while all questions measuring love or comparing incidences of love (both of which require measurement) are good questions for quantitative researchers. Patton writes,

Qualitative data describe.  They take us, as readers, into the time and place of the observation so that we know what it was like to have been there.  They capture and communicate someone else’s experience of the world in his or her own words.  Qualitative data tell a story. ( Patton 2002:47 )

Qualitative researchers are asking different questions about the world than their quantitative colleagues.  Even when researchers are employed in “mixed methods” research ( both quantitative and qualitative), they are using different methods to address different questions of the study.  I do a lot of research about first-generation and working-college college students.  Where a quantitative researcher might ask, how many first-generation college students graduate from college within four years? Or does first-generation college status predict high student debt loads?  A qualitative researcher might ask, how does the college experience differ for first-generation college students?  What is it like to carry a lot of debt, and how does this impact the ability to complete college on time?  Both sets of questions are important, but they can only be answered using specific tools tailored to those questions.  For the former, you need large numbers to make adequate comparisons.  For the latter, you need to talk to people, find out what they are thinking and feeling, and try to inhabit their shoes for a little while so you can make sense of their experiences and beliefs.

Examples of Qualitative Research

You have probably seen examples of qualitative research before, but you might not have paid particular attention to how they were produced or realized that the accounts you were reading were the result of hours, months, even years of research “in the field.”  A good qualitative researcher will present the product of their hours of work in such a way that it seems natural, even obvious, to the reader.  Because we are trying to convey what it is like answers, qualitative research is often presented as stories – stories about how people live their lives, go to work, raise their children, interact with one another.  In some ways, this can seem like reading particularly insightful novels.  But, unlike novels, there are very specific rules and guidelines that qualitative researchers follow to ensure that the “story” they are telling is accurate , a truthful rendition of what life is like for the people being studied.  Most of this textbook will be spent conveying those rules and guidelines.  Let’s take a look, first, however, at three examples of what the end product looks like.  I have chosen these three examples to showcase very different approaches to qualitative research, and I will return to these five examples throughout the book.  They were all published as whole books (not chapters or articles), and they are worth the long read, if you have the time.  I will also provide some information on how these books came to be and the length of time it takes to get them into book version.  It is important you know about this process, and the rest of this textbook will help explain why it takes so long to conduct good qualitative research!

Example 1 : The End Game (ethnography + interviews)

Corey Abramson is a sociologist who teaches at the University of Arizona.   In 2015 he published The End Game: How Inequality Shapes our Final Years ( 2015 ). This book was based on the research he did for his dissertation at the University of California-Berkeley in 2012.  Actually, the dissertation was completed in 2012 but the work that was produced that took several years.  The dissertation was entitled, “This is How We Live, This is How We Die: Social Stratification, Aging, and Health in Urban America” ( 2012 ).  You can see how the book version, which was written for a more general audience, has a more engaging sound to it, but that the dissertation version, which is what academic faculty read and evaluate, has a more descriptive title.  You can read the title and know that this is a study about aging and health and that the focus is going to be inequality and that the context (place) is going to be “urban America.”  It’s a study about “how” people do something – in this case, how they deal with aging and death.  This is the very first sentence of the dissertation, “From our first breath in the hospital to the day we die, we live in a society characterized by unequal opportunities for maintaining health and taking care of ourselves when ill.  These disparities reflect persistent racial, socio-economic, and gender-based inequalities and contribute to their persistence over time” ( 1 ).  What follows is a truthful account of how that is so.

Cory Abramson spent three years conducting his research in four different urban neighborhoods.  We call the type of research he conducted “comparative ethnographic” because he designed his study to compare groups of seniors as they went about their everyday business.  It’s comparative because he is comparing different groups (based on race, class, gender) and ethnographic because he is studying the culture/way of life of a group. [4]   He had an educated guess, rooted in what previous research had shown and what social theory would suggest, that people’s experiences of aging differ by race, class, and gender.  So, he set up a research design that would allow him to observe differences.  He chose two primarily middle-class (one was racially diverse and the other was predominantly White) and two primarily poor neighborhoods (one was racially diverse and the other was predominantly African American).  He hung out in senior centers and other places seniors congregated, watched them as they took the bus to get prescriptions filled, sat in doctor’s offices with them, and listened to their conversations with each other.  He also conducted more formal conversations, what we call in-depth interviews, with sixty seniors from each of the four neighborhoods.  As with a lot of fieldwork , as he got closer to the people involved, he both expanded and deepened his reach –

By the end of the project, I expanded my pool of general observations to include various settings frequented by seniors: apartment building common rooms, doctors’ offices, emergency rooms, pharmacies, senior centers, bars, parks, corner stores, shopping centers, pool halls, hair salons, coffee shops, and discount stores. Over the course of the three years of fieldwork, I observed hundreds of elders, and developed close relationships with a number of them. ( 2012:10 )

When Abramson rewrote the dissertation for a general audience and published his book in 2015, it got a lot of attention.  It is a beautifully written book and it provided insight into a common human experience that we surprisingly know very little about.  It won the Outstanding Publication Award by the American Sociological Association Section on Aging and the Life Course and was featured in the New York Times .  The book was about aging, and specifically how inequality shapes the aging process, but it was also about much more than that.  It helped show how inequality affects people’s everyday lives.  For example, by observing the difficulties the poor had in setting up appointments and getting to them using public transportation and then being made to wait to see a doctor, sometimes in standing-room-only situations, when they are unwell, and then being treated dismissively by hospital staff, Abramson allowed readers to feel the material reality of being poor in the US.  Comparing these examples with seniors with adequate supplemental insurance who have the resources to hire car services or have others assist them in arranging care when they need it, jolts the reader to understand and appreciate the difference money makes in the lives and circumstances of us all, and in a way that is different than simply reading a statistic (“80% of the poor do not keep regular doctor’s appointments”) does.  Qualitative research can reach into spaces and places that often go unexamined and then reports back to the rest of us what it is like in those spaces and places.

Example 2: Racing for Innocence (Interviews + Content Analysis + Fictional Stories)

Jennifer Pierce is a Professor of American Studies at the University of Minnesota.  Trained as a sociologist, she has written a number of books about gender, race, and power.  Her very first book, Gender Trials: Emotional Lives in Contemporary Law Firms, published in 1995, is a brilliant look at gender dynamics within two law firms.  Pierce was a participant observer, working as a paralegal, and she observed how female lawyers and female paralegals struggled to obtain parity with their male colleagues.

Fifteen years later, she reexamined the context of the law firm to include an examination of racial dynamics, particularly how elite white men working in these spaces created and maintained a culture that made it difficult for both female attorneys and attorneys of color to thrive. Her book, Racing for Innocence: Whiteness, Gender, and the Backlash Against Affirmative Action , published in 2012, is an interesting and creative blending of interviews with attorneys, content analyses of popular films during this period, and fictional accounts of racial discrimination and sexual harassment.  The law firm she chose to study had come under an affirmative action order and was in the process of implementing equitable policies and programs.  She wanted to understand how recipients of white privilege (the elite white male attorneys) come to deny the role they play in reproducing inequality.  Through interviews with attorneys who were present both before and during the affirmative action order, she creates a historical record of the “bad behavior” that necessitated new policies and procedures, but also, and more importantly , probed the participants ’ understanding of this behavior.  It should come as no surprise that most (but not all) of the white male attorneys saw little need for change, and that almost everyone else had accounts that were different if not sometimes downright harrowing.

I’ve used Pierce’s book in my qualitative research methods courses as an example of an interesting blend of techniques and presentation styles.  My students often have a very difficult time with the fictional accounts she includes.  But they serve an important communicative purpose here.  They are her attempts at presenting “both sides” to an objective reality – something happens (Pierce writes this something so it is very clear what it is), and the two participants to the thing that happened have very different understandings of what this means.  By including these stories, Pierce presents one of her key findings – people remember things differently and these different memories tend to support their own ideological positions.  I wonder what Pierce would have written had she studied the murder of George Floyd or the storming of the US Capitol on January 6 or any number of other historic events whose observers and participants record very different happenings.

This is not to say that qualitative researchers write fictional accounts.  In fact, the use of fiction in our work remains controversial.  When used, it must be clearly identified as a presentation device, as Pierce did.  I include Racing for Innocence here as an example of the multiple uses of methods and techniques and the way that these work together to produce better understandings by us, the readers, of what Pierce studied.  We readers come away with a better grasp of how and why advantaged people understate their own involvement in situations and structures that advantage them.  This is normal human behavior , in other words.  This case may have been about elite white men in law firms, but the general insights here can be transposed to other settings.  Indeed, Pierce argues that more research needs to be done about the role elites play in the reproduction of inequality in the workplace in general.

Example 3: Amplified Advantage (Mixed Methods: Survey Interviews + Focus Groups + Archives)

The final example comes from my own work with college students, particularly the ways in which class background affects the experience of college and outcomes for graduates.  I include it here as an example of mixed methods, and for the use of supplementary archival research.  I’ve done a lot of research over the years on first-generation, low-income, and working-class college students.  I am curious (and skeptical) about the possibility of social mobility today, particularly with the rising cost of college and growing inequality in general.  As one of the few people in my family to go to college, I didn’t grow up with a lot of examples of what college was like or how to make the most of it.  And when I entered graduate school, I realized with dismay that there were very few people like me there.  I worried about becoming too different from my family and friends back home.  And I wasn’t at all sure that I would ever be able to pay back the huge load of debt I was taking on.  And so I wrote my dissertation and first two books about working-class college students.  These books focused on experiences in college and the difficulties of navigating between family and school ( Hurst 2010a, 2012 ).  But even after all that research, I kept coming back to wondering if working-class students who made it through college had an equal chance at finding good jobs and happy lives,

What happens to students after college?  Do working-class students fare as well as their peers?  I knew from my own experience that barriers continued through graduate school and beyond, and that my debtload was higher than that of my peers, constraining some of the choices I made when I graduated.  To answer these questions, I designed a study of students attending small liberal arts colleges, the type of college that tried to equalize the experience of students by requiring all students to live on campus and offering small classes with lots of interaction with faculty.  These private colleges tend to have more money and resources so they can provide financial aid to low-income students.  They also attract some very wealthy students.  Because they enroll students across the class spectrum, I would be able to draw comparisons.  I ended up spending about four years collecting data, both a survey of more than 2000 students (which formed the basis for quantitative analyses) and qualitative data collection (interviews, focus groups, archival research, and participant observation).  This is what we call a “mixed methods” approach because we use both quantitative and qualitative data.  The survey gave me a large enough number of students that I could make comparisons of the how many kind, and to be able to say with some authority that there were in fact significant differences in experience and outcome by class (e.g., wealthier students earned more money and had little debt; working-class students often found jobs that were not in their chosen careers and were very affected by debt, upper-middle-class students were more likely to go to graduate school).  But the survey analyses could not explain why these differences existed.  For that, I needed to talk to people and ask them about their motivations and aspirations.  I needed to understand their perceptions of the world, and it is very hard to do this through a survey.

By interviewing students and recent graduates, I was able to discern particular patterns and pathways through college and beyond.  Specifically, I identified three versions of gameplay.  Upper-middle-class students, whose parents were themselves professionals (academics, lawyers, managers of non-profits), saw college as the first stage of their education and took classes and declared majors that would prepare them for graduate school.  They also spent a lot of time building their resumes, taking advantage of opportunities to help professors with their research, or study abroad.  This helped them gain admission to highly-ranked graduate schools and interesting jobs in the public sector.  In contrast, upper-class students, whose parents were wealthy and more likely to be engaged in business (as CEOs or other high-level directors), prioritized building social capital.  They did this by joining fraternities and sororities and playing club sports.  This helped them when they graduated as they called on friends and parents of friends to find them well-paying jobs.  Finally, low-income, first-generation, and working-class students were often adrift.  They took the classes that were recommended to them but without the knowledge of how to connect them to life beyond college.  They spent time working and studying rather than partying or building their resumes.  All three sets of students thought they were “doing college” the right way, the way that one was supposed to do college.   But these three versions of gameplay led to distinct outcomes that advantaged some students over others.  I titled my work “Amplified Advantage” to highlight this process.

These three examples, Cory Abramson’s The End Game , Jennifer Peirce’s Racing for Innocence, and my own Amplified Advantage, demonstrate the range of approaches and tools available to the qualitative researcher.  They also help explain why qualitative research is so important.  Numbers can tell us some things about the world, but they cannot get at the hearts and minds, motivations and beliefs of the people who make up the social worlds we inhabit.  For that, we need tools that allow us to listen and make sense of what people tell us and show us.  That is what good qualitative research offers us.

How Is This Book Organized?

This textbook is organized as a comprehensive introduction to the use of qualitative research methods.  The first half covers general topics (e.g., approaches to qualitative research, ethics) and research design (necessary steps for building a successful qualitative research study).  The second half reviews various data collection and data analysis techniques.  Of course, building a successful qualitative research study requires some knowledge of data collection and data analysis so the chapters in the first half and the chapters in the second half should be read in conversation with each other.  That said, each chapter can be read on its own for assistance with a particular narrow topic.  In addition to the chapters, a helpful glossary can be found in the back of the book.  Rummage around in the text as needed.

Chapter Descriptions

Chapter 2 provides an overview of the Research Design Process.  How does one begin a study? What is an appropriate research question?  How is the study to be done – with what methods ?  Involving what people and sites?  Although qualitative research studies can and often do change and develop over the course of data collection, it is important to have a good idea of what the aims and goals of your study are at the outset and a good plan of how to achieve those aims and goals.  Chapter 2 provides a road map of the process.

Chapter 3 describes and explains various ways of knowing the (social) world.  What is it possible for us to know about how other people think or why they behave the way they do?  What does it mean to say something is a “fact” or that it is “well-known” and understood?  Qualitative researchers are particularly interested in these questions because of the types of research questions we are interested in answering (the how questions rather than the how many questions of quantitative research).  Qualitative researchers have adopted various epistemological approaches.  Chapter 3 will explore these approaches, highlighting interpretivist approaches that acknowledge the subjective aspect of reality – in other words, reality and knowledge are not objective but rather influenced by (interpreted through) people.

Chapter 4 focuses on the practical matter of developing a research question and finding the right approach to data collection.  In any given study (think of Cory Abramson’s study of aging, for example), there may be years of collected data, thousands of observations , hundreds of pages of notes to read and review and make sense of.  If all you had was a general interest area (“aging”), it would be very difficult, nearly impossible, to make sense of all of that data.  The research question provides a helpful lens to refine and clarify (and simplify) everything you find and collect.  For that reason, it is important to pull out that lens (articulate the research question) before you get started.  In the case of the aging study, Cory Abramson was interested in how inequalities affected understandings and responses to aging.  It is for this reason he designed a study that would allow him to compare different groups of seniors (some middle-class, some poor).  Inevitably, he saw much more in the three years in the field than what made it into his book (or dissertation), but he was able to narrow down the complexity of the social world to provide us with this rich account linked to the original research question.  Developing a good research question is thus crucial to effective design and a successful outcome.  Chapter 4 will provide pointers on how to do this.  Chapter 4 also provides an overview of general approaches taken to doing qualitative research and various “traditions of inquiry.”

Chapter 5 explores sampling .  After you have developed a research question and have a general idea of how you will collect data (Observations?  Interviews?), how do you go about actually finding people and sites to study?  Although there is no “correct number” of people to interview , the sample should follow the research question and research design.  Unlike quantitative research, qualitative research involves nonprobability sampling.  Chapter 5 explains why this is so and what qualities instead make a good sample for qualitative research.

Chapter 6 addresses the importance of reflexivity in qualitative research.  Related to epistemological issues of how we know anything about the social world, qualitative researchers understand that we the researchers can never be truly neutral or outside the study we are conducting.  As observers, we see things that make sense to us and may entirely miss what is either too obvious to note or too different to comprehend.  As interviewers, as much as we would like to ask questions neutrally and remain in the background, interviews are a form of conversation, and the persons we interview are responding to us .  Therefore, it is important to reflect upon our social positions and the knowledges and expectations we bring to our work and to work through any blind spots that we may have.  Chapter 6 provides some examples of reflexivity in practice and exercises for thinking through one’s own biases.

Chapter 7 is a very important chapter and should not be overlooked.  As a practical matter, it should also be read closely with chapters 6 and 8.  Because qualitative researchers deal with people and the social world, it is imperative they develop and adhere to a strong ethical code for conducting research in a way that does not harm.  There are legal requirements and guidelines for doing so (see chapter 8), but these requirements should not be considered synonymous with the ethical code required of us.   Each researcher must constantly interrogate every aspect of their research, from research question to design to sample through analysis and presentation, to ensure that a minimum of harm (ideally, zero harm) is caused.  Because each research project is unique, the standards of care for each study are unique.  Part of being a professional researcher is carrying this code in one’s heart, being constantly attentive to what is required under particular circumstances.  Chapter 7 provides various research scenarios and asks readers to weigh in on the suitability and appropriateness of the research.  If done in a class setting, it will become obvious fairly quickly that there are often no absolutely correct answers, as different people find different aspects of the scenarios of greatest importance.  Minimizing the harm in one area may require possible harm in another.  Being attentive to all the ethical aspects of one’s research and making the best judgments one can, clearly and consciously, is an integral part of being a good researcher.

Chapter 8 , best to be read in conjunction with chapter 7, explains the role and importance of Institutional Review Boards (IRBs) .  Under federal guidelines, an IRB is an appropriately constituted group that has been formally designated to review and monitor research involving human subjects .  Every institution that receives funding from the federal government has an IRB.  IRBs have the authority to approve, require modifications to (to secure approval), or disapprove research.  This group review serves an important role in the protection of the rights and welfare of human research subjects.  Chapter 8 reviews the history of IRBs and the work they do but also argues that IRBs’ review of qualitative research is often both over-inclusive and under-inclusive.  Some aspects of qualitative research are not well understood by IRBs, given that they were developed to prevent abuses in biomedical research.  Thus, it is important not to rely on IRBs to identify all the potential ethical issues that emerge in our research (see chapter 7).

Chapter 9 provides help for getting started on formulating a research question based on gaps in the pre-existing literature.  Research is conducted as part of a community, even if particular studies are done by single individuals (or small teams).  What any of us finds and reports back becomes part of a much larger body of knowledge.  Thus, it is important that we look at the larger body of knowledge before we actually start our bit to see how we can best contribute.  When I first began interviewing working-class college students, there was only one other similar study I could find, and it hadn’t been published (it was a dissertation of students from poor backgrounds).  But there had been a lot published by professors who had grown up working class and made it through college despite the odds.  These accounts by “working-class academics” became an important inspiration for my study and helped me frame the questions I asked the students I interviewed.  Chapter 9 will provide some pointers on how to search for relevant literature and how to use this to refine your research question.

Chapter 10 serves as a bridge between the two parts of the textbook, by introducing techniques of data collection.  Qualitative research is often characterized by the form of data collection – for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos.  Techniques can be effectively combined, depending on the research question and the aims and goals of the study.   Chapter 10 provides a general overview of all the various techniques and how they can be combined.

The second part of the textbook moves into the doing part of qualitative research once the research question has been articulated and the study designed.  Chapters 11 through 17 cover various data collection techniques and approaches.  Chapters 18 and 19 provide a very simple overview of basic data analysis.  Chapter 20 covers communication of the data to various audiences, and in various formats.

Chapter 11 begins our overview of data collection techniques with a focus on interviewing , the true heart of qualitative research.  This technique can serve as the primary and exclusive form of data collection, or it can be used to supplement other forms (observation, archival).  An interview is distinct from a survey, where questions are asked in a specific order and often with a range of predetermined responses available.  Interviews can be conversational and unstructured or, more conventionally, semistructured , where a general set of interview questions “guides” the conversation.  Chapter 11 covers the basics of interviews: how to create interview guides, how many people to interview, where to conduct the interview, what to watch out for (how to prepare against things going wrong), and how to get the most out of your interviews.

Chapter 12 covers an important variant of interviewing, the focus group.  Focus groups are semistructured interviews with a group of people moderated by a facilitator (the researcher or researcher’s assistant).  Focus groups explicitly use group interaction to assist in the data collection.  They are best used to collect data on a specific topic that is non-personal and shared among the group.  For example, asking a group of college students about a common experience such as taking classes by remote delivery during the pandemic year of 2020.  Chapter 12 covers the basics of focus groups: when to use them, how to create interview guides for them, and how to run them effectively.

Chapter 13 moves away from interviewing to the second major form of data collection unique to qualitative researchers – observation .  Qualitative research that employs observation can best be understood as falling on a continuum of “fly on the wall” observation (e.g., observing how strangers interact in a doctor’s waiting room) to “participant” observation, where the researcher is also an active participant of the activity being observed.  For example, an activist in the Black Lives Matter movement might want to study the movement, using her inside position to gain access to observe key meetings and interactions.  Chapter  13 covers the basics of participant observation studies: advantages and disadvantages, gaining access, ethical concerns related to insider/outsider status and entanglement, and recording techniques.

Chapter 14 takes a closer look at “deep ethnography” – immersion in the field of a particularly long duration for the purpose of gaining a deeper understanding and appreciation of a particular culture or social world.  Clifford Geertz called this “deep hanging out.”  Whereas participant observation is often combined with semistructured interview techniques, deep ethnography’s commitment to “living the life” or experiencing the situation as it really is demands more conversational and natural interactions with people.  These interactions and conversations may take place over months or even years.  As can be expected, there are some costs to this technique, as well as some very large rewards when done competently.  Chapter 14 provides some examples of deep ethnographies that will inspire some beginning researchers and intimidate others.

Chapter 15 moves in the opposite direction of deep ethnography, a technique that is the least positivist of all those discussed here, to mixed methods , a set of techniques that is arguably the most positivist .  A mixed methods approach combines both qualitative data collection and quantitative data collection, commonly by combining a survey that is analyzed statistically (e.g., cross-tabs or regression analyses of large number probability samples) with semi-structured interviews.  Although it is somewhat unconventional to discuss mixed methods in textbooks on qualitative research, I think it is important to recognize this often-employed approach here.  There are several advantages and some disadvantages to taking this route.  Chapter 16 will describe those advantages and disadvantages and provide some particular guidance on how to design a mixed methods study for maximum effectiveness.

Chapter 16 covers data collection that does not involve live human subjects at all – archival and historical research (chapter 17 will also cover data that does not involve interacting with human subjects).  Sometimes people are unavailable to us, either because they do not wish to be interviewed or observed (as is the case with many “elites”) or because they are too far away, in both place and time.  Fortunately, humans leave many traces and we can often answer questions we have by examining those traces.  Special collections and archives can be goldmines for social science research.  This chapter will explain how to access these places, for what purposes, and how to begin to make sense of what you find.

Chapter 17 covers another data collection area that does not involve face-to-face interaction with humans: content analysis .  Although content analysis may be understood more properly as a data analysis technique, the term is often used for the entire approach, which will be the case here.  Content analysis involves interpreting meaning from a body of text.  This body of text might be something found in historical records (see chapter 16) or something collected by the researcher, as in the case of comment posts on a popular blog post.  I once used the stories told by student loan debtors on the website studentloanjustice.org as the content I analyzed.  Content analysis is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest.  In other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue.  This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis.

Where chapter 17 has pushed us towards data analysis, chapters 18 and 19 are all about what to do with the data collected, whether that data be in the form of interview transcripts or fieldnotes from observations.  Chapter 18 introduces the basics of coding , the iterative process of assigning meaning to the data in order to both simplify and identify patterns.  What is a code and how does it work?  What are the different ways of coding data, and when should you use them?  What is a codebook, and why do you need one?  What does the process of data analysis look like?

Chapter 19 goes further into detail on codes and how to use them, particularly the later stages of coding in which our codes are refined, simplified, combined, and organized.  These later rounds of coding are essential to getting the most out of the data we’ve collected.  As students are often overwhelmed with the amount of data (a corpus of interview transcripts typically runs into the hundreds of pages; fieldnotes can easily top that), this chapter will also address time management and provide suggestions for dealing with chaos and reminders that feeling overwhelmed at the analysis stage is part of the process.  By the end of the chapter, you should understand how “findings” are actually found.

The book concludes with a chapter dedicated to the effective presentation of data results.  Chapter 20 covers the many ways that researchers communicate their studies to various audiences (academic, personal, political), what elements must be included in these various publications, and the hallmarks of excellent qualitative research that various audiences will be expecting.  Because qualitative researchers are motivated by understanding and conveying meaning , effective communication is not only an essential skill but a fundamental facet of the entire research project.  Ethnographers must be able to convey a certain sense of verisimilitude , the appearance of true reality.  Those employing interviews must faithfully depict the key meanings of the people they interviewed in a way that rings true to those people, even if the end result surprises them.  And all researchers must strive for clarity in their publications so that various audiences can understand what was found and why it is important.

The book concludes with a short chapter ( chapter 21 ) discussing the value of qualitative research. At the very end of this book, you will find a glossary of terms. I recommend you make frequent use of the glossary and add to each entry as you find examples. Although the entries are meant to be simple and clear, you may also want to paraphrase the definition—make it “make sense” to you, in other words. In addition to the standard reference list (all works cited here), you will find various recommendations for further reading at the end of many chapters. Some of these recommendations will be examples of excellent qualitative research, indicated with an asterisk (*) at the end of the entry. As they say, a picture is worth a thousand words. A good example of qualitative research can teach you more about conducting research than any textbook can (this one included). I highly recommend you select one to three examples from these lists and read them along with the textbook.

A final note on the choice of examples – you will note that many of the examples used in the text come from research on college students.  This is for two reasons.  First, as most of my research falls in this area, I am most familiar with this literature and have contacts with those who do research here and can call upon them to share their stories with you.  Second, and more importantly, my hope is that this textbook reaches a wide audience of beginning researchers who study widely and deeply across the range of what can be known about the social world (from marine resources management to public policy to nursing to political science to sexuality studies and beyond).  It is sometimes difficult to find examples that speak to all those research interests, however. A focus on college students is something that all readers can understand and, hopefully, appreciate, as we are all now or have been at some point a college student.

Recommended Reading: Other Qualitative Research Textbooks

I’ve included a brief list of some of my favorite qualitative research textbooks and guidebooks if you need more than what you will find in this introductory text.  For each, I’ve also indicated if these are for “beginning” or “advanced” (graduate-level) readers.  Many of these books have several editions that do not significantly vary; the edition recommended is merely the edition I have used in teaching and to whose page numbers any specific references made in the text agree.

Barbour, Rosaline. 2014. Introducing Qualitative Research: A Student’s Guide. Thousand Oaks, CA: SAGE.  A good introduction to qualitative research, with abundant examples (often from the discipline of health care) and clear definitions.  Includes quick summaries at the ends of each chapter.  However, some US students might find the British context distracting and can be a bit advanced in some places.  Beginning .

Bloomberg, Linda Dale, and Marie F. Volpe. 2012. Completing Your Qualitative Dissertation . 2nd ed. Thousand Oaks, CA: SAGE.  Specifically designed to guide graduate students through the research process. Advanced .

Creswell, John W., and Cheryl Poth. 2018 Qualitative Inquiry and Research Design: Choosing among Five Traditions .  4th ed. Thousand Oaks, CA: SAGE.  This is a classic and one of the go-to books I used myself as a graduate student.  One of the best things about this text is its clear presentation of five distinct traditions in qualitative research.  Despite the title, this reasonably sized book is about more than research design, including both data analysis and how to write about qualitative research.  Advanced .

Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up .  Chicago: University of Chicago Press. A readable and personal account of conducting qualitative research by an eminent sociologist, with a heavy emphasis on the kinds of participant-observation research conducted by the author.  Despite its reader-friendliness, this is really a book targeted to graduate students learning the craft.  Advanced .

Lune, Howard, and Bruce L. Berg. 2018. 9th edition.  Qualitative Research Methods for the Social Sciences.  Pearson . Although a good introduction to qualitative methods, the authors favor symbolic interactionist and dramaturgical approaches, which limits the appeal primarily to sociologists.  Beginning .

Marshall, Catherine, and Gretchen B. Rossman. 2016. 6th edition. Designing Qualitative Research. Thousand Oaks, CA: SAGE.  Very readable and accessible guide to research design by two educational scholars.  Although the presentation is sometimes fairly dry, personal vignettes and illustrations enliven the text.  Beginning .

Maxwell, Joseph A. 2013. Qualitative Research Design: An Interactive Approach .  3rd ed. Thousand Oaks, CA: SAGE. A short and accessible introduction to qualitative research design, particularly helpful for graduate students contemplating theses and dissertations. This has been a standard textbook in my graduate-level courses for years.  Advanced .

Patton, Michael Quinn. 2002. Qualitative Research and Evaluation Methods . Thousand Oaks, CA: SAGE.  This is a comprehensive text that served as my “go-to” reference when I was a graduate student.  It is particularly helpful for those involved in program evaluation and other forms of evaluation studies and uses examples from a wide range of disciplines.  Advanced .

Rubin, Ashley T. 2021. Rocking Qualitative Social Science: An Irreverent Guide to Rigorous Research. Stanford : Stanford University Press.  A delightful and personal read.  Rubin uses rock climbing as an extended metaphor for learning how to conduct qualitative research.  A bit slanted toward ethnographic and archival methods of data collection, with frequent examples from her own studies in criminology. Beginning .

Weis, Lois, and Michelle Fine. 2000. Speed Bumps: A Student-Friendly Guide to Qualitative Research . New York: Teachers College Press.  Readable and accessibly written in a quasi-conversational style.  Particularly strong in its discussion of ethical issues throughout the qualitative research process.  Not comprehensive, however, and very much tied to ethnographic research.  Although designed for graduate students, this is a recommended read for students of all levels.  Beginning .

Patton’s Ten Suggestions for Doing Qualitative Research

The following ten suggestions were made by Michael Quinn Patton in his massive textbooks Qualitative Research and Evaluations Methods . This book is highly recommended for those of you who want more than an introduction to qualitative methods. It is the book I relied on heavily when I was a graduate student, although it is much easier to “dip into” when necessary than to read through as a whole. Patton is asked for “just one bit of advice” for a graduate student considering using qualitative research methods for their dissertation.  Here are his top ten responses, in short form, heavily paraphrased, and with additional comments and emphases from me:

• Make sure that a qualitative approach fits the research question. The following are the kinds of questions that call out for qualitative methods or where qualitative methods are particularly appropriate: questions about people’s experiences or how they make sense of those experiences; studying a person in their natural environment; researching a phenomenon so unknown that it would be impossible to study it with standardized instruments or other forms of quantitative data collection.
• Study qualitative research by going to the original sources for the design and analysis appropriate to the particular approach you want to take (e.g., read Glaser and Straus if you are using grounded theory )
• Find a dissertation adviser who understands or at least who will support your use of qualitative research methods. You are asking for trouble if your entire committee is populated by quantitative researchers, even if they are all very knowledgeable about the subject or focus of your study (maybe even more so if they are!)
• Really work on design. Doing qualitative research effectively takes a lot of planning.  Even if things are more flexible than in quantitative research, a good design is absolutely essential when starting out.
• Practice data collection techniques, particularly interviewing and observing. There is definitely a set of learned skills here!  Do not expect your first interview to be perfect.  You will continue to grow as a researcher the more interviews you conduct, and you will probably come to understand yourself a bit more in the process, too.  This is not easy, despite what others who don’t work with qualitative methods may assume (and tell you!)
• Have a plan for analysis before you begin data collection. This is often a requirement in IRB protocols , although you can get away with writing something fairly simple.  And even if you are taking an approach, such as grounded theory, that pushes you to remain fairly open-minded during the data collection process, you still want to know what you will be doing with all the data collected – creating a codebook? Writing analytical memos? Comparing cases?  Having a plan in hand will also help prevent you from collecting too much extraneous data.
• Be prepared to confront controversies both within the qualitative research community and between qualitative research and quantitative research. Don’t be naïve about this – qualitative research, particularly some approaches, will be derided by many more “positivist” researchers and audiences.  For example, is an “n” of 1 really sufficient?  Yes!  But not everyone will agree.
• Do not make the mistake of using qualitative research methods because someone told you it was easier, or because you are intimidated by the math required of statistical analyses. Qualitative research is difficult in its own way (and many would claim much more time-consuming than quantitative research).  Do it because you are convinced it is right for your goals, aims, and research questions.
• Find a good support network. This could be a research mentor, or it could be a group of friends or colleagues who are also using qualitative research, or it could be just someone who will listen to you work through all of the issues you will confront out in the field and during the writing process.  Even though qualitative research often involves human subjects, it can be pretty lonely.  A lot of times you will feel like you are working without a net.  You have to create one for yourself.  Take care of yourself.
• And, finally, in the words of Patton, “Prepare to be changed. Looking deeply at other people’s lives will force you to look deeply at yourself.”
• We will actually spend an entire chapter ( chapter 3 ) looking at this question in much more detail! ↵
• Note that this might have been news to Europeans at the time, but many other societies around the world had also come to this conclusion through observation.  There is often a tendency to equate “the scientific revolution” with the European world in which it took place, but this is somewhat misleading. ↵
• Historians are a special case here.  Historians have scrupulously and rigorously investigated the social world, but not for the purpose of understanding general laws about how things work, which is the point of scientific empirical research.  History is often referred to as an idiographic field of study, meaning that it studies things that happened or are happening in themselves and not for general observations or conclusions. ↵
• Don’t worry, we’ll spend more time later in this book unpacking the meaning of ethnography and other terms that are important here.  Note the available glossary ↵

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

In contrast to methodology, methods are more simply the practices and tools used to collect and analyze data.  Examples of common methods in qualitative research are interviews , observations , and documentary analysis .  One’s methodology should connect to one’s choice of methods, of course, but they are distinguishable terms.  See also methodology .

A proposed explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.  The positing of a hypothesis is often the first step in quantitative research but not in qualitative research.  Even when qualitative researchers offer possible explanations in advance of conducting research, they will tend to not use the word “hypothesis” as it conjures up the kind of positivist research they are not conducting.

The foundational question to be addressed by the research study.  This will form the anchor of the research design, collection, and analysis.  Note that in qualitative research, the research question may, and probably will, alter or develop during the course of the research.

An approach to research that collects and analyzes numerical data for the purpose of finding patterns and averages, making predictions, testing causal relationships, and generalizing results to wider populations.  Contrast with qualitative research .

Data collection that takes place in real-world settings, referred to as “the field;” a key component of much Grounded Theory and ethnographic research.  Patton ( 2002 ) calls fieldwork “the central activity of qualitative inquiry” where “‘going into the field’ means having direct and personal contact with people under study in their own environments – getting close to people and situations being studied to personally understand the realities of minutiae of daily life” (48).

The people who are the subjects of a qualitative study.  In interview-based studies, they may be the respondents to the interviewer; for purposes of IRBs, they are often referred to as the human subjects of the research.

The branch of philosophy concerned with knowledge.  For researchers, it is important to recognize and adopt one of the many distinguishing epistemological perspectives as part of our understanding of what questions research can address or fully answer.  See, e.g., constructivism , subjectivism, and  objectivism .

An approach that refutes the possibility of neutrality in social science research.  All research is “guided by a set of beliefs and feelings about the world and how it should be understood and studied” (Denzin and Lincoln 2005: 13).  In contrast to positivism , interpretivism recognizes the social constructedness of reality, and researchers adopting this approach focus on capturing interpretations and understandings people have about the world rather than “the world” as it is (which is a chimera).

The cluster of data-collection tools and techniques that involve observing interactions between people, the behaviors, and practices of individuals (sometimes in contrast to what they say about how they act and behave), and cultures in context.  Observational methods are the key tools employed by ethnographers and Grounded Theory .

Research based on data collected and analyzed by the research (in contrast to secondary “library” research).

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

A method of data collection in which the researcher asks the participant questions; the answers to these questions are often recorded and transcribed verbatim. There are many different kinds of interviews - see also semistructured interview , structured interview , and unstructured interview .

The specific group of individuals that you will collect data from.  Contrast population.

The practice of being conscious of and reflective upon one’s own social location and presence when conducting research.  Because qualitative research often requires interaction with live humans, failing to take into account how one’s presence and prior expectations and social location affect the data collected and how analyzed may limit the reliability of the findings.  This remains true even when dealing with historical archives and other content.  Who we are matters when asking questions about how people experience the world because we, too, are a part of that world.

The science and practice of right conduct; in research, it is also the delineation of moral obligations towards research participants, communities to which we belong, and communities in which we conduct our research.

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Research, according to US federal guidelines, that involves “a living individual about whom an investigator (whether professional or student) conducting research:  (1) Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or  (2) Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.”

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A form of interview that follows a standard guide of questions asked, although the order of the questions may change to match the particular needs of each individual interview subject, and probing “follow-up” questions are often added during the course of the interview.  The semi-structured interview is the primary form of interviewing used by qualitative researchers in the social sciences.  It is sometimes referred to as an “in-depth” interview.  See also interview and  interview guide .

A method of observational data collection taking place in a natural setting; a form of fieldwork .  The term encompasses a continuum of relative participation by the researcher (from full participant to “fly-on-the-wall” observer).  This is also sometimes referred to as ethnography , although the latter is characterized by a greater focus on the culture under observation.

A research design that employs both quantitative and qualitative methods, as in the case of a survey supplemented by interviews.

An epistemological perspective that posits the existence of reality through sensory experience similar to empiricism but goes further in denying any non-sensory basis of thought or consciousness.  In the social sciences, the term has roots in the proto-sociologist August Comte, who believed he could discern “laws” of society similar to the laws of natural science (e.g., gravity).  The term has come to mean the kinds of measurable and verifiable science conducted by quantitative researchers and is thus used pejoratively by some qualitative researchers interested in interpretation, consciousness, and human understanding.  Calling someone a “positivist” is often intended as an insult.  See also empiricism and objectivism.

A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community.

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

A word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data (Saldaña 2021:5).

Usually a verbatim written record of an interview or focus group discussion.

The primary form of data for fieldwork , participant observation , and ethnography .  These notes, taken by the researcher either during the course of fieldwork or at day’s end, should include as many details as possible on what was observed and what was said.  They should include clear identifiers of date, time, setting, and names (or identifying characteristics) of participants.

The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages.  See coding frame and  codebook.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

A detailed description of any proposed research that involves human subjects for review by IRB.  The protocol serves as the recipe for the conduct of the research activity.  It includes the scientific rationale to justify the conduct of the study, the information necessary to conduct the study, the plan for managing and analyzing the data, and a discussion of the research ethical issues relevant to the research.  Protocols for qualitative research often include interview guides, all documents related to recruitment, informed consent forms, very clear guidelines on the safekeeping of materials collected, and plans for de-identifying transcripts or other data that include personal identifying information.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Qualitative Methods
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The word qualitative implies an emphasis on the qualities of entities and on processes and meanings that are not experimentally examined or measured [if measured at all] in terms of quantity, amount, intensity, or frequency. Qualitative researchers stress the socially constructed nature of reality, the intimate relationship between the researcher and what is studied, and the situational constraints that shape inquiry. Such researchers emphasize the value-laden nature of inquiry. They seek answers to questions that stress how social experience is created and given meaning. In contrast, quantitative studies emphasize the measurement and analysis of causal relationships between variables, not processes. Qualitative forms of inquiry are considered by many social and behavioral scientists to be as much a perspective on how to approach investigating a research problem as it is a method.

Denzin, Norman. K. and Yvonna S. Lincoln. “Introduction: The Discipline and Practice of Qualitative Research.” In The Sage Handbook of Qualitative Research . Norman. K. Denzin and Yvonna S. Lincoln, eds. 3 rd edition. (Thousand Oaks, CA: Sage, 2005), p. 10.

Characteristics of Qualitative Research

Below are the three key elements that define a qualitative research study and the applied forms each take in the investigation of a research problem.

• Naturalistic -- refers to studying real-world situations as they unfold naturally; non-manipulative and non-controlling; the researcher is open to whatever emerges [i.e., there is a lack of predetermined constraints on findings].
• Emergent -- acceptance of adapting inquiry as understanding deepens and/or situations change; the researcher avoids rigid designs that eliminate responding to opportunities to pursue new paths of discovery as they emerge.
• Purposeful -- cases for study [e.g., people, organizations, communities, cultures, events, critical incidences] are selected because they are “information rich” and illuminative. That is, they offer useful manifestations of the phenomenon of interest; sampling is aimed at insight about the phenomenon, not empirical generalization derived from a sample and applied to a population.

The Collection of Data

• Data -- observations yield a detailed, "thick description" [in-depth understanding]; interviews capture direct quotations about people’s personal perspectives and lived experiences; often derived from carefully conducted case studies and review of material culture.
• Personal experience and engagement -- researcher has direct contact with and gets close to the people, situation, and phenomenon under investigation; the researcher’s personal experiences and insights are an important part of the inquiry and critical to understanding the phenomenon.
• Empathic neutrality -- an empathic stance in working with study respondents seeks vicarious understanding without judgment [neutrality] by showing openness, sensitivity, respect, awareness, and responsiveness; in observation, it means being fully present [mindfulness].
• Dynamic systems -- there is attention to process; assumes change is ongoing, whether the focus is on an individual, an organization, a community, or an entire culture, therefore, the researcher is mindful of and attentive to system and situational dynamics.

The Analysis

• Unique case orientation -- assumes that each case is special and unique; the first level of analysis is being true to, respecting, and capturing the details of the individual cases being studied; cross-case analysis follows from and depends upon the quality of individual case studies.
• Inductive analysis -- immersion in the details and specifics of the data to discover important patterns, themes, and inter-relationships; begins by exploring, then confirming findings, guided by analytical principles rather than rules.
• Holistic perspective -- the whole phenomenon under study is understood as a complex system that is more than the sum of its parts; the focus is on complex interdependencies and system dynamics that cannot be reduced in any meaningful way to linear, cause and effect relationships and/or a few discrete variables.
• Context sensitive -- places findings in a social, historical, and temporal context; researcher is careful about [even dubious of] the possibility or meaningfulness of generalizations across time and space; emphasizes careful comparative case study analysis and extrapolating patterns for possible transferability and adaptation in new settings.
• Voice, perspective, and reflexivity -- the qualitative methodologist owns and is reflective about her or his own voice and perspective; a credible voice conveys authenticity and trustworthiness; complete objectivity being impossible and pure subjectivity undermining credibility, the researcher's focus reflects a balance between understanding and depicting the world authentically in all its complexity and of being self-analytical, politically aware, and reflexive in consciousness.

Berg, Bruce Lawrence. Qualitative Research Methods for the Social Sciences . 8th edition. Boston, MA: Allyn and Bacon, 2012; Denzin, Norman. K. and Yvonna S. Lincoln. Handbook of Qualitative Research . 2nd edition. Thousand Oaks, CA: Sage, 2000; Marshall, Catherine and Gretchen B. Rossman. Designing Qualitative Research . 2nd ed. Thousand Oaks, CA: Sage Publications, 1995; Merriam, Sharan B. Qualitative Research: A Guide to Design and Implementation . San Francisco, CA: Jossey-Bass, 2009.

Basic Research Design for Qualitative Studies

Unlike positivist or experimental research that utilizes a linear and one-directional sequence of design steps, there is considerable variation in how a qualitative research study is organized. In general, qualitative researchers attempt to describe and interpret human behavior based primarily on the words of selected individuals [a.k.a., “informants” or “respondents”] and/or through the interpretation of their material culture or occupied space. There is a reflexive process underpinning every stage of a qualitative study to ensure that researcher biases, presuppositions, and interpretations are clearly evident, thus ensuring that the reader is better able to interpret the overall validity of the research. According to Maxwell (2009), there are five, not necessarily ordered or sequential, components in qualitative research designs. How they are presented depends upon the research philosophy and theoretical framework of the study, the methods chosen, and the general assumptions underpinning the study. Goals Describe the central research problem being addressed but avoid describing any anticipated outcomes. Questions to ask yourself are: Why is your study worth doing? What issues do you want to clarify, and what practices and policies do you want it to influence? Why do you want to conduct this study, and why should the reader care about the results? Conceptual Framework Questions to ask yourself are: What do you think is going on with the issues, settings, or people you plan to study? What theories, beliefs, and prior research findings will guide or inform your research, and what literature, preliminary studies, and personal experiences will you draw upon for understanding the people or issues you are studying? Note to not only report the results of other studies in your review of the literature, but note the methods used as well. If appropriate, describe why earlier studies using quantitative methods were inadequate in addressing the research problem. Research Questions Usually there is a research problem that frames your qualitative study and that influences your decision about what methods to use, but qualitative designs generally lack an accompanying hypothesis or set of assumptions because the findings are emergent and unpredictable. In this context, more specific research questions are generally the result of an interactive design process rather than the starting point for that process. Questions to ask yourself are: What do you specifically want to learn or understand by conducting this study? What do you not know about the things you are studying that you want to learn? What questions will your research attempt to answer, and how are these questions related to one another? Methods Structured approaches to applying a method or methods to your study help to ensure that there is comparability of data across sources and researchers and, thus, they can be useful in answering questions that deal with differences between phenomena and the explanation for these differences [variance questions]. An unstructured approach allows the researcher to focus on the particular phenomena studied. This facilitates an understanding of the processes that led to specific outcomes, trading generalizability and comparability for internal validity and contextual and evaluative understanding. Questions to ask yourself are: What will you actually do in conducting this study? What approaches and techniques will you use to collect and analyze your data, and how do these constitute an integrated strategy? Validity In contrast to quantitative studies where the goal is to design, in advance, “controls” such as formal comparisons, sampling strategies, or statistical manipulations to address anticipated and unanticipated threats to validity, qualitative researchers must attempt to rule out most threats to validity after the research has begun by relying on evidence collected during the research process itself in order to effectively argue that any alternative explanations for a phenomenon are implausible. Questions to ask yourself are: How might your results and conclusions be wrong? What are the plausible alternative interpretations and validity threats to these, and how will you deal with these? How can the data that you have, or that you could potentially collect, support or challenge your ideas about what’s going on? Why should we believe your results? Conclusion Although Maxwell does not mention a conclusion as one of the components of a qualitative research design, you should formally conclude your study. Briefly reiterate the goals of your study and the ways in which your research addressed them. Discuss the benefits of your study and how stakeholders can use your results. Also, note the limitations of your study and, if appropriate, place them in the context of areas in need of further research.

Chenail, Ronald J. Introduction to Qualitative Research Design. Nova Southeastern University; Heath, A. W. The Proposal in Qualitative Research. The Qualitative Report 3 (March 1997); Marshall, Catherine and Gretchen B. Rossman. Designing Qualitative Research . 3rd edition. Thousand Oaks, CA: Sage, 1999; Maxwell, Joseph A. "Designing a Qualitative Study." In The SAGE Handbook of Applied Social Research Methods . Leonard Bickman and Debra J. Rog, eds. 2nd ed. (Thousand Oaks, CA: Sage, 2009), p. 214-253; Qualitative Research Methods. Writing@CSU. Colorado State University; Yin, Robert K. Qualitative Research from Start to Finish . 2nd edition. New York: Guilford, 2015.

Strengths of Using Qualitative Methods

The advantage of using qualitative methods is that they generate rich, detailed data that leave the participants' perspectives intact and provide multiple contexts for understanding the phenomenon under study. In this way, qualitative research can be used to vividly demonstrate phenomena or to conduct cross-case comparisons and analysis of individuals or groups.

Among the specific strengths of using qualitative methods to study social science research problems is the ability to:

• Obtain a more realistic view of the lived world that cannot be understood or experienced in numerical data and statistical analysis;
• Provide the researcher with the perspective of the participants of the study through immersion in a culture or situation and as a result of direct interaction with them;
• Allow the researcher to describe existing phenomena and current situations;
• Develop flexible ways to perform data collection, subsequent analysis, and interpretation of collected information;
• Yield results that can be helpful in pioneering new ways of understanding;
• Respond to changes that occur while conducting the study ]e.g., extended fieldwork or observation] and offer the flexibility to shift the focus of the research as a result;
• Provide a holistic view of the phenomena under investigation;
• Respond to local situations, conditions, and needs of participants;
• Interact with the research subjects in their own language and on their own terms; and,
• Create a descriptive capability based on primary and unstructured data.

Anderson, Claire. “Presenting and Evaluating Qualitative Research.” American Journal of Pharmaceutical Education 74 (2010): 1-7; Denzin, Norman. K. and Yvonna S. Lincoln. Handbook of Qualitative Research . 2nd edition. Thousand Oaks, CA: Sage, 2000; Merriam, Sharan B. Qualitative Research: A Guide to Design and Implementation . San Francisco, CA: Jossey-Bass, 2009.

Limitations of Using Qualitative Methods

It is very much true that most of the limitations you find in using qualitative research techniques also reflect their inherent strengths . For example, small sample sizes help you investigate research problems in a comprehensive and in-depth manner. However, small sample sizes undermine opportunities to draw useful generalizations from, or to make broad policy recommendations based upon, the findings. Additionally, as the primary instrument of investigation, qualitative researchers are often embedded in the cultures and experiences of others. However, cultural embeddedness increases the opportunity for bias generated from conscious or unconscious assumptions about the study setting to enter into how data is gathered, interpreted, and reported.

Some specific limitations associated with using qualitative methods to study research problems in the social sciences include the following:

• Drifting away from the original objectives of the study in response to the changing nature of the context under which the research is conducted;
• Arriving at different conclusions based on the same information depending on the personal characteristics of the researcher;
• Replication of a study is very difficult;
• Research using human subjects increases the chance of ethical dilemmas that undermine the overall validity of the study;
• An inability to investigate causality between different research phenomena;
• Difficulty in explaining differences in the quality and quantity of information obtained from different respondents and arriving at different, non-consistent conclusions;
• Data gathering and analysis is often time consuming and/or expensive;
• Requires a high level of experience from the researcher to obtain the targeted information from the respondent;
• May lack consistency and reliability because the researcher can employ different probing techniques and the respondent can choose to tell some particular stories and ignore others; and,
• Generation of a significant amount of data that cannot be randomized into manageable parts for analysis.

Research Tip

Human Subject Research and Institutional Review Board Approval

Almost every socio-behavioral study requires you to submit your proposed research plan to an Institutional Review Board. The role of the Board is to evaluate your research proposal and determine whether it will be conducted ethically and under the regulations, institutional polices, and Code of Ethics set forth by the university. The purpose of the review is to protect the rights and welfare of individuals participating in your study. The review is intended to ensure equitable selection of respondents, that you have met the requirements for obtaining informed consent , that there is clear assessment and minimization of risks to participants and to the university [read: no lawsuits!], and that privacy and confidentiality are maintained throughout the research process and beyond. Go to the USC IRB website for detailed information and templates of forms you need to submit before you can proceed. If you are  unsure whether your study is subject to IRB review, consult with your professor or academic advisor.

Chenail, Ronald J. Introduction to Qualitative Research Design. Nova Southeastern University; Labaree, Robert V. "Working Successfully with Your Institutional Review Board: Practical Advice for Academic Librarians." College and Research Libraries News 71 (April 2010): 190-193.

Another Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

NOTE :  For a list of online communities, research centers, indispensable learning resources, and personal websites of leading qualitative researchers, GO HERE .

For a list of scholarly journals devoted to the study and application of qualitative research methods, GO HERE .

• << Previous: 6. The Methodology
• Next: Quantitative Methods >>
• Last Updated: Aug 13, 2024 12:57 PM
• URL: https://libguides.usc.edu/writingguide

Approaches to Qualitative Research in Mathematics Education

Examples of Methodology and Methods

• © 2015
• Angelika Bikner-Ahsbahs 0 ,
• Christine Knipping 1 ,
• Norma Presmeg 2

Faculty 3 of Mathematics and Computer Science, University of Bremen, Bremen, Germany

You can also search for this editor in PubMed   Google Scholar

Faculty 3 of Mathematics and Computer Science, University of Bremen, Germany

Mathematics department, illinois state university, normal, usa.

• Provides innovative new approaches to qualitative research
• Presents a state-of-the-art overview on qualitative research in mathematics education
• Discusses both theoretical depth and practical implication
• Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Mathematics Education (AME)

165k Accesses

928 Citations

15 Altmetric

This is a preview of subscription content, log in via an institution to check access.

Access this book

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

Similar content being viewed by others.

Analyzing Qualitative Data in Mathematics Education

Educational Research on Learning and Teaching Mathematics

Putting the quantitative pieces together to maximize the possibilities for a successful project.

• methodologies in mathematics education
• qualitative content analysis
• qualitative research approaches
• research approach in mathematics education
• research practice in mathematics education
• semiotic approaches to research

Table of contents (19 chapters)

Front matter, grounded theory methodology, grounded theory methods.

• Anne R. Teppo

To See the Wood for the Trees: The Development of Theory from Empirical Interview Data Using Grounded Theory

• Maike Vollstedt

Approaches to Reconstructing Argumentation

Methods for reconstructing processes of argumentation and participation in primary mathematics classroom interaction.

• Götz Krummheuer

Reconstructing Argumentation Structures: A Perspective on Proving Processes in Secondary Mathematics Classroom Interactions

• Christine Knipping, David Reid

Ideal Type Construction

Empirically grounded building of ideal types. a methodical principle of constructing theory in the interpretative research in mathematics education.

Angelika Bikner-Ahsbahs

How Ideal Type Construction Can Be Achieved: An Example

Semiotic research, the question of method in a vygotskian semiotic approach.

• Luis Radford, Cristina Sabena

A Theory on Abstraction and Its Methodology

The nested epistemic actions model for abstraction in context: theory as methodological tool and methodological tool as theory.

• Tommy Dreyfus, Rina Hershkowitz, Baruch Schwarz

Networking of Theories

Advancing research by means of the networking of theories.

• Ivy Kidron, Angelika Bikner-Ahsbahs

A Cross-Methodology for the Networking of Theories: The General Epistemic Need (GEN) as a New Concept at the Boundary of Two Theories

• Angelika Bikner-Ahsbahs, Ivy Kidron

Multi-Level-Analysis

Understanding learning across lessons in classroom communities: a multi-leveled analytic approach.

• Geoffrey B. Saxe, Kenton de Kirby, Marie Le, Yasmin Sitabkhan, Bona Kang

Mixed Methods

“This book, Approaches to Qualitative Research in Mathematics Education: Examples of Methodology and Methods, edited by Angelika Bikner-Ahsbahs, Christine Knipping, and Norma Presmeg, is a timely and valuable addition to the research literature in mathematics education. … The book is to be strongly recommended.” (Keith Jones and Chronoula Voutsina, Educational Studies in Mathematics, Vol. 96, 2017)

“Approaches to Qualitative Research in Mathematics Education: Examples of Methodology and Methods is a clever gift for the skeptics who believe that pursuing truth is only possible through traditional empirical research. … The work of the contributors inspires researchers in the field of mathematics education to replicate thestudies and, more importantly, creates opportunities to further reflect on the ways theories inform qualitative research designs and methods.” (Woong Lim, MAA Reviews, July, 2015)

Editors and Affiliations

Christine Knipping

Norma Presmeg

Bibliographic Information

Book Title : Approaches to Qualitative Research in Mathematics Education

Book Subtitle : Examples of Methodology and Methods

Editors : Angelika Bikner-Ahsbahs, Christine Knipping, Norma Presmeg

Series Title : Advances in Mathematics Education

DOI : https://doi.org/10.1007/978-94-017-9181-6

Publisher : Springer Dordrecht

eBook Packages : Humanities, Social Sciences and Law , Education (R0)

Copyright Information : Springer Science+Business Media Dordrecht 2015

Hardcover ISBN : 978-94-017-9180-9 Published: 05 December 2014

Softcover ISBN : 978-94-024-0688-7 Published: 11 September 2016

eBook ISBN : 978-94-017-9181-6 Published: 26 November 2014

Series ISSN : 1869-4918

Series E-ISSN : 1869-4926

Edition Number : 1

Number of Pages : XV, 592

Number of Illustrations : 94 b/w illustrations, 18 illustrations in colour

Topics : Mathematics Education , Curriculum Studies , Science Education

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Open access
• Published: 15 August 2024

Shared patient information and trust: a qualitative study of a national eHealth system

• Kristine Lundhaug 1 ,
• Arild Faxvaag 2 ,
• Randi Stokke 1 &
• Hege Kristin Andreassen 3 , 1  

BMC Digital Health volume  2 , Article number:  57 ( 2024 ) Cite this article

98 Accesses

Metrics details

In Norway, as in other countries, national eHealth systems, such as the Summary Care Record (SCR), have been implemented to improve the collaboration around patients by sharing patient information between health professionals across healthcare institutions and administrative levels. Although widely implemented across the health and care services in Norway, evaluations of the SCR indicate less use than expected. There is a need for analysis that lays out the visions and expectations of the SCR and contrasts these with detailed observations of use in everyday health professional work. This study adds to the eHealth research field by exploring this reality.

This paper has a qualitative design with an ethnographic approach, including participant observation, qualitative interviews, and a document review. Qualitative individual interviews with 22 health professionals and six weeks of participant observation were conducted, and eight documents were reviewed. The field notes and the interview-transcriptions were analyzed following a stepwise-deductive induction analysis.

The document review identified the expectations and visions of the SCR, including an underlying assumption of trust in shared patient information. However, this assumption is implicit and not recognized as a crucial element for success in the documents. In our observation and interview data, we found that health professionals do not necessarily trust information in the SCR. In fact, several procedures and routines to assess the trustworthiness of SCR information were identified that complicate and disturb the expected use. In our analysis, two main themes characterize the health professionals' handling of the SCR: adapting to workflow and dealing with uncertainty .

Our study illustrates that unconditional trust in shared patient information is an implicit assumption in SCR policy documents, but in their everyday work health professionals do not necessarily unconditionally trust shared patient information. Rather, sharing patient information through technology, such as the SCR, requires of health professionals to critically assess the digital information. The information in the SCR, as all sources of information presented to health professionals, becomes an item for their constant trust-work. Our study is of value to policymakers, health information systems developers, and the field of practice both nationally and internationally.

Peer Review reports

In the early years of digital health information systems (HIS), each institution typically had its own system. This solved the problem of communication and information exchange within the institution but not the need to exchange information across institutional borders.

The clinical encounter between a patient and a health professional (HP) is the building block of everyday medicine. During the encounter, HPs must gather information about the patient's problem, assess information that has been made available, make and execute decisions, and finally document the decisions that were made and subsequently set them in motion. An increasing group of patients have needs that require a multidisciplinary approach at different levels and institutions in the health and care service. To make informed decisions, each HP will need information concerning the decisions made by other HPs. Currently, eHealth systems for information exchange and communication between hospitals, general practitioners (GP), and home care services are expanding worldwide [ 1 , 2 , 3 ].

Huge investments in eHealth systems suggest that big challenges in health and care services, such as the pressure and problems related to an aging population, long-term and complicated chronic diseases, and fragmented healthcare, can be met with technological improvements [ 2 ]. Previous studies have shown that sharing electronic documents is essential in coordinating health and care services across organizational boundaries [ 4 ]. A systematic review exploring factors influential to the implementation of eHealth has found that no single factor was identified as a key facilitator or barrier but that issues around implementation are multi-level and complex [ 4 ]. Other reviews have concluded that eHealth systems could improve access and exchange of information, improve the quality of care, and support policymaking, but underline that for these “benefits to actualize, it is critical to focus on their implementation, which requires attention to more than just the technology" ([ 5 ] p.2046). While trust is a much-researched topic in the health sciences [ 6 ], to our knowledge, research into HP trust in national eHealth systems is lacking.

This study was conducted in Norway. Currently, Norway has two national eHealth systems for sharing patient health information between and across the healthcare sector: "e-prescription" and "the summary care record" (SCR). This study focuses on the latter, SCR. The SCR is the first national digital solution for sharing patients' health information between professionals across different levels and institutions in health and care services in Norway. It is used both in the primary care sector and in hospitals. The SCR is integrated with electronic health records (EHR) and is an electronic service that contains essential health information such as critical information, a pharmaceutical summary, appointment history (hospitals), contact information to the next of kin, and the name and contact information of the GP.

White papers in Norway have for many years pointed out the need for better cooperation and information sharing across all levels of the health and care service, and in 2008, it was determined that a National SCR should be considered [ 7 ]. During 2013–2017, the SCR was implemented in all hospitals, emergency call centers, out-of-duty medical response offices, and 90% of the GP clinics [ 8 ]. The implementation of the SCR in nursing homes and home care services started in 2019, and a full national rollout of the SCR is expected within 2025 [ 9 , 10 ].

Scotland, England [ 3 ], Sweden [ 11 ], and Norway [ 12 ] are among the countries that have implemented nationwide EHRs. A study conducted in Norway shows that eHealth systems, such as the SCR, have been used considerably less than expected by the health authorities before the implementation [ 13 ]. Further, it has been highlighted that the perception of success could differ from those who implemented the technology to those who used it [ 13 ]. One of the arguments for establishing the SCR was to reduce medication errors; to obtain this effect, sharing information, such as the pharmaceutical summary in the SCR, is considered crucial [ 14 ]. However, the problem is complex since HPs perceive obtaining the medication list as fragmented, complex, risky, time-consuming, and causing uncertainty [ 15 ]. This is mainly related to the critical phase of a patient's transition between levels of care [ 15 ].

Even though the Norwegian SCR is intended to be used by nurses, medical doctors (MD), and other HPs, studies have focused on MDs use of the SCR [ 12 ]. Limited research has been done on everyday use of the SCR across professions and levels of care. Our ethnographical approach adds to an interdisciplinary research field on eHealth by including multiple professions: nurses, a physiotherapist, and MDs, and from both primary and specialist health care.

Numerous studies have explored patient trust in HPs, doctor-patient relations, or public trust in health information exchange. Studies on measuring trust in the health system are growing but focus mainly on the relationship among MDs, nurses, and patients and not on relations between humans and technology [ 6 ]. A previous study on the SCR found that trustworthiness in information being shared is an important aspect of MDs use and experience with the SCR [ 12 ]. The study also emphasized that what kind of data sources that is trusted or preferred "is a much-less-explored topic" (12, p. 8) when it comes to shared health information. A recent synthetic review over the last fifty years of trust research in health care emphasizes that "trust plays a critical role in facilitating health care delivery" (16, p. 126) and found that the literature on trust was mostly on patient trust in clinicians [ 16 ]. To our knowledge, there have been few studies on the trust assessment of information in HP workflow and trust assessment of national eHealth systems that share patient information.

Theoretical framework

This article presents an analysis of technology in practice in multidisciplinary and cross-institutional collaborations, anchored in core concepts from Science and Technology Studies (STS) that emphasize the interactions between social and technical elements.

STS is an interdisciplinary field exploring social construction and technology and their interaction. To explore the visions and expectations of a national eHealth system and be able to contrast these with observations of how the system is experienced and used in practice, we lean our analysis on the concepts of "scenario" [ 17 ] and "script" [ 18 ]. These analytical entrances help us unpack the sociotechnical practices and visions embedded in the SCR and thus pave the way for an in-depth understanding of a practice that otherwise might be fleeting and difficult to grasp.

Scenario and script

New technologies are never produced or introduced in a neutral way. Rather, they come with scenarios for the universe the technology is entering. Callon [ 17 ] has illustrated the notion of scenario in his study of the development of an electric car in the early 1970s. In the development of the electric car, the developers and designers have constructed scenarios to shape and imagine the future in which the car would exist. Outlining these scenarios can show how social, economic, and political considerations are built into technology [ 17 ]. This is how we used the notion of scenario to analyze the goals, values, interests, and possibilities that were written, implicit or explicit, in documents about the SCR.

Akrich [ 18 ] has developed the notion of script as a tool to conceptualize how technology designers can inscribe values into technology. The analogy of a film script makes us aware of how there exist defined expectations towards who the actors should be, these actors’ roles and responsibilities, the distribution of tasks between them, and the different actors' needs and interests. The presumptions in a script are not only about the individual character of the various actors, but also about the environment in which the technology will be used and their visions about the world. The analogy to the film metaphor is useful, Akrich [ 18 ] argues, because it shows that technological scripts, like film scripts, are not static but leave a margin of freedom to the actor. To use the film metaphor on our study, we interpret the human actors to be the HPs, and the non-human actor is the SCR that comes with a technological script, inscribed by human actors like developers, programmers and vendors, but played out on scenes where these human actors are not present. There are numerous negotiations and renegotiations between the HPs and the SCR. The HPs use the freedom to interpret, negotiate and renegotiate the script and their roles in it, as well as the roles of the SCR itself. Along with the notion of the script, Akrich and Latour [ 19 ] have developed an extended vocabulary, which can be useful to describe the negotiation and renegotiation between actors. The script is dynamic and can adjust and change; it rescripts. Technology can have a strong or weak script, which refers to the flexibility in the use of the technology. "A strong script suggests a certain kind of use, while a weaker script suggests a larger degree of flexibility" ([ 20 ] p.390). As an analytical tool, script sensitized us to how human actors (HPs) negotiate and adapt the technology (SCR) to their work and how everyday practice adjusts in meeting the technology (SCR).

Hence, this study aims to use a combination of the concepts scenario and script as a lens to review documents on national eHealth systems, and contrast these to our data on how HPs use and experience a national eHealth system in their work. More specifically, we ask the following research questions: What visions and expectations are written into the Norwegian SCR script? How do HPs use and experience the SCR in everyday work?

This study has a qualitative design with an ethnographic approach, including participant observation, qualitative interviews, and a document review. Drawing on Charmaz [ 21 ], the definition of ethnography was "stretched" to involve supplementary data such as documents and interviews and not only participant observations.

Study context and data collection

This study was conducted at a hospital, a home care service, an intermediate unit, and a service allocation unit. They were all located in the same municipality in Norway. The municipality is characterized as a large municipality with more than 20,000 residents [ 22 ]. A purposeful sampling approach was used to recruit the hospital, municipality, units, and participants for this study. In this context, purposeful sampling strategically selects information-rich participants and cases relevant to the research questions [ 23 ]. The municipality was strategically chosen because it had implemented the SCR.

The field work in the intermediate unit lasted for two weeks, in February–March 2021. During that time, individual interviews with five nurses and four MDs from this unit were conducted, in addition to daily observations. Three-week participant observations were conducted in the emergency unit in August–September 2021. During that time, five individual interviews with MDs and two individual interviews with nurses were conducted. The emergency unit at the hospital was strategically selected since the SCR was described as an important tool in an emergency setting [ 24 ]. In the home care service, one-week of participant observation and four individual interviews with nurses were conducted in September 2021. Two individual interviews with HPs in the service allocation unit were conducted over the telephone in December 2021. To summarize, the data collection consists of six weeks of participant observation and 22 individual interviews, as illustrated in Table  1 . The duration of the interviews ranged from 17 to 80 min, with most interviews having a duration of 30 min. The reason for this variation in length is related to the workflow of the interviewees, as they were interviewed during workhours it varied how unpredictable and busy their schedule was and how much time they could set aside for an interview.

Content of observational studies and individual interviews

Observation is a method that "focuses on what people do, while interviews focus on what they say (they do)" (25, p.56). Observation studies can be suitable for exploring and getting insights into interaction and different aspects of a workplace by observing the setting, activities, and actors in their practices [ 25 ] and can better the understanding of the context of the study [ 23 ]. This study used participant observation to gain a deeper understanding of sharing patient information between and across primary and specialist health and care services and how a national eHealth system was used. During the participant observation, the first author followed the HPs around the units. This includes participating in daily and weekly meetings. Most of the participant observation was conducted in the HPs workspaces, which consisted of a desk with a computer, to get an insight into how and when the HPs gathered information about the patients. The first author took field notes during the participant observations to help recall the events in different situations.

Individual interviews were conducted as focused interviews. Focused interviews can be suitable for work-related studies when the interviews occur during work hours, and "the researcher can't expect to have in-depth interviews that last for an hour or longer un-disturbed" (25, p.102). Tjora [ 25 ] argues that focused interviews can be useful when the topic is limited; trust can be gained early in the interview and when the topics to be discussed are not very sensitive or difficult. The focused interviews took place during the participant observations in the different units. Therefore, the interview was conducted at the participants' workplace during work hours, and the location was either a meeting room or an office. The interview guide started with warm-up questions, such as "how long have you worked as a HP?", "what is your position here?" Next, questions such as "how familiar are you with the SCR?" were posed, followed by questions concerning HPs’ experiences with the SCR. The interview guide was developed for this study, which was part of a PhD project in health sciences. The English version of the interview guide is saved as Supplementary file 1.

Data analysis of individual interviews and participant observation

The field notes and the transcription was analyzed by a stepwise-deductive induction analysis (SDI) [ 25 ]. The analysis begins inductively and subsequently draws on existing theory through the analytical phase. The first step of the analysis was inductive empirical close coding, which is inspired by grounded theory [ 21 ]. The coding was grounded in the empirical data and corresponded closely to the detailed description of concrete situations (field notes) or close to the participants' statements (individual interviews). This process prevents the codes from being drawn from theories or research questions and ensures that the codes are grounded in empirical data [ 25 ]. The first author transcribed all the interviews verbatim and performed the empirical inductive coding in the NVivo software, which resulted in approximately 700 empirical-based codes. Some of the field notes were not coded but provided contextual understanding for the authors. The next step was grouping the codes with internal thematic connections relevant to the research questions into code groups, resulting in nine code groups. Two of the code groups were relevant for this article: seeking and sharing information through the SCR and the non-users of the SCR. These code groups were merged and were labeled HPs use and non-use of the SCR. The code groups were further explored, and theory was applied to support the analysis for understanding the empirical material. This was an ongoing back and forth process between the notion of script and the empirical data. The two themes identified through the analysis are adapting to workflow and dealing with uncertainty , as illustrated in Table  2 . During the analysis, uncertainty emerged as a theme. We then further explored uncertainty and found that HPs dealt with uncertainty by doing trust-work.

Document review

The necessity of conducting a document review arose during the analysis of the individual interviews and participant observations. To search for future scenarios and the explicit and implicit visions about the SCR, we conducted a document review. The document review was inspired by a “following the document-issue” approach [ 26 ], the “document-issue” in our case being mentioning’s of the SCR. Following the document-issue means “analysing where it [SCR] emerges in the first place and how it becomes an issue, including which kind of issue" (26, p. 115). The documents were selected based on this approach, which meant we started with the Norwegian "Coordination reform,” a white paper in which the SCR was first mentioned [ 7 ]. Then, we "followed the issue" by selecting white papers relevant to the SCR's development (see Table  1 ) to explore the future scenarios laid out for the SCR. We also included practical documents, such as the website "What is the Summary Care Record?" [ 8 ] and "Summary care record. User guide for best practice" [ 27 ] to explore how these script the SCR. The document review consisted of six policy and strategic documents and two practical documents (see Table  1 ). Our theoretical approach, STS, means that we interpret documents as a form of technology that is never completely neutral. "They come from somewhere and they are integral to the very issues and controversies that unfold in society" (26, p.3). Documents were imported into NVivo software, scrutinized, and coded through the theoretical lens of scenarios and script. The coding resulted in 49 codes focusing on the implicit or explicit visions of the SCR, and three themes were identified through the document review: solving the problem of coordination and information sharing , the red icon: a national alert system , and the idea of a seamless information system.

In this section, we present our main findings. First, we present the visions and expectations of the SCR identified in the document review. Next, we explain how these expectations are experienced and lived out among the HPs per the analysis of participant observations and individual interviews.

Findings from the document review

Three themes were identified through the documents review: solving the problem of coordination and information sharing , the red icon: a national alert system , and the idea of a seamless information system .

Solving the problem of coordination and information sharing

The policy documents describe the healthcare sector as fragmented and consisting of siloed systems with problems related to coordination, information sharing, and access to necessary information, such as medication lists and critical patient information, in emergencies [ 7 , 24 , 28 ]. The patient health information is stored in equally siloed systems, reflecting the healthcare sector institutions (GPs, the municipalities' health and care services, hospitals, and private specialists) who base their choice of EHR systems on their local needs [ 24 ]. This inhibits access and information gathering across health institutions in complex patient trajectories [ 24 ]. Lack of coordination and information sharing between health and care services is a risk to patient safety [ 7 , 24 , 28 ]. The need for better coordination and information sharing across all levels of the health and care service is presented in numerous white papers in Norway. Published in 2009, the white paper "The coordination reform" recognizes that coordination within the health and care services had been a problem for many years and that the health and care sector needed to develop better coordination; this is where the SCR (then called the national core journal) was mentioned for the first time in a white paper [ 7 ]. Published in 2012, the white paper "One citizen-one Journal" [ 24 ] emphasizes the need to modernize the ICT platform and work for a standard solution for the entire health and care sector; subsequently, the SCR was established. The first national eHealth strategy in Norway was published in 2017 [ 29 ]. The national eHealth strategy was established to create a common direction for digitalization nationally and to contribute to achieving political goals in the health and care sector. The national eHealth strategy builds on the white paper "One citizen-one Journal" [ 24 ]. The document review show that the political goal is to establish stronger national coordination of digitalization work in the health and care sector [ 30 , 31 ]. National eHealth solutions, including the SCR, are described as the "cornerstone of the digital interaction structure" and as essential for coherent health and care services [ 30 ]. The SCR is presented as an important part of the solution to fix the coordination problems described in the policy and strategic documents.

The red icon: a national alert system

Expectations of the SCR to function as a national alert system and potentially be lifesaving in emergencies, were described in the documents [ 8 ]. Furthermore, the documents indicate that the SCR will be an essential tool for HPs providing quick access to patients’ critical health information, regardless of where the patient is receiving treatment. According to the practical user guide [ 27 ], HPs are expected to click on a SCR icon in their local EHR system to access the patient's SCR. This is the case in all health and care services that have implemented the SCR, and the SCR icon is identical in all EHR systems. The icon appears in colors blue or red. The SCR icon color is a symbol for alerting HPs if the patient has any critical information stored just by looking at the color of the SCR icon, before opening the SCR. If the icon is red, this signal that MDs have registered critical information about the patient (severe allergies, implants, special disorders), while a blue icon signals that the patient does not have any critical information registered in the SCR. Only MDs are allowed to enter critical information [ 27 ]. Citizens can also register certain information themselves, such as primary contact information, information about being an organ donor, disease history, or special needs in connection with diminished sight, hearing, or the need for a translator [ 8 , 27 ].

The idea of a seamless information system

The document review revealed that the SCR's vision is that the HIS will compile current, trustworthy, essential information about patients available across institutional levels. The SCR was established to increase patient safety by giving HPs easy access to updated information such as medication lists, allergies, and other critical information [ 8 , 24 , 28 ]. The documents express an expectation that the SCR will increase patient safety, giving HPs updated information about the patient in acute situations. This includes the patient's medication list when the HPs lack up-to-date information about the patient in their local journal EHR systems [ 24 , 27 , 31 ]. The SCR is described as helping HPs gather information about the patient in one place, ensuring the HP does not waste time logging into different systems. Furthermore, the SCR is expected to prevent the patient from repeating their medical history each time they meet a new HP [ 24 ]. According to the documents, most of the information in the SCR will automatically be extracted from national registers. This includes the prescription intermediary; contact information to the patient’s next of kin (name, address, telephone numbers); the patient’s GP; and admission history to the specialist health service [ 8 ].

The documents describe a health and care service that needs a national eHealth system as a solution to problems with coordination and information sharing. There is an expectation that the SCR will function as an alert system in an emergency setting and act as a “seamless system” of information sharing across the health and care services. These expectations and visions constitute the scenario the SCR is entering.

Findings from the analysis of observation and interview data

In this section, we present our analysis of HPs’ negotiations and experiences in daily use of the SCR. Two main themes were developed through the analysis: adapting to workflow and dealing with uncertainty (see Table  2 ).

Adapting to workflow

In two of the sites where observations and interviews were conducted, the SCR was part of the MDs information-gathering routine when a new patient was admitted. MDs in the emergency unit checked the SCR on every patient and preferably before the medical examination of patients. MDs in the intermediate unit did not have the same routine or the need to check the SCR before examining every patient, as they often knew their patients from previous admissions.

When MDs gathered information about a patient, regardless of context, they often started in their local EHR system and read through the admission report. Then they checked the SCR by clicking on the icon of the SCR that is an integrated part of their local EHR. Typical information MDs gathered from the SCR included: critical information, whether the patient was married, had kids, the next of kin, the GP, age, and the pharmaceutical summary. We observed, and were told, that the MDs checked the SCR regardless of the SCR icon's color. As one MD in the intermediate unit said, "I would never trust that there wasn't any information there." If the MDs in the emergency unit did not have the chance to check the SCR before the patient's medical examination, they checked it as soon as possible. Their first priority was to check if the patient had any critical information, and the pharmaceutical summary was second. They often experienced that critical information was stored in their local EHR system and not in the SCR. Only a few MDs had ever registered critical information themselves in the patient's SCR. "I think doctors should register critical information more often, since I have experienced that the information has been useful," said one MD in the emergency unit. Some of the MDs reflected on the concept of “critical information” and how and by who such information should be registered.

The MDs in the emergency unit experienced that the SCR made a difference in emergencies by providing information about medical allergies and diagnoses of the patients. They also found it helpful when the patient was a tourist, since they had no previous information about the patient in their local EHR system. Through the SCR, the MDs could see where the patient has previously been admitted (hospitals), and they could contact that hospital. As one MD in the emergency unit said, " If a patient is unknown to us or unconscious, the SCR is the go-to."

For the nurses in the emergency unit, the SCR was not part of the information-gathering routine. The nurses occasionally used their local EHR, but they typically used the "folder," which was a physical paper folder containing the patient's ID band and ID tags, a paper manually filled out by nurses during the patient’s examination, and a medication sheet manually filled out by MDs. The nurses explained that if they saw that the icon was red, they did not check the SCR themselves, rather they made sure to let the MD know.

The nurses in the intermediate unit had experienced that when patients were admitted from the hospital there was often a note from the hospital saying: "check the SCR.” When the nurses checked, the SCR contained critical information about the patient. Hence, nurses at the intermediate unit found the information in the SCR valuable in emergencies. As a nurse expressed in an interview:

It was at night, and we received a patient from the emergency room. The patient did not have any papers from the emergency room. We had no information except the patient’s name and social security number. We then admitted the patient to our local EHR system and got access to SCR through that. We then saw the necessary medical information until the doctor came the next day. SCR was the only place we could look for information because the patient had nothing with him but himself. –Nurse (intermediate unit)

The SCR was part of the information-gathering routine for the MDs at the intermediate unit, but it had not become a routine for the nurses. The nurses in the intermediate unit felt that the hospital staff was unsure whether the intermediate unit had routines for checking the SCR as a part of their information-gathering routines, since the hospital staff often explicitly wrote "check the SCR" in their discharge summaries.

The pharmaceutical summary was the primary use of the SCR for the MDs, and it could be time-consuming to gather information for the medication list to a patient.

I mostly use it (SCR) to check medicines. Many of our patients are quite sick and have a lot of medication, and they often don't know what they're taking themselves. If you tell them, some patients know the medicine's name, but if not, it can be quite hopeless. In that way, the SCR is helpful, so I don't know how we would have worked without it. It would have been cumbersome. –MD (emergency unit)

Though MDs at the units involved in our study had adopted the SCR into their workflow, there were only a few nurses in the intermediate unit that used the SCR. The nurses and the physiotherapist in the service allocative unit, the home care service, and the emergency unit did not use the SCR. The reasons why these HPs did not use the SCR varied. Some did not know what the SCR was, and others did not know if they had access to it. Some had heard about SCR from their colleges. As a nurse in the intermediate unit said, "I have to admit, I don't really know what the SCR is." The HPs reflected on, both during the interviews and observations, the purpose of the SCR since they had already obtained the discharge report and medication list from elsewhere. These HPs had access to the SCR but described the SCR as a tool that the MDs used, and they did not find a reason why they had to use it as well. The HPs emphasized that they get their information elsewhere, like their local EHR system, and did not find the SCR as a useful source of information. "I've only looked at it, but I've had no need for it," said a physiotherapist in the service allocation unit.

During the observation study, a conversation about the SCR emerged, and some nurses started discussing the SCR. Primarily, the discussions centered on whether or not they had access. One of the nurses pointed out they had to have a chip in their id-card to put in the keyboard to access the SCR. During the conversation, several of the nurses expressed that they did not bother to go to the IT service, located in another building in the municipality, to get the chip in their id-card that was required before they could get access to the SCR.

Dealing with uncertainty

The time a HP spends on gathering information varies, and HP have several sources of information. Typical information sources include previously discharged reports, the SCR, their local EHR, the admission report, the patient itself, and next of kin. The high number of sources of information imply uncertainty could play out among HPs in cases where there is discrepancy between different sources.

There are so many places to gather information. There is double and triple and quadruple journaling. The medication list is enough to drive you crazy. There is one medication list written on paper in the hospital, one in the SCR, one in the general practitioner system, and one medication list in the system that the homecare nurses use. There can easily be five different places for a completely average old patient. When a patient goes back and forth from the hospital, there are often mistakes in the medication list. –MD (intermediate unit)

Some HPs felt like the SCR was just another place they had to check when gathering information about a patient. The uncertainty that the HPs experienced was embedded in the complex system of multiple sources of information that they had to navigate through.

The MDs in the emergency and intermediate unit emphasized that the complete medication history in the SCR made a difference in obtaining a comprehensive picture of their patient's medical history. However, the pharmaceutical summary in the SCR also brought up uncertainty. Medication management was a primary concern and there was frustration around the uncertainty in the multiple lists. The MDs were frustrated over how time-consuming it was to ensure the medication list was current and correct. During the observational study, some MDs mentioned that it felt like they were detectives trying to get the right puzzle pieces to solve the "case" of getting the medication list up-to-date. The MDs had to use at least two or three sources in the medication reconciliation. The information sources are the patient, the next of kin, the home care service, the medication list from the hospital, previous discharge reports, information in their local EHR system, and the pharmaceutical summary in the SCR.

The MDs spent a lot of time on medication reconciliation. The uncertainty in the medication list could last for days. One MD in the intermediate unit expressed, "It can take several days to be sure that what is written there is correct." The MDs emphasized that there are too many sources of information in medication reconciliation, and the uncertainty plays out when different sources give different information about the patient's medications. If a patient was admitted from the home care services or nursing home, it was "common knowledge" among the HPs at the hospital that the pharmaceutical summary in the SCR would be incorrect.

What the patient physically consumes of medication is only known by the home care service, or the patient itself, the SCR can come close, and sometimes it is entirely identical. Sometimes there can be a discrepancy there as well. The home care service writes in their local EHR systems and not in SCR. We've only got one more place in a way, but it's a slightly better place than many of the others. –MD (intermediate unit)

According to the MDs, the updated and correct medication list would not be found in the SCR when a patient was admitted from a nursing home or had home care services. In such cases, they depended on receiving the medication list or an admission report from the nursing home or home care service. They expressed that getting an overview of the patient's medication use was complex.

Though the information is automatically extracted from national registers, HPs don't necessarily trust the information relayed through the SCR. When a MD is new in the emergency unit, they offer a training course that includes using the SCR. During this training course, the MDs "were told that they can't trust the medication list in the SCR if the home care service controls the medications to the patients" said one MD at the emergency unit. In these cases, they were encouraged to contact the home care services by telephone.

The idea behind the SCR is that one gathers information from various health organizations and also towards general practitioners, home care services, and nursing homes is very good, but not optimal. There are several pitfalls, meaning you must take it with a pinch of salt. One cannot blindly trust the SCR. –MD (emergency unit)

The MDs had other sources of information they trusted more than the SCR, such as the information in their local EHR system, the admission report, previous discharge reports and spoken information from home care nurses.

To summarize our findings, the HPs experienced uncertainties and altered workflows in the wake of the implementation of the SCR. How HPs dealt with these challenges in their daily work, and these new ways of working have been interpreted as a kind of trust-work. Trust-work can be understood as a way of dealing with uncertainty, in line with other researchers who see trust in relation to uncertainty and risk [ 32 ].

The purpose of this study was to gain better understanding of the visions and expectations of the SCR and of how HPs descript and rescript the SCR in their daily professional life.

The policy and strategic documents show the visions and expectations of the scenario for the SCR, and the practical user guides gives us information on the designer's user manual: the script of the SCR. The political goal of the SCR is a solution to solve the problems of information sharing faced by the health and care service. In the daily workflows of Norwegian health care institutions, the SCR is considered just another tool for information gathering; however, it needs to be checked and validated by human actors, hence creating more work.

Considering our findings from the empirical study, we have observed an assumption that is only slightly mentioned: information sharing requires that HPs who use the information have a high degree of trust in the information being shared. In the consultation note establishing the national SCR [ 28 ], the focus is on the MDs lack of trust in the pharmaceutical summary [ 28 ]. The document Roadmap for development and implementation of national eHealth solutions [ 30 ] mentions that shared information must be up-to-date and complete as well as the possibilities for establishing a trust model for data and document sharing regarding access control to which HPs gets access to patient health information across different levels in the health and care service [ 30 ].

HPs are critically evaluating information and it is a core aspect of HPs practice. This aspect is not problematized through the documents, but we argue it is an underlying assumption. Trust in others to interpret the information gathered from technology is essential for high-quality care [ 33 ]. The vision of the SCRs script can only work if all users trust all the actors involved, both people and the technology. For the SCR to function as planned, HPs who enter information in the SCR must trust that those who retrieve the information understand and interpret the information correctly. In addition, HPs must also have confidence in the system that makes the information available to those who need it. The HPs who retrieve information must, in turn, have confidence that those who entered the information are competent and that the system can be trusted, continuously updated, and the information always available. This assumption of complete trust in other actors is not explicitly described in the SCR vision but lies as an unspoken premise. Our analysis shows that this becomes problematic when HPs rescript the SCR. HPs do not entirely trust shared information. On the contrary, HPs include critical assessment of information in all stages of their work. Previous studies on using eHealth system found that HPs have more trust in shared information if they receive information from colleagues they already know [ 34 ]. The information being shared in the SCR is not necessarily entered by colleagues that the HPs know.

Our findings showed that the SCR was scripted as an alert system for HPs by the SCR having a symbolic color system. However, the alert system had little to no function for the HPs. We found that HPs checked the SCR regardless of the symbolic color because they did not trust it. Our findings are consistent with other Norwegian studies, that MDs do not trust the coloring system of the SCR and that "a blue icon did not equal a lack of critical information" (12, p.8). Our findings indicate that MDs, in spite of frequent use, did not trust the pharmaceutical summary entirely. Rather, they experienced it could raise more questions than answers when the MDs were trying to update the medication list, and it led to additional work.

Though the SCR's vision and scripting express expectations that it should provide easy access to updated information gathered in one place, the HPs experiences were ambivalent. The SCR held a dual role for MDs, as it could ease the information gathering, but also complicate and introduce more uncertainties. This does not mean that the national eHealth system SCR does not have a function or is considered useless. The SCR contributes to getting patient information out of the siloed system, as intended. HPs find that the information in the SCR has made a difference.

The SCR has a strong script: the information is mainly automatically updated, and there is little room for free text. This gives HPs little room for flexibility in using the SCR. Our findings reveal challenging aspects of the vision of seamless shared information within the SCR. Based in our findings, we claim that a seamless information system [ 35 ] may be impossible to achieve due to HPs constant and ongoing trust-work in their everyday practice. In an already complex system of information, the SCR holds a dual role; it is a valuable source of information gathering but simultaneously an add-on; a new source of uncertainty. HPs must always filter and distil as much information as they can for every patient they meet. The ongoing trust-work includes constant critical assessment of any information they gather about a patient. This is a core aspect of a HPs work. In our study, the HPs were constantly checking the SCR, regardless of the symbolic color system, and integrating this checking in the totality of the information trust-work that they do every day. In their ongoing trust-work, HPs will relate to multiple other HPs and other sources of information. They will ask for confirmation and assess information all the time. Vos et al. [ 36 ] suggest that HPs must develop multifaceted trust for a more coordinated and collaborative use of the EHR system. To achieve multifaced trust, "health professionals need to be able to retrieve, understand and trust each other's information" (36, p.10). Trust involves assessing not only patient information but also the sender of the information and the SCR as a HIS. Trust in HISs, like trust in other humans and other written sources, will, and should, never be unconditional.

On the contrary, our health care system relies on HPs continuous critical assessment of information, the sources of information, and the systems containing the information. This trust-work will always be an integral part of any HPs workflow. Expectations of shared information systems to reduce the uncertainty HPs face when in front of a new patient must take this into consideration. Information trust-work is, and must be, at the core of HP performance. HIS can facilitate but never replace the critical assessment of information that all HPs need to perform when treating a patient. We argue that critical trust-work is an essential and integral part of all HP practice. Regardless of the quality, size, and design of HISs that share patient information, there might always be issues related to HPs ability to trust the information in the systems. Indeed, assessing and double-checking information is part of health professionalism. Hence, the expectations of the system to solve the problem of coordination and information sharing, to function as a perfect alarm system, and to work seamlessly might never be met. However, this does not mean that national eHealth systems cannot improve healthcare quality. They will, or are, to some extent, already good enough to be a viable part of the provision of healthcare service communication. Still, as our study highlights, future scenarios should include expectations of trust-work related to national HIS and not overlook this core aspect of high-quality professional healthcare.

Some limitations should be acknowledged in this study that could have affected the interpretation of the results. One researcher conducted all the individual interviews, participant observation, and the empirical inductive coding alone. A methodological strength could have been if a second researcher had done some of the data collection or coded the empirical data. However, the research team had regular meetings where the grouping of codes and the analysis were thoroughly discussed between the authors. The researcher producing the data was new to health care settings when entering the field, and this qualitative fieldwork can thus be described as a study in an unfamiliar culture; the internal terminology was hard to understand since the HPs used internal jargon and foreign words [ 25 ]. This can be seen as a limitation but also a strength since it allowed the researcher to ask open questions and maybe see situations differently than the HPs, as in our analysis where the HPs trust-work became important.

The data collection was conducted during COVID-19, which affected the participant observation since the researcher had to keep a two-meter distance from HPs. This affected where and how the participant observation could be carried out in the home care service, the intermediate unit, and the emergency unit. There was not always enough room for the researcher to observe; therefore, the researcher had to adjust where the observation could happen in the different units. Due the COVID-19, two individual interviews had to be conducted over the phone. Phone interviews have limitations since we could not see each other's facial expressions and body language.

The document review was a small part of the data collection compared to the participant observation and individual interviews. Other policy documents, Official Norwegian Reports, or other practical documents could have been included in the document review. Still, the selection of which documents were included was narrowed down due to limitations in the scope of the research project. A limitation of the document review was that it was carried out with the lens of searching for future scenarios and explicit and implicit visions about the SCR; this can be a limitation since the researchers searched explicitly through the lens and, therefore, lacked the overall nuanced perspective.

This study has explored the visions and expectations that constitute the scenario for the national eHealth system SCR through a document review and studied how HPs descript and rescript the SCR in their everyday work. While the visions and expectations of the national eHealth system SCR assume that HPs will unconditionally trust the system and the information shared, we found that this is not the case. Our study illuminate how the SCR script is de-scripted and re-scripted in ways that demand of human actors, HPs, to double check the trustworthiness of SCR information in various ways. Through the de-scripting and re-scripting of the SCR, HPs include new tasks of critical assessment of information from the SCR in all stages of their work. Sharing patient information through technology requires trust-work by the HPs, especially when the information is being shared with HPs outside the institutions from which the patient information originates. Our study thus implies that trust-work deserves more attention in the interdisciplinary field of eHealth, especially regarding technology that enables shared patient information.

Availability of data and materials

The datasets generated during and/or analysed during the current study are not publicly available due to ethical restrictions regarding data protection issues and the study-specific consent text and procedure, but anonymized data are available from the corresponding author upon reasonable request.

Abbreviations

Health information system

• Health professional

Summary Care Record

Electronic health record

Medical doctor

Science and technology studies

Stepwisedeductive induction analysis

General practitioner

Fragidis LL, Chatzoglou PD. Implementation of a nationwide electronic health record (EHR): The international experience in 13 countries. Int J Health Care Qual Assur. 2018;31(2):116–30. https://doi.org/10.1108/IJHCQA-09-2016-0136 .

Article   PubMed   Google Scholar  

Garrety K, McLoughlin I, Dalley A, Wilson R, Yu P. National electronic health record systems as `wicked projects’: The Australian experience. Inform Polity: An Int J Govern Democracy Inform Age. 2016;21(4):367–81. https://doi.org/10.3233/IP-160389 .

Greenhalgh T, Morris L, Wyatt JC, Thomas G, Gunning K. Introducing a nationally shared electronic patient record: Case study comparison of Scotland, England, Wales and Northern Ireland. Int J Med Inform. 2013;82(5):e125–38. https://doi.org/10.1016/j.ijmedinf.2013.01.002 .

Ross J, Stevenson F, Lau R, Murray E. Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update). Implement Sci. 2016;11(1):146.

Article   PubMed   PubMed Central   Google Scholar  

Scheibner J, Sleigh J, Ienca M, Vayena E. Benefits, challenges, and contributors to success for national eHealth systems implementation: a scoping review. J Am Med Inform Assoc. 2021;28(9):2039–49. https://doi.org/10.1093/jamia/ocab096 .

Ozawa S, Sripad P. How do you measure trust in the health system? A systematic review of the literature. Soc Sci Med. 2013;91:10–4.

St.Meld.nr.47 (2008–2009). Samhandlingsreformen. Rett behandling - på rett sted- til rett tid (The Coordination Reform. Proper Treatment-At the Right Place and Time) Oslo, Norway: Ministry of Health and Care Services 2009 [cited 2022 November 12]. Available from: https://www.regjeringen.no/no/dokumenter/stmeld-nr-47-2008-2009-/id567201/ .

Norsk Helsenett (Norwegian Health Network). Hva er kjernejournal (what is the SCR): Norsk Helsenett, ; 2022 [cited 2022 December 22]. Available from: https://www.nhn.no/tjenester/kjernejournal/hva-er-kjernejournal .

Direktoratet for e-helse (The Directorate of e-health). Kjernejournal til sykehjem og hjemmetjenester (Program pasientens legemiddelliste) (SCR to nursing home and home care services) 2020 [updated june 16 2020; cited 2022 December 27]. Available from: https://www.ehelse.no/prosjekt/kjernejournal-til-sykehjem-og-hjemmetjenester .

Direktoratet for e-helse (The Directorate of e-health). Plan for realisering av Nasjonal e-helsestrategi (Plan for realization of national e-health strategy);2023 [cited 2023 January 19]. Available from: https://www.ehelse.no/strategi/nasjonal-e-helsestrategi-for-helse-og-omsorgssektoren/_/attachment/inline/1305a1d3-23a5-45ce-9b06-39e770509597:52d85fffdbbd20633361e41d41bf23ba8f593fde/Plan%20for%20realisering%20av%20Nasjonal%20e-helsestrategi.pdf .

Sellberg N, Eltes J. The Swedish Patient Portal and Its Relation to the National Reference Architecture and the Overall eHealth Infrastructure. In: Aanestad M, Grisot M, Hanseth O, Vassilakopoulou P, editors. Information Infrastructures within European Health Care: Working with the Installed Base. Springer Copyright; 2017. p. 225–44. https://doi.org/10.1007/978-3-319-51020-0_14 .

Dyb K, Warth LL. The Norwegian National Summary Care Record: a qualitative analysis of doctors’ use of and trust in shared patient information. BMC Health Serv Res. 2018;18(1):252. https://doi.org/10.1186/s12913-018-3069-y .

Warth LL, Dyb K. eHealth initiatives; the relationship between project work and institutional practice. BMC Health Serv Res. 2019;19(1):520. https://doi.org/10.1186/s12913-019-4346-0 .

Dyb K, Warth LL. Implementing eHealth Technologies: The Need for Changed Work Practices to Reduce Medication Errors. Studies inHealth Technology Informatics. 2019;262:83–6. https://doi.org/10.3233/SHTI190022 .

Article   Google Scholar  

Manskow US, Kristiansen TT. Challenges Faced by Health Professionals in Obtaining Correct Medication Information in the Absence of a Shared Digital Medication List. Pharmacy. 2021;9(1):46. https://doi.org/10.3390/pharmacy9010046 .

Taylor LA, Nong P, Platt J. Fifty Years of Trust Research in Health Care: A Synthetic Review. Milbank Q. 2023;101(1):126–78. https://doi.org/10.1111/1468-0009.12598 .

Callon M. Society in the Making: The Study of Technology as a Tool for Sociological Analysis. In: Bijker WE, Hughes TP, Pinch TJ, editors. In The Social Construction of Technical Systems: New Directions in the Sociology and History of Technology. Cambridge, Mass. and London: MIT Press; 1987. p. 83–103.

Google Scholar  

Akrich M. The De-Scription of technical objects. In: Bijker W, Law J, editors. Shaping Technology, Building Society: Studies on Sociotechnical Changes. Cambridge: MIT Press; 1992. p. 205–24.

Akrich M, Latour B. A summary of a Convenient Vocabulary for the Semiotics of Human and Nonhuman Assemblies. In: Bijker W, Law J, editors. Shaping Technology, Building Society: Studies of Sociotechnical Change. Cambridge, Massachusetts: MIT Press; 1992. p. 259–64.

Aune M. Users versus utilities: the domestication of an energy controlling technology. In: Jamison A, Rohracher H, editors. Technol Stud Sustain Dev. 2002(39).

Charmaz K. Constructing grounded theory. 2nd ed. London: Sage; 2014.

Kringlebotten M, Langørgen A. Gruppering av kommuner etter folkemengde og økonomiske rammebetingelser 2020/48. Statistisk sentralbyrå Statistics Norway; Descember 21 2022. Report No. 2020;2020:48.

Patton MQ. Qualitative Research & Evaluation Methods. 4th ed. United States of America: SAGE Publications, Inc.; 2015.

Meld.St.9 (2012–2013). Èn innbygger- èn journal (One citizent- one journal) Oslo, Norway; 2012 [cited 2022 November 04]. Available from: https://www.regjeringen.no/no/dokumenter/meld-st-9-20122013/id708609/?ch=1 .

Tjora AH. Qualitative research as stepwise-deductive induction. Abingdon, Oxon: Routledge; 2018.

Book   Google Scholar  

Asdal K, Reinertsen H. Doing document analysis. A Practice-oriented Method. United Kingdom: SAGE Publication Ltd; 2022.

Norsk helsenett. Kjernejournal. Veiledning i god prakis i bruk av kjernejournal. (Summary Care Record. Userguide of best practice; 2021. Available from: Veiledning i god praksis for bruk av kjernejournal.pdf. (nhn.no).

Helse- og omsorgsdepartementet. Høringsnotat. Etablering av nasjonal kjernejournal; 2011 [cited 2023 January 10]. Available from: https://www.regjeringen.no/no/dokumenter/etablering-av-nasjonal-kjernejournal/id651187/?expand=horingsnotater .

Direktoratet for e-helse (The Directorate of e-health). Nasjonal e-helsestrategi og mål 2017-2022 (national e-strategy and goal 2017-2022); 2017 [cited 2022 November 04.]. Available from: https://www.ehelse.no/publikasjoner/nasjonal-e-helsestrategi-og-mal-2017-2022 .

Direktoratet for e-helse (The Directorate of e-health). Veikart for utvikling og innføring av nasjonale e-helseløsninger 2021-2026 (Roadmap for developement and implementations of national eHealth solutions 2021-2026); 2022 [cited 2022 December 15]. Available from: https://www.ehelse.no/publikasjoner/veikart-for-utvikling-og-innforing-av-nasjonale-e-helselosninger-2021-2026-versjon-22.3 .

Direktoratet for e-helse (The Directorate of e-health). Plan for e-helse 2019-2022, Vedlegg til Nasjonal e-helsestrategi (2017-2022); 2019. [cited 2022 December 15]. Available from: https://www.ehelse.no/publikasjoner/plan-for-e-helse-2019-2022 .

Frederiksen M. Trust in the face of uncertainty: a qualitative study of intersubjective trust and risk. Int Rev Sociol. 2014;24(1):130–44. https://doi.org/10.1080/03906701.2014.894335 .

Raj M, Wilk AS, Platt JE. Dynamics of Physicians’ Trust in Fellow Health Care Providers and the Role of Health Information Technology. Med Care Res Rev. 2021;78(4):338–49. https://doi.org/10.1177/1077558719892349 .

Zwaanswijk M, Verheij RA, Wiesman FJ, Friele RD. Benefits and problems of electronic information exchange as perceived by health care professionals: an interview study. BMC Health Serv Res. 2011;11(1):256. https://doi.org/10.1186/1472-6963-11-256 .

Aanestad M, Olaussen I. IKT og samhandling i helsesektoren: digitale lappetepper eller sømløs integrasjon? Trondheim: Tapir akademisk forlag; 2010. ISBN: 978-82-519-2646-1.

Vos JFJ, Boonstra A, Kooistra A, Seelen M, van Offenbeek M. The influence of electronic health record use on collaboration among medical specialties. BMC Health Serv Res. 2020;20(1):676. https://doi.org/10.1186/s12913-020-05542-6 .

Download references

Acknowledgements

Thanks to all participants who willingly took part in the study.

Open access funding provided by Norwegian University of Science and Technology The first author is a doctoral candidate employed at NTNU Norwegian University of Science and Technology. No external funding was received for this study.

Author information

Authors and affiliations.

Department of Health Sciences in Gjøvik, Faculty of Medicine and Health Sciences,, NTNU, Centre for Care Research East, Norwegian University of Science and Technology (NTNU), Teknologiveien 22, Gjøvik, NO-2815, Norway

Kristine Lundhaug, Randi Stokke & Hege Kristin Andreassen

Department of Movement Science and Neuromedicine, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology (NTNU), Faculty of Medicine and Health Sciences, Trondheim, N-7491, Norway

Arild Faxvaag

UiT The Arctic University of Norway (UiT), Faculty of Health Sciences, Institute of Health and Care Sciences, PO Box 6050 Langnes, Tromsø, N-9037, Norway

Hege Kristin Andreassen

You can also search for this author in PubMed   Google Scholar

Contributions

KL: conceptualization, methodology, formal analysis, investigation, writing original draft preparations. AF: conceptualization, methodology, analysis, review, editing and supervision. RS: conceptualization, methodology, analysis, review, editing and supervision. HKA: conceptualization, methodology, analysis, review, editing and supervision. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Kristine Lundhaug .

Ethics declarations

Ethics approval and consent to participate.

The Regional Committee for Medical and Health Research Ethics approved the exemption from the duty of confidentiality (Ref: 141144), and the Norwegian Center for Research Data (Ref: 919576) approved the study before the beginning of the data collection process. The health and welfare director in the municipality approved that the municipality could participate in the research project. After approval, they facilitated contact between the researcher and the different units. The units were contacted, informed about the study, and decided whether to participate. The hospital's head of emergency medical care was contacted and informed about the study and approved participation. The study is registered at the data protection supervisor at the hospital. All methods were carried out in accordance with relevant guidelines and the declaration of Helsinki. Written informed consent was obtained from all the participants. The participants were informed of their right to withdraw from the study without stating a reason. They were assured that confidentiality would be maintained concerning the transcribed data (anonymized systematically) and in any publications resulting from the study. All the participants were asked to participate and informed about the research project by the first author, and they all agreed to audio-record the individual interviews.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Lundhaug, K., Faxvaag, A., Stokke, R. et al. Shared patient information and trust: a qualitative study of a national eHealth system. BMC Digit Health 2 , 57 (2024). https://doi.org/10.1186/s44247-024-00108-6

Download citation

Received : 15 December 2023

Accepted : 05 June 2024

Published : 15 August 2024

DOI : https://doi.org/10.1186/s44247-024-00108-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• National eHealth system
• Summary care record
• Ethnographic approach

BMC Digital Health

ISSN: 2731-684X

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• DOI: 10.59455/qietp.21
• Corpus ID: 271850721

Development and Use of the Alphabetical Analysis Technique in Qualitative Research

• Sadegül Akbaba Altun
• Published in Qualitative Inquiry in… 4 August 2024
• Qualitative Inquiry in Education: Theory &amp; Practice

Tables from this paper

18 References

Qualitative research: a guide to design and implementation.

• Highly Influential

Qualitative analysis: what it is and how to begin.

Ηow to use artificial intelligence (ai) as a resource, methodological and analysis tool in qualitative research, a new method using llms for keypoints generation in qualitative data analysis, qualitative research for education: an introduction to theories and methods, interviews : an introduction to qualitative research interviewing, the logic of qualitative survey research and its position in the field of social research methods, nitel veri analizi ve temel i̇lkeleri, qualitative evaluation and research methods, 2nd ed., transforming qualitative data: description, analysis, and interpretation, related papers.

Showing 1 through 3 of 0 Related Papers

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

A comprehensive research agenda for integrating ecological principles into the transportation sector.

1. Introduction to Transportation Ecology: Distant Roots for a Contemporary Study Field

1.1. developing the transportation ecology concept.

• The lack of integration of multiple transportation modes into traditional ecological studies. Road ecology has primarily focused on the impacts of roads, often neglecting how different transportation systems interact and their combined effects on the environment. Transportation ecology aims to fill this gap by studying the interactions and cumulative impacts of various transportation modes.
• The limited focus on climate change and energy consumption in Road Ecology. While road ecology has examined localized environmental impacts, it has not fully addressed the broader implications of transportation emissions and energy use. Transportation Ecology can actively research how transportation systems contribute to climate change and exploring sustainable transportation solutions to reduce carbon footprints.
• The lack of integration with urban and regional planning (an area where traditional road ecology has fallen short). Transportation Ecology typically does not address the integration of transportation systems within urban planning frameworks. Transportation Ecology, however, includes the role of transportation in urban sprawl, land use, and regional development, promoting eco-friendly urban and mobility planning practice that consider environmental sustainability.
• Human health and socioeconomic impacts of transportation systems have also been underrepresented in road ecology. Traditional studies rarely consider how transportation affects air quality, noise levels, and overall human well-being. Transportation ecology can fill this gap by investigating these impacts and addressing socioeconomic disparities in transportation access.
• Lastly, technological innovations in transportation, such as electric vehicles and autonomous transportation, have not been extensively explored in traditional road ecology. Transportation Ecology can help evaluating the environmental benefits and potential drawbacks of these new technologies, aiming to understand their implications for ecological sustainability.

1.2. Rationale of the Paper

2. transportation and infrastructure operations’ impacts on flora and fauna: materials and methods to describe the phenomena, 2.1. mitigating impacts due to transportation operations and infrastructure.

• Not constructing the infrastructure at all.
• Modifying the route.
• Building the infrastructure underground.
• Creation of structures to reduce risk and safeguard zones or alert animals: This includes crossing structures, barriers, ramps, reflectors, and lighting.
• Habitat modifications: For instance, planting unattractive or unpleasant vegetation along the road.
• Enhancing road permeability: This involves the installation of overhead or underground crossings.
• Modifying the roadbed: Lowering or raising the roadbed relative to the surrounding terrain to reduce traffic disturbances, particularly noise.
• Implementing noise mitigation devices: These can be used both on vehicles and infrastructure.
• Adopting less polluting traction systems: This is essential for reducing environmental impact [ 56 ].
• Creation of compensation areas larger than the impacted areas: These areas should be forested, wetland, etc., rather than making improvements in areas equal in size to the impacted ones.
• Proximity to impacted areas: These compensation areas should be located as close as possible to the impacted areas but outside the zones of influence.
• Re-creation of pre-existing ecological conditions: It is preferable to recreate the same ecological conditions that existed before, rather than introducing different conditions.
• Quality improvement: The aim should be to enhance the quality of ecological conditions compared to pre-existing ones rather than simply restoring the same quality level.

2.2. Additional Issues

3. regulatory requirements, the european vision, 3.1. regulations on strategic assessment for transport infrastructures.

• Conservative: Aimed at maintaining or restoring natural habitats and populations of wild species.
• Contractual: Ensuring compliance through agreements.
• Preventive: Designed to avoid degradation and disturbances around the affected sites.

3.2. Additional Environmental Regulatory Requirements

• Evidently, a multidisciplinary approach is essential for the effective application of these regulatory tools, SEA and EIA included, to Transportation Ecology. However, the lack of established practice and awareness suggests these regulations may be overlooked due to several factors:
• Firstly, their comprehensive scope spans multiple sectors and industries, potentially diluting specific focus on Transportation Ecology.
• Secondly, sector-specific regulations that are already in force may take precedence over broader regulations such as the Due Diligence Directive and the Nature Restoration Law.
• Lastly, corporate strategies often prioritize compliance with regulations that directly and immediately affect operational aspects, such as emission reduction targets, coherently with NDCs and national regulations, and waste management, rather than long-term ecological considerations like Transportation Ecology.
• To address these gaps, targeted efforts are required, with increased advocacy and awareness being of particular importance. In addition, the development of practical solutions is essential, including the formulation of specific guidelines for Transportation Ecology actions, the provision of financial and technical support for projects in this field to encourage implementation by transit operators, and the undertaking of research and data collection to highlight the impacts of transit operations and infrastructure on biodiversity, thereby demonstrating the benefits of integrating Transportation Ecology into broader sustainability efforts and within regular traffic planning and infrastructure management practice.

4. Quantifying Tangible Benefits for Transit Operators

4.1. a scenario of potential monetary benefits, 4.1.1. benefits associated with improvements.

• C f is the new collision frequency per year (unit)
• E is the number of events (unit)
• R r is the reduction rate (%)
• Fc i is the improved average fuel cost (EUR)
• Fc c is the current average fuel cost (EUR)
• P is the expected improvement in fuel efficiency (%)
• S v are the savings per vehicle (EUR)

4.1.2. Potential Revenues by Increasing the Attractiveness of the Service

• ROI p is the potential return on investment a transit company can expect to see as a result of achieving more transportation ecology consciousness (EUR)
• C dc is the number of conscious consumers (%)
• M a is the average marketing budget (EUR)
• S a is the average increase in sales per customer (%)

5. Discussion around a Prospective Road Map for Transportation Infrastructure and Operations with Transportation Ecology in Mind

5.1. adapting the roadmap to diverse transportation contexts, 5.2. the role of the stakeholders: from management to awareness to education, 6. concluding remarks, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Forman, R.T.; Sperling, D.; Bissonette, J.A.; Clevenger, A.P.; Cutshall, C.D.; Dale, V.H.; Fahrig, L.; France, R.L.; Goldman, C.R.; Heanue, K. Road Ecology—Science and Solutions ; Island Press: Washington, DC, USA, 2003; pp. 12–35. [ Google Scholar ]
• Odum, E.P. Fundamentals of Ecology ; W.B. Saunders: Philadelphia, PA, USA, 1971. [ Google Scholar ]
• von Herrmann, F.W. (Ed.) Martin Heidegger. Vorträge und Aufsätze (1936–1953) ; Klostermann: Frankfurt am Main, Germany, 2000; Volume 8. [ Google Scholar ]
• Takeda, F. An approach to make regional developmental effect of road construction compatible with environmental conservation. Stud. Reg. Sci. 1971 , 2 , 37–45. [ Google Scholar ] [ CrossRef ]
• Scheerer, H. Die Auswirkungen des Fernstrassenbaus und des Fernverkehrs auf Die Natuerliche Umwelt ; Report N. 23; Deutscher Rat für Landespflege: Bonn, Germany, 1974. [ Google Scholar ]
• Mathe, P. Ökologische Gesichtspunkte beim Ausbau und bei der Asphaltierung von Wegen und Strassen mit Verdichtung der Verkehrsnetze auch im landlichen Raum. Forum Stadte-Hyg. 1977 , 28 , 189–193. [ Google Scholar ]
• Fielenbach, R. Strasse und Ökologie. Str. Autob. 1975 , 26 , 52–54. [ Google Scholar ]
• Bellport, B.P. US Bureau of Reclamation -USBR specifications focus on environment. Civ. Eng. 1971 , 41 , 50–52. [ Google Scholar ]
• Muskett, C.J.; Jones, M.P. The dispersal of lead, cadmium and nickel from motor vehicles and effects on roadside invertebrate macrofauna. Environ. Pollut. A 1980 , 23 , 231–242. [ Google Scholar ] [ CrossRef ]
• Ofomata, G.E.K. Impact of road building, urbanisation and general infrastructural development on the Nigerian rainforest ecosystem. Landsc. Plan. 1981 , 8 , 21–29. [ Google Scholar ] [ CrossRef ]
• Davis, J.B.; George, J.J. Invertebrates as indicators of urban and motorway discharges. Sci. Total Environ. 1987 , 59 , 291–302. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dyment, R. Pure waters project includes restoration. Water Sew. Work. 1974 , 121 , 52–53. [ Google Scholar ]
• Bacsó, J.; Kis-Varga, M.; Kovács, P.; Kalinka, G. Investigation on the accumulation of lead and other metals in plants caused by motor-traffic and smelting. J. Radioanal. Nucl. Chem. 1984 , 81 , 59–65. [ Google Scholar ] [ CrossRef ]
• Tam, N.F.Y.; Liu, W.K.; Wong, M.H.; Wong, Y.S. Heavy metal pollution in roadside urban parks and gardens in Hong Kong. Sci. Total Environ. 1987 , 59 , 325–328. [ Google Scholar ] [ CrossRef ]
• Bischofsberger, W. Ueber den Einfluss der Tausalze auf Grund- und Oberflaechenwasser. Str. Tiefbau 1985 , 39 , 6–10. [ Google Scholar ]
• Scott, N.E.; Davison, A.W. The distribution and ecology of coastal species on roadsides. Vegetation 1985 , 62 , 433–440. [ Google Scholar ] [ CrossRef ]
• Morikawa, T. The influence of road dust on asthmatic children. Jpn. J. Allergol. 1985 , 34 , 297–304. [ Google Scholar ]
• Healy, M.A.; Aslam, M. The distribution of lead in a roadside environment and its consequences for health. Public Health 1980 , 94 , 78–88. [ Google Scholar ] [ CrossRef ]
• van der Ree, R.; Jaeger, J.A.G.; van der Grift, E.A.; Clevenger, A.P. Effects of roads and traffic on wildlife populations and landscape function: Road ecology is moving towards larger scales. Ecol. Soc. 2011 , 16 , 48. [ Google Scholar ] [ CrossRef ]
• Ellenberg, H.; Müller, K.; Stottele, T.; Walper, K.H. Straßen-Ökologie: Auswirkungen von Autobahnen und Straßen auf Ökosysteme deutscher Landschaften. Okol. Straße 1981 , 3 , 19–122. [ Google Scholar ]
• Forman, R.T.T. Road ecology: A solution for the giant embracing us. Landsc. Ecol. 1998 , 13 , III–V. [ Google Scholar ] [ CrossRef ]
• Harris, L.D.; Scheck, J. From implications to applications: The dispersal corridor principle applied to the conservation of biological diversity. In Nature Conservation 2: The Role of Corridors ; Saunders, D.A., Hobbs, R.J., Eds.; Surrey Beatty and Sons: Chipping Norton, UK, 1991; pp. 189–220. [ Google Scholar ]
• Coffin, A.W. From roadkill to road ecology: A review of the ecological effects of roads. J. Transp. Geogr. 2007 , 15 , 396–406. [ Google Scholar ] [ CrossRef ]
• Forman, R.T.T.; Alexander, L.E. Roads and their major ecological effects. Ann. Rev. Ecol. Syst. 1998 , 29 , 207–232. [ Google Scholar ] [ CrossRef ]
• Voller, J.; Harrison, S. (Eds.) Connectivity. In Conservation Biology Principles for Forested Landscapes ; UBC Press: Vancouver, BC, Canada, 1998; pp. 76–97. [ Google Scholar ]
• Parminter, J.V. Natural disturbance ecology. In Conservation Biology Principles for Forested Landscapes ; Voller, J., Harrison, S., Eds.; UBC Press: Vancouver, BC, Canada, 1998; pp. 3–41. [ Google Scholar ]
• Sousa, W.P. The Role of Disturbance in Natural Communities. Ann. Rev. Ecol. Syst. 1984 , 15 , 353–391. [ Google Scholar ] [ CrossRef ]
• Hunt, A.; Dickens, H.J.; Whelan, R.J. Movement of mammals through tunnels under railway lines. Aust. Zool. 1987 , 24 , 89–93. [ Google Scholar ] [ CrossRef ]
• Yanes, M.; Velasco, J.M.; Suárez, F. Permeability of roads and railways to vertebrates: The importance of culverts. Biol. Conserv. 1995 , 71 , 217–222. [ Google Scholar ] [ CrossRef ]
• De Santo, R.S.; Smith, D.G. An introduction to issues of habitat fragmentation relative to transportation corridors with special reference to high-speed rail (HSR). Environ. Manag. 1993 , 17 , 111–114. [ Google Scholar ] [ CrossRef ]
• Rodriguez, A.; Crema, G.; Delibes, M. Use of non-wildlife passages across a high speed railway by terrestrial vertebrates. J. Appl. Ecol. 1996 , 33 , 1527–1540. [ Google Scholar ] [ CrossRef ]
• Kaczensky, P.; Knauer, F.; Krze, B.; Jonozovic, M.; Adamic, M.; Gossow, H. The impact of high speed, high volume traffic axes on brown bears in Slovenia. Biol. Conserv. 2003 , 111 , 191–204. [ Google Scholar ] [ CrossRef ]
• Popp, J.N.; Boyle, S.P. Railway ecology: Underrepresented in science? Basic Appl. Geol. 2017 , 19 , 84–89. [ Google Scholar ] [ CrossRef ]
• Barrientos, R.; Ascensão, F.; Beja, P.; Pereira, H.M.; Borda-de-Água, L. Railway ecology vs. road ecology: Similarities and differences. Eur. J. Wildl. Res. 2019 , 65 , 12. [ Google Scholar ] [ CrossRef ]
• Borda-de-Água, L.; Barrientos, R.; Beja, P.; Pereira, H.M. Railway Ecology ; Springer: Cham, Switzerland, 2017. [ Google Scholar ]
• Karlson, M.; Mortberg, U.; Balfors, B. Road ecology in environmental impact assessment. Eur. J. Wildl. Res. 2014 , 48 , 10–19. [ Google Scholar ] [ CrossRef ]
• Santos, G.; Behrendt, H.; Maconi, L.; Shirvani, T.; Teytelboym, A. Part I: Externalities and economic policies in road transport. Res. Transp. Econ. 2010 , 28 , 2–45. [ Google Scholar ] [ CrossRef ]
• Iuell, B. Wildlife and Traffic: A European Handbook for Identifying Conflicts and Designing Solutions ; KNNV Publishers: BM Zeist, The Netherlands, 2003. [ Google Scholar ]
• Togridou, A. Rail: A test bed for biodiversity innovations. TCR South—Rev. 2023 , 26 , 60–63. [ Google Scholar ]
• Goldfarb, B. City of Glass.31.10.2023. Available online: https://www.biographic.com/city-of-glass/ (accessed on 29 March 2024).
• Transport Infrastructure Ireland. Design and Delivery of Soft Landscape Treatments in Urban Transport Environments ; Technical Report; TIE: Dublin, Ireland, 2003; pp. 111–113. [ Google Scholar ]
• Naumann, S.; Davis, M.; Kaphengst, T.; Pieterse, M.; Rayment, M. Design, Implementation and Cost Elements of Green Infrastructure Projects ; Ecologic Institute and GHK Consulting: Berlin, Germany, 2011; pp. 63–109. [ Google Scholar ]
• Biodiversity Andiinfrastructure Synergies and Opportunities for European Transport Networks—BISON. BISON Policy Brief. Available online: https://bison-transport.eu/2023/10/16/bison-policy-briefs/ (accessed on 29 March 2024).
• Wilansky, J.; Jaeger, J.A.G. Predicting the effectiveness of wildlife fencing along roads using an individual-based model: How do fence-following distances influence the fence-end effect? Ecol. Model. 2024 , 495 , 110784. [ Google Scholar ] [ CrossRef ]
• Wall, A.E.; Biffi, D.; Ackel, A.; Moody, R.W.; Stevens, T.K.; Williams, D.A. Small towns limit dispersal and reduce genetic diversity in populations of Texas horned lizards. Ecol. Evol. 2024 , 14 , e70112. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Červinka, J.; Riegert, J.; Grill, S.; Šálek, M. Large-scale evaluation of carnivore road mortality: The effect of landscape and local scale characteristics. Mammal Res. 2015 , 60 , 233–243. [ Google Scholar ] [ CrossRef ]
• Finch, D.; Schofield, H.; Mathews, F. Traffic noise playback reduces the activity and feeding behaviour of free-living bats. Environ. Pollut. 2020 , 263 , 114405. [ Google Scholar ] [ CrossRef ]
• Ortiz-Urbina, E.; Diaz-Balteiro, L.; Iglesias-Merchan, C. Influence of anthropogenic noise for predicting cinereous vulture nest distribution. Sustainability 2020 , 12 , 503. [ Google Scholar ] [ CrossRef ]
• Madadi, H.; Moradi, H.; Soffianian, A.; Salmanmahiny, A.; Senn, J.; Geneletti, D. Degradation of natural habitats by roads: Comparing land-take and noise effect zone. Environ. Impact Assess. Rev. 2017 , 65 , 147–155. [ Google Scholar ] [ CrossRef ]
• Ghadirian, O.; Moradi, H.; Madadi, H.; Lotfi, A.; Senn, J. Identifying noise disturbance by roads on wildlife: A case study in central Iran. SN Appl. Sci. 2019 , 1 , 808. [ Google Scholar ] [ CrossRef ]
• Francis, C.D.; Barber, J.R. A framework for understanding noise impacts on wildlife: An urgent conservation priority. Front. Ecol. Environ. 2013 , 11 , 305–313. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Blackwell, B.F.; Devault, T.L.; Seamans, T.W. Understanding and Mitigating the Negative Effects of Road Lighting on Ecosystems. In Handbook of Road Ecology ; van der Ree, R., Smith, D.J., Grilo, C., Eds.; Wiley: Chichester, UK, 2015; pp. 143–150. [ Google Scholar ]
• Roedenbeck, I.A.; Fahrig, L.; Findlay, C.S.; Houlahan, J.E.; Jaeger, J.A.; Klar, N.; Kramer-Schadt, S.; van der Grift, E.A. The Rauischholzhausen agenda for road ecology. Ecol. Soc. 2007 , 12 , 11. [ Google Scholar ] [ CrossRef ]
• Forman, R.T.T.; Deblinger, R.D. The ecological road-effect zone of a Massachusetts (USA) suburban highway. Conserv. Biol. 2000 , 14 , 36–46. [ Google Scholar ] [ CrossRef ]
• Ciabò, S.; Fabrizio, M.; Ricci, S.; Merten, A. Strade—Life ; Regione Umbria: Perugia, Italy, 2015. [ Google Scholar ]
• Selva, N.; Kreft, S.; Kati, V.; Schluck, M.; Jonsson, B.G.; Mihok, B.; Okarma, H. Roadless and Low-Traffic Areas as Conservation Targets in Europe. Environ. Manag. 2011 , 48 , 865–877. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Loss, S.R.; Will, T.; Loss, S.S.; Marra, P.P. Bird–building collisions in the United States: Estimates of annual mortality and species vulnerability. Condor: Ornithol. Appl. 2014 , 116 , 8–23. [ Google Scholar ] [ CrossRef ]
• Calvario, E.; Sebasti, S.; Copiz, R.; Salomone, F.; Brunelli, M.; Tallone, G.; Blasi, C. Habitat e Specie di Interesse Comunitario nel Lazio ; Regione Lazio—ARP: Rome, Italy, 2009. [ Google Scholar ]
• Bissonette, J.A.; Kassar, C.A.; Cook, L.J. Assessment of Costs Associated with Deer–Vehicle Collisions: Human Death and Injury, Vehicle Damage, and Deer Loss. Hum.-Wildl. Interact. 2008 , 2 , 9. [ Google Scholar ]
• Huijser, M.P.; Duffield, J.W.; Clevenger, A.P.; Ament, R.J.; McGowen, P.T. Cost–benefit analyses of mitigation measures aimed at reducing collisions with large ungulates in the United States and Canada; a decision support tool. Ecol. Soc. 2009 , 14 , 15. [ Google Scholar ] [ CrossRef ]
• Conover, M.R.; Pitt, W.C.; Kessler, K.K.; DuBow, T.J.; Sanborn, W.A. Review of human injuries, illnesses, and economic losses caused by wildlife in the United States. Wildl. Soc. Bull. 1995 , 23 , 407–414. [ Google Scholar ]
• Bíl, M.; Grilo, C.; Kubeček, J.; Sedoník, J.; Andrášik, R.; Cícha, V.; Favilli, F.; Stauder, J.; Schwingshackl, F.; Michael, K.; et al. Wildlife Vehicle Collisions: Road Ecology, Monitoring and Mitigation, Citizen Science, Pedagogical and Socioeconomic Aspects. Report ERASMUS+ “EnVeROS” Report, Intellectual Output 1. 2019. Available online: http://www.enveros.eu/ (accessed on 28 June 2024).
• Automobil Club d’Italia—ACI, 2022. Distribuzione Parco Veicoli per Anno, Comune Capoluogo e Categoria—Open Parco Veicoli Database. 2022. Available online: https://opv.aci.it/WEBDMCircolante/ (accessed on 28 June 2024).
• Baccelli, O. Scenari e Prospettive Dell’elettrificazione del Trasporto Pubblico su Strada. Un’innovativa Analisi di Benchmark: Il TCRO—Total Cost and Revenues of Ownership. Metodologia e Risultati per l’Italia, Presentation at Bocconi University Milan. 16 November 2021. Available online: https://www.enelfoundation.org/content/dam/enel-foundation/topics/2021/11/211116%20GREEN%20per%20evento%20su%20TCRO%20caso%20Italia_per%20evento%20v7def.pdf (accessed on 29 June 2024).
• Seiler, A. The Toll of the Automobile—Wildlife and Roads in Sweden. Ph.D. Thesis, Department of Conservation Biology, Swedish University of Agricutural Sciences, Uppsala, Sweden, 2003. [ Google Scholar ]
• Intesa SanPaolo Preventivo Assicurazione Autobus, Autocarri, Caravan, Furgoni, Natanti, Mezzi Agricoli 2024. Available online: https://www.intesasanpaoloassicura.com/preventivatore-altriveicoli (accessed on 29 June 2024).
• Clevenger, A.P.; Chruszcz, B.; Gunson, K.E. Highway mitigation fencing reduces wildlife-vehicle collisions. Wildl. Soc. Bull. 2001 , 29 , 646–653. [ Google Scholar ]
• Gagnon, J.W.; Dodd, N.L.; Sprague, S.C.; Ogren, K.S.; Loberger, C.D.; Schweinsburg, R.E. Animal-activated highway crosswalk: Long-term impact on elk-vehicle collisions, vehicle speeds, and motorist braking response. Hum. Dimens. Wildl. 2019 , 24 , 132–147. [ Google Scholar ] [ CrossRef ]
• Guccione, M.; Gori, M.; Bajo, N. Tutela della Connettività Ecologica del Territorio e Infrastrutture Lineari ; ISPRA: Rome, Italy, 2008. [ Google Scholar ]
• Robertson, R.D.; Delavary, M.; Lyon, C.; Vanlaar, W. Effect of eco-driving on commercial motor vehicle driver collision risk. J. Saf. Res. 2024 , 89 , 190–196. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Litman, T. Traffic Calming: Benefits, Costs and Equity Impacts ; Victoria Transport Policy Institute: Victoria, BC, Canada, 1999. [ Google Scholar ]
• Haworth, N.; Symmons, M. Driving to Reduce Fuel Consumption and Improve Road Safety. In Proceedings of the Road Safety Research, Policing and Education Conference 2001, Melbourne, VIC, Australia, 18–20 November 2001. [ Google Scholar ]
• Romero, C.A.; Correa, P.; Ariza Echeverri, E.A.; Vergara, D. Strategies for Reducing Automobile Fuel Consumption. Appl. Sci. 2024 , 14 , 910. [ Google Scholar ] [ CrossRef ]
• ATAC Bilancio di Esercizio al. 31 December 2022. Available online: https://trasparenza.atac.roma.it/moduli/downloadFile.php?file=oggetto_allegati/232111122320O__OBilancio+ATAC+al+31.12.2022.pdf (accessed on 29 June 2024).
• KPMG Greener-Journeys-Maximising-the-Benefits-of-Local-Bus-Services. 2023. Available online: https://greener-vision.com/wp-content/uploads/2020/07/Greener-Journeys-Maximising-the-benefits-of-local-bus-services.pdf (accessed on 29 June 2024).
• Sustainable Bus. UK Funding Streams for Zero Emission Buses, Well Explained. Available online: https://www.sustainable-bus.com/news/uk-funding-zero-emission-buses-well-explained/ (accessed on 29 June 2024).
• Piaggio, V. The Global Inclusion Imperative. Available online: https://www.kantar.com/inspiration/brands/the-global-inclusion-imperative (accessed on 22 December 2023).
• Hlaváč, V.; Anděl, P.; Matoušová, J.; Dostál, I.; Strnad, M. Wildlife and Traffic in the Carpathians. Guidelines How to Minimize Impact of Transport Infrastructure Development on Nature in the Carpathian Countries. Danube Transnational Programme TRANSGREEN Project ; The State Nature Conservancy of the Slovak Republic: Banská Bystrica, Slovakia, 2019. [ Google Scholar ]
• van der Grift, E.A.; van der Ree, R.; Fahrig, L.; Findlay, S.; Houlahan, J.; Jaeger, J.A.G.; Klar, N.; Madriñan, L.F. Evaluating the effectiveness of road mitigation measures. Biodivers. Conserv. 2013 , 22 , 425–448. [ Google Scholar ] [ CrossRef ]
• Corazza, M.V. Ecologia delle Infrastrutture Viarie a tutela dell’ambiente: Concetti e criteri per una ricerca allargata alle discipline trasportistiche. Ing. Ferrov. 2022 , 10 , 787–808. [ Google Scholar ]
• Mihai, F.C.; Schneider, P.; Eva, M. Engineering and green infrastructure in mitigating emerging urban environmental threats. In Handbook of Ecological and Ecosystem Engineering ; Narasimha, M., Prasad, V., Eds.; Wiley: Chichester, UK, 2021; pp. 95–121. [ Google Scholar ]
• Herrera, D.J.; Fidino, M.; Luther, D.; Mullinax, J.M.; Gallo, T. Historical park planning is associated with modern-day patterns of bird diversity in cities. Landsc. Urban Plan. 2024 , 249 , 105132. [ Google Scholar ] [ CrossRef ]
• Kirk, H.; Soanes, K.; Amati, M.; Bekessy, S.; Harrison, L.; Parris, K.; Ramalho, C.; Van de Ree, R.; Threlfall, C. Ecological connectivity as a planning tool for the conservation of wildlife in cities. MethodsX 2023 , 10 , 101989. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Smythe, T.; McCann, J.A.; Fox, C. Spatial Planning for busy waterways: A case study of innovative waterways management in the San Francisco Bay region. In Case Studies of Marine Spatial Planning Report Series ; McCann, J., Ed.; Coastal Resources Center and Rhode Island Sea Grant College Program, URI Graduate School of Oceanography: Narragansett, RI, USA, 2016; pp. 56–62. [ Google Scholar ]
• Utrecht Municipality. Green-Roofed Bus Shelters in Utrecht. Available online: https://www.utrecht.nl/city-of-utrecht/green-roofed-bus-shelters-in-utrecht (accessed on 1 August 2024).
• Granger, B.; Burnham, R.; Vagle, S. Soundscape additions from vessels related to transit speed, direction and manoeuvres. Can. Acoust. 2023 , 51 , 39–48. [ Google Scholar ]
• Grand Paris Express. Protecting the Environment during the Build. Available online: https://view.genially.com/606574ffda2fe30ce2f43bf5 (accessed on 1 August 2024).
• Erftemeijer, P.L.A.; Lewis, R.R.R., III. Environmental impacts of dredging on seagrasses: A review. Mar. Poll. Bull. 2006 , 52 , 1553–1572. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bahía, R.; Lambertucci, S.A.; Speziale, K.L. Anthropogenic city noise affects the vocalizations of key forest birds. Biodivers. Conserv. 2024 , 33 , 2405–2421. [ Google Scholar ] [ CrossRef ]
• Nikolopoulos, A.; Sigray, P.; Andersson, M.; Carlström, J.; Lalander, E. 2016: BIAS Implementation. Plan—Monitoring and Assessment Guidance for Continuous Low Frequency Sound in the Baltic Sea, Technical Report. 2016. Available online: www.bias-project.eu (accessed on 1 August 2024).
• Zellmer, A.J.; Goto, B.S. Urban wildlife corridors: Building bridges for wildlife and people. Front. Sustain. Cities 2022 , 4 , 954089. [ Google Scholar ] [ CrossRef ]
• Ledoux, L.; Crooks, S.; Jordan, A.; Turner, R.K. Implementing EU biodiversity policy: UK experiences. Land Use Policy 2000 , 17 , 257–268. [ Google Scholar ] [ CrossRef ]
• Rautjärvi, S.; MacGregor-Fors, I. Where economic parity meets ecology: Neither biodiversity nor ecosystem integrity values relate to wealth in the context of a medium-sized Finnish city. Ambio 2024 , 53 , 292–298. [ Google Scholar ] [ CrossRef ]
• Dasgupta, P. The Economics of Biodiversity ; Cambridge University Press: Cambridge, UK, 2024; pp. 107–113. [ Google Scholar ]
• Li, T.; Tian, W.; Zhang, S.; Wang, S. Environmental regulation, high-quality economic development and ecological capital utilization. Front. Environ. Sci. 2024 , 12 , 1325289. [ Google Scholar ] [ CrossRef ]
• Mudalige, U.; Carver, S. Unveiling Sri Lanka’s Wilderness: GIS-Based Modelling of Wilderness Attributes. Land 2024 , 13 , 402. [ Google Scholar ] [ CrossRef ]
• Andersson, E.; McPhearson, T.; Pickett, S.T.A. From urban ecology to urban enquiry: How to build cumulative and context-sensitive understandings. Ambio 2024 , 53 , 813–825. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Moore, L.J.; Arietta, A.A.; Spencer, D.T.; Huijser, M.P.; Walder, B.L.; Abra, F.D. On the road without a map: Why we need an “Ethic of Road Ecology”. Front. Ecol. Evol. 2021 , 9 , 774286. [ Google Scholar ] [ CrossRef ]
• Owais, M. Deep Learning for Integrated Origin–Destination Estimation and Traffic Sensor Location Problems. IEEE Trans. Intell. Transp. Syst. 2024 , 25 , 6501–6513. [ Google Scholar ] [ CrossRef ]
• Owais, M.; Moussa, G.S.; Hussain, K.F. Robust deep learning architecture for traffic flow estimation from a subset of link sensors. J. Transp. Eng. A 2020 , 146 , 04019055. [ Google Scholar ] [ CrossRef ]
• Owais, M.; Osman, M.K. Complete hierarchical multi-objective genetic algorithm for transit network design problem. Expert Syst. Appl. 2018 , 114 , 143–154. [ Google Scholar ] [ CrossRef ]
• Owais, M.; Osman, M.K.; Moussa, G.S. Multi-Objective Transit Route Network Design as Set Covering Problem. IEEE Trans. Intell. Transp. Syst. 2016 , 17 , 670–679. [ Google Scholar ] [ CrossRef ]
• Corazza, M.V.; Di Mascio, P.; Esposito, G. Airports as Sensitive Areas to Mitigate Air Pollution: Evidence from a Case Study in Rome. Environments 2022 , 9 , 108. [ Google Scholar ] [ CrossRef ]
• Di Mascio, P.; Corazza, M.V.; Rosa, N.R.; Moretti, L. Optimization of Aircraft Taxiing Strategies to Reduce the Impacts of Landing and Take-Off Cycle at Airports. Sustainability 2022 , 14 , 9692. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Corazza, M.V. A Comprehensive Research Agenda for Integrating Ecological Principles into the Transportation Sector. Sustainability 2024 , 16 , 7081. https://doi.org/10.3390/su16167081

Corazza MV. A Comprehensive Research Agenda for Integrating Ecological Principles into the Transportation Sector. Sustainability . 2024; 16(16):7081. https://doi.org/10.3390/su16167081

Corazza, Maria Vittoria. 2024. "A Comprehensive Research Agenda for Integrating Ecological Principles into the Transportation Sector" Sustainability 16, no. 16: 7081. https://doi.org/10.3390/su16167081

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals