quantitative case study design

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
• Research Process Flow Chart
• Research Alignment 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
• 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: Apr 2, 2024 6:35 PM
• URL: https://resources.nu.edu/researchtools

Ohio State nav bar

The Ohio State University

• BuckeyeLink
• Find People
• Search Ohio State

Basic Research Design

What is research design.

• Definition of Research Design : A procedure for generating answers to questions, crucial in determining the reliability and relevance of research outcomes.
• Importance of Strong Designs : Strong designs lead to answers that are accurate and close to their targets, while weak designs may result in misleading or irrelevant outcomes.
• Criteria for Assessing Design Strength : Evaluating a design’s strength involves understanding the research question and how the design will yield reliable empirical information.

The Four Elements of Research Design (Blair et al., 2023)

• The MIDA Framework : Research designs consist of four interconnected elements – Model (M), Inquiry (I), Data strategy (D), and Answer strategy (A), collectively referred to as MIDA.
• Theoretical Side (M and I): This encompasses the researcher’s beliefs about the world (Model) and the target of inference or the primary question to be answered (Inquiry).
• Empirical Side (D and A): This includes the strategies for collecting (Data strategy) and analyzing or summarizing information (Answer strategy).
• Interplay between Theoretical and Empirical Sides : The theoretical side sets the research challenges, while the empirical side represents the researcher’s responses to these challenges.
• Relation among MIDA Components: The diagram above shows how the four elements of a design are interconnected and how they relate to both real-world and simulated quantities.
• Parallelism in Design Representation: The illustration highlights two key parallelisms in research design – between actual and simulated processes, and between the theoretical (M, I) and empirical (D, A) sides.
• Importance of Simulated Processes: The parallelism between actual and simulated processes is crucial for understanding and evaluating research designs.
• Balancing Theoretical and Empirical Aspects : Effective research design requires a balance between theoretical considerations (models and inquiries) and empirical methodologies (data and answer strategies).

Research Design Principles (Blair et al., 2023)

• Integration of Components: Designs are effective not merely due to their individual components but how these components work together.
• Focus on Entire Design: Assessing a design requires examining how each part, such as the question, estimator, and sampling method, fits into the overall design.
• Importance of Diagnosis: The evaluation of a design’s strength lies in diagnosing the whole design, not just its parts.
• Strong Design Characteristics: Designs with parallel theoretical and empirical aspects tend to be stronger.
• The M:I:D:A Analogy: Effective designs often align data strategies with models and answer strategies with inquiries.
• Flexibility in Models: Good designs should perform well even under varying world scenarios, not just under expected conditions.
• Broadening Model Scope: Designers should consider a wide range of models, assessing the design’s effectiveness across these.
• Robustness of Inquiries and Strategies: Inquiries should yield answers and strategies should be applicable regardless of variations in real-world events.
• Diagnosis Across Models: It’s important to understand for which models a design excels and for which it falters.
• Specificity of Purpose: A design is deemed good when it aligns with a specific purpose or goal.
• Balancing Multiple Criteria: Designs should balance scientific precision, logistical constraints, policy goals, and ethical considerations.
• Diverse Goals and Assessments: Different designs may be optimal for different goals; the purpose dictates the design evaluation.
• Early Planning Benefits: Designing early allows for learning and improving design properties before data collection.
• Avoiding Post-Hoc Regrets: Early design helps avoid regrets related to data collection or question formulation.
• Iterative Improvement: The process of declaration, diagnosis, and redesign improves designs, ideally done before data collection.
• Adaptability to Changes: Designs should be flexible to adapt to unforeseen circumstances or new information.
• Expanding or Contracting Feasibility: The scope of feasible designs may change due to various practical factors.
• Continual Redesign: The principle advocates for ongoing design modification, even post research completion, for robustness and response to criticism.
• Improvement Through Sharing: Sharing designs via a formalized declaration makes it easier for others to understand and critique.
• Enhancing Scientific Communication: Well-documented designs facilitate better communication and justification of research decisions.
• Building a Design Library: The idea is to contribute designs to a shared library, allowing others to learn from and build upon existing work.

The Basics of Social Science Research Designs (Panke, 2018)

Deductive and inductive research.

• Starting Point: Begins with empirical observations or exploratory studies.
• Development of Hypotheses: Hypotheses are formulated after initial empirical analysis.
• Case Study Analysis: Involves conducting explorative case studies and analyzing dynamics at play.
• Generalization of Findings: Insights are then generalized across multiple cases to verify their applicability.
• Application: Suitable for novel phenomena or where existing theories are not easily applicable.
• Example Cases: Exploring new events like Donald Trump’s 2016 nomination or Russia’s annexation of Crimea in 2014.
• Theory-Based: Starts with existing theories to develop scientific answers to research questions.
• Hypothesis Development: Hypotheses are specified and then empirically examined.
• Empirical Examination: Involves a thorough empirical analysis of hypotheses using sound methods.
• Theory Refinement: Results can refine existing theories or contribute to new theoretical insights.
• Application: Preferred when existing theories relate to the research question.
• Example Projects: Usually explanatory projects asking ‘why’ questions to uncover relationships.

Explanatory and Interpretative Research Designs

• Definition: Explanatory research aims to explain the relationships between variables, often addressing ‘why’ questions. It is primarily concerned with identifying cause-and-effect dynamics and is typically quantitative in nature. The goal is to test hypotheses derived from theories and to establish patterns that can predict future occurrences.
• Definition: Interpretative research focuses on understanding the deeper meaning or underlying context of social phenomena. It often addresses ‘how is this possible’ questions, seeking to comprehend how certain outcomes or behaviors are produced within specific contexts. This type of research is usually qualitative and prioritizes individual experiences and perceptions.
• Explanatory Research: Poses ‘why’ questions to explore causal relationships and understand what factors influence certain outcomes.
• Interpretative Research: Asks ‘how is this possible’ questions to delve into the processes and meanings behind social phenomena.
• Explanatory Research: Relies on established theories to form hypotheses about causal relationships between variables. These theories are then tested through empirical research.
• Interpretative Research: Uses theories to provide a framework for understanding the social context and meanings. The focus is on constitutive relationships rather than causal ones.
• Explanatory Research: Often involves studying multiple cases to allow for comparison and generalization. It seeks patterns across different scenarios.
• Interpretative Research: Typically concentrates on single case studies, providing an in-depth understanding of that particular case without necessarily aiming for generalization.
• Explanatory Research: Aims to produce findings that can be generalized to other similar cases or populations. It seeks universal or broad patterns.
• Interpretative Research: Offers detailed insights specific to a single case or context. These findings are not necessarily intended to be generalized but to provide a deep understanding of the particular case.

Qualitative, Quantitative, and Mixed-method Projects

• Definition: Qualitative research is exploratory and aims to understand human behavior, beliefs, feelings, and experiences. It involves collecting non-numerical data, often through interviews, focus groups, or textual analysis. This method is ideal for gaining in-depth insights into specific phenomena.
• Example in Education: A qualitative study might involve conducting in-depth interviews with teachers to explore their experiences and challenges with remote teaching during the pandemic. This research would aim to understand the nuances of their experiences, challenges, and adaptations in a detailed and descriptive manner.
• Definition: Quantitative research seeks to quantify data and generalize results from a sample to the population of interest. It involves measurable, numerical data and often uses statistical methods for analysis. This approach is suitable for testing hypotheses or examining relationships between variables.
• Example in Education: A quantitative study could involve surveying a large number of students to determine the correlation between the amount of time spent on homework and their academic achievement. This would involve collecting numerical data (hours of homework, grades) and applying statistical analysis to examine relationships or differences.
• Definition: Mixed-method research combines both qualitative and quantitative approaches, providing a more comprehensive understanding of the research problem. It allows for the exploration of complex research questions by integrating numerical data analysis with detailed narrative data.
• Example in Education: A mixed-method study might investigate the impact of a new teaching method. The research could start with quantitative methods, like administering standardized tests to measure learning outcomes, followed by qualitative methods, such as conducting focus groups with students and teachers to understand their perceptions and experiences with the new teaching method. This combination provides both statistical results and in-depth understanding.
• Research Questions: What kind of information is needed to answer the questions? Qualitative for “how” and “why”, quantitative for “how many” or “how much”, and mixed methods for a comprehensive understanding of both the breadth and depth of a phenomenon.
• Nature of the Study: Is the study aiming to explore a new area (qualitative), confirm hypotheses (quantitative), or achieve both (mixed-method)?
• Resources Available: Time, funding, and expertise available can influence the choice. Qualitative research can be more time-consuming, while quantitative research may require specific statistical skills.
• Data Sources: Availability and type of data also guide the methodology. Existing numerical data might lean towards quantitative, while studies requiring personal experiences or opinions might be qualitative.

References:

Blair, G., Coppock, A., & Humphreys, M. (2023).  Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign . Princeton University Press.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

Quantitative study designs: Case Control

Quantitative study designs.

• Introduction
• Cohort Studies
• Randomised Controlled Trial

Case Control

• Cross-Sectional Studies
• Study Designs Home

In a Case-Control study there are two groups of people: one has a health issue (Case group), and this group is “matched” to a Control group without the health issue based on characteristics like age, gender, occupation. In this study type, we can look back in the patient’s histories to look for exposure to risk factors that are common to the Case group, but not the Control group. It was a case-control study that demonstrated a link between carcinoma of the lung and smoking tobacco . These studies estimate the odds between the exposure and the health outcome, however they cannot prove causality. Case-Control studies might also be referred to as retrospective or case-referent studies. 

Stages of a Case-Control study

This diagram represents taking both the case (disease) and the control (no disease) groups and looking back at their histories to determine their exposure to possible contributing factors.  The researchers then determine the likelihood of those factors contributing to the disease.

(FOR ACCESSIBILITY: A case control study is likely to show that most, but not all exposed people end up with the health issue, and some unexposed people may also develop the health issue)

Which Clinical Questions does Case-Control best answer?

Case-Control studies are best used for Prognosis questions.

For example: Do anticholinergic drugs increase the risk of dementia in later life? (See BMJ Case-Control study Anticholinergic drugs and risk of dementia: case-control study )

What are the advantages and disadvantages to consider when using Case-Control?

* Confounding occurs when the elements of the study design invalidate the result. It is usually unintentional. It is important to avoid confounding, which can happen in a few ways within Case-Control studies. This explains why it is lower in the hierarchy of evidence, superior only to Case Studies.

What does a strong Case-Control study look like?

A strong study will have:

• Well-matched controls, similar background without being so similar that they are likely to end up with the same health issue (this can be easier said than done since the risk factors are unknown). 
• Detailed medical histories are available, reducing the emphasis on a patient’s unreliable recall of their potential exposures. 

What are the pitfalls to look for?

• Poorly matched or over-matched controls.  Poorly matched means that not enough factors are similar between the Case and Control. E.g. age, gender, geography. Over-matched conversely means that so many things match (age, occupation, geography, health habits) that in all likelihood the Control group will also end up with the same health issue! Either of these situations could cause the study to become ineffective. 
• Selection bias: Selection of Controls is biased. E.g. All Controls are in the hospital, so they’re likely already sick, they’re not a true sample of the wider population. 
• Cases include persons showing early symptoms who never ended up having the illness. 

Critical appraisal tools 

To assist with critically appraising case control studies there are some tools / checklists you can use.

CASP - Case Control Checklist

JBI – Critical appraisal checklist for case control studies

CEBMA – Centre for Evidence Based Management  – Critical appraisal questions (focus on leadership and management)

STROBE - Observational Studies checklists includes Case control

SIGN - Case-Control Studies Checklist

NCCEH - Critical Appraisal of a Case Control Study for environmental health

Real World Examples

Smoking and carcinoma of the lung; preliminary report

• Doll, R., & Hill, A. B. (1950). Smoking and carcinoma of the lung; preliminary report.  British Medical Journal ,  2 (4682), 739–748. Retrieved from  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2038856/
• Key Case-Control study linking tobacco smoking with lung cancer
• Notes a marked increase in incidence of Lung Cancer disproportionate to population growth.
• 20 London Hospitals contributed current Cases of lung, stomach, colon and rectum cancer via admissions, house-physician and radiotherapy diagnosis, non-cancer Controls were selected at each hospital of the same-sex and within 5 year age group of each.
• 1732 Cases and 743 Controls were interviewed for social class, gender, age, exposure to urban pollution, occupation and smoking habits.
• It was found that continued smoking from a younger age and smoking a greater number of cigarettes correlated with incidence of lung cancer.

Anticholinergic drugs and risk of dementia: case-control study

• Richardson, K., Fox, C., Maidment, I., Steel, N., Loke, Y. K., Arthur, A., . . . Savva, G. M. (2018). Anticholinergic drugs and risk of dementia: case-control study. BMJ , 361, k1315. Retrieved from  http://www.bmj.com/content/361/bmj.k1315.abstract .
• A recent study linking the duration and level of exposure to Anticholinergic drugs and subsequent onset of dementia.
• Anticholinergic Cognitive Burden (ACB) was estimated in various drugs, the higher the exposure (measured as the ACB score) the greater likeliness of onset of dementia later in life.
• Antidepressant, urological, and antiparkinson drugs with an ACB score of 3 increased the risk of dementia. Gastrointestinal drugs with an ACB score of 3 were not strongly linked with onset of dementia.
• Tricyclic antidepressants such as Amitriptyline have an ACB score of 3 and are an example of a common area of concern.

Omega-3 deficiency associated with perinatal depression: Case-Control study 

• Rees, A.-M., Austin, M.-P., Owen, C., & Parker, G. (2009). Omega-3 deficiency associated with perinatal depression: Case control study. Psychiatry Research , 166(2), 254-259. Retrieved from  http://www.sciencedirect.com/science/article/pii/S0165178107004398 .
• During pregnancy women lose Omega-3 polyunsaturated fatty acids to the developing foetus.
• There is a known link between Omgea-3 depletion and depression
• Sixteen depressed and 22 non-depressed women were recruited during their third trimester
• High levels of Omega-3 were associated with significantly lower levels of depression.
• Women with low levels of Omega-3 were six times more likely to be depressed during pregnancy.

References and Further Reading

Doll, R., & Hill, A. B. (1950). Smoking and carcinoma of the lung; preliminary report. British Medical Journal, 2(4682), 739–748. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2038856/

Greenhalgh, Trisha. How to Read a Paper: the Basics of Evidence-Based Medicine, John Wiley & Sons, Incorporated, 2014. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/deakin/detail.action?docID=1642418 .

Himmelfarb Health Sciences Library. (2019). Study Design 101: Case-Control Study. Retrieved from https://himmelfarb.gwu.edu/tutorials/studydesign101/casecontrols.cfm   

Hoffmann, T., Bennett, S., & Del Mar, C. (2017). Evidence-Based Practice Across the Health Professions (Third edition. ed.): Elsevier. 

Lewallen, S., & Courtright, P. (1998). Epidemiology in practice: case-control studies. Community Eye Health, 11(28), 57.  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1706071/  

Pelham, B. W. a., & Blanton, H. (2013). Conducting research in psychology : measuring the weight of smoke /Brett W. Pelham, Hart Blanton (Fourth edition. ed.): Wadsworth Cengage Learning. 

Rees, A.-M., Austin, M.-P., Owen, C., & Parker, G. (2009). Omega-3 deficiency associated with perinatal depression: Case control study. Psychiatry Research, 166(2), 254-259. Retrieved from http://www.sciencedirect.com/science/article/pii/S0165178107004398

Richardson, K., Fox, C., Maidment, I., Steel, N., Loke, Y. K., Arthur, A., … Savva, G. M. (2018). Anticholinergic drugs and risk of dementia: case-control study. BMJ, 361, k1315. Retrieved from http://www.bmj.com/content/361/bmj.k1315.abstract

Statistics How To. (2019). Case-Control Study: Definition, Real Life Examples. Retrieved from https://www.statisticshowto.com/case-control-study/  

• << Previous: Randomised Controlled Trial
• Next: Cross-Sectional Studies >>
• Last Updated: Feb 29, 2024 4:49 PM
• URL: https://deakin.libguides.com/quantitative-study-designs

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 2 April 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.

Single Case Research Design

• First Online: 10 November 2021

Cite this chapter

• Stefan Hunziker 3 &
• Michael Blankenagel 3  

3755 Accesses

2 Citations

This chapter addresses the peculiarities, characteristics, and major fallacies of single case research designs. A single case study research design is a collective term for an in-depth analysis of a small non-random sample. The focus on this design is on in-depth. This characteristic distinguishes the case study research from other research designs that understand the individual case as a rather insignificant and interchangeable aspect of a population or sample. Also, researchers find relevant information on how to write a single case research design paper and learn about typical methodologies used for this research design. The chapter closes with referring to overlapping and adjacent research designs.

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

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Baškarada, S. (2014). Qualitative case studies guidelines. The Qualitative Report, 19 (40), 1–25.

Google Scholar  

Berg, B., & Lune, H. (2012). Qualitative research methods for the social sciences. Pearson.

Bryman, A. (2004). Social research methods (2nd ed.). Oxford University Press, 592.

Burns, R. B. (2000). Introduction to research methods. United States of America.

Creswell, J. W. (2013). Qualitative inquiry and research design. Choosing among five approaches (3rd ed.). SAGE.

Darke, P., Shanks, G., & Broadbent, M. (1998). Successfully completing case study research: Combining rigour, relevance and pragmatism. Inform Syst J, 8 (4), 273–289.

Article   Google Scholar  

Dey, I. (1999). Grounding grounded theory: Guidelines for qualitative inquiry . Academic Press.

Dick, B. (2005). Grounded theory: A thumbnail sketch. Retrieved 11 June 2021 from http://www.scu.edu.au/schools/gcm/ar/arp/grounded.html .

Dooley, L. M. (2002). Case study research and theory building. Advances in Developing Human Resources, 4 (3), 335–354.

Edmonds, W. A., & Kennedy, T. D. (2012). An applied reference guide to research designs: Quantitative, qualitative, and mixed methods . Thousand Oaks, CA: Sage.

Edmondson, A. & McManus, S. (2007). Methodological fit in management field research. The Academy of Management Review, 32 (4), 1155–1179.

Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14 (4), 532–550.

Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research . Sociology Press.

Flynn, B. B., Sakakibara, S., Schroeder, R. G., Bates, K. A., & Flynn, E. J. (1990). Empirical research methods in operations management. Journal of Operations Management, 9 (2), 250–284.

Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12 (2), 219–245.

General Accounting Office (1990). Case study evaluations. Retrieved May 15, 2021, from https://www.gao.gov/assets/pemd-10.1.9.pdf .

Gomm, R. (2000). Case study method. Key issues, key texts . SAGE.

Halaweh, M. (2012). Integration of grounded theory and case study: An exemplary application from e-commerce security perception research. Journal of Information Technology Theory and Application (JITTA), 13 (1).

Hancock, D., & Algozzine, B. (2016). Doing case study research: A practical guide for beginning researchers (3rd ed.). Teachers College Press.

Hekkala, R. (2007). Grounded theory—the two faces of the methodology and their manifestation in IS research. In Proceedings of the 30th Information Systems Research Seminar in Scandinavia IRIS, 11–14 August, Tampere, Finland (pp. 1–12).

Hyett, N., Kenny, A., & Dickson-Swift, V. (2014). Methodology or method? A critical review of qualitative case study reports. International Journal of Qualitative Studies on Health and Well-Being, 9 , 23606.

Keating, P. J. (1995). A framework for classifying and evaluating the theoretical contributions of case research in management accounting. Journal of Management Accounting Research, 7 , 66.

Levy, J. S. (2008). Case studies: Types, designs, and logics of inference. Conflict Management and Peace Science, 25 (1), 1–18.

Meyer, J.-A., & Kittel-Wegner, E. (2002). Die Fallstudie in der betriebswirtschaftlichen Forschung und Lehre . Stiftungslehrstuhl für ABWL, insb. kleine und mittlere Unternehmen, Universität.

Mitchell, J. C. (1983). Case and situation analysis. The Sociological Review, 31 (2), 187–211.

Ng, Y. N. K. & Hase, S. (2008). Grounded suggestions for doing a grounded theory business research. Electronic Journal on Business Research Methods, 6 (2).

Ng. (2005). A principal-distributor collaboration moden in the crane industry. Ph.D. Thesis, Graduate College of Management, Southern Cross University, Australia.

Ridder, H.-G. (2016). Case study research. Approaches, methods, contribution to theory. Sozialwissenschaftliche Forschungsmethoden (vol. 12). Rainer Hampp Verlag.

Ridder, H.-G. (2017). The theory contribution of case study research designs. Business Research, 10 (2), 281–305.

Maoz, Z. (2002). Case study methodology in international studies: from storytelling to hypothesis testing. In F. P. Harvey & M. Brecher (Eds.). Evaluating methodology in international studies . University of Michigan Press.

May, T. (2011). Social research: Issues, methods and process . Open University Press/Mc.

Merriam, S. B. (2009). Qualitative research in practice: Examples for discussion and analysis .

Onwuegbuzie, A. J., Leech, N. L., & Collins, K. M. (2012). Qualitative analysis techniques for the review of the literature. Qualitative Report, 17 (56).

Piekkari, R., Welch, C., & Paavilainen, E. (2009). The case study as disciplinary convention. Organizational Research Methods, 12 (3), 567–589.

Stake, R. E. (1995). The art of case study research . Sage.

Stake, R. E. (2005). Qualitative case studies. The SAGE handbook of qualitative research (3rd ed.), ed. N. K. Denzin & Y. S. Lincoln (pp. 443–466).

Strauss, A. L., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques . Sage publications.

Strauss, A. L., & Corbin, J. (1998). Basics of qualitative research techniques and procedures for developing grounded theory . Sage.

Tight, M. (2003). Researching higher education . Society for Research into Higher Education; Open University Press.

Tight, M. (2010). The curious case of case study: A viewpoint. International Journal of Social Research Methodology, 13 (4), 329–339.

Walsham, G. (2006). Doing interpretive research. European Journal of Information Systems, 15 (3), 320–330.

Welch, C., Piekkari, R., Plakoyiannaki, E., & Paavilainen-Mäntymäki, E. (2011). Theorising from case studies: Towards a pluralist future for international business research. Journal of International Business Studies, 42 (5), 740–762.

Woods, M. (2009). A contingency theory perspective on the risk management control system within Birmingham City Council. Management Accounting Research, 20 (1), 69–81.

Yin, R. K. (1994). Discovering the future of the case study. Method in evaluation research. American Journal of Evaluation, 15 (3), 283–290.

Yin, R. K. (2014). Case study research. Design and methods (5th ed.). SAGE.

Download references

Author information

Authors and affiliations.

Wirtschaft/IFZ – Campus Zug-Rotkreuz, Hochschule Luzern, Zug-Rotkreuz, Zug , Switzerland

Stefan Hunziker & Michael Blankenagel

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Stefan Hunziker .

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature

About this chapter

Hunziker, S., Blankenagel, M. (2021). Single Case Research Design. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_8

Download citation

DOI : https://doi.org/10.1007/978-3-658-34357-6_8

Published : 10 November 2021

Publisher Name : Springer Gabler, Wiesbaden

Print ISBN : 978-3-658-34356-9

Online ISBN : 978-3-658-34357-6

eBook Packages : Business and Economics (German Language)

Share this chapter

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

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Have a language expert improve your writing

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

• Knowledge Base

Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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

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

Research bias

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

Cite this Scribbr article

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

McCombes, S. (2023, June 22). Types of Research Designs Compared | Guide & Examples. Scribbr. Retrieved April 2, 2024, from https://www.scribbr.com/methodology/types-of-research/

Is this article helpful?

Shona McCombes

Other students also liked, what is a research design | types, guide & examples, qualitative vs. quantitative research | differences, examples & methods, what is a research methodology | steps & tips, what is your plagiarism score.

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
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.)

Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation.

Chapter 5 case study quantitative data findings.

• Introduction

This chapter provides the results of the analysis of quantitative data from three different sources:

• Staff activity: task time distribution. Observations of staff activities were undertaken in each study ward to understand the types of tasks undertaken by staff and the proportion of time spent on each. Staff were shadowed by a researcher who logged their activities.
• Staff travel distances. These were collected by staff wearing pedometers. These data were collected before and after the shadowing sessions.
• Staff experience surveys. Staff surveys on each ward were conducted before and after the move to the new hospital and these data provide a comparison of perceptions of the ward environment in the old and new wards.

The survey probed perceptions of many aspects of the ward environment before and after the move. As discussed in Chapter 3 , the trust, the designers and stakeholders held various expectations about the benefits of the 100% single room design. We examined whether or not these expectations (or hypotheses about the effect of the move) were fulfilled. Specifically, the new hospital was designed to increase patient comfort, prevent infections, reduce numbers of patient falls, reduce patient stress, increase patient-centred care and increase the time spent by nurses on direct care (see Appendix 16 ). Concerns were raised about the possible reduction in staff observing and monitoring patients, increased travel distances and patient isolation.

This chapter primarily addresses the following two research questions:

• What are the advantages and disadvantages of a move to all single rooms for staff?
• Does the move to all single rooms affect staff experience and well-being and their ability to deliver effective and high-quality care?
• Staff activity: task time distribution results

Preliminary analysis showed that five activity categories accounted for 78% of observation data before the move and 83% of observation data after the move. This meant that numbers in the remaining categories were too low for analysis, so all subsequent analyses were confined to these five categories: direct care, indirect care, professional communication, medication tasks and ward-related activities. Proportion of time was derived by calculating the duration of each event from its start and end time, and then aggregating duration by activity for each observation session. The number of events for each activity was also counted ( Table 23 ).

Observations (events) per session before and after new build

Proportion of time spent in each type of activity was analysed using a general linear model with proportion of time as the dependent variable. The first model consisted of a single independent variable for before and after the new build and was used to ascertain the effect of the move to a new build, prior to adjusting for other variables. To this model were added ward (maternity, surgical, older people, AAU), staff group (midwife, RN, HCA) and day of the week. This second model was used to ascertain the effect of the move to the new build having adjusted for these variables.

Events were defined as a switch of activity (either to a new activity or to continue a previously interrupted activity) and were captured by a new entry in the PDA. The number of events (new or continuation of a previous activity) per hour was modelled in the same way except that a generalised linear model with a Poisson distribution and shift length in hours specified as offset (equivalent to modelling the hourly rate) was fitted to the data. An unadjusted analysis (before and after the new build only) and adjusted analysis (before and after the new build, ward, staff group and day of week) were performed.

Analysis of medication tasks was confined to RNs only. The fact that RMs work only on the postnatal ward means that it would not be possible to interpret whether any obtained results were due to the effect of the professional group or the ward. Therefore, staff group (i.e. midwives) was dropped from this model. On average the number of events (either new or continuations of previous activities) observed per session was higher before the move than after (177 vs. 153).

However, the move to the new build did not result in a significant change to the proportion of time spent on different activities ( Table 24 ). Although there was an increase in the proportion of direct care, indirect care, professional communication and medication tasks and a decrease in ward-related activities such as cleaning, bed making and stocking the utility room in adjusted analyses, none of these changes was statistically significant (see Table 24 ).

Mean proportion of time spent in each type of activity before and after move

Table 25 shows results for the analysis of the number of events per hour. The adjusted number of recorded events per hour decreased significantly for direct care ( p  = 0.039) and professional communication ( p  = 0.002), and increased significantly for medication tasks. A decrease in the number of events per hour for an activity, and no change in the proportion of time spent on that activity, suggests that there were fewer interruptions during these tasks and work was, therefore, less fragmented. This interpretation is supported by qualitative data showing that nurses could focus on direct care and communication tasks more easily in the single room environment. Staff had difficulty locating each other and also felt reluctant to interrupt a colleague providing direct care in a single room, and there were more frequent structured opportunities for professional communication within the small nursing teams.

Number of events per hour by type of activity before and after move

The number of events per hour increased significantly for medication tasks ( p  = 0.001), showing increased fragmentation for this task. Again, this interpretation is supported by the qualitative data showing that when staff entered a patient room to administer medication they were likely to engage in other direct care activities; thus medication administration was not carried out in a single medication round, but integrated into patient care activities generally.

We also assessed the changes in patients’ contact time per patient-day to check if nurses spent more time with the patient instead of doing other activities. The analysis draws on day shift observation data (based on 118.5 hours of staff shadowing before the move and 254.5 hours after the move). The proportion of contact time was applied to the total NHPPD to provide an estimate of the patients’ contact time per patient-day (see Table 26 ).

Patients’ contact time per patient-day before and after move in the case study wards

After the move, the contact time per patient-days increased in all units, apart from surgery, where there was a decrease in direct care and an increase in indirect care activities, for example medication activities and professional communication, and essential ward/patient care activities.

These changes are the result of a combination of two factors: a change in the proportion of care (i.e. an increase/decrease in the time spent with the patient) and a change in NHPPD (i.e. an increase/decrease in the number of nurses working full-time during a day).

• Staff travel distances results

Statistical analysis

The data were analysed using a repeated measures general linear mixed model (GLMM) with steps per hour as the dependent variable and pre/post new build, ward (maternity, surgical, older people, AAU), observation session (repeated measure), staff group (midwife, RN, HCA) and day of the week as independent variables. The first GLMM analysis investigated the main effects of ward, pre/post move, staff group and day of the week. The second GLMM analysis investigated the interactions between pre/post move and ward, and between pre/post move and staff group. Because midwives were employed only on the maternity ward, there was potential confounding between the effects of ward and staff type. Initial analyses confirmed that removing maternity from the analyses improved the fit of the models. The first sensitivity analysis added a variable to the model that indicated whether or not a member of staff contributed to both the pre- and post-build samples. Only five staff contributed to both. The effect on the overall results was minor. A second sensitivity analysis fitted a model to first observation session data only, but allowed data to repeat across individual staff before and after the build. We report the results below, including where sensitivity analyses identified differences.

The data set contains information on 140 sessions collected on 53 staff (49%) prior to and 56 staff (51%) after the new build. A number of staff contributed more than one observation session: 85 provided one session, 18 provided two sessions, five provided three sessions and one provided four sessions. There were 73 sessions (52%) collected prior to the new build and 67 sessions (48%) after the new build. The average numbers of sessions per member of staff were 1.38 and 1.20, respectively. A small number of staff ( n  = 5, 4%) were observed at both times (one RN and four HCAs). Table 27 shows descriptive data for ward and staff group.

Steps per hour before and after new build

The unadjusted means (see Table 27 ) show an increase in the number of steps per hour for all wards and staff groups. Staff working on the older people’s ward (from 664 to 845) and RNs (from 639 to 827) have seen the biggest increases.

Table 28 shows results for the main effects of ward, pre/post move, staff group and day of the week. The number of steps per hour increased significantly from a mean of 715 before the move to a mean of 839 [ F (1,83) = 10.36; p  = 0.002] after the move. HCAs took significantly more steps per hour than nurses [ F (1,83) = 8.01; p  = 0.006]. There were also significant differences between days of the week [ F (4,21) = 3.40; p  = 0.027]. There was no significant difference between wards in the distances travelled ( Table 29 ).

F -tests on main effects

Mean steps per hour by wards, pre-/post move, staff group and day of the week

Table 30 shows results for the interactions between pre/post move and ward, and between pre/post move and staff group. Neither of the two interactions was statistically significant.

F -tests on interaction effects

The estimated marginal means ( Table 31 ) showed that there was an increase from pre to post build across all wards. Although the size of this increase did not differ significantly between wards, the increases in the surgical and older people’s wards were larger than for the AAU. RNs experienced a larger increase (from 624 to 811) in the number of steps per hour (from 3.74 to 4.86 miles) than HCAs (from 828 to 862 steps; from 4.96 to 5.17 miles).

Mean steps per hour for the interactions

The estimated marginal means from the second sensitivity analysis suggested a decrease in the number of steps per hour for the AAU from 901 to 836 and for HCAs from 876 to 855, rather than an increase as shown in Table 31 . The change in means for the remaining two wards and for RNs, from pre to post build, were in the same direction, and of the same order of magnitude (see Table 31 ).

• Staff experience survey

Because of staff leave, shift patterns and staff turnover during the course of the study, it was not possible to use a completely within-subjects design, in which the pre- and post-move surveys were completed by the same people. Despite this, 19 participants did complete surveys at both times, which meant a mixed within- and between-subjects design. One potential problem with this is that the subgroup who completed both surveys could have been sensitised to the research questions and, therefore, could have been more likely to report differences after the move than those who completed only one survey; that would bias our results. We addressed this by treating the design as a between-subjects design and checking for bias by comparing the results of our analyses for the whole group with separate within-subjects analyses on the subgroup who completed both surveys. The results were identical except for a small difference: perceptions of the effect of the accommodation on the delivery of care approached significance (0.099) in the within-subjects analysis whereas for the whole group this effect was significant (0.011). This can be attributed to lack of power in the subsample of 19. On this basis we proceeded with the analysis by treating the ‘before’ and ‘after’ samples as independent groups.

There were 152 items in the staff survey. Our approach to analysis was multifaceted. First, we explored the potential for grouping questions into subscales that would summarise a topic area. We thematically analysed the questions to determine those that were likely to be measuring attitudes to related aspects of the ward design, and then tested these subscales using statistical reliability analysis. Where reliability was not adequate we revised the items in the subscales until we had identified coherent subscales. These were then analysed using independent sample t -tests to determine if post-move responses were significantly different from the pre-move scores for each subscale. Similar analyses were undertaken for the teamwork and safety climate scales. Qualitative open-ended questions were analysed thematically using a content analytic approach. The well-being and stress items were compared before and after the move using the Pearson chi-squared test and Fisher’s exact test when expected frequencies were less than 5.

One of the aims of the study was to investigate if there were differences between the case study wards in their perceptions of the positives and negatives of the new single room accommodation. However, the relatively small number of staff in each of the case study wards meant that it was not possible to explore this question statistically. We therefore used correspondence analysis and perceptual mapping to examine the interaction between ward attributes and case study wards. Correspondence analysis is an exploratory mapping tool that allows visualisation of relationships in the data that would be difficult to identify if presented in a table. 114 It is related to other techniques such as factor analysis and multidimensional scaling. It does not rely on significance testing and is best viewed as an exploratory technique that provides insights into the similarities and differences between two variables. 115 Correspondence analysis does not address questions of whether or not there were differences in ratings between the attributes (e.g. whether or not privacy for patients was rated more highly than staff teamwork). Instead, it focuses on the differences between case study wards and the interaction between ratings and wards. It allows an examination of to what extent which wards are associated with particular ratings. In this way it allows us to qualitatively explore the quantitative data.

Ward environment survey subscales

Ten reliable subscales were formed. Table 32 shows the subscales and example items from each.

Description of subscales

Appendix 19 contains a complete list of all items used for each subscale.

Table 33 summarises the statistical analysis of the subscales showing means, Cronbach’s alpha and the number of items for each subscale before and after the move. According to accepted criteria, 115 alpha above 0.60 is acceptable for exploratory analyses, above 0.70 is acceptable for confirmatory purposes and above 0.80 is good for confirmatory purposes. Obtained coefficients were generally good, ranging mostly between 0.67 and 0.92. The lowest alpha, of 0.53, was obtained for the family/visitors subscale after the move, suggesting that this subscale is not internally consistent. However, the pre-move alpha was good (0.70), so it was decided to retain this subscale for exploratory purposes.

Mean subscale scores and reliability analysis before and after the move

Table 34 shows the results of independent sample t -tests comparing subscale scores before and after the move. Staff perceived significant improvements in the efficiency of the physical environment, the patient amenity, the effect of the environment on infection control, patient privacy, and family and visitors. The largest increases were found for perceptions of infection control and patient privacy. Perceptions of the effect of the ward environment on teamwork and care delivery were significantly more negative after the move. There were no significant differences in staff perceptions of staff facilities, patient safety and staff safety.

Results of t -tests comparing perceptions of the ward environment before and after the move

Although all subscales showed moderate to very good reliability, changes were not uniform for all items in every subscale; there were some exceptions to the overall trend. Overall ratings for the subscale ‘efficiency of physical environment’ increased, but ratings for the item ‘ward design/layout minimises walking distances for staff’ decreased. These perceptions were confirmed by our findings from the analysis of travel distances showing that staff took significantly more steps after than before the move. Some aspects of the design increased the amenity of the ward for staff but others did not. For example, staff toilet facilities, locker facilities and space at staff bases were rated more highly but ratings for social interaction and natural light decreased. These positive and negative aspects meant there was no significant difference in staff amenity before and after the move. The new ward was rated as much more positive for patients but there were reduced scores for three items after the move: social contact between patients, ability of patients to see staff and way finding. All aspects of teamwork and training were rated less positively, except for the item ‘discussing patient care with colleagues’, which increased. This finding is supported by our analysis of observation data showing that professional communication activities were less fragmented.

Although there were no significant differences in the effect of the ward layout on perceptions of patient safety, examination of the items showed that ratings for two items increased (‘minimising risk to patients of physical/verbal abuse from other patients/visitors’ and ‘minimising the risk of medication errors’) while ratings for two items decreased (‘responding to patient calls for assistance’ and ‘minimising the risk of falls/injury to patients’). This suggests that, although staff thought some risks to safety were reduced, they perceived an increased risk of falls and delays in responding to calls for assistance. Staff perceptions of a rise in risk of falls are detailed in Chapter 6 . Staff also reported being unable to hear calls for assistance when in a single room with a patient.

There were five items that did not fit into any of the subscales. These items were analysed singly using Fisher’s exact test and the results are shown in Table 35 . There was a significant relationship between the move and ratings for the number and location of hand basins, ease of keeping patient areas clean and quiet, and the overall comfort of patients, which all increased after the move. There was no relationship between the move and judgements of whether or not the location of the dirty utility room (where bedpans are stored and disposed of) reduces cross-contamination.

Results of single-item analyses

The distribution of responses for the four significant items showed that significantly more staff rated these aspects of single room accommodation as more positive after the move than before ( Tables 36 – 39 ).

Distribution of responses for the item ‘Number and location of CHWBs supports good hand hygiene’

Distribution of responses for the item ‘Easy to keep patient care areas clean’

Distribution of responses for the item ‘Overall comfort of patients’

Distribution of responses for the item ‘Easy to keep patient care areas quiet’

Expectations before the move and reality after the move

Before the move, staff were asked to rate on a five-point scale whether they thought single rooms would be better or worse for different aspects of clinical work (e.g. minimising the risk of patient falls, maintaining patient confidentiality, knowing when other staff might need help). After the move they again rated whether single rooms were better or worse for clinical work, thus providing a measure of whether or not their expectations about single rooms were met in reality. The questions were a subset of 23 questions from the first part of the survey and were analysed using Fisher’s exact test.

Results ( Table 40 ) showed that staff perceptions of whether or not single rooms were better than multibedded wards changed after the move for five items. Staff perceptions of whether or not single rooms were better for responding to calls for assistance, knowing when other staff might need help and minimising walking distances were rated as worse or much worse by significantly more staff after than before the move. Staff rated single rooms as positive for patient sleep and rest and for interactions between patients and visitors after the move.

Relationship between expectations before the move and reality after the move

Tables 41 – 45 show the distribution of significant responses.

Distribution of responses for the item ‘Responding to patient calls for assistance’

Distribution of responses for the item ‘Minimising staff walking distances’

Distribution of responses for the item ‘Patient sleep and rest’

Distribution of responses for the item ‘Knowing when other staff might need a helping hand’

Distribution of responses for the item ‘Patient interaction with visitors’

Teamwork and safety climate survey

To take into account our changes to the survey, we combined the four items about the quality of communication with doctors, nurses, nursing assistants and AHPs with the items in the information handover subscale to form a new subscale of seven items. Although this is different from the scales reported by Hutchinson et al. , 98 reliability analysis confirmed the original factor structure of the survey. There were two teamwork subscales and three safety climate subscales with good to high reliability ( Table 46 ). See Appendix 20 for a list of the items contained in each subscale.

Mean scores for all subscales decreased following the move. Independent sample t -tests showed that ratings for information handover and communication decreased significantly following the move [ t  = 3.23, degrees of freedom (df) = 108, p  = 0.002], indicating that information exchange and sharing within teams was perceived to be worse after the move. There were no other significant differences.

Correspondence analysis

Correspondence analysis transforms cross-tabulated data into a biplot showing distances between variables. In this study, case study ward was a column variable and mean questionnaire subscale score was a row variable (see Table 33 ). As appropriate when analysing mean scores, Euclidean distance was used and standardisation by removing row means was used. 114 , 116 This means that differences between the subscale means were not represented in the perceptual map, as we were not interested in whether or not, for example, infection control was rated more highly than privacy. Differences between wards, contained in the columns, were of interest and are represented in the perceptual map. Separate analyses were conducted for before and after the move and for the ward attributes and teamwork/safety climate survey.

Figure 11 shows perceptual maps of the association between ward attributes and wards before and after the move. The pre-move map shows that the points on the map were dispersed, indicating that the ratings were not strongly associated with particular wards. There was one exception in that ratings for the efficiency of the physical environment, privacy and infection control were higher for the older people’s ward than for the other wards. The post-move map shows that the highest ratings for the efficiency of the physical environment, the delivery of care, the staff facilities and teamwork were obtained in the older people’s and surgical wards, indicated by proximity on the map. Ratings for patient amenity, infection control, privacy and family/visitors were highest for the surgical ward. High ratings for patient safety were obtained in maternity and the surgical ward. Ratings for staff safety were similar in the older people’s, surgical and maternity wards. The acute assessment ward was not associated with any particular ward attributes, as was the case before the move.

Perceptual maps of (a) pre- and (b) post-move ward attributes by ward.

Figure 12 shows perceptual maps before and after the move of the association between teamwork/safety climate ratings and wards. The teamwork/safety climate survey consisted of two teamwork subscales – team input into decisions, and information handover and communication – and three safety climate subscales – attitudes to safety within own team, overall confidence in safety of organisation and perceptions of management attitudes to safety. The pre-move map shows that ratings of input into decisions, information and handover, and overall confidence in safety of the organisation were highest for the acute assessment ward. Ratings of safety attitudes within the team and management attitudes to safety were highest for the surgical ward. After the move, the surgical ward had the highest ratings for safety attitudes within the team, overall attitudes to safety and management; ratings for team input into decisions and information handover and communication were highest for the older people’s ward. Ratings for all safety climate subscales decreased in the acute assessment ward, which is indicated on the perceptual map by its location in a quadrant by itself. Maternity scores did not show a consistent pattern.

Perceptual map of (a) pre- and (b) post-move ratings of teamwork/safety climate by ward. Att., attitude; mgt., management.

These maps reveal some differences between wards in perceptions of the ward environment and show that perceptions were different before and after the move.

Staff ward preferences

Nursing staff were asked to indicate whether they would prefer single rooms, multibedded accommodation or a combination. There was a range of views ( Figure 13 ). In each phase, fewer than 18% of staff indicated a preference for 100% single rooms. The most common preference in each phase was a combination of 50% of beds in single rooms and 50% in bays (see Figure 13 ). In the pre-move survey, more staff reported a preference for more beds in bays ( n  = 20) than in the post-move phase ( n  = 12).

Nurse preferences for single room or multibedded accommodation.

Staff stress and well-being

There were five categorical questions about staff well-being that investigated whether or not they had experienced injuries and harassment in the previous 12 months ( Table 47 ). There were three items about job stress that asked participants to rate their stress on a five-point Likert scale ( Table 48 ) . Results showed no differences in staff well-being and stress before and after the move.

Relationship between move and staff well-being

Relationship between move and staff stress

Staff were asked 10 questions about their satisfaction with their own performance of various tasks during their last shift, and one question about their overall job satisfaction. Results ( Table 49 ) showed no significant effect for any of the job satisfaction items.

Relationship between job satisfaction and move

Qualitative survey data

Four open-ended questions were used to gain qualitative data about staff attitudes. The questions were:

• What two things do you think would most improve the current ward environment for staff?
• What two things do you think would most improve the current ward environment for patients?
• What two things are you most looking forward to in relation to the move to 100% single rooms in the new hospital?
• What two things are you most concerned about in relation to the move to 100% single rooms in the new hospital?
• What two things do you like the most about single room wards in the new hospital?
• What two things do you dislike most about single room wards in the new hospital?

In the following sections we present the results of the thematic analysis with frequency data (almost equal numbers of staff responded before and after the move, n  = 55 and n  = 54 respectively) and examples from participants’ written responses where appropriate. Table 50 shows that staff identified a number of things that would improve the ward accommodation for patients. The need for more space, improved patient facilities, privacy, and rest and sleep were largely met, since there were fewer people identifying these as needs after the move. However, the need for improved patient–staff ratios and a day room to provide patient social interaction were still reported after the move.

What would improve the current ward environment for patients ? Response frequencies

The need that staff perceived before the move for space around patient beds and staffing levels had decreased after the move ( Table 51 ). However, ventilation/heating/lighting, access to equipment and supplies and facilities for staff, including staff bases, were identified as needing improvement after move. In addition there was a need for improvements in monitoring patients, keeping track of colleagues, reducing isolation and reducing walking distances. These have all been identified by other parts of our results (see Chapter 6 ).

What would improve the current ward environment for staff ? Response frequencies

Staff were asked about the features of the ward they were most looking forward to in the pre-move phase, and most liked in the post-move phase ( Table 52 ). Results showed that staff most liked the increased patient privacy, patient sleep and rest, increased space, working in a modern environment and improved patient bathroom facilities.

What are you most looking forward to/do you most like about 100% single room accommodation? Response frequencies

Table 53 shows that staff were most concerned about being able to monitor patients, patient isolation and the risk of falls. Being unable to find staff and increased walking distances also emerged as features staff disliked about single rooms.

What are you most concerned about/do you most dislike about 100% single room accommodation? Response frequencies

• Most staff would prefer a mix of single rooms and multibedded rooms on wards.
• Staff activity events observed per session were higher after the move and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.
• A significant increase in medication tasks among recorded events suggests medication administration was integrated into patient care activities and was not undertaken as a medication ‘round’.
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards and for RNs/RMs.
• efficiency in carrying out tasks
• patient amenity, including comfort, space, sleep, light and ventilation
• infection control
• patient privacy
• patient interaction with family/visitors and their involvement in care.
• In open comments, staff most liked the increased patient privacy, working in a modern environment, improved patient sleep and rest, and space around the bedside.
• delivery of care, including factors such as spending time with patients, communication with patients, monitoring patients and remaining close to patients, responding to calls for assistance, minimising the risks to staff, minimising walking distances and staff spending time with patients
• teamwork, including being able to locate staff, obtain assistance from colleagues, informal learning, keeping team members updated, discussing care with colleagues and knowing when other staff might need help.
• In addition, in open comments staff were most concerned about patient isolation, the risk of falls and staff isolation.
• There were no perceived differences in staff amenity and patient and staff safety.
• Ratings for information handover and communication decreased significantly following the move. This suggests that information exchange and sharing within teams – and between professions – was perceived to be worse after the move.
• Different wards valued different aspects of the ward environment.
• Ratings for staff toilet facilities, locker facilities and space at staff bases were rated more highly but ratings for social interaction and natural light decreased.
• No differences were found in staff job satisfaction, well-being or stress before and after the move.
• The need for improved patient–staff ratios and a day room to provide patient social interaction was still reported after the move.

Included under terms of UK Non-commercial Government License .

• Cite this Page Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings.
• PDF version of this title (96M)

In this Page

Other titles in this collection.

• Health Services and Delivery Research

Recent Activity

• Case study quantitative data findings - Evaluating a major innovation in hospita... Case study quantitative data findings - Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers