case study on quantitative research

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

Quantitative study designs: Case Studies/ Case Report/ Case Series

Quantitative study designs.

• Introduction
• Cohort Studies
• Randomised Controlled Trial
• Case Control
• Cross-Sectional Studies
• Study Designs Home

Case Study / Case Report / Case Series

Some famous examples of case studies are John Martin Marlow’s case study on Phineas Gage (the man who had a railway spike through his head) and Sigmund Freud’s case studies, Little Hans and The Rat Man. Case studies are widely used in psychology to provide insight into unusual conditions.

A case study, also known as a case report, is an in depth or intensive study of a single individual or specific group, while a case series is a grouping of similar case studies / case reports together.

A case study / case report can be used in the following instances:

• where there is atypical or abnormal behaviour or development
• an unexplained outcome to treatment
• an emerging disease or condition

The stages of a Case Study / Case Report / Case Series

Which clinical questions does Case Study / Case Report / Case Series best answer?

Emerging conditions, adverse reactions to treatments, atypical / abnormal behaviour, new programs or methods of treatment – all of these can be answered with case studies /case reports / case series. They are generally descriptive studies based on qualitative data e.g. observations, interviews, questionnaires, diaries, personal notes or clinical notes.

What are the advantages and disadvantages to consider when using Case Studies/ Case Reports and Case Series ?

What are the pitfalls to look for?

One pitfall that has occurred in some case studies is where two common conditions/treatments have been linked together with no comprehensive data backing up the conclusion. A hypothetical example could be where high rates of the common cold were associated with suicide when the cohort also suffered from depression.

Critical appraisal tools 

To assist with critically appraising Case studies / Case reports / Case series there are some tools / checklists you can use.

JBI Critical Appraisal Checklist for Case Series

JBI Critical Appraisal Checklist for Case Reports

Real World Examples

Some Psychology case study / case report / case series examples

Capp, G. (2015). Our community, our schools : A case study of program design for school-based mental health services. Children & Schools, 37(4), 241–248. A pilot program to improve school based mental health services was instigated in one elementary school and one middle / high school. The case study followed the program from development through to implementation, documenting each step of the process.

Cowdrey, F. A. & Walz, L. (2015). Exposure therapy for fear of spiders in an adult with learning disabilities: A case report. British Journal of Learning Disabilities, 43(1), 75–82. One person was studied who had completed a pre- intervention and post- intervention questionnaire. From the results of this data the exposure therapy intervention was found to be effective in reducing the phobia. This case report highlighted a therapy that could be used to assist people with learning disabilities who also suffered from phobias.

Li, H. X., He, L., Zhang, C. C., Eisinger, R., Pan, Y. X., Wang, T., . . . Li, D. Y. (2019). Deep brain stimulation in post‐traumatic dystonia: A case series study. CNS Neuroscience & Therapeutics. 1-8. Five patients were included in the case series, all with the same condition. They all received deep brain stimulation but not in the same area of the brain. Baseline and last follow up visit were assessed with the same rating scale.

References and Further Reading  

Greenhalgh, T. (2014). How to read a paper: the basics of evidence-based medicine. (5th ed.). New York: Wiley.

Heale, R. & Twycross, A. (2018). What is a case study? Evidence Based Nursing, 21(1), 7-8.

Himmelfarb Health Sciences Library. (2019). Study design 101: case report. Retrieved from https://himmelfarb.gwu.edu/tutorials/studydesign101/casereports.cfm

Hoffmann T., Bennett S., Mar C. D. (2017). Evidence-based practice across the health professions. Chatswood, NSW: Elsevier.

Robinson, O. C., & McAdams, D. P. (2015). Four functional roles for case studies in emerging adulthood research. Emerging Adulthood, 3(6), 413-420.

• << Previous: Cross-Sectional Studies
• Next: Study Designs Home >>
• Last Updated: Jun 13, 2024 10:34 AM
• URL: https://deakin.libguides.com/quantitative-study-designs

What is case study research?

Last updated

8 February 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.

Organization

Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take. 

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

Analyze case study research

Dovetail streamlines case study research to help you uncover and share actionable insights

• What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:

Explanatory

An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it. 

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”

Descriptive

An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand. 

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."

Exploratory

Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others. 

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study. 

The researchers also get an in-depth look at a particular subject from different viewpoints.  Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth. 

The findings can then be used further to generalize whether they would also apply in a different environment.  Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”

Instrumental

Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory. 

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making. 

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.” 

• When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

• What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information. 

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

• What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 6 February 2023

Last updated: 6 October 2023

Last updated: 5 February 2023

Last updated: 16 April 2023

Last updated: 7 March 2023

Last updated: 9 March 2023

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next.

Users report unexpectedly high data usage, especially during streaming sessions.

Users find it hard to navigate from the home page to relevant playlists in the app.

It would be great to have a sleep timer feature, especially for bedtime listening.

I need better filters to find the songs or artists I’m looking for.

Log in or sign up

Get started for free

• 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

Descriptive Research Design – Types, Methods and...

Exploratory Research – Types, Methods and...

Textual Analysis – Types, Examples and Guide

Basic Research – Types, Methods and Examples

Correlational Research – Methods, Types and...

Explanatory Research – Types, Methods, Guide

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.

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 June 28, 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, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

• 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

788k Accesses

1063 Citations

37 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]

Warning: The NCBI web site requires JavaScript to function. more...

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

• Open access
• Published: 28 June 2024

Perceived efficacy of case analysis as an assessment method for clinical competencies in nursing education: a mixed methods study

• Basma Mohammed Al Yazeedi   ORCID: orcid.org/0000-0003-2327-6918 1 ,
• Lina Mohamed Wali Shakman 1 ,
• Sheeba Elizabeth John Sunderraj   ORCID: orcid.org/0000-0002-9171-7239 1 ,
• Harshita Prabhakaran   ORCID: orcid.org/0000-0002-5470-7066 1 ,
• Judie Arulappan 1 ,
• Erna Judith Roach   ORCID: orcid.org/0000-0002-5817-8886 1 ,
• Aysha Al Hashmi 1 , 2 &
• Zeinab Al Azri   ORCID: orcid.org/0000-0002-3376-9380 1  

BMC Nursing volume  23 , Article number:  441 ( 2024 ) Cite this article

Metrics details

Case analysis is a dynamic and interactive teaching and learning strategy that improves critical thinking and problem-solving skills. However, there is limited evidence about its efficacy as an assessment strategy in nursing education.

This study aimed to explore nursing students’ perceived efficacy of case analysis as an assessment method for clinical competencies in nursing education.

This study used a mixed methods design. Students filled out a 13-item study-advised questionnaire, and qualitative data from the four focus groups was collected. The setting of the study was the College of Nursing at Sultan Qaboos University, Oman. Descriptive and independent t-test analysis was used for the quantitative data, and the framework analysis method was used for the qualitative data.

The descriptive analysis of 67 participants showed that the mean value of the perceived efficacy of case analysis as an assessment method was 3.20 (SD = 0.53), demonstrating an 80% agreement rate. Further analysis indicated that 78.5% of the students concurred with the acceptability of case analysis as an assessment method (mean = 3.14, SD = 0.58), and 80.3% assented its association with clinical competencies as reflected by knowledge and cognitive skills (m = 3.21, SD = 0.60). No significant difference in the perceived efficacy between students with lower and higher GPAs (t [61] = 0.05, p  > 0.05) was identified Three qualitative findings were discerned: case analysis is a preferred assessment method for students when compared to MCQs, case analysis assesses students’ knowledge, and case analysis assesses students’ cognitive skills.

Conclusions

This study adds a potential for the case analysis to be acceptable and relevant to the clinical competencies when used as an assessment method. Future research is needed to validate the effectiveness of case analysis exams in other nursing clinical courses and examine their effects on academic and clinical performance.

Peer Review reports

Introduction

Nurses play a critical role in preserving human health by upholding core competencies [ 1 ]. Clinical competence in nursing involves a constant process of acquiring knowledge, values, attitudes, and abilities to deliver safe and high-quality care [ 2 , 3 ]. Nurses possessing such competencies can analyze and judge complicated problems, including those involving crucial patient care, ethical decision-making, and nurse-patient disputes, meeting the constantly altering health needs [ 4 , 5 ]. To optimize the readiness of the new graduates for the challenging clinical work environment needs, nurse leaders call for integrating clinical competencies into the nursing curriculum [ 6 , 7 ] In 2021, the American Association of Colleges of Nursing (AACN) released updated core competencies for professional nursing education [ 8 ]. These competencies were classified into ten fundamental essentials, including knowledge of nursing practice and person-centered care (e.g. integrate assessment skills in practice, diagnose actual or potential health problems and needs, develop a plan of care), representing clinical core competencies.

Nursing programs emphasize clinical competencies through innovative and effective teaching strategies, including case-based teaching (CBT) [ 9 ]. CBT is a dynamic teaching method that enhances the focus on learning goals and increases the chances of the instructor and students actively participating in teaching and learning [ 10 , 11 ]. Additionally, it improves the students’ critical thinking and problem-solving skills and enriches their capacity for independent study, cooperation capacity, and communication skills [ 12 , 13 , 14 , 15 ]. It also broadens students’ perspectives and helps develop greater creativity in fusing theory and practice [ 16 , 17 , 18 , 19 , 20 ]. As the learning environment significantly impacts the students’ satisfaction, case analysis fosters a supportive learning atmosphere and encourages active participation in learning, ultimately improving their satisfaction [ 21 , 22 ].

In addition to proper teaching strategies for clinical competencies, programs are anticipated to evaluate the students’ attainment of such competencies through effective evaluation strategies [ 23 ]. However, deploying objective assessment methods for the competencies remains challenging for most educators [ 24 ]. The standard assessment methods used in clinical nursing courses, for instance, include clinical evaluations (direct observation), skills checklists, Objective Structured Clinical Examination (OSCE), and multiple-choice questions (MCQs) written exams [ 25 ]. MCQs tend to test the recall of factual information rather than the application of knowledge and cognitive skills, potentially leading to assessment inaccuracies [ 26 ].

Given the aforementioned outcomes of CBT, the deployment of case analysis as a clinical written exam is more closely aligned with the course’s expected competencies. A mixed methods study was conducted among forty nursing students at the University of Southern Taiwan study concluded that the unfolding case studies create a safe setting where nursing students can learn and apply their knowledge to safe patient care [ 6 ]. In a case analysis, the patient’s sickness emerges in stages including the signs and symptoms of the disease, urgent care to stabilize the patient, and bedside care to enhance recovery. Thus, unfolding the case with several scenarios helps educators track students’ attained competencies [ 27 ]. However, case analysis as an assessment method is sparsely researched [ 28 ]. A literature review over the past five years yielded no studies investigating case analysis as an assessment method, necessitating new evidence. There remains uncertainty regarding its efficacy as an assessment method, particularly from the students’ perspectives [ 29 ]. In this study, we explored the undergraduate nursing students’ perceived efficacy of case analysis as an assessment method for clinical competencies. Results from this study will elucidate the position of case analysis as an assessment method in nursing education. The potential benefits are improved standardization of clinical assessment and the ability to efficiently evaluate a broad range of competencies.

Research design

Mixed-method research with a convergent parallel design was adopted in the study. This approach intends to converge two data types (quantitative and qualitative) at the interpretation stage to ensure an inclusive research problem analysis [ 30 ]. The quantitative aspect of the study was implemented through a cross-sectional survey. The survey captured the perceived efficacy of using case analysis as an assessment method in clinical nursing education. The qualitative part of the study was carried out through a descriptive qualitative method using focus groups to provide an in-depth understanding of the perceived strengths experienced by the students.

Study setting

Data were collected in the College of Nursing at Sultan Qaboos University (SQU), Oman, during the Spring and Fall semesters of 2023. At the end of each clinical course, the students have a clinical written exam and a clinical practical exam, which constitute their final exam. Most clinical courses use multiple-choice questions (MCQs) in their written exam. However, the child health clinical course team initiated the case analysis as an assessment method in the clinical written exam, replacing the MCQs format.

Participants

For this study, the investigators invited undergraduate students enrolled in the child health nursing clinical course in the Spring and Fall semesters of 2023. Currently, the only course that uses case analysis is child health. Other courses use MCQs. A total enumeration sampling technique was adopted. All the students enrolled in child health nursing clinical courses in the Spring and Fall 2023 semesters were invited to participate in the study. In the Spring, 36 students registered for the course, while 55 students were enrolled in the Fall. We included students who completed the case analysis as a final clinical written exam on the scheduled exam time. Students who did not show up for the exam during the scheduled time and students not enrolled in the course during the Spring and Fall of 2023 were excluded. Although different cases were used each semester, both had the same structure and level of complexity. Further, both cases were peer-reviewed.

Case analysis format

The format presents open-ended questions related to a clinical case scenario. It comprises three main sections: Knowledge, Emergency Room, and Ward. The questions in the sections varied in difficulty based on Bloom’s cognitive taxonomy levels, as presented in Table  1 . An answer key was generated to ensure consistency among course team members when correcting the exam. Three experts in child health nursing peer-reviewed both the case analysis exam paper and the answer key paper. The students were allocated two hours to complete the exam.

Study instruments

Quantitative stage.

The researchers developed a study questionnaire to meet the study objectives. It included two parts. The first was about the demographic data, including age, gender, type of residence, year in the program, and cumulative grade point average (GPA). The second part comprised a 13-item questionnaire assessing the perceived efficacy of case analysis as an assessment method. The perceived efficacy was represented by the acceptability of case analysis as an assessment method (Items 1–5 and 13) and the association with clinical competencies (Items 6 to 12). Acceptability involved format organization and clarity, time adequacy, alignment with course objectives, appropriateness to students’ level, and recommendation for implementation in other clinical nursing courses. Clinical competencies-related items were relevant to knowledge (motivation to prepare well for the exam, active learning, interest in topics, collaboration while studying) and cognitive skills (critical thinking, decision-making, and problem-solving skills) (The questionnaire is attached as a supplementary document).

The questionnaire is answered on a 4-point Likert scale: 1 = strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree. Higher scores indicated better perceived efficacy and vice versa. The tool underwent content validity testing with five experts in nursing clinical education, resulting in an item-content validity index ranging from 0.7 to 1. The Cronbach alpha was 0.83 for acceptability and 0.90 for clinical competencies.

Qualitative stage

For the focus group interviews, the investigators created a semi-structured interview guide to obtain an in-depth understanding of the students’ perceived strengths of case analysis as an assessment method. See Table  2 .

Data collection

Data was collected from the students after they gave their written informed consent. Students were invited to fill out the study questionnaire after they completed the case analysis as a clinical written exam.

All students in the child health course were invited to participate in focus group discussions. Students who approached the PI to participate in the focus group discussion were offered to participate in four different time slots. So, the students chose their time preferences. Four focus groups were conducted in private rooms at the College of Nursing. Two trained and bilingual interviewers attended the focus groups, one as a moderator while the other took notes on the group dynamics and non-verbal communication. The discussion duration ranged between 30 and 60 min. After each discussion, the moderator transcribed the audio recording. The transcriptions were rechecked against the audio recording for accuracy. Later, the transcriptions were translated into English by bilingual researchers fluent in Arabic and English for the analysis.

Rigor and trustworthiness

The rigor and trustworthiness of the qualitative method were enhanced using multiple techniques. Firstly, quantitative data, literature reviews, and focus groups were triangulated. Participants validated the summary after each discussion using member checking to ensure the moderator’s understanding was accurate. Third, the principal investigator (PI) reflected on her assumptions, experiences, expectations, and feelings weekly. In addition, the PI maintained a detailed audit trail of study details and progress. The nursing faculty conducted the study with experience in qualitative research and nursing education. This report was prepared following the Standard for Reporting Qualitative Research (SRQR) protocol [ 31 ].

Data analysis

Quantitative data were entered in SPSS version 24 and analyzed using simple descriptive analysis using means, standard deviations, and percentages. After computing the means of each questionnaire item, an average of the means was calculated to identify the perceived efficacy rate. A similar technique was used to calculate the rate of acceptability and clinical competencies. The percentage was calculated based on the mean: gained score/total score* 100. In addition, the investigators carried out an independent t-test to determine the relationship between the perceived efficacy and students’ GPA.

The qualitative data were analyzed using the framework analysis method. In our analysis, we followed the seven interconnected stages of framework analysis: (1) transcription, (2) familiarization with the interview, (3) coding, (4) developing a working analytical framework, (5) applying the analytical framework, (6) charting data into framework matrix and (7) interpreting the data [ 32 ]. Two members of the team separately analyzed the transcriptions. Then, they discussed the coding, and discrepancies were solved with discussion.

Mixed method integration

In our study, the quantitative and qualitative data were analyzed separately, and integration occurred at the interpretation level by merging the data [ 33 ]. As a measure of integration between qualitative and quantitative data, findings were assessed through confirmation, expansion, and discordance. If both data sets confirmed each other’s findings, it was considered confirmation, and if they expanded each other’s insight, it was considered expansion. Discordance was determined if the findings were contradictory.

Ethical considerations

Ethical approval was obtained from the Research and Ethics Committee of the College of Nursing, SQU (CON/NF/2023/18). Informed consent was collected, and no identifiable information was reported. For the focus group interviews, students were reassured that their grades were finalized, and their participation would not affect their grades. Also, the interviewers were instructed to maintain a non-judgmental and non-biased position during the interview. Data were saved in a locked cabinet inside a locked office room. The electronic data were saved in a password-protected computer.

The results section will present findings from the study’s quantitative and qualitative components. The integration of the two data types is described after each qualitative finding.

Quantitative findings

We analyzed the data of 67 participants, representing a 73.6% response rate. The mean age was 21.0 years old (SD 0.73) and 36.4% were male students. See Table  3 for more details.

The descriptive analysis showed that the mean value of the perceived efficacy of case analysis as an assessment method was 3.20 (SD = 0.53), demonstrating an 80% agreement rate. Further analysis indicated that 78.5% of the students concurred the acceptability of case analysis as an assessment method (mean = 3.14, SD = 0.58) and 80.3% (m = 3.21, SD = 0.60) assented the clinical competencies associated with it.

For the items representing acceptability, 81.8% of the students agreed that the case analysis was written clearly, and 80.3% reported that it was well organized. As per the questions, 81% described they were appropriate to their level, and 79.8% agreed upon their alignment with the course objectives. Moreover, the time allocated was adequate for 74.5% of the students, and 73.5% recommend using case analysis as an evaluation strategy for other clinical written examinations.

Regarding the clinical competencies, 77.3% of students agreed that the case analysis motivated them to prepare well for the exam, 81.3% reported that it encouraged them to be active in learning, and 81.0% indicated that it stimulated their interest in the topics discussed in the course. Additionally, 76.5% of the students agreed that the case analysis encouraged them to collaborate with other students when studying for the exam. Among the students, 82.5% reported that the case analysis as an assessment method enhanced their critical thinking skills, 81.0% agreed that it helped them practice decision-making skills, and 81.8% indicated that it improved their problem-solving abilities. See Table  4 .

The independent t-test analysis revealed no significant difference in the perceived efficacy between students with lower and higher GPAs (t [61] = 0.05, p  > 0.05). Further analysis showed that the means of acceptability and clinical competencies were not significantly different between the lower GPA group and higher GPA group, t [62] = 0.72, p  > 0.05 and t [63] = -0.83, p  > 0.05, respectively (Table  5 ).

Qualitative findings

A total of 22 had participated in four focus groups, each group had 5–6 students. The qualitative framework analysis revealed three main findings; case analysis is a preferred assessment method to students when compared to MCQs, case analysis assesses students’ knowledge, and case analysis assesses students’ cognitive skills.

Qualitative Finding 1: case analysis is a preferred assessment method to students when compared to MCQs

Most of the students’ statements about the case analysis as an assessment method were positive. One student stated, “Previously, we have MCQs in clinical exams, but they look as if they are theory exams. This exam makes me deal with cases like a patient, which is good for clinical courses.” . At the same time, many students conveyed optimism about obtaining better grades with this exam format. A student stated, “Our grades, with case analysis format, will be better, … may be because we can write more in open-ended questions, so we can get some marks, in contrast to MCQs where we may get it right or wrong” . On the other hand, a few students suggested adding multiple-choice questions, deleting the emergency department section, and lessening the number of care plans in the ward section to secure better grades.

Although the case analysis was generally acceptable to students, they have repeatedly expressed a need to allocate more time for this type of exam. A student stated, “The limited time with the type of questions was a problem, …” . When further discussion was prompted to understand this challenge, we figured that students are not used to handwriting, which has caused them to be exhausted during the exam. An example is “writing is time-consuming and energy consuming in contrast to MCQs …” . These statements elucidate that the students don’t necessarily mind writing but recommend more practice as one student stated, “More experience of this type of examination is required, more examples during clinical practice are needed.” Some even recommended adopting this format with other clinical course exams by saying “It’s better to start this method from the first year for the new cohort and to apply it in all other courses.”

Mixed Methods Inference 1: Confirmation and Expansion

The abovementioned qualitative impression supports the high acceptability rate in quantitative analysis. In fact, there is a general agreement that the case analysis format surpasses the MCQs when it comes to the proper evaluation strategies for clinical courses. Expressions in the qualitative data revealed more details, such as the limited opportunities to practice handwriting, which negatively impacted the perceived adequacy of exam time.

Qualitative Finding 2: case analysis assesses students’ knowledge

Students conferred that they were reading more about the disease pathophysiology, lab values, and nursing care plans, which they did not usually do with traditional means of examination. Examples of statements include “… before we were not paying attention to the normal lab results but …in this exam, we went back and studied them which was good for our knowledge” and “we cared about the care plan. In previous exams, we were not bothered by these care plans”. Regarding the burden that could be perceived with this type of preparation, the students expressed that this has helped them prepare for the theory course exam; as one student said, “We also focus on theory lectures to prepare for this exam …. this was very helpful to prepare us for the theory final exam as well.” However, others have highlighted the risks of limiting the exam’s content to one case analysis. The argument was that some students may have not studied the case completely or been adequately exposed to the case in the clinical setting. To solve this risk, the students themselves advocated for frequent case group discussions in the clinical setting as stated by one student: “There could be some differences in the cases that we see during our clinical posting, for that I recommend that instructors allocate some time to gather all the students and discuss different cases.” Also, the participants advocated for more paper-based case analysis exercises as it is helpful to prepare them for the exams and enhance their knowledge and skills.

Mixed Methods Inferences 2: Confirmation and Expansion

The qualitative finding supports the quantitative data relevant to items 6, 7, and 8. Students’ expressions revealed more insights, including the acquisition of deeper knowledge, practicing concept mapping, and readiness for other course-related exams. At the same time, students recommended that faculty ensure all students’ exposure to common cases in the clinical setting for fair exam preparation.

Qualitative Finding 3. case analysis assesses students’ cognitive skills

Several statements conveyed how the case analysis format helped the students use their critical thinking and analysis skills. One student stated, “It, the case analysis format, enhanced our critical thinking skills as there is a case with given data and we analyze the case….” . Therefore, the case analysis format as an exam is potentially a valid means to assess the student’s critical thinking skills. Students also conveyed that the case analysis format helped them link theory to practice and provided them with the platform to think like real nurses and be professional. Examples of statements are: “…we connect our knowledge gained from theory with the clinical experience to get the answers…” and “The questions were about managing a case, which is what actual nurses are doing daily.” Another interesting cognitive benefit to case analysis described by the students was holistic thinking. For example, one student said, “Case analysis format helped us to see the case as a whole and not only from one perspective.”

Mixed Methods Inferences 3: Confirmation

The quantitative data indicated mutual agreement among the students that the case analysis enhanced their critical thinking, decision-making, and problem-solving skills. The students’ statements from the interviews, including critical thinking, linking theory to practice, and holistic thinking, further supported these presumptions.

This research presents the findings from a mixed methods study that explored undergraduate nursing students’ perceived efficacy of using case analysis as an assessment method. The perceived efficacy was reflected through acceptability and association with two core competencies: knowledge and cognitive skills. The study findings showed a high rate of perceived efficacy of case analysis as an assessment method among nursing students. Additionally, three findings were extracted from the qualitative data that further confirmed the perceived efficacy: (1) case analysis is a preferred assessment method to students compared to MCQs, (2) case analysis assesses students’ knowledge, and (3) case analysis assesses students’ cognitive skills. Moreover, the qualitative findings revealed details that expanded the understanding of the perceived efficacy among nursing students.

Previous literature reported students’ preference for case analysis as a teaching method. A randomized controlled study investigated student’s satisfaction levels with case-based teaching, in addition to comparing certain outcomes between a traditional teaching group and a case-based teaching group. They reported that most students favored the use of case-based teaching, whom at the same time had significantly better OSCE scores compared to the other group [ 34 ]. As noted, this favorable teaching method ultimately resulted in better learning outcomes and academic performance. Although it may be challenging since no answer options are provided, students appreciate the use of case analysis format in their exams because it aligns better with the course objectives and expected clinical competencies. The reason behind students’ preference for case analysis is that it allows them to interact with the teaching content and visualize the problem, leading to a better understanding. When case analysis is used as an assessment method, students can connect the case scenario presented in the exam to their clinical training, making it more relevant.

In this study, students recognized the incorporation of nursing knowledge in the case analysis exam. They also acknowledged improved knowledge and learning abilities similar to those observed in case-based teaching. Boney et al. (2015) reported that students perceived increased learning gains and a better ability to identify links between different concepts and other aspects of life through case-based teaching [ 35 ]. Additionally, case analysis as an exam promotes students’ in-depth acquirement of knowledge through the type of preparation it entails. Literature suggested that case-based teaching promotes self-directed learning with high autonomous learning ability [ 34 , 36 ]. Thus, better achievement in the case analysis exam could be linked with a higher level of knowledge, making it a suitable assessment method for knowledge integration in nursing care.

The findings of this study suggest that case analysis can be a useful tool for evaluating students’ cognitive skills, such as critical thinking, decision-making, and problem-solving. A randomized controlled study implied better problem-solving abilities among the students in the case-based learning group compared to those in the traditional teaching methods group [ 12 ]. Moreover, students in our study conveyed that case analysis as an exam was an opportunity for them to think like real nurses. Similar to our findings, a qualitative study on undergraduate nutrition students found that case-based learning helped students develop professional competencies for their future practice, in addition to higher-level cognitive skills [ 37 ]. Therefore, testing students through case analysis allows educators to assess the student’s readiness for entry-level professional competencies, including the thinking process. Also, to evaluate students’ high-level cognitive skills according to Bloom’s taxonomy (analysis, synthesis, and evaluation), which educators often find challenging.

Case analysis as an assessment method for clinical courses is partially integrated in case presentation or OSCE evaluation methods. However, the written format is considered to be more beneficial for both assessment and learning processes. A qualitative study was conducted to examine the impact of paper-based case learning versus video-based case learning on clinical decision-making skills among midwifery students. The study revealed that students paid more attention and were able to focus better on the details when the case was presented in a paper format [ 38 ]. Concurrently, the students in our study recommended more paper-based exercises, which they believed would improve their academic performance.

This study has possible limitations. The sample size was small due to the limited experience of case analysis as a clinical written exam in the program. Future studies with larger sample sizes and diverse nursing courses are needed for better generalizability.

Implications

Little evidence relates to the efficacy of case analysis as an evaluation method, suggesting the novelty of this study. Despite the scarcity of case-based assessment studies, a reader can speculate from this study’s findings that there is a potential efficacy of case analysis as an assessment method in nursing education. Future research is warranted to validate the effectiveness of case-analysis assessment methods and investigate the effects of case-analysis exams on academic and clinical performance.

Overall, our findings are in accordance with the evidence suggesting students’ perceived efficacy of case analysis as a teaching method. This study adds a potential for the case analysis to be acceptable and relevant to the clinical competencies when used as an assessment method. Future research is needed to validate the effectiveness of case analysis exams in other nursing clinical courses and examine their effects on academic and clinical performance.

Data availability

The datasets used and/or analyzed during the current study are available fromthe Principal Investigator (BAY) upon reasonable request.

Iriarte-Roteta A, Lopez‐Dicastillo O, Mujika A, Ruiz‐Zaldibar C, Hernantes N, Bermejo‐Martins E, Pumar‐Méndez MJ. Nurses’ role in health promotion and prevention: a critical interpretive synthesis. J Clin Nurs. 2020;29(21–22):3937–49. https://doi.org/10.1111/jocn.15441

Article   PubMed   Google Scholar  

Fukada M. Nursing competency: definition, structure and development. Yonago Acta Med. 2018;61(1):001–7. https://doi.org/10.33160/yam.2018.03.001

Article   Google Scholar  

Nabizadeh-Gharghozar Z, Alavi NM, Ajorpaz NM. Clinical competence in nursing: a hybrid concept analysis. Nurse Educ Today. 2021;97:104728. https://doi.org/10.1016/j.nedt.2020.104728

Allande-Cussó R, Fernández-García E, Porcel-Gálvez AM. Defining and characterising the nurse–patient relationship: a concept analysis. Nurs Ethics. 2021;29(2):462–84. https://doi.org/10.1177/09697330211046651

Butts JB, Rich KL. Nursing ethics: across the curriculum and into practice. Jones & Bartlett Learning; 2019.

Chen W, Shah UV, Brechtelsbauer C. A framework for hands-on learning in chemical engineering education—training students with the end goal in mind. Educ Chem Eng. 2019;28:25–9.

Willman A, Bjuresäter K, Nilsson J. Newly graduated registered nurses’ self-assessed clinical competence and their need for further training. Nurs Open. 2020;7(3):720–30. https://doi.org/10.1002/nop2.443

Article   PubMed   PubMed Central   Google Scholar  

American Association of Colleges of Nursing. (2021). The essentials: Core competencies for professional nursing education. In. Retrieved from n.d.). American Association of Colleges of Nursing. https://www.aacnnursing.org/Portals/0/PDFs/Publications/Essentials-2021.pdf

Kaur G, Rehncy J, Kahal KS, Singh J, Sharma V, Matreja PS, Grewal H. Case-based learning as an effective tool in teaching pharmacology to undergraduate medical students in a large group setting. J Med Educ Curric Dev. 2020;7:2382120520920640.

Patiraki E, Katsaragakis S, Dreliozi A, Prezerakos P. Nursing care plans based on NANDA, nursing interventions classification, and nursing outcomes classification: the investigation of the effectiveness of an educational intervention in Greece. Int J Nurs Knowl. 2017;28:88–93.

Cui C, Li Y, Geng D, Zhang H, Jin C. The effectiveness of evidence-based nursing on development of nursing students ‘critical thinking: a meta-analysis. Nurse Educ Today. 2018;65:46–53.

Bi M, Zhao Z, Yang J, Wang Y. Comparison of case-based learning and traditional method in teaching postgraduate students of medical oncology. Med Teach. 2019;41(10):1124–8.

Seshan V, Matua GA, Raghavan D, Arulappan J, Al Hashmi I, Roach EJ, Prince EJ. Case study analysis as an effective teaching strategy: perceptions of undergraduate nursing students from a Middle Eastern Country. SAGE Open Nurs. 2021;7:23779608211059265.

PubMed   PubMed Central   Google Scholar  

Slieman TA, Camarata T. Case-based group learning using concept maps to achieve multiple educational objectives and behavioral outcomes. J Med Educ Curric Dev. 2019;6:2382120519872510.

Yu Z, Hu R, Ling S, Zhuang J, Chen Y, Chen M, Lin Y. Effects of blended versus offline case-centered learning on the academic performance and critical thinking ability of undergraduate nursing students: a cluster randomized controlled trial. Nurse Educ Pract. 2021;53:103080.

Chan AW, Chair SY, Sit JW, Wong EM, Lee DT, Fung OW. Case-based web learning versus face-to-face learning: a mixed-method study on university nursing students. J Nurs Res. 2016;24(1):31–40.

Hong S, Yu P. Comparison of the effectiveness of two styles of case-based learning implemented in lectures for developing nursing students’ critical thinking ability: a randomized controlled trial. Int J Nurs Stud. 2017;68:16–24.

Shohani M, Bastami M, Gheshlaghi LA, Nasrollahi A. Nursing student’s satisfaction with two methods of CBL and lecture-based learning. BMC Med Educ. 2023;23(1):1–5.

Tan KW. Using Teaching Cases for Achieving Bloom’s High-Order Cognitive Levels: An Application in Technically-Oriented Information Systems Course (2017). 2017 Proceedings. 1. http://aisel.aisnet.org/siged2017/1

Farashahi M, Tajeddin M. Effectiveness of teaching methods in business education: a comparison study on the learning outcomes of lectures, case studies and simulations. Int J Manage Educ. 2018;16(1):131–42.

Google Scholar  

Farha RJA, Zein MH, Al Kawas S. Introducing integrated case-based learning to clinical nutrition training and evaluating students’ learning performance. J Taibah Univ Med Sci. 2021;16(4):558–64.

Picciano AG. Theories and frameworks for Online Education: seeking an Integrated Model. Online Learn. 2017;213:166–90.

Bezanilla MJ, Fernández-Nogueira D, Poblete M, Galindo-Domínguez H. Methodologies for teaching-learning critical thinking in higher education: the teacher’s view. Think Skills Creativity. 2019;33:100584.

Immonen K, Oikarainen A, Tomietto M, Kääriäinen M, Tuomikoski A-M, Kaučič BM, Perez-Canaveras RM. Assessment of nursing students’ competence in clinical practice: a systematic review of reviews. Int J Nurs Stud. 2019;100:103414.

Oermann MH, Gaberson KB, De Gagne JC, NPD-BC C. Evaluation and testing in nursing education. Springer Publishing Company; 2024.

McCarty T. (2020). How to Build Assessments for Clinical Learners. Roberts Academic Medicine Handbook: A Guide to Achievement and Fulfillment for Academic Faculty, 83–90.

Gholami M, Changaee F, Karami K, Shahsavaripour Z, Veiskaramian A, Birjandi M. Effects of multiepisode case-based learning (CBL) on problem-solving ability and learning motivation of nursing students in an emergency care course. J Prof Nurs. 2021;37(3):612–9.

King N. (2016, April). Case-based exams for learning and assessment: Experiences in an information systems course [ Confeence presentation]. In 2016 IEEE Global Engineering Education Conference (EDUCON) , Abu Dhabi, UAE.

Pereira D, Flores MA, Niklasson L. Assessment revisited: a review of research in Assessment and evaluation in Higher Education. Assess Evaluation High Educ. 2016;41(7):1008–32.

Creswell JW, Poth CN. Qualitative inquiry and research design: choosing among five approaches. SAGE; 2016.

O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research. Acad Med. 2014;89(9):1245–51.

Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. 2013;13(1). https://doi.org/10.1186/1471-2288-13-117

Fetters MD, Curry LA, Creswell JW. Achieving integration in mixed methods designs—principles and practices. Health Serv Res. 2013;48(6pt2):2134–56. https://doi.org/10.1111/1475-6773.12117

Liu L, Li M, Zheng Q, Jiang H. The effects of case-based teaching in nursing skill education: cases do matter. INQUIRY. J Health Care Organ Provis Financing. 2020;57:004695802096442.

Bonney KM. Case study teaching methods improve student performance and perceptions of learning gains. J Microbiol Biology Educ. 2015;16(1):21–8.

Rezaee R, Mosalanejad L. The effects of case-based team learning on students’ learning, self-regulation, and self-direction. Global J Health Sci. 2015;7(4):295.

Harman T, Bertrand B, Greer A, Pettus A, Jennings J, Wall-Bassett E, Babatunde OT. Case-based learning facilitates critical thinking in undergraduate nutrition education: students describe the big picture. J Acad Nutr Dietetics. 2015;115(3):378–88.

Nunohara K, Imafuku R, Saiki T, Bridges SM, Kawakami C, Tsunekawa K, Niwa M, Fujisaki K, Suzuki Y. (2020). How does video case-based learning influence clinical decision-making by midwifery students? An exploratory study. BMC Med Educ, 20 (1).

Download references

Acknowledgements

The authors wish to thank the nursing students at SQU who voluntarily participated in this study.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and affiliations.

Sultan Qaboos University, Al Khodh 66, Muscat, 123, Oman

Basma Mohammed Al Yazeedi, Lina Mohamed Wali Shakman, Sheeba Elizabeth John Sunderraj, Harshita Prabhakaran, Judie Arulappan, Erna Judith Roach, Aysha Al Hashmi & Zeinab Al Azri

Oman College of Health Science, Norht Sharqia Branch, Ibra 66, Ibra, 124, Oman

Aysha Al Hashmi

You can also search for this author in PubMed   Google Scholar

Contributions

Dr. Basma Mohammed Al Yazeedi contributed to conceptualization, methods, data collection, data analysis, writing the draft, and reviewing the final draft. Ms. Lina Mohamed Wali Shakman contributed to conceptualization, data collection, data analysis, writing the draft, and reviewing the final draft. Ms. Sheeba Elizabeth John Sunderraj contributed to conceptualization, methods, data collection, writing the draft, and reviewing the final draft.Ms. Harshita Prabhakaran contributed to conceptualization, data collection, writing the draft, and reviewing the final draft.Dr. Judie Arulappan contributed to conceptualization and reviewing the final draft.Dr. Erna Roach contributed to conceptualization writing the draft and reviewing the final draft.Ms. Aysha Al Hashmi contributed to the conceptualization and reviewing the final draft. Dr. Zeinab Al Azri contributed to data collection, data analysis, writing the draft, and reviewing the final draft.All auhors reviewed and approved the final version of the manuscirpt.

Corresponding author

Correspondence to Zeinab Al Azri .

Ethics declarations

Ethics approval and consent to participate.

The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Research and Ethics Committee of the College of Nursing, Sultan Qaboos University SQU (CON/NF/2023/18). All data was held and stored following the SQU data policy retention. Informed consent to participate was obtained from all of the participants in the study.

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:

The questionnaire used in this study is attached as a supplementary document.

Supplementary Material 2

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.

Yazeedi, B.M.A., Shakman, L.M.W., Sunderraj, S.E.J. et al. Perceived efficacy of case analysis as an assessment method for clinical competencies in nursing education: a mixed methods study. BMC Nurs 23 , 441 (2024). https://doi.org/10.1186/s12912-024-02102-9

Download citation

Received : 07 April 2024

Accepted : 17 June 2024

Published : 28 June 2024

DOI : https://doi.org/10.1186/s12912-024-02102-9

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-analysis
• Clinical competency
• Nursing education

BMC Nursing

ISSN: 1472-6955

• General enquiries: [email protected]

Research Brief: Mobility Safety for California’s Affordable Housing Residents: Co-locating Improvements

Nearly 60% of all affordable housing in Alameda County is within 500 feet of a high-injury street and 40% of affordable housing is within 100 feet of one. Kyler Blodgett

New research brief highlights mobility safety needs for California’s affordable housing residents and provides recommendations to better connect mobility safety improvements with anticipated affordable housing developments.

A new research brief, Mobility Safety for California’s Affordable Housing Residents: Co-locating Improvements , authored by UC Berkeley SafeTREC's Kyler Blodgett explores how California is rapidly building affordable housing, yet vulnerable road users who will live in these units are often left out of current policies and funding programs that link housing and transportation. This research brief explores the gap in the literature and California’s policy priorities related to residents’ mobility and housing. It then analyzes data for Alameda County, finding that approximately 40% of government-funded affordable developments are within 100 ft of the pedestrian High Injury Network. It concludes with recommendations for municipalities and funding agencies wishing to better connect mobility safety improvements with anticipated affordable housing developments.

The California Department of Housing and Community Development two priority issues, over-commuting and housing and transportation affordability, do not address the critical question of whether California cities are co-locating housing developments with mobility safety upgrades to ensure that the new residents at a given site can navigate their neighborhood safely when walking, biking, or using a wheelchair.

Unique active transportation needs for key resident populations

Because of state funding incentives and set-asides, many affordable developments target one of a few resident types: seniors, families (including children), and “Special Needs” residents with mental or cognitive disabilities. Each of these groups has unique mobility safety challenges.

Based on income alone, affordable housing residents are likely to have different travel patterns from their middle and upper income counterparts:

• Low-income households with cars take nearly 15% more walking trips and 33% more cycling trips per week than higher-income households (Ghimire and Bardaka, 2023).

Current Practice

Currently, most housing development projects incorporate mobility safety in one of a few ways:

• Some projects are required to do an environmental impact report under the California Environmental Quality Act (CEQA), which requires a traffic impact study with details on the safety of pedestrians and bicyclists. However, most new 100% affordable housing in California is exempt from the CEQA process. Furthermore, current law is clear that these studies can optionally consider the safety of vulnerable road users, including people using personal assistive mobility devices and unhoused people, but do not have to.
• Current planning code provides for certain street safety measures like minimum spacing, driveway placement, and curb cuts.
• The jurisdiction may create a specific plan for the development to abide by, which includes enhanced mobility safety improvements.

Quantitative Case Study: Alameda County

• Nearly 60% of all affordable housing sites in Alameda County (176 of 303 sites) is within 500 feet of a high-injury street and 40% of affordable housing (119 of 303 sites) is within 100 feet of one.
• These percentages are slightly higher for affordable housing dedicated to vulnerable residents, which is worrisome given the unique mobility needs of the populations served.

Figure 1: Number of Affordable Housing Sites by proximity to the HIN (Alameda County)

What can cities and state agencies do to better link mobility safety improvements with anticipated affordable housing development?

Strengthen mobility safety-related objective design standards.

Strengthening the mobility safety lens in objective design standards could do two things:

• It could ensure that housing sites are developed according to mobility safety best practices.
• If larger cities with staff capacity develop robust safety standards within their Objective Design Standards, these could be adapted by other jurisdictions that might not have the resources to do so themselves.

Emphasize Vulnerable Road User Safety Improvements in AHSC Scoring, in addition to VMT

Increasing the proportion of points related to pedestrian and bicyclist safety would better center mobility safety as a key part of housing project selection, and would formalize an incentive for developers and city partners to build out safe mobility infrastructure.

Connect Complete Streets Mandates with Affordable Housing Siting

In California, a jurisdiction’s Housing Element must now include locations for planned housing for the upcoming eight years. Municipalities could simultaneously prioritize these neighborhoods for complete streets upgrades to support mobility safety for residents.

Read the full research brief.

This research brief was prepared in cooperation with the California Office of Traffic Safety (OTS). The opinions, findings, and conclusions expressed in this publication are those of the author(s) and not necessarily those of OTS.

Funding for this program was provided by a grant from the California Office of Traffic Safety (OTS) through the National Highway Traffic Safety Administration (NHTSA).

• Complete Streets topic page
• Pedestrian Safety topic page
• Research Brief topic page
• Road User Behavior topic page
• Safe System topic page
• Bicycle safety topic page

Information

• Author Services

Initiatives

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

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

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

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

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

Original Submission Date Received: .

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

Article Menu

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

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

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

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

A case study investigating the relational well-being of international students at hohai university nanjing, jiangsu province of china.

Share and Cite

Ying, H.; Khoso, A.R.; Bhutto, S. A Case Study Investigating the Relational Well-Being of International Students at Hohai University Nanjing, Jiangsu Province of China. Behav. Sci. 2024 , 14 , 544. https://doi.org/10.3390/bs14070544

Ying H, Khoso AR, Bhutto S. A Case Study Investigating the Relational Well-Being of International Students at Hohai University Nanjing, Jiangsu Province of China. Behavioral Sciences . 2024; 14(7):544. https://doi.org/10.3390/bs14070544

Ying, Haihua, Abdul Rasool Khoso, and Shahnaz Bhutto. 2024. "A Case Study Investigating the Relational Well-Being of International Students at Hohai University Nanjing, Jiangsu Province of China" Behavioral Sciences 14, no. 7: 544. https://doi.org/10.3390/bs14070544

Article Metrics

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

Subscribe to receive issue release notifications and newsletters from MDPI journals

Towards routine proteome profiling of FFPE tissue: Insights from a 1,200 case pan-cancer study

• Find this author on Google Scholar
• Find this author on PubMed
• Search for this author on this site
• ORCID record for Johanna Tueshaus
• ORCID record for Stephan Eckert
• ORCID record for Matthew The
• ORCID record for Carolin Mogler
• ORCID record for Bernhard Küster
• For correspondence: [email protected]
• Info/History
• Preview PDF

Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. Here, we did so by analyzing 1,220 tumors from six cancer entities processed over the course of three years. Key findings include the need for a new normalization method ensuring equal and reproducible sample loading for LC-MS/MS analysis across cohorts, showing that tumors can, on average, be profiled to a depth of >4,000 proteins and discovering that current software fails to process such large data sets. We report the first comprehensive pan-cancer proteome expression resource for FFPE material comprising 11,000 proteins which is of immediate utility to the scientific community by way of a web resource. It enables a range of analysis including quantitative comparisons of proteins between patients or cohorts or the discovery of protein fingerprints representing the tissue of origin, or proteins enriched in certain cancer entities.

View the discussion thread.

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

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

• Animal Behavior and Cognition (5413)
• Biochemistry (12215)
• Bioengineering (9144)
• Bioinformatics (30154)
• Biophysics (15487)
• Cancer Biology (12586)
• Cell Biology (18073)
• Clinical Trials (138)
• Developmental Biology (9753)
• Ecology (14625)
• Epidemiology (2067)
• Evolutionary Biology (18780)
• Genetics (12551)
• Genomics (17226)
• Immunology (12318)
• Microbiology (29046)
• Molecular Biology (12054)
• Neuroscience (63244)
• Paleontology (463)
• Pathology (1938)
• Pharmacology and Toxicology (3369)
• Physiology (5187)
• Plant Biology (10810)
• Scientific Communication and Education (1710)
• Synthetic Biology (3006)
• Systems Biology (7541)
• Zoology (1692)