quantitative research methods case study

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Locating Theoretical and Conceptual Frameworks This link opens in a new window
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

Was this resource helpful?

• << Previous: Thematic Data Analysis in Qualitative Design
• Next: Journal Article Reporting Standards (JARS) >>
• Last Updated: May 29, 2024 8:05 AM
• URL: https://resources.nu.edu/researchtools

• Open access
• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

789k Accesses

40 Altmetric

Metrics details

The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Peer Review reports

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Yin RK: Case study research, design and method. 2009, London: Sage Publications Ltd., 4

Google Scholar  

Keen J, Packwood T: Qualitative research; case study evaluation. BMJ. 1995, 311: 444-446.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sheikh A, Halani L, Bhopal R, Netuveli G, Partridge M, Car J, et al: Facilitating the Recruitment of Minority Ethnic People into Research: Qualitative Case Study of South Asians and Asthma. PLoS Med. 2009, 6 (10): 1-11.

Article   Google Scholar  

Pinnock H, Huby G, Powell A, Kielmann T, Price D, Williams S, et al: The process of planning, development and implementation of a General Practitioner with a Special Interest service in Primary Care Organisations in England and Wales: a comparative prospective case study. Report for the National Co-ordinating Centre for NHS Service Delivery and Organisation R&D (NCCSDO). 2008, [ http://www.sdo.nihr.ac.uk/files/project/99-final-report.pdf ]

Robertson A, Cresswell K, Takian A, Petrakaki D, Crowe S, Cornford T, et al: Prospective evaluation of the implementation and adoption of NHS Connecting for Health's national electronic health record in secondary care in England: interim findings. BMJ. 2010, 41: c4564-

Pearson P, Steven A, Howe A, Sheikh A, Ashcroft D, Smith P, the Patient Safety Education Study Group: Learning about patient safety: organisational context and culture in the education of healthcare professionals. J Health Serv Res Policy. 2010, 15: 4-10. 10.1258/jhsrp.2009.009052.

Article   PubMed   Google Scholar  

van Harten WH, Casparie TF, Fisscher OA: The evaluation of the introduction of a quality management system: a process-oriented case study in a large rehabilitation hospital. Health Policy. 2002, 60 (1): 17-37. 10.1016/S0168-8510(01)00187-7.

Stake RE: The art of case study research. 1995, London: Sage Publications Ltd.

Sheikh A, Smeeth L, Ashcroft R: Randomised controlled trials in primary care: scope and application. Br J Gen Pract. 2002, 52 (482): 746-51.

PubMed   PubMed Central   Google Scholar  

King G, Keohane R, Verba S: Designing Social Inquiry. 1996, Princeton: Princeton University Press

Doolin B: Information technology as disciplinary technology: being critical in interpretative research on information systems. Journal of Information Technology. 1998, 13: 301-311. 10.1057/jit.1998.8.

George AL, Bennett A: Case studies and theory development in the social sciences. 2005, Cambridge, MA: MIT Press

Eccles M, the Improved Clinical Effectiveness through Behavioural Research Group (ICEBeRG): Designing theoretically-informed implementation interventions. Implementation Science. 2006, 1: 1-8. 10.1186/1748-5908-1-1.

Article   PubMed Central   Google Scholar  

Netuveli G, Hurwitz B, Levy M, Fletcher M, Barnes G, Durham SR, Sheikh A: Ethnic variations in UK asthma frequency, morbidity, and health-service use: a systematic review and meta-analysis. Lancet. 2005, 365 (9456): 312-7.

Sheikh A, Panesar SS, Lasserson T, Netuveli G: Recruitment of ethnic minorities to asthma studies. Thorax. 2004, 59 (7): 634-

CAS   PubMed   PubMed Central   Google Scholar  

Hellström I, Nolan M, Lundh U: 'We do things together': A case study of 'couplehood' in dementia. Dementia. 2005, 4: 7-22. 10.1177/1471301205049188.

Som CV: Nothing seems to have changed, nothing seems to be changing and perhaps nothing will change in the NHS: doctors' response to clinical governance. International Journal of Public Sector Management. 2005, 18: 463-477. 10.1108/09513550510608903.

Lincoln Y, Guba E: Naturalistic inquiry. 1985, Newbury Park: Sage Publications

Barbour RS: Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?. BMJ. 2001, 322: 1115-1117. 10.1136/bmj.322.7294.1115.

Mays N, Pope C: Qualitative research in health care: Assessing quality in qualitative research. BMJ. 2000, 320: 50-52. 10.1136/bmj.320.7226.50.

Mason J: Qualitative researching. 2002, London: Sage

Brazier A, Cooke K, Moravan V: Using Mixed Methods for Evaluating an Integrative Approach to Cancer Care: A Case Study. Integr Cancer Ther. 2008, 7: 5-17. 10.1177/1534735407313395.

Miles MB, Huberman M: Qualitative data analysis: an expanded sourcebook. 1994, CA: Sage Publications Inc., 2

Pope C, Ziebland S, Mays N: Analysing qualitative data. Qualitative research in health care. BMJ. 2000, 320: 114-116. 10.1136/bmj.320.7227.114.

Cresswell KM, Worth A, Sheikh A: Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare. BMC Med Inform Decis Mak. 2010, 10 (1): 67-10.1186/1472-6947-10-67.

Article   PubMed   PubMed Central   Google Scholar  

Malterud K: Qualitative research: standards, challenges, and guidelines. Lancet. 2001, 358: 483-488. 10.1016/S0140-6736(01)05627-6.

Article   CAS   PubMed   Google Scholar  

Yin R: Case study research: design and methods. 1994, Thousand Oaks, CA: Sage Publishing, 2

Yin R: Enhancing the quality of case studies in health services research. Health Serv Res. 1999, 34: 1209-1224.

Green J, Thorogood N: Qualitative methods for health research. 2009, Los Angeles: Sage, 2

Howcroft D, Trauth E: Handbook of Critical Information Systems Research, Theory and Application. 2005, Cheltenham, UK: Northampton, MA, USA: Edward Elgar

Book   Google Scholar  

Blakie N: Approaches to Social Enquiry. 1993, Cambridge: Polity Press

Doolin B: Power and resistance in the implementation of a medical management information system. Info Systems J. 2004, 14: 343-362. 10.1111/j.1365-2575.2004.00176.x.

Bloomfield BP, Best A: Management consultants: systems development, power and the translation of problems. Sociological Review. 1992, 40: 533-560.

Shanks G, Parr A: Positivist, single case study research in information systems: A critical analysis. Proceedings of the European Conference on Information Systems. 2003, Naples

Pre-publication history

The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2288/11/100/prepub

Download references

Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

Author information

Authors and affiliations.

Division of Primary Care, The University of Nottingham, Nottingham, UK

Sarah Crowe & Anthony Avery

Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Kathrin Cresswell, Ann Robertson & Aziz Sheikh

School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sarah Crowe .

Additional information

Competing interests.

The authors declare that they have no competing interests.

Authors' contributions

AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article.

Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

Download citation

Received : 29 November 2010

Accepted : 27 June 2011

Published : 27 June 2011

DOI : https://doi.org/10.1186/1471-2288-11-100

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

• Case Study Approach
• Electronic Health Record System
• Case Study Design
• Case Study Site
• Case Study Report

BMC Medical Research Methodology

ISSN: 1471-2288

• General enquiries: [email protected]

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
• J Korean Med Sci
• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

Whatever field you're in, ATLAS.ti puts your data to work for you

Download a free trial of ATLAS.ti to turn your data into insights.

Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

Ready to jumpstart your research with ATLAS.ti?

Conceptualize your research project with our intuitive data analysis interface. Download a free trial today.

Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

Ready to analyze your data with ATLAS.ti?

See how our intuitive software can draw key insights from your data with a free trial today.

The Case Study as Research Method: A Practical Handbook

Qualitative Research in Accounting & Management

ISSN : 1176-6093

Article publication date: 21 June 2011

Scapens, R.W. (2011), "The Case Study as Research Method: A Practical Handbook", Qualitative Research in Accounting & Management , Vol. 8 No. 2, pp. 201-204. https://doi.org/10.1108/11766091111137582

Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

This book aims to provide case‐study researchers with a step‐by‐step practical guide to “help them conduct the study with the required degree of rigour” (p. xi).

It seeks to “demonstrate that the case study is indeed a scientific method” (p. 104) and to show “the usefulness of the case method as one tool in the researcher's methodological arsenal” (p. 105). The individual chapters cover the various stages in conducting case‐study research, and each chapter sets out a number of practical steps which have to be taken by the researcher. The following are the eight stages/chapters and, in brackets, the number of steps in each stages:

Assessing appropriateness and usefulness (4).

Ensuring accuracy of results (21).

Preparation (6).

Selecting cases (4).

Collecting data (7).

Analyzing data (4).

Interpreting data (3).

Reporting results (4).

It is particularly noticeable that ensuring accuracy of results has by far the largest number of number of steps – 21 steps compared to seven or fewer steps in the other stages. This reflects Gagnon's concern to demonstrate the scientific rigour of case‐study research. In the forward, he explains that the book draws on his experience in conducting his own PhD research, which was closely supervised by three professors, one of whom was inclined towards quantitative research. Consequently, his research was underpinned by the principles and philosophy of quantitative research. This is clearly reflected in the approach taken in this book, which seeks to show that case‐study research is just as rigorous and scientific as quantitative research, and it can produce an objective and accurate representation of the observed reality.

There is no discussion of the methodological issues relating to the use of case‐study research methods. This is acknowledged in the forward, although Gagnon refers to them as philosophical or epistemological issues (p. xii), as he tends to use the terms methodology and method interchangeably – as is common in quantitative research. Although he starts (step 1.1) by trying to distance case and other qualitative research from the work of positivists, arguing that society is socially constructed, he nevertheless sees social reality as objective and independent of the researcher. So for Gagnon, the aim of case research is to accurately reflect that reality. At various points in the book the notion of interpretation is used – evidence is interpreted and the (objective) case findings have to be interpreted.

So although there is a distancing from positivist research (p. 1), the approach taken in this book retains an objective view of the social reality which is being researched; a view which is rather different to the subjective view of reality taken by many interpretive case researchers. This distinction between an objective and a subjective view of the social reality being researched – and especially its use in contrasting positivist and interpretive research – has its origins the taxonomy of Burrell and Morgan (1979) . Although there have been various developments in the so‐called “objective‐subjective debate”, and recently some discussion in relation to management accounting research ( Kakkuri‐Knuuttila et al. , 2008 ; Ahrens, 2008 ), this debate is not mentioned in the book. Nevertheless, it is clear that Gagnon is firmly in the objective camp. In a recent paper, Johnson et al. (2006, p. 138) provide a more contemporary classification of the different types of qualitative research. In their terms, the approach taken in this book could be described as neo‐empiricist – an approach which they characterise as “qualitative positivists”.

The approach taken in this handbook leaves case studies open to the criticisms that they are a small sample, and consequently difficult to generalise, and to arguments that case studies are most appropriate for exploratory research which can subsequently be generalised though quantitative research. Gagnon explains that this was the approach he used after completing his thesis (p. xi). The handbook only seems to recognise two types of case studies, namely exploratory and raw empirical case studies – the latter being used where “the researcher is interested in a subject without having formed any preconceived ideas about it” (p. 15) – which has echoes of Glaser and Strauss (1967) . However, limiting case studies to these two types ignores other potential types; in particular, explanatory case studies which are where interpretive case‐study research can make important contributions ( Ryan et al. , 2002 ).

This limited approach to case studies comes through in the practical steps which are recommended in the handbook, and especially in the discussion of reliability and validity. The suggested steps seem to be designed to keep very close to the notions of reliability and validity used in quantitative research. There is no mention of the recent discussion of “validity” in interpretive accounting research, which emphasises the importance of authenticity and credibility and their implications for writing up qualitative and case‐study research ( Lukka and Modell, 2010 ). Although the final stage of Gagnon's handbook makes some very general comments about reporting the results, it does not mention, for example, Baxter and Chua's (2008) paper in QRAM which discusses the importance of demonstrating authenticity, credibility and transferability in writing qualitative research.

Despite Gagnon's emphasis on traditional notions of reliability and validity the handbook provides some useful practical advice for all case‐study researchers. For example, case‐study research needs a very good research design; case‐study researchers must work hard to gain access to and acceptance in the research settings; a clear strategy is needed for data collection; the case researcher should create field notes (in a field notebook, or otherwise) to record all the thoughts, ideas, observations, etc. that would not otherwise be collected; and the vast amount of data that case‐study research can generate needs to be carefully managed. Furthermore, because of what Gagnon calls the “risk of mortality” (p. 54) (i.e. the risk that access to a research site may be lost – for instance, if the organisation goes bankrupt) it is crucial for some additional site(s) to be selected at the outset to ensure that the planned research can be completed. This is what I call “insurance cases” when talking to my own PhD students. Interestingly, Gagnon recognises the ethical issues involved in doing case studies – something which is not always mentioned by the more objectivist type of case‐study researchers. He emphasises that it is crucial to honour confidentiality agreements, to ensure data are stored securely and that commitments are met and promises kept.

There is an interesting discussion of the advantages and disadvantages of using computer methods in analysing data (in stage 6). However, the discussion of coding appears to be heavily influenced by grounded theory, and is clearly concerned with producing an accurate reflection of an objective reality. In addition, Gagnon's depiction of case analysis is overly focussed on content analysis – possibly because it is a quantitative type of technique. There is no reference to the other approaches available to qualitative researchers. For example, there is no mention of the various visualisation techniques set out in Miles and Huberman (1994) .

To summarise, Gagnon's book is particularly useful for case‐study researchers who see the reality they are researching as objective and researcher independent. However, this is a sub‐set of case‐study researchers. Although some of the practical guidance offered is relevant for other types of case‐study researchers, those who see multiple realities in the social actors and/or recognise the subjectivity of the research process might have difficulty with some of the steps in this handbook. Gagnon's aim to show that the case study is a scientific method, gives the handbook a focus on traditional (quantitatively inspired) notions rigour and validity, and a tendency to ignore (or at least marginalise) other types of case study research. For example, the focus on exploratory cases, which need to be supplemented by broad based quantitative research, overlooks the real potential of case study research which lies in explanatory cases. Furthermore, Gagnon is rather worried about participant research, as the researcher may play a role which is “not consistent with scientific method” (p. 42), and which may introduce researcher bias and thereby damage “the impartiality of the study” (p. 53). Leaving aside the philosophical question about whether any social science research, including quantitative research, can be impartial, this stance could severely limit the potential of case‐study research and it would rule out both the early work on the sociology of mass production and the recent calls for interventionist research. Clearly, there could be a problem where a researcher is trying to sell consulting services, but there is a long tradition of social researchers working within organisations that they are studying. Furthermore, if interpretive research is to be relevant for practice, researchers may have to work with organisations to introduce new ideas and new ways of analysing problems. Gagnon would seem to want to avoid all such research – as it would not be “impartial”.

Consequently, although there is some good practical advice for case study researchers in this handbook, some of the recommendations have to be treated cautiously, as it is a book which sees case‐study research in a very specific way. As mentioned earlier, in the Forward Gagnon explicitly recognises that the book does not take a position on the methodological debates surrounding the use of case studies as a research method, and he says that “The reader should therefore use and judge this handbook with these considerations in mind” (p. xii). This is very good advice – caveat emptor .

Ahrens , T. ( 2008 ), “ A comment on Marja‐Liisa Kakkuri‐Knuuttila ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 291 ‐ 7 , Kari Lukka and Jaakko Kuorikoski.

Baxter , J. and Chua , W.F. ( 2008 ), “ The field researcher as author‐writer ”, Qualitative Research in Accounting & Management , Vol. 5 No. 2 , pp. 101 ‐ 21 .

Burrell , G. and Morgan , G. ( 1979 ), Sociological Paradigms and Organizational Analysis , Heinneman , London .

Glaser , B.G. and Strauss , A.L. ( 1967 ), The Discovery of Grounded Theory: Strategies for Qualitative Research , Aldine , New York, NY .

Johnson , P. , Buehring , A. , Cassell , C. and Symon , G. ( 2006 ), “ Evaluating qualitative management research: towards a contingent critieriology ”, International Journal of Management Reviews , Vol. 8 No. 3 , pp. 131 ‐ 56 .

Kakkuri‐Knuuttila , M.‐L. , Lukka , K. and Kuorikoski , J. ( 2008 ), “ Straddling between paradigms: a naturalistic philosophical case study on interpretive research in management accounting ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 267 ‐ 91 .

Lukka , K. and Modell , S. ( 2010 ), “ Validation in interpretive management accounting research ”, Accounting, Organizations and Society , Vol. 35 , pp. 462 ‐ 77 .

Miles , M.B. and Huberman , A.M. ( 1994 ), Qualitative Data Analysis: A Source Book of New Methods , 2nd ed. , Sage , London .

Ryan , R.J. , Scapens , R.W. and Theobald , M. ( 2002 ), Research Methods and Methodology in Finance and Accounting , 2nd ed. , Thomson Learning , London .

Related articles

All feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

• Privacy Policy

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Phenomenology – Methods, Examples and Guide

Focus Groups – Steps, Examples and Guide

Correlational Research – Methods, Types and...

Triangulation in Research – Types, Methods and...

Qualitative Research Methods

Mixed Methods Research – Types & Analysis

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Prevent plagiarism, run a free check.

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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 Reference Generator.

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 5 July 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

Is this article helpful?

Shona McCombes

Other students also liked, correlational research | guide, design & examples, a quick guide to experimental design | 5 steps & examples, descriptive research design | definition, methods & examples.

• Search Menu

Sign in through your institution

• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Urban Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Literature
• Classical Reception
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Papyrology
• Greek and Roman Epigraphy
• Greek and Roman Law
• Greek and Roman Archaeology
• Late Antiquity
• Religion in the Ancient World
• Social History
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Emotions
• History of Agriculture
• History of Education
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Evolution
• Language Reference
• Language Acquisition
• Language Variation
• Language Families
• Lexicography
• Linguistic Anthropology
• Linguistic Theories
• Linguistic Typology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies (Modernism)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Media
• Music and Religion
• Music and Culture
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Science
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Politics
• Law and Society
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Neuroanaesthesia
• Clinical Neuroscience
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Toxicology
• Medical Oncology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Medical Ethics
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Security
• Computer Games
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Psychology
• Cognitive Neuroscience
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Ethics
• Business Strategy
• Business History
• Business and Technology
• Business and Government
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic History
• Economic Systems
• Economic Methodology
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Management of Land and Natural Resources (Social Science)
• Natural Disasters (Environment)
• Social Impact of Environmental Issues (Social Science)
• Sustainability
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• Ethnic Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• International Political Economy
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Behaviour
• Political Economy
• Political Institutions
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Political Theory
• Politics and Law
• Politics of Development
• Public Policy
• Public Administration
• Qualitative Political Methodology
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

A newer edition of this book is available.

• < Previous chapter
• Next chapter >

22 Case Study Research: In-Depth Understanding in Context

Helen Simons, School of Education, University of Southampton

• Published: 01 July 2014
• Cite Icon Cite
• Permissions Icon Permissions

This chapter explores case study as a major approach to research and evaluation. After first noting various contexts in which case studies are commonly used, the chapter focuses on case study research directly Strengths and potential problematic issues are outlined and then key phases of the process. The chapter emphasizes how important it is to design the case, to collect and interpret data in ways that highlight the qualitative, to have an ethical practice that values multiple perspectives and political interests, and to report creatively to facilitate use in policy making and practice. Finally, it explores how to generalize from the single case. Concluding questions center on the need to think more imaginatively about design and the range of methods and forms of reporting requiredto persuade audiences to value qualitative ways of knowing in case study research.

Personal account

• Sign in with email/username & password
• Get email alerts
• Save searches
• Purchase content
• Activate your purchase/trial code
• Add your ORCID iD

Institutional access

Sign in with a library card.

• Sign in with username/password
• Recommend to your librarian
• Institutional account management
• Get help with access

Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:

IP based access

Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.

Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.

• Click Sign in through your institution.
• Select your institution from the list provided, which will take you to your institution's website to sign in.
• When on the institution site, please use the credentials provided by your institution. Do not use an Oxford Academic personal account.
• Following successful sign in, you will be returned to Oxford Academic.

If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.

Enter your library card number to sign in. If you cannot sign in, please contact your librarian.

Society Members

Society member access to a journal is achieved in one of the following ways:

Sign in through society site

Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:

• Click Sign in through society site.
• When on the society site, please use the credentials provided by that society. Do not use an Oxford Academic personal account.

If you do not have a society account or have forgotten your username or password, please contact your society.

Sign in using a personal account

Some societies use Oxford Academic personal accounts to provide access to their members. See below.

A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.

Some societies use Oxford Academic personal accounts to provide access to their members.

Viewing your signed in accounts

Click the account icon in the top right to:

• View your signed in personal account and access account management features.
• View the institutional accounts that are providing access.

Signed in but can't access content

Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.

For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.

Our books are available by subscription or purchase to libraries and institutions.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Rights and permissions
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Have a language expert improve your writing

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

• Knowledge Base

Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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

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

Research bias

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

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

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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

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

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

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

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.

Streefkerk, R. (2023, June 22). Qualitative vs. Quantitative Research | Differences, Examples & Methods. Scribbr. Retrieved July 7, 2024, from https://www.scribbr.com/methodology/qualitative-quantitative-research/

Is this article helpful?

Raimo Streefkerk

Other students also liked, what is quantitative research | definition, uses & methods, what is qualitative research | methods & examples, mixed methods research | definition, guide & examples, what is your plagiarism score.

• Open access
• Published: 05 July 2024

Integrating virtual patients into undergraduate health professions curricula: a framework synthesis of stakeholders’ opinions based on a systematic literature review

• Joanna Fąferek 1 ,
• Pierre-Louis Cariou 2 ,
• Inga Hege 3 ,
• Anja Mayer 4 ,
• Luc Morin 2 ,
• Daloha Rodriguez-Molina 5 ,
• Bernardo Sousa-Pinto 6 &
• Andrzej A. Kononowicz 7  

BMC Medical Education volume  24 , Article number:  727 ( 2024 ) Cite this article

60 Accesses

1 Altmetric

Metrics details

Virtual patients (VPs) are widely used in health professions education. When they are well integrated into curricula, they are considered to be more effective than loosely coupled add-ons. However, it is unclear what constitutes their successful integration. The aim of this study was to identify and synthesise the themes found in the literature that stakeholders perceive as important for successful implementation of VPs in curricula.

We searched five databases from 2000 to September 25, 2023. We included qualitative, quantitative, mixed-methods and descriptive case studies that defined, identified, explored, or evaluated a set of factors that, in the perception of students, teachers, course directors and researchers, were crucial for VP implementation. We excluded effectiveness studies that did not consider implementation characteristics, and studies that focused on VP design factors. We included English-language full-text reports and excluded conference abstracts, short opinion papers and editorials. Synthesis of results was performed using the framework synthesis method with Kern’s six-step model as the initial framework. We appraised the quality of the studies using the QuADS tool.

Our search yielded a total of 4808 items, from which 21 studies met the inclusion criteria. We identified 14 themes that formed an integration framework. The themes were: goal in the curriculum; phase of the curriculum when to implement VPs; effective use of resources; VP alignment with curricular learning objectives; prioritisation of use; relation to other learning modalities; learning activities around VPs; time allocation; group setting; presence mode; VPs orientation for students and faculty; technical infrastructure; quality assurance, maintenance, and sustainability; assessment of VP learning outcomes and learning analytics. We investigated the occurrence of themes across studies to demonstrate the relevance of the framework. The quality of the studies did not influence the coverage of the themes.

Conclusions

The resulting framework can be used to structure plans and discussions around implementation of VPs in curricula. It has already been used to organise the curriculum implementation guidelines of a European project. We expect it will direct further research to deepen our knowledge on individual integration themes.

Peer Review reports

Introduction

Virtual patients (VPs) are defined as interactive computer simulations of real-life clinical scenarios for the purpose of health professions training, education, or assessment [ 1 ]. Several systematic reviews have demonstrated that learning using VPs is associated with educational gains when compared to no intervention and is non-inferior to traditional, non-computer-aided, educational methods [ 2 , 3 , 4 ]. This conclusion holds true across several health professions, including medicine [ 3 , 5 ], nursing [ 6 ] and pharmacy [ 7 ]. The strength of VPs in health professions education lies in fostering clinical reasoning [ 4 , 6 , 8 ] and related communication skills [ 5 , 7 , 9 ]. At the same time, the research syntheses report high heterogeneity of obtained results [ 2 , 4 ]. Despite suggestions in the literature that VPs that are well integrated into curricula are more effective than loosely coupled add-ons [ 5 , 10 , 11 ], there is no clarity on what constitutes successful integration. Consequently, the next important step in the research agenda around VPs is to investigate strategies for effectively implementing VPs into curricula [ 9 , 12 , 13 ].

In the context of healthcare innovation, implementation is the process of uptaking a new finding, policy or technology in the routine practice of health services [ 14 , 15 , 16 ]. In many organisations, innovations are rolled out intuitively, which at times ends in failure even though the new tool has previously shown good results in laboratory settings [ 17 ]. A large review of over 500 implementation studies showed that better-implemented health promotion programs yield 2–3 times larger mean effect sizes than poorly implemented ones [ 18 ]. Underestimation of the importance and difficulty of implementation processes is costly and may lead to unjustified attribution of failure to the new product, while the actual problem is inadequate methods for integration of the innovation into practice [ 15 ].

The need for research into different ways of integrating computer technology into medical schools was recognised by Friedman as early as 1994 [ 19 ]. However, studies of the factors and processes of technology implementation in medical curricula have long been scarce [ 12 ]. While the terminology varies across studies, we will use the terms introduction, integration, incorporation , and implementation of VPs into curricula interchangeably. Technology adoption is the decision to use a new technology in a curriculum, and we view it as the first phase of implementation. In an early guide to the integration of VPs into curricula, Huwendiek et al. recommended, based on their experience, the consideration of four aspects relevant to successful implementation: blending face-to-face learning with on-line VP sessions; designing collaborative learning around VPs; allowing students flexibility in deciding when/where/how to learn with VPs; and constructively aligning learning objectives with suitable VPs and matched assessment [ 20 ]. In a narrative review of VPs in medical curricula, Cendan and Lok identified a few practices which are recommended for the use of VPs in curricula: filling gaps in clinical experience with standardised and safe practice, replacing paper cases with interactive models showing variations in clinical presentations, and providing individualised feedback based on objective observation of student activities. These authors also highlighted cost as a significant barrier to the implementation process [ 21 ]. Ellaway and Davies proposed a theoretical construct based on Activity Theory to relate VPs to their use and to link to other educational interventions in curricula [ 22 ]. However, a systematic synthesis of the literature on the identified integration factors and steps relevant to VP implementation is lacking.

The context of this study was a European project called iCoViP (International Collection of Virtual Patients; https://icovip.eu ) , which involved project partners from France, Germany, Poland, Portugal, and Spain and succeeded in creating a collection of 200 open-access VPs available in 6 languages to support clinical reasoning education [ 23 ]. Such a collection would benefit from being accompanied by integration guidelines to inform potential users on how to implement the collection into their curricula. However, guidelines require frameworks to structure the recommendations. Existing integration frameworks are limited in scope for a specific group of health professions, were created mostly for evaluation rather than guidance, or are theoretical or opinion-based, without an empirical foundation [ 24 , 25 , 26 ].

Inspired by the methodological development of qualitative literature synthesis [ 27 ], we decided to build a mosaic of the available studies in order to identify and describe what stakeholders believe is important when planning the integration of VPs into health professions curricula. The curriculum stakeholders in our review included students, teachers, curriculum planners, and researchers in health professions education. We aimed to develop a framework that would configure existing research on curriculum implementations, structure future practice guidelines, and inform research agendas in order to strengthen the evidence behind the recommendations.

Therefore, the research aim of this study was to identify and synthesise themes across the literature that, in stakeholders’ opinions, are important for the successful implementation of VPs in health professions curricula.

This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework [ 28 ].

Eligibility criteria

We selected studies whose main objective was to define, identify, explore, or evaluate a set of factors that, in the view of the authors or study participants, contribute to the successful implementation of VPs in curricula. Table  1 summarises the inclusion and exclusion criteria.

The curricula in which VPs were included targeted undergraduate health professions students, such as human medicine, dentistry, nursing, or pharmacy programs. We were interested in the perspectives of all possible stakeholders engaged in planning or directly affected by undergraduate health professions curricula, such as students, teachers, curriculum planners, course directors, and health professions education researchers. We excluded postgraduate and continuing medical education curricula, faculty development courses not specifically designed to prepare a faculty to teach an undergraduate curriculum with VPs, courses for patients, as well as education at secondary school level and below. Also excluded were alternative and complementary medicine programs and programs in which students do not interact with human patients, such as veterinary medicine.

Similar to the previous systematic review [ 4 ], we excluded from the review VP simulations that required non-standard computer equipment (like virtual reality headsets) and those in which the VP was merely a static case vignette without interaction or the VP was simulated by a human (e.g., a teacher answering emails from students as a virtual patient). We included studies in which VPs were presented in the context of health professions curricula; we excluded studies in which VPs were used as extracurricular activities (e.g., one-time learning opportunities, such as conference workshops) or merely as part of laboratory experimentation.

We included all studies that presented original research, and we excluded editorials and opinion papers. Systematic reviews were included in the first stage so we could manually search for references in order to detect relevant studies that had potentially been omitted. We included studies that aimed to comprehensively identify or evaluate external contextual factors relevant for the integration of VPs into curricula or that examined activities around VPs and the organisational, curricular and accreditation context (the constructed and framed layers of activities in Ellaway & Davies’ model [ 22 ]). As the goal was to investigate integration strategies, we excluded VP design studies that looked into techniques for authoring VPs or researched technical or pedagogical mechanisms encoded in VPs that could not be easily altered (i.e., encoded layer of VP activities [ 22 ]). As we looked into studies that comprehensively investigated a set of integration factors that are important in the implementation process, we excluded studies that focus on program effectiveness (i.e., whether or not a VP integration worked) but do not describe in detail how the VPs were integrated into curricula or investigate what integration factors contributed to the implementation process. We also excluded studies that focused on a single integration factor as our goal was to explore the broad perspective of stakeholders’ opinions on what factors matter in integration of VPs into curricula.

We only included studies published in English as we aimed to qualitatively analyse the stakeholders’ opinions in depth and did not want to rely on translations. We chose the year 2000 as the starting point for inclusion. We recognise that VPs were used before this date but also acknowledge the significant shift in infrastructure from offline technologies to the current web-based platforms, user-friendly graphical web browsers, and broadband internet, all of which appeared around the turn of the millennium. Additionally, VP literature before 2000 was mainly focused on demonstrating technology rather than integrating these tools into curricula [ 12 , 19 ].

Information sources and search

We systematically searched the following five bibliographic databases: MEDLINE (via PubMed), EMBASE (via Elsevier), Educational Resource Information Center (ERIC) (via EBSCO), CINAHL Complete (via EBSCO), Web of Science (via Clarivate). The search strategies are presented in Supplementary Material S1 . We launched the first query on March 8, 2022, and the last update was carried out on September 25, 2023. The search results were imported into the Rayyan on-line software [ 29 ]. Duplicate items were removed. Each abstract was screened by at least two reviewers working independently. In the case of disagreement between reviewers, we included the abstract for full text analysis. Next, we downloaded the full text of the included abstracts, and pairs of reviewers analysed the content in order to determine whether they met the inclusion criteria. In the case of disagreement, a third reviewer was consulted to arbitrate the decision.

Data extraction and analysis

Reviewers working independently extracted relevant characteristics of the included studies to an online spreadsheet. We extracted such features as the country in which the study was conducted, the study approach, the data collection method, the year of implementation in the curriculum, the medical topic of the VPs, the type and number of participants, the number of included VPs, the type of VP software, and the provenance of the cases (e.g., self-developed, part of a commercial database or open access repository).

The qualitative synthesis followed the five steps of the framework synthesis method [ 27 , pp. 188–190]. In the familiarisation phase (step 1), the authors who were involved previously in the screening and data extraction process read the full text versions of the included studies to identify text segments containing opinions on how VPs should be implemented into curricula.

Next, after a working group discussion, we selected David Kern’s six-step curriculum development [ 30 ] for the pragmatic initial frame (step 2). Even though it is not a VP integration framework in itself, we regarded it as a “best fit” to configure a broad range of integration factors spanning the whole process of curriculum development. David Kern’s model is often used for curriculum design and reform and has also been applied in the design of e-learning curricula [ 31 ]. Through a series of asynchronous rounds of comments, on-line meetings and one face-to-face workshop that involved a group of stakeholders from the iCoViP project, we iteratively clustered the recommendations into the themes that emerged. Each theme was subsumed to one of Kern’s six-steps in the initial framework. Next, we formulated definitions of the themes.

In the indexing phase (step 3), two authors (JF and AK) systematically coded the results and discussion sections of all the included empirical studies, line-by-line, using the developed themes as a coding frame. Text segments grouped into individual themes were comparatively analysed for consistency and to identify individual topics within themes. Coding was performed using MaxQDA software for qualitative analysis (MaxQDA, version 22.5 [ 32 ]). Disagreements were discussed and resolved by consensus, leading to iterative refinements of the coding frame, clarifications of definitions, and re-coding until a final framework was established.

Subsequently, the studies were charted (step 4) into tables in order to compare their characteristics. Similar papers were clustered based on study design to facilitate closer comparisons. A quality appraisal of the included studies was then performed using a standardised tool. Finally, a visual representation of the framework was designed and discussed among the research team, allowing for critical reflection on the consistency of the themes.

In the concluding step (step 5), in order to ensure the completeness and representativeness of the framework for the analysed body of literature, we mapped the themes from the developed framework to the studies in which they were found, and we analysed how individual themes corresponded to the conceptual and implementation evaluation models identified during the review. We looked for patterns and attempted to interpret them. We also looked for inconsistencies and tensions in the studies to identify potential areas for future research.

Quality appraisal of the included studies

To appraise the quality of the included studies, we selected the QuADS (Quality Assessment with Diverse Studies) tool [ 33 ], which is suitable for assessing the quality of studies with diverse designs, including mixed- or multi-method studies. This tool consists of 13 items on a four-point scale (0: not reported; 1: reported but inadequate; 2: reported and partially adequate; 3: sufficiently reported). QuADS has previously been successfully used in synthesis of studies in the field of health professions education [ 34 ] and technology-enhanced learning environments [ 35 ]. The included qualitative studies, quantitative surveys, and mixed-methods interview studies were independently assessed by two reviewers (JF, AK). The results were then compared; if differences arose, the justifications were discussed and a final judgement was reached by consensus. Following the approach taken by Goagoses et al. [ 35 ], we divided the studies into three groups, depending on the summary quality score: weak (≤ 49% of QuADS points); medium (50–69%) and high (≥ 70%) study quality.

Characteristics of the included studies

The selection process for the included studies is presented in Fig.  1 .

PRISMA flowchart of the study selection process

Our search returned a total of 4808 items. We excluded duplicate records ( n  = 2201), abstracts not meeting the inclusion criteria ( n  = 2526), and complete reports ( n  = 59) after full text analysis. In the end, 21 studies met our inclusion criteria.

Types of included studies

In the analysis of the 21 included studies, 18 were classified as empirical studies, while three studies were identified as theoretical or evaluation models.

The purpose of the 18 empirical studies was to survey or directly observe the reaction of stakeholders to curriculum integration strategies in order to identify or describe the relevant factors (Table  2 ). Study types included qualitative ( n  = 4) [ 11 , 36 , 37 , 38 ], mixed-methods ( n  = 4) [ 39 , 40 , 41 , 42 ], quantitative survey ( n  = 4) [ 10 , 43 , 44 , 45 ], and descriptive case studies ( n  = 6) [ 46 , 47 , 48 , 49 , 50 , 51 ]. Data collection methods included questionnaires ( n  = 9) [ 10 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 48 ], focus groups and small group interviews ( n  = 8) [ 11 , 36 , 37 , 38 , 39 , 41 , 42 , 48 ], system log analyses ( n  = 3) [ 44 , 47 , 48 ], direct observations ( n  = 1) [ 44 ], or narrative descriptions of experiences with integration ( n  = 5) [ 46 , 47 , 49 , 50 , 51 ]. The vast majority of studies reported experiences from integration of VPs into medical curricula ( n  = 15). Two studies reported integration of VPs into nursing programs [ 40 , 51 ], one in a dentistry [ 40 ] and one in a pharmacy program [ 41 ]. One study was unspecific about the health professions program [ 46 ].

The remaining three of the included studies represented a more theoretical approach: one aimed to create a conceptual model [ 25 ]; the other two [ 24 , 26 ] presented evaluation models of the integration process (Table  3 ). We analysed them separately, considering their different structures, and we mapped the components of these models to our framework in the last stage of the framework synthesis.

Themes in the developed framework

The developed framework (Table  4 ), which we named the iCoViP Virtual Patient Curriculum Integration Framework (iCoViP Framework), contains 14 themes and 51 topic codes. The final version of the codebook used in the study can be found in Supplementary Material  S2 . Below, we describe the individual themes.

General needs assessment

In the Goal theme, we coded perceptions regarding appropriate general uses of VPs in curricula. This covers the competencies to be trained using VPs, but also unique strengths and limitations of VPs as a learning method that should influence decisions regarding their adoption in curricula.

A common opinion was that VPs should target clinical reasoning skills and subskills such as acquisition/organisation of clinical information, development of illness scripts (sign, symptoms, risk factors, knowledge of disease progress over time), patient-centred care (including personal preferences and cultural competencies in patient interaction) [ 11 , 36 , 37 , 38 , 39 , 40 , 42 , 43 , 44 , 45 , 46 , 49 , 50 , 51 ]. According to these opinions, a strength of VPs is their potential for self-directed learning in an authentic, practice-relevant, safe environment that gives opportunities for reflection and “productive struggle” [ 37 , 39 , 49 ]. VPs also make it possible for students to practise decision-making in undifferentiated patient cases and observe the development of disease longitudinally [ 45 ]. For instance, some students valued the potential of VPs as a tool that integrates basic knowledge with clinical application in a memorable experience:

We associate a disease more to a patient than to the textbook. If I saw the patient, saw the photo and questioned the patient in the program, I will remember more easily, I’ll have my flashback of that pathology more than if I only studied my class notes or a book. {Medical student, 4th year, Columbia} [ 36 ].

Another perceived function of VPs is to help fill gaps in curricula and clinical experiences [ 36 , 37 , 38 , 42 , 45 , 50 ]. This supporting factor for the implementation of VPs in curricula is particularly strong when combined with the need to meet regulatory requirements [ 42 ].

Varying opinions were expressed regarding the aim of VPs to represent rare diseases (or common conditions but with unusual symptoms) [ 43 , 48 ] versus common clinical pictures [ 37 , 40 ]. Another tension arose when considering whether VPs should be used to introduce new factual/conceptual knowledge versus serving as a knowledge application and revision tool:

The students, however, differed from leaders and teachers in assuming that VPS should offer a reasonable load of factual knowledge with each patient. More as a surprise came the participants’ preference for usual presentations of common diseases. [ 40 ].

Limitations of VPs were voiced when the educational goal was related to physical contact and hands-on training because, in some aspects of communication skills, physical examination, or application of medical equipment, VPs clearly have inferior properties to real patients, human actors or physical mannequins [ 36 , 51 ].

Targeted needs assessment

The Phase theme described the moment in curricula when the introduction of VPs was regarded as adequate. According to some opinions, VPs should be introduced early in curricula to provide otherwise limited exposure to real patients [ 39 , 43 ]:

Students of the pre-clinical years show a high preference in the adoption of VPs as learning activities. That could be explained from the lack of any clinical contact with real patients in their two first years of study and their willingness to have early, even virtual, clinical encounters. [ 43 ].

The tendency to introduce VPs early in curricula was confronted with the problem of students’ limited core knowledge as they were required to use VPs before they had learnt about the features of the medical conditions they were supposed to recognise [ 41 , 48 ]. At the other end of the time axis, we did not encounter opinions that specified when it would be too late to use VPs in curricula. Even final-year students stated that they preferred to augment their clinical experience with VPs [ 43 ].

In the Resources theme, we gathered opinions regarding the cost and assets required for the integration of VPs into curricula. Cost can be a barrier that, if not addressed properly, can slow down or even stop an implementation, therefore it should be addressed early in the implementation process. This includes monetary funds [ 42 ] and availability of adequately qualified personnel [ 38 ] and their time [ 47 ].

For instance, it was found that if a faculty member is primarily focused on clinical work, their commitment to introducing innovation in VPs will be limited and will tend to revert to previous practices unless additional resources are provided to support the change [ 38 ].

The Resources theme also included strategies to follow when there is only a limited number of resources to implement VPs in a curriculum. Some suggested solutions included the sharing of VPs with other institutions [ 50 ], the exchange of know-how on the implementation of VPs with more experienced institutions and networks of excellence [ 38 , 42 ], and increasing faculties’ awareness of the benefits of using VPs, also in terms of reduced workload after the introduction of VPs in curricula [ 38 ]. Finally, another aspect of this theme was the (lack of) awareness of the cost of implementing VPs in curricula across stakeholder groups [ 40 ].

Goals and objectives

The Alignment theme grouped utterances highlighting the importance of selecting the correct VP content for curricula and matching VPs with several elements of curricula, such as learning objectives, the content of VPs across different learning forms, as well as the need to adapt VPs to local circumstances. The selection criteria included discussion regarding the number of VPs [ 36 ], fine-grained learning objectives that could be achieved using VPs [ 42 , 50 ], and selection of an appropriate difficulty level, which preferably should gradually increase [ 11 , 49 ].

It was noticed that VPs can be used to systematically cover a topic. For example, they can align with implementation of clinical reasoning themes in curricula [ 38 ] or map a range of diseases that are characteristic of a particular region of interest, thereby filling gaps in important clinical exposure and realistically representing the patient population [ 36 ].

Several approaches were mentioned regarding the alignment of VPs with curricula that include the selection of learning methods adjusted to the type of learning objectives [ 45 ], introduction of VPs in small portions in relevant places in curricula to avoid large-scale changes [ 38 ], alignment of VP content with assessment [ 39 ], and the visibility of this alignment by explicitly presenting the specific learning objectives addressed by VPs [ 49 ]. It is crucial to retain cohesion of educational content across a range of learning modalities:

I worked through a VP, and then I went to the oncology ward where I saw a patient with a similar disease. After that we discussed the disease. It was great that it was all so well coordinated and it added depth and some [sic!] much needed repetition to the case. {Medical student, 5th year, Germany} [ 11 ].

We also noted unresolved dilemmas, such as whether to present VPs in English as the modern lingua franca to support the internationalisation of studies, versus the need to adapt VPs to the local native language of learners in order to improve accessibility and perceived relevance [ 50 ].

Prioritisation

Several studies presented ideas for achieving higher Prioritisation of VPs in student agendas. The common but “heavy-handed” approach to increase motivation was to make completion of VPs a mandatory requirement to obtain course credits [ 36 , 48 , 51 ]. However, this approach was then often criticised for promoting superficial learning and lack of endorsement for self-directed learning [ 47 ]. Motivation was reported to increase when content was exam-relevant [ 11 ].

According to yet another mentioned strategy, motivation comes with greater engagement of teachers who intensively reference VPs in their classes and often give meaningful feedback regarding their use [ 40 ] or construct group activities around them [ 46 ]. It was suggested that VPs ought to have dedicated time for their use which should not compete with activities with obviously higher priorities, such as meeting real patients [ 37 ].

Another idea for motivation was adjustment of VPs to local needs, language and culture. It was indicated that it would be helpful to promote VPs’ authenticity by stressing the similarity of presented scenarios to problems clinicians encounter in clinical practice (e.g., using teacher testimonials [ 48 ]). Some students saw VPs as being more relevant when they are comprehensively described in course guides and syllabi [ 39 ]. The opinions about VPs that circulate among more-experienced students are also important:

Definitely if the year above kind of approves of something you definitely think you need it. {Medical student, 3rd year, UK} [ 39 ].

Peer opinion was also important for teachers, who were reported to be more likely to adopt VPs in their teaching if they have heard positive opinions from colleagues using them, know the authors of VP cases, or respect organisations that endorse the use of VP software [ 38 , 42 ]:

I was amazed because it was a project that seemed to have incredible scope, it was huge. I was impressed that there was the organization to really roll out and develop all these cases and have this national organization involved. {Clerkship director, USA} [ 42 ].

Educational strategies

The Relation theme contained opinions about the connections between VPs and other types of learning activities. This theme was divided into preferences regarding which types of activities should be replaced or extended by VPs, and the relative order in which they should appear in curricula. We noticed general warnings that VPs should not be added on top of existing activities as this is likely to cause work overload for students [ 10 , 45 ]. The related forms of education that came up in the discussions were expository methods like lectures and reading assignments (e.g., textbooks, websites), small group discussions in seminars (e.g., problem-based learning [PBL] sessions, follow-up seminars), alternative forms of simulations (e.g., simulated patients, human patient simulators), clinical teaching (i.e., meeting with real patients and bedside learning opportunities), and preparation for assessments.

Lectures were seen as a form of providing core knowledge that could later be applied in VPs:

Working through the VP before attending the lecture was not as useful to me as attending the lecture before doing the VP. I feel I was able to get more out of the VP when I first attended the lecture in which the substance and procedures were explained. {Medical student, 5th year, Germany} [ 11 ].

Textbooks were helpful as a source of reference knowledge while solving VPs that enabled students to reflect while applying this knowledge in clinical context. Such a learning scenario was regarded impossible in front of real patients:

But here it’s very positive right now when we really don’t know everything about rheumatic diseases, that we can sit with our books at the same time as we have a patient in front of us. {Medical student, 3rd year, Sweden} [ 37 ].

Seminars (small group discussions) were perceived as learning events that motivate students to work intensively with VPs and as an opportunity to ask questions about them [ 11 , 46 , 47 ], with the warning that teachers should not simply repeat the content of VPs as this would be boring [ 44 ]. The reported combination of VPs with simulated patients made it possible to increase the fidelity of the latter by means of realistic representation of clinical signs (e.g., cranial nerve palsies) [ 48 ]. It was noticed that VPs can connect different forms of simulation, “turn[ing] part-task training into whole-task training” [ 46 ], or allow more thorough and nuanced preparation for other forms of simulation (e.g., mannequin-based simulation) [ 46 ]. A common thread in the discussion was the relation between VPs and clinical teaching [ 10 , 11 , 37 , 39 , 45 , 46 ]. The opinions included warnings against spending too much time with VPs at the expense of bedside teaching [ 37 , 51 ]. The positive role of VPs was highlighted in preparing for clinical experience or as a follow-up to meeting real patients because working with VPs is not limited by time and is not influenced by emotions [ 37 ].

Huwendiek et al. [ 11 ] suggested a complete sequence of activities which has found confirmation in some other studies [ 48 ]: lectures, VP, seminars and, finally, real patients. However, we also identified alternative solutions, such as VPs that are discussed between lectures as springboards to introduce new concepts [ 49 ]. In addition, some studies concluded that students should have the right to decide which form of learning they prefer in order to achieve their learning objectives [ 38 , 48 ], but this conflicts with limited resources, a problem the students seem not to consider when expressing their preferences.

In the Activities theme, we grouped statements about tasks constructed by teachers around VPs. This includes teachers asking questions to probe whether students have understood the content of VPs, and guiding students in their work with VPs [ 11 , 49 ]. Students were also expected to ask their teachers questions to clarify content [ 43 ]. Some educators felt that students trained using VPs ask too many questions instead of relying more on their clinical reasoning skills and asking fewer, but more pertinent questions [ 38 ].

Students were asked to compare two or more VPs with similar symptoms to recognise key diagnostic features [ 11 ] and to reflect on cases, discuss their decisions, and summarise VPs to their peers or document them in a standardised form [ 11 , 46 , 49 , 51 ]. Another type of activity was working with textbooks while solving VP cases [ 37 ] or following a standard/institutional checklist [ 51 ]. Finally, some students expected more activities around VPs and felt left alone to struggle with learning with VPs [ 37 ].

Implementation

Another theme grouped stakeholders’ opinions regarding Time. A prominent topic was the time required for VP activities. Some statements provided the exact amount of time allocated to VP activities (e.g., one hour a week [ 51 ]), sometimes suggesting that it should be increased. There were several comments from students complaining about insufficient time allocated for VP activities:

There was also SO much information last week and with studying for discretionary IRATs constantly, I felt that I barely had enough time to synthesize the information and felt burdened by having a deadline for using the simulation. {Medical student, 2nd year, USA} [ 48 ].

Interestingly, the perceived lack of time was sometimes interpreted by researchers as a matter of students not assigning high enough priority to VP tasks because they do not consider them relevant [ 39 ].

Some students expected their teachers to help them with time management. Mechanisms for this included explicitly allocated time slots for work with VPs, declaration of the required time spent on working with VPs, and setting deadlines for task completion:

Without a time limit we can say: I’ll check the cases later, and then nothing happens; but if there’s a time limit, well, this week I see cardiac failure patients etc. It’s more practical for us and also for the teachers, I think. {Medical student, 4th year, Columbia} [ 36 ].

This expectation conflicts with the views that students should learn to self-regulate their activities, that setting a minimum amount of time that students should spend working with VPs will discourage them from doing more, and that deadlines cause an acute burst of activity shortly before them, but no activity otherwise [ 47 , 48 ].

Finally, it was interesting to notice that some educators and students perceived VPs as a more time-efficient way of collecting clinical experience than meeting real patients [ 37 , 38 ].

The Group theme included preferences for working alone or in a group. The identified comments revealed tensions between the benefits of working in groups, such as gaining new perspectives, higher motivation thanks to teamwork, peer support:

You get so much more from the situation when you discuss things with someone else, than if you would be working alone. {Medical student, 3rd year, Sweden} [ 37 ].

and the flexibility of working alone [ 43 , 44 , 46 , 49 ]. Some studies reported on their authors’ experiences in selection of group size [ 11 , 48 ]. It was also noted that smaller groups motivated more intensive work [ 41 , 44 ].

In the Presence theme, we coded preferences regarding whether students should work on VPs in a computer lab, a shared space, seminar rooms, or at home. Some opinions valued flexibility in selecting the place of work (provided a good internet connection is available) [ 11 , 36 ]. Students reported working from home in order to prepare well for work in a clinical setting:

... if you can work through a VP at home, you can check your knowledge about a certain topic by working through the relevant VP to see how you would do in a more realistic situation. {Medical student, 5th year, Germany} [ 11 ].

Some elements of courses related to simulated patient encounters had to be done during obligatory face-to-face training in a simulation lab (e.g., physical examination) that accompanied work with VPs [ 51 ]. Finally, it was observed that VPs offer sufficient flexibility to support different forms of blended learning scenarios [ 46 ]. Synchronous collaborative learning can be combined with asynchronous individual learning, which is particularly effective when there is a need for collaboration between geographically dispersed groups [ 46 ], for instance if a school has more than one campus.

Orientation

In the Orientation theme, we included all comments that relate to the need for teacher training, the content of teacher training courses, and the form of preparation of faculty members and students for using VPs. Knowledge and skills mentioned as useful for the faculty were awareness about how VPs fit into curricula [ 42 ], small-group facilitation skills, clinical experience [ 11 ], and experience with online learning [ 38 ]. Teachers expected to be informed about the advantages/disadvantages and evidence of effectiveness of VPs [ 38 ]. For students, the following prerequisites were identified: the ability to operate VP tools and experience with online learning in general, high proficiency of the language in which the VPs are presented and, for some scenarios (e.g., learning by design), also familiarity with VP methodology [ 38 , 47 , 48 , 50 , 51 ]. It was observed that introduction of VPs is more successful when both teachers and students are familiar with the basics of clinical reasoning theory and explicit teaching methods [ 38 ].

Forms of student orientation that were also identified regarding the use of VPs included demonstrations and introductions at the start of learning units [ 42 ], handouts and email reminders, publication of online schedules for assigned VPs, and expected time to complete them [ 11 , 48 ].

Infrastructure

The Infrastructure theme grouped stakeholders’ requirements regarding the technical environment in which VPs work. This included the following aspects: stable internet connection, secure login, usability of the user interface, robust software (well tested for errors and able to handle many simultaneous users), interoperability (e.g., support for the standardised exchange of VPs between universities) and access to an IT helpdesk [ 11 , 40 , 42 , 47 , 50 ]. It was noticed that technical glitches can have a profound influence on the perceived success of VP integration:

Our entire team had some technical difficulties, whether during the log-in process or during the patient interviews themselves and felt that our learning was somewhat compromised by this. {Medical student, 2nd year, USA} [ 48 ].

Evaluating the effectiveness

Sustainability & quality.

In the Sustainability & Quality theme, we indexed statements regarding the need to validate and update VP content, and its alignment with curricular goals and actual assessment to respond to changes in local conditions and regulatory requirements [ 45 ].

The need to add new cases to VP collections that are currently in use was mentioned [ 40 ]. This theme also included the requirement to evaluate students’ opinions on VPs using questionnaires, feedback sessions and observations [ 47 , 48 , 49 ]. Some of the stakeholders required evidence regarding the quality of VPs before they decided to adopt them [ 38 , 42 , 50 ]. Interestingly, it was suggested that awareness of the need for quality control of VPs varied between stakeholder groups, with low estimation of the importance of this factor among educational leaders:

Leaders also gave very low scores to both case validation and case exchange with other higher education institutions (the latter finding puts into perspective the current development of VPS interoperability standards). The leaders’ lack of interest in case validation may reflect a de facto conviction, that it is the ‘shell’ that validates the content. [ 40 ].

The Assessment theme encompasses a broad selection of topics related to various forms of using VPs in the assessment of educational outcomes related to VPs. This includes general comments on VPs as an assessment form, use of VPs in formative and summative assessment, as well as the use of learning analytics methods around VPs.

General topics identified in this theme included which learning objectives should be assessed with VPs, such as the ability to conduct medical diagnostic processes effectively [ 36 ], the authenticity of VPs as a form of examination [ 36 ], the use of VPs for self-directed assessment [ 11 , 39 , 43 , 46 ], and the emotions associated with assessment using VPs, e.g., reduced stress and a feeling of competitiveness [ 36 , 48 ].

Other topics discussed in the context of assessment included the pedagogical value of using VPs for assessments [ 36 ], such as the improved retention of information through reflection on diagnostic errors made with VPs [ 48 ], and VPs’ ability to illustrate the consequences of students’ errors [ 46 ]. Methods of providing feedback during learning with VPs were also described [ 11 ]. It was highlighted that data from assessments using VPs can aid teachers in planning future training [ 49 , 51 ]. Furthermore, it was observed that feedback from formative assessments with VPs motivates students to engage more deeply in their future learning [ 10 , 41 , 47 ]:

It definitely helped what we did wrong and what we should have caught, because there was a lot that I missed and I didn’t realize it until I got the feedback and in the feedback it also said where you would find it most of the time and why you would have looked there in the first place. {Pharmacy student, 4th year, Canada} [ 41 ].

In several papers [ 42 , 47 , 48 , 51 ], suggestions were made regarding the types of metrics that can be used to gauge students’ performance (e.g., time to complete tasks related to VPs, the accuracy of answers given in the context of VPs, recall and precision in selecting key features in the diagnostic process, the order of selecting diagnostic methods, and the quality of medical documentation prepared by students from VPs). The use of specific metrics and the risks associated with them were discussed. For instance, time spent on a task was sometimes seen as a metric of decision efficiency (a speed-based decision score) that should be minimised [ 48 ], or as an indicator of diligence in VP analysis that should be maximised [ 47 ]. Time measurements in on-line environments can be influenced by external factors like parallel learning using different methods (e.g. consulting a textbook) or interruptions unrelated to learning [ 47 ].

Finally, the analysed studies discussed summative aspects of assessment, including arguments regarding the validity of using VPs in assessments [ 51 ], the need to ensure alignment between VPs and examination content [ 49 ], and the importance of VP assessment in relation to other forms of assessment (e.g., whether it should be part of high-stakes examinations) [ 40 , 51 ]. The studies also explored forms of assessment that should be used to test students’ assimilation of content delivered through VPs [ 47 ], the challenges related to assessing clinical reasoning [ 38 ], and the risk of academic dishonesty in grading based on VP performance [ 48 ].

Mapping of the literature using the developed framework

We mapped the occurrence of the iCoViP Framework themes across the included empirical studies, as presented in Fig.  2 .

Code matrix of the occurrence of themes in the included empirical studies

Table  5 displays a pooled number of studies in which each theme occurred. The three most frequently covered themes were Prioritisation , Goal , and Alignment . These themes were present in approx. 90% of the analysed papers. Each theme from the framework appeared in at least four studies. The least-common themes, present in fewer than one-third of studies, were Phase , Presence , and Resources .

We mapped the iCoViP Framework to the three identified existing theoretical and evaluation models (Fig.  3 ).

Mapping of the existing integration models to the iCoViP Framework

None of the compared models contained a category that could not be mapped to the themes from the iCoViP Framework. The model by Georg & Zary [ 25 ] covered the fewest themes from our framework, including only the common categories of Goal, Alignment, Activities and Assessment . The remaining two models by Huwendiek et al. [ 24 ] and Kleinheksel & Ritzhaupt [ 26 ] underpinned integration quality evaluation tools and covered the majority of themes (9 out of 14 each). There were three themes not covered by any of the models: Phase, Resources, and Presence .

Quality assessment of studies

The details of the quality appraisal of the empirical studies using the QuADS tool are presented in Supplementary Material S3 . The rated papers had medium (50–69%; [ 39 , 40 , 43 ]) to high quality (≥ 70%; [ 10 , 11 , 36 , 37 , 38 , 41 , 42 , 44 , 45 ]). Owing to the difficulty in identifying the study design elements in the included descriptive case studies [ 46 , 47 , 48 , 49 , 50 , 51 ], we decided against assessing their methodological quality with the QuADS tool. This difficulty can also be interpreted as indicative of the low quality of the studies in this group.

The QuADS quality criterion that was most problematic in the reported studies was the inadequate involvement of stakeholders in study design. Most studies reported the involvement of students or teachers only in questionnaire pilots, but not in the conceptualisation of the research. Another issue was the lack of explicit referral to the theoretical frameworks upon which the studies were based. Finally, in many of the studies, participants were selected using convenience sampling, or the authors did not report purposeful selection of the study group.

We found high-quality studies in qualitative, quantitative, and mixed-methods research. There was no statistical correlation between study quality and the number of topics covered. For sensitivity analysis, we excluded all medium-quality and descriptive studies from the analysis; this did not reduce the number of iCoViP Framework topics covered by the remaining high-quality studies.

In our study, we synthesised the literature that describes stakeholders’ perceptions of the implementation of VPs in health professions curricula. We systematically analysed research reports from a mix of study designs that provided a broad perspective on the relevant factors. The main outcome of this study is the iCoViP Framework, which represents a mosaic of 14 themes encompassing many specific topics encountered by stakeholders when reflecting on VPs in health professions curricula. We examined the prevalence of the identified themes in the included studies to justify the relevance of the framework. Finally, we assessed the quality of the analysed studies.

Significance of the results

The significance of the developed framework lies in its ability to provide the health professions education community with a structure that can guide VP implementation efforts and serve as a scaffold for training and research in the field of integration of VPs in curricula. The developed framework was immediately applied in the structuring of the iCoViP Curriculum Implementation Guideline. This dynamic document, available on the website of the iCoViP project [ https://icovip.eu/knowledge-base ], presents the recommendations taken from the literature review and the project partners’ experiences with how to implement VPs, particularly the collection of 200 VPs developed during the iCoViP project [ 23 ]. To improve the accessibility of this guideline, we have added a glossary with definitions of important terms. We have already been using the framework to structure faculty development courses on the topic of teaching with VPs.

It is clear from our study that the success of integrating VPs into curricula depends on the substantial effort that is required of stakeholders to make changes in the learning environment to enable VPs to work well in the context of local health professions education programs. The wealth of themes discussed in the literature around VPs confirms what is known from implementation science: the quality of the implementation is as important as the quality of the product [ 15 ]. This might be disappointing for those who hope VPs are a turnkey solution that can be easily purchased to save time, under the misconception that implementation will occur effortlessly.

Our review also makes it evident that implementation of VPs is a team endeavour. Without understanding, acceptance and mutual support at all levels of the institutional hierarchy and a broad professional background, different aspects of the integration of VPs into curricula will not match. Students should not be left to their own devices when using VPs. They need to understand the relevance of the learning method used in a given curriculum by observing teachers’ engagement in the routine use of VPs, and they should properly understand the relationship between VPs and student assessment. Despite the IT-savviness of many students, they should be shown how and when to use VPs, while also allowing room for creative, self-directed learning. Finally, students should not get the impression that their use of VPs comes at the expense of something they give higher priority, such as direct patient contact or teacher feedback. Teachers facilitating learning with VPs should be convinced of their utility and effectiveness, and they need to know how to use VPs by themselves before recommending them to students. It is important that teachers are aware that VPs, like any other teaching resources, require quality control linked with perpetual updates. They should feel supported by more-experienced colleagues and an IT helpdesk if methodological or technical issues arise. Last but not least, curriculum managers should recognise the benefits and limitations of VPs, how they align with institutional goals, and that their adoption requires both time and financial resources for sustainment. All of this entails communication, coordinated efforts, and shared decision-making during the implementation of VPs in curricula.

Implications for the field

Per Nilsen has divided implementation theories, models and frameworks into three broad categories: process models, determinant frameworks and evaluation models [ 16 ]. We view the iCoViP Framework primarily as a process model. This perspective originates from the initial framework we adopted in our systematic review, namely Kern’s 6-steps curriculum development process [ 30 ], which facilitates the grouping of curricula integration factors into discrete steps and suggests a specific order in which to address implementation tasks. Our intention in using this framework was also to structure how-to guidelines, which are another hallmark of process models. As already noted by Nilsen and as is evident in Kern’s model, implementation process models are rarely applied linearly in practice and require a pragmatic transition between steps, depending on the situation.

The boundary between the classes of implementation models is blurred [ 16 ] and there is significant overlap. It is therefore not surprising that the iCoViP framework can be interpreted through the lens of a determinant framework which configures many factors (facilitators and barriers) that influence VP implementation in curricula. Nilsen’s category of determinant frameworks includes the CFIR framework [ 52 ], which was also chosen by Kassianos et al. to structure their study included in this review [ 38 ]. A comparison of the themes emerging from their study and our framework indicates a high degree of agreement (as depicted in Fig.  2 ). We interpret this as a positive indication of research convergence. Our framework extends this research by introducing numerous fine-grained topic codes that are characteristic of VP integration into curricula.

The aim of our research was not to develop an evaluation framework. For this purpose, the two evaluation tools available in the literature by Huwendiek et al. [ 24 ] and Kleinheksel & Ritzhaupt [ 26 ] are suitable. However, the factors proposed in our framework can further inform and potentially extend existing or new tools for assessing VP integration.

Despite the plethora of available implementation science theories and models [ 16 ], their application in health professions curricula is limited [ 15 ]. The studies included in the systematic review only occasionally reference implementation sciences theories directly (exceptions are CFIR and UTAUT [ 38 ], Rogers’ Diffusion of Innovation Theory [ 26 , 42 ] and Surry’s RIPPLES model [ 42 ]). However, it is important to acknowledge that implementation science is itself an emerging field that is gradually gaining recognition. Furthermore, as noticed by Dubrowski & Dubrowski [ 17 ], the direct application of general implementation science models does not guarantee success and requires verification and adaptation.

Limitations and strengths

This study is based on stakeholders’ perceptions of the integration of VPs into curricula. The strength of the evidence behind the recommendations expressed in the analysed studies is low from a positivist perspective as it is based on subjective opinions. However, by adopting a more interpretivist stance in this review, our goal is not to offer absolute, ready-to-copy recommendations. Instead, we aim to provide a framework that organises the implementation themes identified in the literature into accessible steps. It is beyond the scope of this review to supply an inventory of experimental evidence for the validity of the recommendations in each topic, as was intended in previous systematic reviews [ 4 ]. We recognise that, for some themes, it will always be challenging to achieve a higher level of evidence due to practical constraints in organising studies that experiment with different types of curricula. The complexity, peculiarities, and context-dependency of implementation likely preclude one-size-fits-all recommendations for VP integration. Nevertheless, even in such a situation, a framework for sorting through past experiences with integration of VPs proves valuable for constructing individual solutions that fit a particular context.

The aim of our study was to cover experiences from different health professions programs in the literature synthesis. However, with a few exceptions, the results show a dominance of medical programs in research on VP implementation in curricula. This, although beyond the authors’ control, limits the applicability of our review findings. The data clearly indicates a need for more research into the integration of VPs into health professions curricula other than medicine.

The decision to exclude single-factor studies from the framework synthesis is justified by our aim to provide a comprehensive overview of the integration process. Nevertheless, recommendations from identified single-factor studies [ 53 , 54 , 55 ] were subsequently incorporated into the individual themes in the iCoViP project implementation guideline. We did not encounter any studies on single factors that failed to align with any of the identified themes within the framework. Due to practical reasons concerning the review’s feasibility, we did not analyse studies in languages other than English and did not explore non-peer-reviewed grey literature databases. However, we recognise the potential of undertaking such activities in preparing future editions of the iCoViP guideline as we envisage this resource as an evolving document.

We acknowledge that our systematic review was shaped by the European iCoViP project [ 23 ]. However, we did not confine our study to just a single VP model, thereby encompassing a broad range of technical implementations. The strength of this framework synthesis lies in the diversity of its contributors affiliated with several European universities in different countries, who were at different stages of their careers, and had experience with various VP systems.

Further research

The iCoViP framework, by charting a map of themes around VP integration in health professions curricula, provides a foundation for further, more focused research on individual themes. The less-common themes or conflicts and inconsistencies in recommendations found in the literature synthesis may be a promising starting point.

An example of this is the phase of the curriculum into which a given VP fits. We see that proponents of early and late introduction of VPs use different arguments. The recommendation that VPs should be of increasing difficulty seems to be valid, but what is missing is the detail of what this means in practice. We envisage that this will be researched by exploring models of integration that cater for different levels of student expertise.

There are also varying opinions between those who see VPs as tools for presenting rare, intriguing cases, and those who see the commonality and practice relevance of the clinical problems presented in VPs as the most important factor. However, these opposing stances can be harmonised by developing a methodology to establish a well-balanced case-mix of VPs with different properties depending upon the needs of the learners and curricular context. Another point of division is the recognition of VPs as a tool for internationalising studies and supporting student mobility, versus the expectation that VPs should be adapted to local circumstances. These disparate beliefs can be reconciled by research into the design of activities around VPs that explicitly addresses the different expectations and confirm or refute their usefulness.

A significant barrier to the adoption of VPs is cost. While universities are occasionally willing to make a one-off investment in VPs for prestige or research purposes, the field needs more sustainable models. These should be suitable for different regions of the world and demonstrate how VPs can be maintained at a high level of quality in the face of limited time and resources. This is particularly important in low-resource countries and those affected by crises (e.g., war, natural disasters, pandemics), where the need for VPs is even greater than in developed countries due to the shortage of health professionals involved in teaching [ 56 ]. However, most of the studies included in our systematic review are from high-income countries. This shows a clear need for more research into the implementation of VPs in health professions curricula in developing countries.

Finally, an interesting area for future research is the interplay of different types of simulation modalities in curricula. The studies we reviewed do not recommend one type of simulation over another as each method has its unique advantages. In line with previous suggestions [ 46 ], we see a need for further research into practical implementation methods of such integrated simulation scenarios in curricula.

Stakeholders’ perceptions were structured into 14 themes by this framework synthesis of mixed methods studies on the curricular integration of VPs. We envision that teachers, course directors and curriculum designers will benefit from this framework when they decide to introduce VPs in their teaching. We anticipate that our summary will inspire health professions education researchers to conduct new studies that will deepen our understanding of how to effectively and efficiently implement VPs in curricula. Last but not least, we hope that our research will empower students to express their expectations regarding how they would like to learn with VPs in curricula, thus helping them to become better health professionals in the future.

Data availability

All datasets produced and analysed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

• Virtual patients

International Collection of Virtual Patients

Quality Assessment with Diverse Studies

Liaison Committee on Medical Education (LCME) accreditation standard

Computer-assisted Learning in Paediatrics Program

Problem-Based Learning

Ellaway R, Poulton T, Fors U, McGee JB, Albright S. Building a virtual patient commons. Med Teach. 2008;30:170–4.

Article   Google Scholar  

Cook DA, Erwin PJ, Triola MM. Computerized virtual patients in Health professions Education: a systematic review and Meta-analysis. Acad Med. 2010;85:1589–602.

Consorti F, Mancuso R, Nocioni M, Piccolo A. Efficacy of virtual patients in medical education: a meta-analysis of randomized studies. Comput Educ. 2012;59:1001–8.

Kononowicz AA, Woodham LA, Edelbring S, Stathakarou N, Davies D, Saxena N, et al. Virtual Patient Simulations in Health Professions Education: systematic review and Meta-analysis by the Digital Health Education Collaboration. J Med Internet Res. 2019;21:e14676.

Lee J, Kim H, Kim KH, Jung D, Jowsey T, Webster CS. Effective virtual patient simulators for medical communication training: a systematic review. Med Educ. 2020;54:786–95.

Foronda CL, Fernandez-Burgos M, Nadeau C, Kelley CN, Henry MN. Virtual Simulation in nursing education: a systematic review spanning 1996 to 2018. Simul Healthc J Soc Simul Healthc. 2020;15:46–54.

Richardson CL, White S, Chapman S. Virtual patient technology to educate pharmacists and pharmacy students on patient communication: a systematic review. BMJ Simul Technol Enhanc Learn. 2020;6:332–8.

Plackett R, Kassianos AP, Mylan S, Kambouri M, Raine R, Sheringham J. The effectiveness of using virtual patient educational tools to improve medical students’ clinical reasoning skills: a systematic review. BMC Med Educ. 2022;22:365.

Kelly S, Smyth E, Murphy P, Pawlikowska T. A scoping review: virtual patients for communication skills in medical undergraduates. BMC Med Educ. 2022;22:429.

Berman N, Fall LH, Smith S, Levine DA, Maloney CG, Potts M, et al. Integration strategies for using virtual patients in clinical clerkships. Acad Med. 2009;84:942–9.

Huwendiek S, Duncker C, Reichert F, De Leng BA, Dolmans D, Van Der Vleuten CPM, et al. Learner preferences regarding integrating, sequencing and aligning virtual patients with other activities in the undergraduate medical curriculum: a focus group study. Med Teach. 2013;35:920–9.

Cook DA. The Research we still are not doing: an agenda for the study of computer-based learning. Acad Med. 2005;80:541–8.

Berman NB, Fall LH, Maloney CG, Levine DA. Computer-assisted instruction in Clinical Education: a Roadmap to increasing CAI implementation. Adv Health Sci Educ. 2008;13:373–83.

Eccles MP, Mittman BS. Welcome to implementation science. Implement Sci. 2006;1:1, 1748-5908-1–1.

Dubrowski R, Barwick M, Dubrowski A. I wish I knew this Before… an implementation science primer and model to Guide Implementation of Simulation Programs in Medical Education. In: Safir O, Sonnadara R, Mironova P, Rambani R, editors. Boot Camp Approach to Surgical Training. Cham: Springer International Publishing; 2018. pp. 103–21.

Chapter   Google Scholar  

Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci. 2015;10:53.

Dubrowski R, Dubrowski A. Why should implementation science matter in simulation-based health professions education? Cureus. 2018. https://doi.org/10.7759/cureus.3754 .

Google Scholar  

Durlak JA, DuPre EP. Implementation matters: a review of research on the influence of implementation on program outcomes and the factors affecting implementation. Am J Community Psychol. 2008;41:327–50.

Friedman C. The research we should be doing. Acad Med. 1994;69:455–7.

Huwendiek S, Muntau AC, Maier EM, Tönshoff B, Sostmann K. E-Learning in Der Medizinischen Ausbildung: Leitfaden Zum Erfolgreichen Einsatz in Der Pädiatrie. Monatsschr Kinderheilkd. 2008;156:458–63.

Cendan J, Lok B. The use of virtual patients in medical school curricula. Adv Physiol Educ. 2012;36:48–53.

Ellaway RH, Davies D. Design for learning: deconstructing virtual patient activities. Med Teach. 2011;33:303–10.

Mayer A, Da Silva Domingues V, Hege I, Kononowicz AA, Larrosa M, Martínez-Jarreta B, et al. Planning a Collection of virtual patients to train clinical reasoning: a blueprint representative of the European Population. Int J Environ Res Public Health. 2022;19:6175.

Huwendiek S, Haider HR, Tönshoff B, Leng BD. Evaluation of curricular integration of virtual patients: development of a student questionnaire and a reviewer checklist within the electronic virtual patient (eVIP) project. Bio-Algorithms Med-Syst. 2009;5:35–44.

Georg C, Zary N. Web-based virtual patients in nursing education: development and validation of theory-anchored design and activity models. J Med Internet Res. 2014;16:e105.

Kleinheksel AJ, Ritzhaupt AD. Measuring the adoption and integration of virtual patient simulations in nursing education: an exploratory factor analysis. Comput Educ. 2017;108:11–29.

Gough D, Oliver S, Thomas J. An introduction to systematic reviews. SAGE; 2017.

Moher D, Liberati A, Tetzlaff J, Altman DG, for the PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339(jul21 1):b2535–2535.

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5:210.

Thomas PA, Kern DE, Hughes MT, Chen BY, editors. Curriculum development for medical education: a six-step approach. Third edition. Baltimore: Johns Hopkins University Press; 2016.

Chen BY, Kern DE, Kearns RM, Thomas PA, Hughes MT, Tackett S. From modules to MOOCs: application of the Six-Step Approach to Online Curriculum Development for Medical Education. Acad Med. 2019;94:678–85.

VERBI Software. MAXQDA 2022.5. Software. 2023. maxqda.com.

Harrison R, Jones B, Gardner P, Lawton R. Quality assessment with diverse studies (QuADS): an appraisal tool for methodological and reporting quality in systematic reviews of mixed- or multi-method studies. BMC Health Serv Res. 2021;21:144.

Opie JE, McLean SA, Vuong AT, Pickard H, McIntosh JE. Training of lived experience workforces: a Rapid Review of Content and outcomes. Adm Policy Ment Health Ment Health Serv Res. 2023;50:177–211.

Goagoses N, Suovuo T, Bgt, Winschiers-Theophilus H, Suero Montero C, Pope N, Rötkönen E, et al. A systematic review of social classroom climate in online and technology-enhanced learning environments in primary and secondary school. Educ Inf Technol. 2024;29:2009–42.

Botezatu M, Hult H, Fors UG. Virtual patient simulation: what do students make of it? A focus group study. BMC Med Educ. 2010;10:91.

Edelbring S, Dastmalchi M, Hult H, Lundberg IE, Dahlgren LO. Experiencing virtual patients in clinical learning: a phenomenological study. Adv Health Sci Educ. 2011;16:331–45.

Kassianos AP, Plackett R, Kambouri MA, Sheringham J. Educators’ perspectives of adopting virtual patient online learning tools to teach clinical reasoning in medical schools: a qualitative study. BMC Med Educ. 2023;23:424.

McCarthy D, O’Gorman C, Gormley G. Intersecting virtual patients and microbiology: fostering a culture of learning. Ulster Med J. 2015;84(3):173-8.

Botezatu M, Hult Hå, Kassaye Tessma M, Fors UGH. As time goes by: stakeholder opinions on the implementation and use of a virtual patient simulation system. Med Teach. 2010;32:e509–16.

Dahri K, MacNeil K, Chan F, Lamoureux E, Bakker M, Seto K, et al. Curriculum integration of virtual patients. Curr Pharm Teach Learn. 2019;11:1309–15.

Schifferdecker KE, Berman NB, Fall LH, Fischer MR. Adoption of computer-assisted learning in medical education: the educators’ perspective: adoption of computer-assisted learning in medical education. Med Educ. 2012;46:1063–73.

Dafli E, Fountoukidis I, Hatzisevastou-Loukidou C, D Bamidis P. Curricular integration of virtual patients: a unifying perspective of medical teachers and students. BMC Med Educ. 2019;19:416.

Edelbring S, Broström O, Henriksson P, Vassiliou D, Spaak J, Dahlgren LO, et al. Integrating virtual patients into courses: follow-up seminars and perceived benefit. Med Educ. 2012;46:417–25.

Lang VJ, Kogan J, Berman N, Torre D. The evolving role of online virtual patients in Internal Medicine Clerkship Education nationally. Acad Med. 2013;88:1713–8.

Ellaway R, Topps D, Lee S, Armson H. Virtual patient activity patterns for clinical learning. Clin Teach. 2015;12:267–71.

Hege I, Ropp V, Adler M, Radon K, Mäsch G, Lyon H, et al. Experiences with different integration strategies of case-based e-learning. Med Teach. 2007;29:791–7.

Hirumi A, Johnson T, Reyes RJ, Lok B, Johnsen K, Rivera-Gutierrez DJ, et al. Advancing virtual patient simulations through design research and interPLAY: part II—integration and field test. Educ Technol Res Dev. 2016;64:1301–35.

Kulasegaram K, Mylopoulos M, Tonin P, Bernstein S, Bryden P, Law M, et al. The alignment imperative in curriculum renewal. Med Teach. 2018;40:443–8.

Fors UGH, Muntean V, Botezatu M, Zary N. Cross-cultural use and development of virtual patients. Med Teach. 2009;31:732–8.

Kelley CG. Using a virtual patient in an Advanced Assessment Course. J Nurs Educ. 2015;54:228–31.

Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.

Zary N, Johnson G, Fors U. Web-based virtual patients in dentistry: factors influencing the use of cases in the Web‐SP system. Eur J Dent Educ. 2009;13:2–9.

Maier EM, Hege I, Muntau AC, Huber J, Fischer MR. What are effects of a spaced activation of virtual patients in a pediatric course? BMC Med Educ. 2013;13:45.

Johnson TR, Lyons R, Kopper R, Johnsen KJ, Lok BC, Cendan JC. Virtual patient simulations and optimal social learning context: a replication of an aptitude–treatment interaction effect. Med Teach. 2014;36:486–94.

Mayer A, Yaremko O, Shchudrova T, Korotun O, Dospil K, Hege I. Medical education in times of war: a mixed-methods needs analysis at Ukrainian medical schools. BMC Med Educ. 2023;23:804.

Download references

Acknowledgements

The authors would like to thank Zuzanna Oleniacz and Joanna Ożga for their contributions in abstract screening and data extraction, as well as all the participants who took part in the iCoViP project and the workshops.

The study has been partially funded by the ERASMUS + program, iCoViP project (International Collection of Virtual Patients) from European Union grant no. 2020-1-DE01-KA226-005754 and internal funds from Jagiellonian University Medical College (N41/DBS/001125).

Author information

Authors and affiliations.

Center for Innovative Medical Education, Jagiellonian University Medical College, Medyczna 7, Krakow, 30-688, Poland

Joanna Fąferek

Faculty of Medicine, Paris Saclay University, Le Kremlin-Bicetre, 94270, France

Pierre-Louis Cariou & Luc Morin

Paracelsus Medical University, Prof.-Ernst-Nathan-Str. 1, 90419, Nürnberg, Germany

Medical Education Sciences, University of Augsburg, 86159, Augsburg, Germany

Institute and Clinic for Occupational, Social and Environmental Medicine, LMU University Hospital, 80336, Munich, Germany

Daloha Rodriguez-Molina

Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal

Bernardo Sousa-Pinto

Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Medyczna 7, Krakow, 30-688, Poland

Andrzej A. Kononowicz

You can also search for this author in PubMed   Google Scholar

Contributions

JF and AK conceived the idea for the study. JF coordinated the research team activities. All authors contributed to the writing of the review protocol. AK designed the literature search strategies. All authors participated in screening and data extraction. JF retrieved and managed the abstracts and full-text articles. JF and AK performed qualitative analysis of the data and quality appraisal. AK, JF and IH designed the illustrations for this study. All authors interpreted the analysis and contributed to the discussion. JF and AK drafted the manuscript. PLC, IH, AM, LM, DRM, BSP read and critically commented on the manuscript. All authors gave final approval of the version submitted.

Corresponding authors

Correspondence to Joanna Fąferek or Andrzej A. Kononowicz .

Ethics declarations

Ethics approval and consent to participate.

Systematic review of literature - not applicable.

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.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary material 2, supplementary material 3, supplementary material 4, 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/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Fąferek, J., Cariou, PL., Hege, I. et al. Integrating virtual patients into undergraduate health professions curricula: a framework synthesis of stakeholders’ opinions based on a systematic literature review. BMC Med Educ 24 , 727 (2024). https://doi.org/10.1186/s12909-024-05719-1

Download citation

Received : 20 March 2024

Accepted : 27 June 2024

Published : 05 July 2024

DOI : https://doi.org/10.1186/s12909-024-05719-1

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

• Curriculum development
• Systematic review
• Framework synthesis

BMC Medical Education

ISSN: 1472-6920

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

Risk and outcomes of healthcare-associated infections in three hospitals in Bobo Dioulasso, 2022 (Burkina Faso): a longitudinal study

• Find this author on Google Scholar
• Find this author on PubMed
• Search for this author on this site
• ORCID record for Arsene HEMA
• For correspondence: [email protected]
• Info/History
• Preview PDF

Abstract Background Healthcare-associated infections (HAIs) are one of the most common adverse events in healthcare and represent a major public health problem. However, 40% to 70% of HAIs are considered to be preventable. The present study was conducted to analyze the incidence, etiological factors, and outcomes of HAIs through active surveillance in three hospitals in the city of Bobo Dioulasso. Methods A prospective, longitudinal, multicenter study was conducted from May 1th to November 30rd, 2022, in two district hospitals (DO and Dafra) and the Sourô Sanou Teaching Hospital (CHUSS). Consenting patients hospitalized for reasons other than infection, cancer, immunosuppression in the postoperative care ward of DO or of Dafra district hospitals, intensive care unit (ICU)/CHUSS, neonatal ward/CHUSS, and gynecology and obstetrics postoperative care ward/CHUSS during a 2-month inclusion period in district hospitals and 4 months for CHUSS wards. For this study, we used the operational definitions of the French Technical Committee for Nosocomial Infections and Healthcare-associated Infections, with slight modifications. Logistic regression was used to analyze predictors of HAIs. Results. Of the 664 patients enrolled, 166 experienced an HAI, with a cumulative incidence rate of 25% (CI: 21.7%-28.3%) or an incidence density rate of 36.7 per 1000 patient-days (CI: 31.7-42.9). Surgical site infections (SSI) (44%), followed by neonatal infections (42%) were the most common HAIs. Enterobacteriaceae represented 60% of the bacteria identified in HAIs, and 38.9% of them were extended spectrum β-lactamase (EBLSE) producers. Factors associated with HAIs were admission in the neonatal ward (aOR=7.4; CI:1.3-42.7), ICU (aOR=3.7; CI:1.4-9.5), hospital stay longer than 2 days (aOR=2.1; CI:1.2-3.4), or male sex (aOR=1.8; CI:1.1-3.1). In addition, HAIs were associated with longer follow-up, hospitalization, and mortality (18.1%; 95% CI:12.1 - 24.4). Deaths were only recorded in the ICU and neonatal ward, with case fatality rates of 45.4% (95% CI: 27.5 - 63.4) and 21.4% (95% CI: 11.6 - 31.3), respectively, p=0.019. Conclusions The incidence of HAIs was relatively high in the three hospitals in Bobo Dioulasso. A national strategy to reduce HAIs should be implemented to achieve better control of HAIs.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

The author(s) received no specific funding for this work.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics approval and consent to participate The study was approved by the National Health Ethics Committee of Burkina Faso. An information sheet was given to each participant. After reading this information sheet, those who desired to participate in the study signed a free and informed consent form. The data collected were anonymized to ensure their confidentiality.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

The datasets, including deidentified provider quantitative and qualitative data, used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

View the discussion thread.

Thank you for your interest in spreading the word about medRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Citation Manager Formats

• EndNote (tagged)
• EndNote 8 (xml)
• RefWorks Tagged
• Ref Manager
• Tweet Widget
• Facebook Like
• Google Plus One

Subject Area

• Epidemiology
• Addiction Medicine (330)
• Allergy and Immunology (651)
• Anesthesia (174)
• Cardiovascular Medicine (2502)
• Dentistry and Oral Medicine (306)
• Dermatology (210)
• Emergency Medicine (386)
• Endocrinology (including Diabetes Mellitus and Metabolic Disease) (883)
• Epidemiology (11979)
• Forensic Medicine (10)
• Gastroenterology (719)
• Genetic and Genomic Medicine (3904)
• Geriatric Medicine (365)
• Health Economics (653)
• Health Informatics (2519)
• Health Policy (973)
• Health Systems and Quality Improvement (934)
• Hematology (350)
• HIV/AIDS (813)
• Infectious Diseases (except HIV/AIDS) (13484)
• Intensive Care and Critical Care Medicine (778)
• Medical Education (386)
• Medical Ethics (106)
• Nephrology (416)
• Neurology (3657)
• Nursing (205)
• Nutrition (544)
• Obstetrics and Gynecology (708)
• Occupational and Environmental Health (681)
• Oncology (1902)
• Ophthalmology (555)
• Orthopedics (230)
• Otolaryngology (299)
• Pain Medicine (243)
• Palliative Medicine (71)
• Pathology (463)
• Pediatrics (1072)
• Pharmacology and Therapeutics (444)
• Primary Care Research (434)
• Psychiatry and Clinical Psychology (3290)
• Public and Global Health (6330)
• Radiology and Imaging (1341)
• Rehabilitation Medicine and Physical Therapy (779)
• Respiratory Medicine (845)
• Rheumatology (389)
• Sexual and Reproductive Health (384)
• Sports Medicine (334)
• Surgery (426)
• Toxicology (51)
• Transplantation (179)
• Urology (156)

Quantifying the impact of urbanization on regional climate based on a partial differential method: a case study of vapor pressure deficit

• She, Dunxian

The rapid development of urbanization has significant impacts on regional climate, and thereby affects the hydrological characteristics of urban areas. Urban hydrological models have been mainly focused on the changes in hydrological response caused by complex urban underlying surfaces and urban pipe network construction in previous studies, while there is a need to strengthen research on the climate change patterns caused by urbanization. Vapor pressure deficit (VPD) is a key indicator for studying water cycle in climate system, and it has a close relationship with hydrological processes such as precipitation, evapotranspiration, and surface water transport. However, as a meteorological indicator affected by multiple factors, a deep understanding of the quantitative analysis method for the contribution of different factors to VPD changes is still lacking. This study uses a urban-rural station pairing method to analyze the impact of urbanization and proposes a method based on partial differential equations to quantitatively explore the contribution of different factors to urban-rural VPD difference. Taking daily-scale data of urban-rural paired stations in mainland China as an example, the study finds that urbanization significantly increases VPD in the core urban areas, and the urban-rural VPD difference gradually expands over time, showing significant seasonal and geographical variations. The method based on partial differential equations can effectively capture the trend of the urban-rural VPD difference, thereby confirming the validity of the derived method for evaluating the contributions. Relative humidity is the main factor contributing to the urban-rural differences in VPD in most regions, but shows a different pattern in some plateau continental climate regions. This study establishes a framework for analyzing the impact of urbanization on specific meteorological indicators, especially providing a way to quantify the contribution of factors causing urban climate change, which is of reference value for further considering the uniqueness of urban climate in the construction of urban hydrological models.

No Sources Found

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

Research on the renewal of multi-story high-density urban landscape based on property rights land—a case study of the self-built liu houses in zherong, fujian province, 1. introduction, 2. materials and methods, 3. results: case study—zherong county, fujian province.

• ② History of the County
• (ii) Regional Morphological Feature: Lack of Certain Regularity
• (iii) Architectural Feature: Significant Diversity Exists
• (iv) Summary of Early Liu House Features
• ② Sample B: Mid-term Liu Houses (i) Morphology and Structure of Land Parcels
• (ii) Regional Morphological Feature: Pattern with Certain Regularity
• (iii) Architectural Feature: Balanced Similarity and Diversity
• (iv) Summary of Mid-Term Liu House Features
• ③ Sample C: Late Liu Houses (i) Morphology and Structure of Land Parcels
• (ii) Regional Morphological Feature: Strong Regularity
• (iii) Architectural Features: Highly Unified Similarity
• (iv) Summary of Late Liu House Features
• ② Estimation-Based Renovation Framework of Liu House Areas in Zherong

4. Discussion

• (ii) Individual Construction Driven by the Residents
• ② Mid-term Liu Houses: Morphological and Architectural Features Affected by both the Government and the Residents (i) Land Parcels Shaped by Administrative Planning
• (ii) Collective Construction Driven by Spontaneously Formed Resident Groups
• ③ Late Liu Houses: Unified Morphological and Architectural Features Under Developers’ Direct Control (i) Commercial Real Estate Development
• (ii) Unified Construction Led by Commercial Developers

5. Conclusions

Author contributions, data availability statement, conflicts of interest.

• Zherong County Local Chronicles Compilation Committee. Zherong County Chronicle (1949–1995) ; Zhonghua Book Company: Beijing, China, 1995; pp. 78–79. [ Google Scholar ]
• Dong, W.; Gao, X.; Han, W.; Wang, J. Renewal Framework for Self-Built Houses in “Village-to-Community” Areas with a Focus on Safety and Resilience. Buildings 2023 , 13 , 3003. [ Google Scholar ] [ CrossRef ]
• Jiao, L.; Wu, Y.; Fang, K.; Liu, X. Typo-Morphological Approaches for Maintaining the Sustainability of Local Traditional Culture: A Case Study of the Damazhan and Xiaomazhan Historical Area in Guangzhou. Buildings 2023 , 13 , 2351. [ Google Scholar ] [ CrossRef ]
• Conzen, M.R.G. Alnwick, Northumberland: A Study in Town-Plan Analysis ; Institute of British Geographers Publication: London, UK, 1960; Volume 27. [ Google Scholar ]
• Tian, Y.; Gu, K.; Tao, W. Urban Morphology and Conservation Planning. City Plan. Rev. 2010 , 34 , 21–26. [ Google Scholar ]
• Tian, Y. Management Units of Urban Morphology: Significance, Formation and Application. City Plan. Rev. 2021 , 45 , 9–16. [ Google Scholar ]
• Liang, J.; Sun, H. Parcel: An Important Factor for Urban Land Use Control Insights from the American Zoning System. City Plan. Rev. 2000 , 6 , 40–42. [ Google Scholar ]
• Ungureanu, T.; Șoimoșan, T.M. An Integrated Analysis of the Urban Form of Residential Areas in Romania. Buildings 2023 , 13 , 2525. [ Google Scholar ] [ CrossRef ]
• Duan, J.; Qiu, G. Burgage Cycle and Micro-Intervention Planning and Design. City Plan. Rev. 2010 , 8 , 24–28. [ Google Scholar ]
• Meng, Y.; Zhai, H.; Teoh, B.K.; Tiong, R.L.K.; Huang, S.; Cen, D.; Cui, C. A Data-Driven Method for Constructing the Spatial Database of Traditional Villages—A Case Study of Courtyard Residential Typologies in Yunnan, China. Buildings 2023 , 13 , 2956. [ Google Scholar ] [ CrossRef ]
• Muratori, S. Studi per una operante storia urbana di Venezia. In Quadro Generale Dalle Origini Agli Sviluppi Attuali , 2nd ed.; Istituto Poligrafico dello Stato: Roma, Italy, 1960. [ Google Scholar ]
• Muratori, S.; Bollati, R.; Bollati, S.; Marinucci, G. Studi per una Operante Storia Urbana di Roma ; Centro Studi di Storia Urbanistica: Roma, Italy, 1963. [ Google Scholar ]
• Chen, J.; Yao, S.; Tian, Y. The Theory and Localization About Typo-morphological Approach. Urban Plan. Int. 2017 , 32 , 57–64. [ Google Scholar ] [ CrossRef ]
• Caniggia, G.; Maffei, G.L. Architectural Composition and Building Typology: Interpreting Basic Building ; Alinea Editrice: Florence, Italy, 2001. [ Google Scholar ]
• El Moussaoui, M. Architectural Typology and Its Influence on Authentic Living. Buildings 2024 , 14 , 754. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Li, N.; Cao, Z.; Wang, K. Research on the Renewal of Multi-Story High-Density Urban Landscape Based on Property Rights Land—A Case Study of the Self-Built Liu Houses in Zherong, Fujian Province. Buildings 2024 , 14 , 1998. https://doi.org/10.3390/buildings14071998

Li N, Cao Z, Wang K. Research on the Renewal of Multi-Story High-Density Urban Landscape Based on Property Rights Land—A Case Study of the Self-Built Liu Houses in Zherong, Fujian Province. Buildings . 2024; 14(7):1998. https://doi.org/10.3390/buildings14071998

Li, Ningyuan, Zhenyu Cao, and Ka Wang. 2024. "Research on the Renewal of Multi-Story High-Density Urban Landscape Based on Property Rights Land—A Case Study of the Self-Built Liu Houses in Zherong, Fujian Province" Buildings 14, no. 7: 1998. https://doi.org/10.3390/buildings14071998

Article Metrics

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

Subscribe to receive issue release notifications and newsletters from MDPI journals