case study in empirical research

• University of Memphis Libraries
• Research Guides

Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

• Introduction
• Database Tools
• Search Terms
• Image Descriptions

Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

• << Previous: Home
• Next: Identifying Empirical Research >>
• Last Updated: Apr 2, 2024 11:25 AM
• URL: https://libguides.memphis.edu/empirical-research

Research Methodologies Guide

• Action Research
• Bibliometrics

Case Studies

• Content Analysis
• Digital Scholarship This link opens in a new window
• Documentary
• Ethnography
• Focus Groups
• Grounded Theory
• Life Histories/Autobiographies
• Longitudinal
• Participant Observation
• Qualitative Research (General)
• Quasi-Experimental Design
• Usability Studies

"A case study is an empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident" (Yin, 1994).

It refers to a type of research in which a case (an event, issue, population, or other item being studied) is analyzed, often through the use of multiple methods of analysis.

Tools commonly used in case studies include:

• Observations

For more information about case studies, review the resources below:

Books and articles

• Five Misunderstandings About Case Study Research [pdf] An article reflecting on common issues in case study research.
• Case Study Research and Applications by Robert K. Yin Publication Date: 2017
• Qualitative Research Through Case Studies by Max Travers Publication Date: 2001
• Unravelling the Mysteries of Case Study Research by Marilyn L. Taylor; Mikael Søndergaard Publication Date: 2017

Additional Resources

• Case Studies A tutorial on case study research from Colorado State University.
• Case Study - Wikipedia, the free encyclopedia. Wikipedia can be a useful place to start your research- check the citations at the bottom of the article for more information.
• << Previous: Bibliometrics
• Next: Content Analysis >>
• Last Updated: Dec 19, 2023 2:12 PM
• URL: https://instr.iastate.libguides.com/researchmethods

• Privacy Policy

Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Questionnaire – Definition, Types, and Examples

Observational Research – Methods and Guide

Quantitative Research – Methods, Types and...

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

Survey Research – Types, Methods, Examples

• Open access
• Published: 10 November 2020

Case study research for better evaluations of complex interventions: rationale and challenges

• Sara Paparini   ORCID: orcid.org/0000-0002-1909-2481 1 ,
• Judith Green 2 ,
• Chrysanthi Papoutsi 1 ,
• Jamie Murdoch 3 ,
• Mark Petticrew 4 ,
• Trish Greenhalgh 1 ,
• Benjamin Hanckel 5 &
• Sara Shaw 1  

BMC Medicine volume  18 , Article number:  301 ( 2020 ) Cite this article

17k Accesses

42 Citations

35 Altmetric

Metrics details

The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear.

Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports.

Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

Peer Review reports

The need for methodological development to address the most urgent challenges in health research has been well-documented. Many of the most pressing questions for public health research, where the focus is on system-level determinants [ 1 , 2 ], and for health services research, where provisions typically vary across sites and are provided through interlocking networks of services [ 3 ], require methodological approaches that can attend to complexity. The need for methodological advance has arisen, in part, as a result of the diminishing returns from randomised controlled trials (RCTs) where they have been used to answer questions about the effects of interventions in complex systems [ 4 , 5 , 6 ]. In conditions of complexity, there is limited value in maintaining the current orientation to experimental trial designs in the health sciences as providing ‘gold standard’ evidence of effect.

There are increasing calls for methodological pluralism [ 7 , 8 ], with the recognition that complex intervention and context are not easily or usefully separated (as is often the situation when using trial design), and that system interruptions may have effects that are not reducible to linear causal pathways between intervention and outcome. These calls are reflected in a shifting and contested discourse of trial design, seen with the emergence of realist [ 9 ], adaptive and hybrid (types 1, 2 and 3) [ 10 , 11 ] trials that blend studies of effectiveness with a close consideration of the contexts of implementation. Similarly, process evaluation has now become a core component of complex healthcare intervention trials, reflected in MRC guidance on how to explore implementation, causal mechanisms and context [ 12 ].

Evidence about the context of an intervention is crucial for questions of external validity. As Woolcock [ 4 ] notes, even if RCT designs are accepted as robust for maximising internal validity, questions of transferability (how well the intervention works in different contexts) and generalisability (how well the intervention can be scaled up) remain unanswered [ 5 , 13 ]. For research evidence to have impact on policy and systems organisation, and thus to improve population and patient health, there is an urgent need for better methods for strengthening external validity, including a better understanding of the relationship between intervention and context [ 14 ].

Policymakers, healthcare commissioners and other research users require credible evidence of relevance to their settings and populations [ 15 ], to perform what Rosengarten and Savransky [ 16 ] call ‘careful abstraction’ to the locales that matter for them. They also require robust evidence for understanding complex causal pathways. Case study research, currently under-utilised in public health and health services evaluation, can offer considerable potential for strengthening faith in both external and internal validity. For example, in an empirical case study of how the policy of free bus travel had specific health effects in London, UK, a quasi-experimental evaluation (led by JG) identified how important aspects of context (a good public transport system) and intervention (that it was universal) were necessary conditions for the observed effects, thus providing useful, actionable evidence for decision-makers in other contexts [ 17 ].

The overall approach of case study research is based on the in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings. Empirical case studies typically enable dynamic understanding of complex challenges rather than restricting the focus on narrow problem delineations and simple fixes. Case study research is a diverse and somewhat contested field, with multiple definitions and perspectives grounded in different ways of viewing the world, and involving different combinations of methods. In this paper, we raise awareness of such plurality and highlight the contribution that case study research can make to the evaluation of complex system-level interventions. We review some of the challenges in exploiting the current evidence base from empirical case studies and conclude by recommending that further guidance and minimum reporting criteria for evaluation using case studies, appropriate for audiences in the health sciences, can enhance the take-up of evidence from case study research.

Case study research offers evidence about context, causal inference in complex systems and implementation

Well-conducted and described empirical case studies provide evidence on context, complexity and mechanisms for understanding how, where and why interventions have their observed effects. Recognition of the importance of context for understanding the relationships between interventions and outcomes is hardly new. In 1943, Canguilhem berated an over-reliance on experimental designs for determining universal physiological laws: ‘As if one could determine a phenomenon’s essence apart from its conditions! As if conditions were a mask or frame which changed neither the face nor the picture!’ ([ 18 ] p126). More recently, a concern with context has been expressed in health systems and public health research as part of what has been called the ‘complexity turn’ [ 1 ]: a recognition that many of the most enduring challenges for developing an evidence base require a consideration of system-level effects [ 1 ] and the conceptualisation of interventions as interruptions in systems [ 19 ].

The case study approach is widely recognised as offering an invaluable resource for understanding the dynamic and evolving influence of context on complex, system-level interventions [ 20 , 21 , 22 , 23 ]. Empirically, case studies can directly inform assessments of where, when, how and for whom interventions might be successfully implemented, by helping to specify the necessary and sufficient conditions under which interventions might have effects and to consolidate learning on how interdependencies, emergence and unpredictability can be managed to achieve and sustain desired effects. Case study research has the potential to address four objectives for improving research and reporting of context recently set out by guidance on taking account of context in population health research [ 24 ], that is to (1) improve the appropriateness of intervention development for specific contexts, (2) improve understanding of ‘how’ interventions work, (3) better understand how and why impacts vary across contexts and (4) ensure reports of intervention studies are most useful for decision-makers and researchers.

However, evaluations of complex healthcare interventions have arguably not exploited the full potential of case study research and can learn much from other disciplines. For evaluative research, exploratory case studies have had a traditional role of providing data on ‘process’, or initial ‘hypothesis-generating’ scoping, but might also have an increasing salience for explanatory aims. Across the social and political sciences, different kinds of case studies are undertaken to meet diverse aims (description, exploration or explanation) and across different scales (from small N qualitative studies that aim to elucidate processes, or provide thick description, to more systematic techniques designed for medium-to-large N cases).

Case studies with explanatory aims vary in terms of their positioning within mixed-methods projects, with designs including (but not restricted to) (1) single N of 1 studies of interventions in specific contexts, where the overall design is a case study that may incorporate one or more (randomised or not) comparisons over time and between variables within the case; (2) a series of cases conducted or synthesised to provide explanation from variations between cases; and (3) case studies of particular settings within RCT or quasi-experimental designs to explore variation in effects or implementation.

Detailed qualitative research (typically done as ‘case studies’ within process evaluations) provides evidence for the plausibility of mechanisms [ 25 ], offering theoretical generalisations for how interventions may function under different conditions. Although RCT designs reduce many threats to internal validity, the mechanisms of effect remain opaque, particularly when the causal pathways between ‘intervention’ and ‘effect’ are long and potentially non-linear: case study research has a more fundamental role here, in providing detailed observational evidence for causal claims [ 26 ] as well as producing a rich, nuanced picture of tensions and multiple perspectives [ 8 ].

Longitudinal or cross-case analysis may be best suited for evidence generation in system-level evaluative research. Turner [ 27 ], for instance, reflecting on the complex processes in major system change, has argued for the need for methods that integrate learning across cases, to develop theoretical knowledge that would enable inferences beyond the single case, and to develop generalisable theory about organisational and structural change in health systems. Qualitative Comparative Analysis (QCA) [ 28 ] is one such formal method for deriving causal claims, using set theory mathematics to integrate data from empirical case studies to answer questions about the configurations of causal pathways linking conditions to outcomes [ 29 , 30 ].

Nonetheless, the single N case study, too, provides opportunities for theoretical development [ 31 ], and theoretical generalisation or analytical refinement [ 32 ]. How ‘the case’ and ‘context’ are conceptualised is crucial here. Findings from the single case may seem to be confined to its intrinsic particularities in a specific and distinct context [ 33 ]. However, if such context is viewed as exemplifying wider social and political forces, the single case can be ‘telling’, rather than ‘typical’, and offer insight into a wider issue [ 34 ]. Internal comparisons within the case can offer rich possibilities for logical inferences about causation [ 17 ]. Further, case studies of any size can be used for theory testing through refutation [ 22 ]. The potential lies, then, in utilising the strengths and plurality of case study to support theory-driven research within different methodological paradigms.

Evaluation research in health has much to learn from a range of social sciences where case study methodology has been used to develop various kinds of causal inference. For instance, Gerring [ 35 ] expands on the within-case variations utilised to make causal claims. For Gerring [ 35 ], case studies come into their own with regard to invariant or strong causal claims (such as X is a necessary and/or sufficient condition for Y) rather than for probabilistic causal claims. For the latter (where experimental methods might have an advantage in estimating effect sizes), case studies offer evidence on mechanisms: from observations of X affecting Y, from process tracing or from pattern matching. Case studies also support the study of emergent causation, that is, the multiple interacting properties that account for particular and unexpected outcomes in complex systems, such as in healthcare [ 8 ].

Finally, efficacy (or beliefs about efficacy) is not the only contributor to intervention uptake, with a range of organisational and policy contingencies affecting whether an intervention is likely to be rolled out in practice. Case study research is, therefore, invaluable for learning about contextual contingencies and identifying the conditions necessary for interventions to become normalised (i.e. implemented routinely) in practice [ 36 ].

The challenges in exploiting evidence from case study research

At present, there are significant challenges in exploiting the benefits of case study research in evaluative health research, which relate to status, definition and reporting. Case study research has been marginalised at the bottom of an evidence hierarchy, seen to offer little by way of explanatory power, if nonetheless useful for adding descriptive data on process or providing useful illustrations for policymakers [ 37 ]. This is an opportune moment to revisit this low status. As health researchers are increasingly charged with evaluating ‘natural experiments’—the use of face masks in the response to the COVID-19 pandemic being a recent example [ 38 ]—rather than interventions that take place in settings that can be controlled, research approaches using methods to strengthen causal inference that does not require randomisation become more relevant.

A second challenge for improving the use of case study evidence in evaluative health research is that, as we have seen, what is meant by ‘case study’ varies widely, not only across but also within disciplines. There is indeed little consensus amongst methodologists as to how to define ‘a case study’. Definitions focus, variously, on small sample size or lack of control over the intervention (e.g. [ 39 ] p194), on in-depth study and context [ 40 , 41 ], on the logic of inference used [ 35 ] or on distinct research strategies which incorporate a number of methods to address questions of ‘how’ and ‘why’ [ 42 ]. Moreover, definitions developed for specific disciplines do not capture the range of ways in which case study research is carried out across disciplines. Multiple definitions of case study reflect the richness and diversity of the approach. However, evidence suggests that a lack of consensus across methodologists results in some of the limitations of published reports of empirical case studies [ 43 , 44 ]. Hyett and colleagues [ 43 ], for instance, reviewing reports in qualitative journals, found little match between methodological definitions of case study research and how authors used the term.

This raises the third challenge we identify that case study reports are typically not written in ways that are accessible or useful for the evaluation research community and policymakers. Case studies may not appear in journals widely read by those in the health sciences, either because space constraints preclude the reporting of rich, thick descriptions, or because of the reported lack of willingness of some biomedical journals to publish research that uses qualitative methods [ 45 ], signalling the persistence of the aforementioned evidence hierarchy. Where they do, however, the term ‘case study’ is used to indicate, interchangeably, a qualitative study, an N of 1 sample, or a multi-method, in-depth analysis of one example from a population of phenomena. Definitions of what constitutes the ‘case’ are frequently lacking and appear to be used as a synonym for the settings in which the research is conducted. Despite offering insights for evaluation, the primary aims may not have been evaluative, so the implications may not be explicitly drawn out. Indeed, some case study reports might properly be aiming for thick description without necessarily seeking to inform about context or causality.

Acknowledging plurality and developing guidance

We recognise that definitional and methodological plurality is not only inevitable, but also a necessary and creative reflection of the very different epistemological and disciplinary origins of health researchers, and the aims they have in doing and reporting case study research. Indeed, to provide some clarity, Thomas [ 46 ] has suggested a typology of subject/purpose/approach/process for classifying aims (e.g. evaluative or exploratory), sample rationale and selection and methods for data generation of case studies. We also recognise that the diversity of methods used in case study research, and the necessary focus on narrative reporting, does not lend itself to straightforward development of formal quality or reporting criteria.

Existing checklists for reporting case study research from the social sciences—for example Lincoln and Guba’s [ 47 ] and Stake’s [ 33 ]—are primarily orientated to the quality of narrative produced, and the extent to which they encapsulate thick description, rather than the more pragmatic issues of implications for intervention effects. Those designed for clinical settings, such as the CARE (CAse REports) guidelines, provide specific reporting guidelines for medical case reports about single, or small groups of patients [ 48 ], not for case study research.

The Design of Case Study Research in Health Care (DESCARTE) model [ 44 ] suggests a series of questions to be asked of a case study researcher (including clarity about the philosophy underpinning their research), study design (with a focus on case definition) and analysis (to improve process). The model resembles toolkits for enhancing the quality and robustness of qualitative and mixed-methods research reporting, and it is usefully open-ended and non-prescriptive. However, even if it does include some reflections on context, the model does not fully address aspects of context, logic and causal inference that are perhaps most relevant for evaluative research in health.

Hence, for evaluative research where the aim is to report empirical findings in ways that are intended to be pragmatically useful for health policy and practice, this may be an opportune time to consider how to best navigate plurality around what is (minimally) important to report when publishing empirical case studies, especially with regards to the complex relationships between context and interventions, information that case study research is well placed to provide.

The conventional scientific quest for certainty, predictability and linear causality (maximised in RCT designs) has to be augmented by the study of uncertainty, unpredictability and emergent causality [ 8 ] in complex systems. This will require methodological pluralism, and openness to broadening the evidence base to better understand both causality in and the transferability of system change intervention [ 14 , 20 , 23 , 25 ]. Case study research evidence is essential, yet is currently under exploited in the health sciences. If evaluative health research is to move beyond the current impasse on methods for understanding interventions as interruptions in complex systems, we need to consider in more detail how researchers can conduct and report empirical case studies which do aim to elucidate the contextual factors which interact with interventions to produce particular effects. To this end, supported by the UK’s Medical Research Council, we are embracing the challenge to develop guidance for case study researchers studying complex interventions. Following a meta-narrative review of the literature, we are planning a Delphi study to inform guidance that will, at minimum, cover the value of case study research for evaluating the interrelationship between context and complex system-level interventions; for situating and defining ‘the case’, and generalising from case studies; as well as provide specific guidance on conducting, analysing and reporting case study research. Our hope is that such guidance can support researchers evaluating interventions in complex systems to better exploit the diversity and richness of case study research.

Availability of data and materials

Not applicable (article based on existing available academic publications)

Abbreviations

Qualitative comparative analysis

Quasi-experimental design

Randomised controlled trial

Diez Roux AV. Complex systems thinking and current impasses in health disparities research. Am J Public Health. 2011;101(9):1627–34.

Article   Google Scholar  

Ogilvie D, Mitchell R, Mutrie N, M P, Platt S. Evaluating health effects of transport interventions: methodologic case study. Am J Prev Med 2006;31:118–126.

Walshe C. The evaluation of complex interventions in palliative care: an exploration of the potential of case study research strategies. Palliat Med. 2011;25(8):774–81.

Woolcock M. Using case studies to explore the external validity of ‘complex’ development interventions. Evaluation. 2013;19:229–48.

Cartwright N. Are RCTs the gold standard? BioSocieties. 2007;2(1):11–20.

Deaton A, Cartwright N. Understanding and misunderstanding randomized controlled trials. Soc Sci Med. 2018;210:2–21.

Salway S, Green J. Towards a critical complex systems approach to public health. Crit Public Health. 2017;27(5):523–4.

Greenhalgh T, Papoutsi C. Studying complexity in health services research: desperately seeking an overdue paradigm shift. BMC Med. 2018;16(1):95.

Bonell C, Warren E, Fletcher A. Realist trials and the testing of context-mechanism-outcome configurations: a response to Van Belle et al. Trials. 2016;17:478.

Pallmann P, Bedding AW, Choodari-Oskooei B. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med. 2018;16:29.

Curran G, Bauer M, Mittman B, Pyne J, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217–26. https://doi.org/10.1097/MLR.0b013e3182408812 .

Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015 [cited 2020 Jun 27];350. Available from: https://www.bmj.com/content/350/bmj.h1258 .

Evans RE, Craig P, Hoddinott P, Littlecott H, Moore L, Murphy S, et al. When and how do ‘effective’ interventions need to be adapted and/or re-evaluated in new contexts? The need for guidance. J Epidemiol Community Health. 2019;73(6):481–2.

Shoveller J. A critical examination of representations of context within research on population health interventions. Crit Public Health. 2016;26(5):487–500.

Treweek S, Zwarenstein M. Making trials matter: pragmatic and explanatory trials and the problem of applicability. Trials. 2009;10(1):37.

Rosengarten M, Savransky M. A careful biomedicine? Generalization and abstraction in RCTs. Crit Public Health. 2019;29(2):181–91.

Green J, Roberts H, Petticrew M, Steinbach R, Goodman A, Jones A, et al. Integrating quasi-experimental and inductive designs in evaluation: a case study of the impact of free bus travel on public health. Evaluation. 2015;21(4):391–406.

Canguilhem G. The normal and the pathological. New York: Zone Books; 1991. (1949).

Google Scholar  

Hawe P, Shiell A, Riley T. Theorising interventions as events in systems. Am J Community Psychol. 2009;43:267–76.

King G, Keohane RO, Verba S. Designing social inquiry: scientific inference in qualitative research: Princeton University Press; 1994.

Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q. 2004;82(4):581–629.

Yin R. Enhancing the quality of case studies in health services research. Health Serv Res. 1999;34(5 Pt 2):1209.

CAS   PubMed   PubMed Central   Google Scholar  

Raine R, Fitzpatrick R, Barratt H, Bevan G, Black N, Boaden R, et al. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Health Serv Deliv Res. 2016 [cited 2020 Jun 30];4(16). Available from: https://www.journalslibrary.nihr.ac.uk/hsdr/hsdr04160#/abstract .

Craig P, Di Ruggiero E, Frohlich KL, E M, White M, Group CCGA. Taking account of context in population health intervention research: guidance for producers, users and funders of research. NIHR Evaluation, Trials and Studies Coordinating Centre; 2018.

Grant RL, Hood R. Complex systems, explanation and policy: implications of the crisis of replication for public health research. Crit Public Health. 2017;27(5):525–32.

Mahoney J. Strategies of causal inference in small-N analysis. Sociol Methods Res. 2000;4:387–424.

Turner S. Major system change: a management and organisational research perspective. In: Rosalind Raine, Ray Fitzpatrick, Helen Barratt, Gywn Bevan, Nick Black, Ruth Boaden, et al. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Health Serv Deliv Res. 2016;4(16) 2016. https://doi.org/10.3310/hsdr04160.

Ragin CC. Using qualitative comparative analysis to study causal complexity. Health Serv Res. 1999;34(5 Pt 2):1225.

Hanckel B, Petticrew M, Thomas J, Green J. Protocol for a systematic review of the use of qualitative comparative analysis for evaluative questions in public health research. Syst Rev. 2019;8(1):252.

Schneider CQ, Wagemann C. Set-theoretic methods for the social sciences: a guide to qualitative comparative analysis: Cambridge University Press; 2012. 369 p.

Flyvbjerg B. Five misunderstandings about case-study research. Qual Inq. 2006;12:219–45.

Tsoukas H. Craving for generality and small-N studies: a Wittgensteinian approach towards the epistemology of the particular in organization and management studies. Sage Handb Organ Res Methods. 2009:285–301.

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

Mitchell JC. Typicality and the case study. Ethnographic research: A guide to general conduct. Vol. 238241. 1984.

Gerring J. What is a case study and what is it good for? Am Polit Sci Rev. 2004;98(2):341–54.

May C, Mort M, Williams T, F M, Gask L. Health technology assessment in its local contexts: studies of telehealthcare. Soc Sci Med 2003;57:697–710.

McGill E. Trading quality for relevance: non-health decision-makers’ use of evidence on the social determinants of health. BMJ Open. 2015;5(4):007053.

Greenhalgh T. We can’t be 100% sure face masks work – but that shouldn’t stop us wearing them | Trish Greenhalgh. The Guardian. 2020 [cited 2020 Jun 27]; Available from: https://www.theguardian.com/commentisfree/2020/jun/05/face-masks-coronavirus .

Hammersley M. So, what are case studies? In: What’s wrong with ethnography? New York: Routledge; 1992.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach. BMC Med Res Methodol. 2011;11(1):100.

Luck L, Jackson D, Usher K. Case study: a bridge across the paradigms. Nurs Inq. 2006;13(2):103–9.

Yin RK. Case study research and applications: design and methods: Sage; 2017.

Hyett N, A K, Dickson-Swift V. Methodology or method? A critical review of qualitative case study reports. Int J Qual Stud Health Well-Being. 2014;9:23606.

Carolan CM, Forbat L, Smith A. Developing the DESCARTE model: the design of case study research in health care. Qual Health Res. 2016;26(5):626–39.

Greenhalgh T, Annandale E, Ashcroft R, Barlow J, Black N, Bleakley A, et al. An open letter to the BMJ editors on qualitative research. Bmj. 2016;352.

Thomas G. A typology for the case study in social science following a review of definition, discourse, and structure. Qual Inq. 2011;17(6):511–21.

Lincoln YS, Guba EG. Judging the quality of case study reports. Int J Qual Stud Educ. 1990;3(1):53–9.

Riley DS, Barber MS, Kienle GS, Aronson JK, Schoen-Angerer T, Tugwell P, et al. CARE guidelines for case reports: explanation and elaboration document. J Clin Epidemiol. 2017;89:218–35.

Download references

Acknowledgements

Not applicable

This work was funded by the Medical Research Council - MRC Award MR/S014632/1 HCS: Case study, Context and Complex interventions (TRIPLE C). SP was additionally funded by the University of Oxford's Higher Education Innovation Fund (HEIF).

Author information

Authors and affiliations.

Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

Sara Paparini, Chrysanthi Papoutsi, Trish Greenhalgh & Sara Shaw

Wellcome Centre for Cultures & Environments of Health, University of Exeter, Exeter, UK

Judith Green

School of Health Sciences, University of East Anglia, Norwich, UK

Jamie Murdoch

Public Health, Environments and Society, London School of Hygiene & Tropical Medicin, London, UK

Mark Petticrew

Institute for Culture and Society, Western Sydney University, Penrith, Australia

Benjamin Hanckel

You can also search for this author in PubMed   Google Scholar

Contributions

JG, MP, SP, JM, TG, CP and SS drafted the initial paper; all authors contributed to the drafting of the final version, and read and approved the final manuscript.

Corresponding author

Correspondence to Sara Paparini .

Ethics declarations

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

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

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

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.

Paparini, S., Green, J., Papoutsi, C. et al. Case study research for better evaluations of complex interventions: rationale and challenges. BMC Med 18 , 301 (2020). https://doi.org/10.1186/s12916-020-01777-6

Download citation

Received : 03 July 2020

Accepted : 07 September 2020

Published : 10 November 2020

DOI : https://doi.org/10.1186/s12916-020-01777-6

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Qualitative
• Case studies
• Mixed-method
• Public health
• Health services research
• Interventions

BMC Medicine

ISSN: 1741-7015

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

What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 36min read

Ever wondered how we gather the facts, unveil hidden truths, and make informed decisions in a world filled with questions? Empirical research holds the key.

In this guide, we'll delve deep into the art and science of empirical research, unraveling its methods, mysteries, and manifold applications. From defining the core principles to mastering data analysis and reporting findings, we're here to equip you with the knowledge and tools to navigate the empirical landscape.

What is Empirical Research?

Empirical research is the cornerstone of scientific inquiry, providing a systematic and structured approach to investigating the world around us. It is the process of gathering and analyzing empirical or observable data to test hypotheses, answer research questions, or gain insights into various phenomena. This form of research relies on evidence derived from direct observation or experimentation, allowing researchers to draw conclusions based on real-world data rather than purely theoretical or speculative reasoning.

Characteristics of Empirical Research

Empirical research is characterized by several key features:

• Observation and Measurement : It involves the systematic observation or measurement of variables, events, or behaviors.
• Data Collection : Researchers collect data through various methods, such as surveys, experiments, observations, or interviews.
• Testable Hypotheses : Empirical research often starts with testable hypotheses that are evaluated using collected data.
• Quantitative or Qualitative Data : Data can be quantitative (numerical) or qualitative (non-numerical), depending on the research design.
• Statistical Analysis : Quantitative data often undergo statistical analysis to determine patterns , relationships, or significance.
• Objectivity and Replicability : Empirical research strives for objectivity, minimizing researcher bias . It should be replicable, allowing other researchers to conduct the same study to verify results.
• Conclusions and Generalizations : Empirical research generates findings based on data and aims to make generalizations about larger populations or phenomena.

Importance of Empirical Research

Empirical research plays a pivotal role in advancing knowledge across various disciplines. Its importance extends to academia, industry, and society as a whole. Here are several reasons why empirical research is essential:

• Evidence-Based Knowledge : Empirical research provides a solid foundation of evidence-based knowledge. It enables us to test hypotheses, confirm or refute theories, and build a robust understanding of the world.
• Scientific Progress : In the scientific community, empirical research fuels progress by expanding the boundaries of existing knowledge. It contributes to the development of theories and the formulation of new research questions.
• Problem Solving : Empirical research is instrumental in addressing real-world problems and challenges. It offers insights and data-driven solutions to complex issues in fields like healthcare, economics, and environmental science.
• Informed Decision-Making : In policymaking, business, and healthcare, empirical research informs decision-makers by providing data-driven insights. It guides strategies, investments, and policies for optimal outcomes.
• Quality Assurance : Empirical research is essential for quality assurance and validation in various industries, including pharmaceuticals, manufacturing, and technology. It ensures that products and processes meet established standards.
• Continuous Improvement : Businesses and organizations use empirical research to evaluate performance, customer satisfaction, and product effectiveness. This data-driven approach fosters continuous improvement and innovation.
• Human Advancement : Empirical research in fields like medicine and psychology contributes to the betterment of human health and well-being. It leads to medical breakthroughs, improved therapies, and enhanced psychological interventions.
• Critical Thinking and Problem Solving : Engaging in empirical research fosters critical thinking skills, problem-solving abilities, and a deep appreciation for evidence-based decision-making.

Empirical research empowers us to explore, understand, and improve the world around us. It forms the bedrock of scientific inquiry and drives progress in countless domains, shaping our understanding of both the natural and social sciences.

How to Conduct Empirical Research?

So, you've decided to dive into the world of empirical research. Let's begin by exploring the crucial steps involved in getting started with your research project.

1. Select a Research Topic

Selecting the right research topic is the cornerstone of a successful empirical study. It's essential to choose a topic that not only piques your interest but also aligns with your research goals and objectives. Here's how to go about it:

• Identify Your Interests : Start by reflecting on your passions and interests. What topics fascinate you the most? Your enthusiasm will be your driving force throughout the research process.
• Brainstorm Ideas : Engage in brainstorming sessions to generate potential research topics. Consider the questions you've always wanted to answer or the issues that intrigue you.
• Relevance and Significance : Assess the relevance and significance of your chosen topic. Does it contribute to existing knowledge? Is it a pressing issue in your field of study or the broader community?
• Feasibility : Evaluate the feasibility of your research topic. Do you have access to the necessary resources, data, and participants (if applicable)?

2. Formulate Research Questions

Once you've narrowed down your research topic, the next step is to formulate clear and precise research questions . These questions will guide your entire research process and shape your study's direction. To create effective research questions:

• Specificity : Ensure that your research questions are specific and focused. Vague or overly broad questions can lead to inconclusive results.
• Relevance : Your research questions should directly relate to your chosen topic. They should address gaps in knowledge or contribute to solving a particular problem.
• Testability : Ensure that your questions are testable through empirical methods. You should be able to gather data and analyze it to answer these questions.
• Avoid Bias : Craft your questions in a way that avoids leading or biased language. Maintain neutrality to uphold the integrity of your research.

3. Review Existing Literature

Before you embark on your empirical research journey, it's essential to immerse yourself in the existing body of literature related to your chosen topic. This step, often referred to as a literature review, serves several purposes:

• Contextualization : Understand the historical context and current state of research in your field. What have previous studies found, and what questions remain unanswered?
• Identifying Gaps : Identify gaps or areas where existing research falls short. These gaps will help you formulate meaningful research questions and hypotheses.
• Theory Development : If your study is theoretical, consider how existing theories apply to your topic. If it's empirical, understand how previous studies have approached data collection and analysis.
• Methodological Insights : Learn from the methodologies employed in previous research. What methods were successful, and what challenges did researchers face?

4. Define Variables

Variables are fundamental components of empirical research. They are the factors or characteristics that can change or be manipulated during your study. Properly defining and categorizing variables is crucial for the clarity and validity of your research. Here's what you need to know:

• Independent Variables : These are the variables that you, as the researcher, manipulate or control. They are the "cause" in cause-and-effect relationships.
• Dependent Variables : Dependent variables are the outcomes or responses that you measure or observe. They are the "effect" influenced by changes in independent variables.
• Operational Definitions : To ensure consistency and clarity, provide operational definitions for your variables. Specify how you will measure or manipulate each variable.
• Control Variables : In some studies, controlling for other variables that may influence your dependent variable is essential. These are known as control variables.

Understanding these foundational aspects of empirical research will set a solid foundation for the rest of your journey. Now that you've grasped the essentials of getting started, let's delve deeper into the intricacies of research design.

Empirical Research Design

Now that you've selected your research topic, formulated research questions, and defined your variables, it's time to delve into the heart of your empirical research journey – research design . This pivotal step determines how you will collect data and what methods you'll employ to answer your research questions. Let's explore the various facets of research design in detail.

Types of Empirical Research

Empirical research can take on several forms, each with its own unique approach and methodologies. Understanding the different types of empirical research will help you choose the most suitable design for your study. Here are some common types:

• Experimental Research : In this type, researchers manipulate one or more independent variables to observe their impact on dependent variables. It's highly controlled and often conducted in a laboratory setting.
• Observational Research : Observational research involves the systematic observation of subjects or phenomena without intervention. Researchers are passive observers, documenting behaviors, events, or patterns.
• Survey Research : Surveys are used to collect data through structured questionnaires or interviews. This method is efficient for gathering information from a large number of participants.
• Case Study Research : Case studies focus on in-depth exploration of one or a few cases. Researchers gather detailed information through various sources such as interviews, documents, and observations.
• Qualitative Research : Qualitative research aims to understand behaviors, experiences, and opinions in depth. It often involves open-ended questions, interviews, and thematic analysis.
• Quantitative Research : Quantitative research collects numerical data and relies on statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys.

Your choice of research type should align with your research questions and objectives. Experimental research, for example, is ideal for testing cause-and-effect relationships, while qualitative research is more suitable for exploring complex phenomena.

Experimental Design

Experimental research is a systematic approach to studying causal relationships. It's characterized by the manipulation of one or more independent variables while controlling for other factors. Here are some key aspects of experimental design:

• Control and Experimental Groups : Participants are randomly assigned to either a control group or an experimental group. The independent variable is manipulated for the experimental group but not for the control group.
• Randomization : Randomization is crucial to eliminate bias in group assignment. It ensures that each participant has an equal chance of being in either group.
• Hypothesis Testing : Experimental research often involves hypothesis testing. Researchers formulate hypotheses about the expected effects of the independent variable and use statistical analysis to test these hypotheses.

Observational Design

Observational research entails careful and systematic observation of subjects or phenomena. It's advantageous when you want to understand natural behaviors or events. Key aspects of observational design include:

• Participant Observation : Researchers immerse themselves in the environment they are studying. They become part of the group being observed, allowing for a deep understanding of behaviors.
• Non-Participant Observation : In non-participant observation, researchers remain separate from the subjects. They observe and document behaviors without direct involvement.
• Data Collection Methods : Observational research can involve various data collection methods, such as field notes, video recordings, photographs, or coding of observed behaviors.

Survey Design

Surveys are a popular choice for collecting data from a large number of participants. Effective survey design is essential to ensure the validity and reliability of your data. Consider the following:

• Questionnaire Design : Create clear and concise questions that are easy for participants to understand. Avoid leading or biased questions.
• Sampling Methods : Decide on the appropriate sampling method for your study, whether it's random, stratified, or convenience sampling.
• Data Collection Tools : Choose the right tools for data collection, whether it's paper surveys, online questionnaires, or face-to-face interviews.

Case Study Design

Case studies are an in-depth exploration of one or a few cases to gain a deep understanding of a particular phenomenon. Key aspects of case study design include:

• Single Case vs. Multiple Case Studies : Decide whether you'll focus on a single case or multiple cases. Single case studies are intensive and allow for detailed examination, while multiple case studies provide comparative insights.
• Data Collection Methods : Gather data through interviews, observations, document analysis, or a combination of these methods.

Qualitative vs. Quantitative Research

In empirical research, you'll often encounter the distinction between qualitative and quantitative research . Here's a closer look at these two approaches:

• Qualitative Research : Qualitative research seeks an in-depth understanding of human behavior, experiences, and perspectives. It involves open-ended questions, interviews, and the analysis of textual or narrative data. Qualitative research is exploratory and often used when the research question is complex and requires a nuanced understanding.
• Quantitative Research : Quantitative research collects numerical data and employs statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys. Quantitative research is ideal for testing hypotheses and establishing cause-and-effect relationships.

Understanding the various research design options is crucial in determining the most appropriate approach for your study. Your choice should align with your research questions, objectives, and the nature of the phenomenon you're investigating.

Data Collection for Empirical Research

Now that you've established your research design, it's time to roll up your sleeves and collect the data that will fuel your empirical research. Effective data collection is essential for obtaining accurate and reliable results.

Sampling Methods

Sampling methods are critical in empirical research, as they determine the subset of individuals or elements from your target population that you will study. Here are some standard sampling methods:

• Random Sampling : Random sampling ensures that every member of the population has an equal chance of being selected. It minimizes bias and is often used in quantitative research.
• Stratified Sampling : Stratified sampling involves dividing the population into subgroups or strata based on specific characteristics (e.g., age, gender, location). Samples are then randomly selected from each stratum, ensuring representation of all subgroups.
• Convenience Sampling : Convenience sampling involves selecting participants who are readily available or easily accessible. While it's convenient, it may introduce bias and limit the generalizability of results.
• Snowball Sampling : Snowball sampling is instrumental when studying hard-to-reach or hidden populations. One participant leads you to another, creating a "snowball" effect. This method is common in qualitative research.
• Purposive Sampling : In purposive sampling, researchers deliberately select participants who meet specific criteria relevant to their research questions. It's often used in qualitative studies to gather in-depth information.

The choice of sampling method depends on the nature of your research, available resources, and the degree of precision required. It's crucial to carefully consider your sampling strategy to ensure that your sample accurately represents your target population.

Data Collection Instruments

Data collection instruments are the tools you use to gather information from your participants or sources. These instruments should be designed to capture the data you need accurately. Here are some popular data collection instruments:

• Questionnaires : Questionnaires consist of structured questions with predefined response options. When designing questionnaires, consider the clarity of questions, the order of questions, and the response format (e.g., Likert scale , multiple-choice).
• Interviews : Interviews involve direct communication between the researcher and participants. They can be structured (with predetermined questions) or unstructured (open-ended). Effective interviews require active listening and probing for deeper insights.
• Observations : Observations entail systematically and objectively recording behaviors, events, or phenomena. Researchers must establish clear criteria for what to observe, how to record observations, and when to observe.
• Surveys : Surveys are a common data collection instrument for quantitative research. They can be administered through various means, including online surveys, paper surveys, and telephone surveys.
• Documents and Archives : In some cases, data may be collected from existing documents, records, or archives. Ensure that the sources are reliable, relevant, and properly documented.

To streamline your process and gather insights with precision and efficiency, consider leveraging innovative tools like Appinio . With Appinio's intuitive platform, you can harness the power of real-time consumer data to inform your research decisions effectively. Whether you're conducting surveys, interviews, or observations, Appinio empowers you to define your target audience, collect data from diverse demographics, and analyze results seamlessly.

By incorporating Appinio into your data collection toolkit, you can unlock a world of possibilities and elevate the impact of your empirical research. Ready to revolutionize your approach to data collection?

Book a Demo

Data Collection Procedures

Data collection procedures outline the step-by-step process for gathering data. These procedures should be meticulously planned and executed to maintain the integrity of your research.

• Training : If you have a research team, ensure that they are trained in data collection methods and protocols. Consistency in data collection is crucial.
• Pilot Testing : Before launching your data collection, conduct a pilot test with a small group to identify any potential problems with your instruments or procedures. Make necessary adjustments based on feedback.
• Data Recording : Establish a systematic method for recording data. This may include timestamps, codes, or identifiers for each data point.
• Data Security : Safeguard the confidentiality and security of collected data. Ensure that only authorized individuals have access to the data.
• Data Storage : Properly organize and store your data in a secure location, whether in physical or digital form. Back up data to prevent loss.

Ethical Considerations

Ethical considerations are paramount in empirical research, as they ensure the well-being and rights of participants are protected.

• Informed Consent : Obtain informed consent from participants, providing clear information about the research purpose, procedures, risks, and their right to withdraw at any time.
• Privacy and Confidentiality : Protect the privacy and confidentiality of participants. Ensure that data is anonymized and sensitive information is kept confidential.
• Beneficence : Ensure that your research benefits participants and society while minimizing harm. Consider the potential risks and benefits of your study.
• Honesty and Integrity : Conduct research with honesty and integrity. Report findings accurately and transparently, even if they are not what you expected.
• Respect for Participants : Treat participants with respect, dignity, and sensitivity to cultural differences. Avoid any form of coercion or manipulation.
• Institutional Review Board (IRB) : If required, seek approval from an IRB or ethics committee before conducting your research, particularly when working with human participants.

Adhering to ethical guidelines is not only essential for the ethical conduct of research but also crucial for the credibility and validity of your study. Ethical research practices build trust between researchers and participants and contribute to the advancement of knowledge with integrity.

With a solid understanding of data collection, including sampling methods, instruments, procedures, and ethical considerations, you are now well-equipped to gather the data needed to answer your research questions.

Empirical Research Data Analysis

Now comes the exciting phase of data analysis, where the raw data you've diligently collected starts to yield insights and answers to your research questions. We will explore the various aspects of data analysis, from preparing your data to drawing meaningful conclusions through statistics and visualization.

Data Preparation

Data preparation is the crucial first step in data analysis. It involves cleaning, organizing, and transforming your raw data into a format that is ready for analysis. Effective data preparation ensures the accuracy and reliability of your results.

• Data Cleaning : Identify and rectify errors, missing values, and inconsistencies in your dataset. This may involve correcting typos, removing outliers, and imputing missing data.
• Data Coding : Assign numerical values or codes to categorical variables to make them suitable for statistical analysis. For example, converting "Yes" and "No" to 1 and 0.
• Data Transformation : Transform variables as needed to meet the assumptions of the statistical tests you plan to use. Common transformations include logarithmic or square root transformations.
• Data Integration : If your data comes from multiple sources, integrate it into a unified dataset, ensuring that variables match and align.
• Data Documentation : Maintain clear documentation of all data preparation steps, as well as the rationale behind each decision. This transparency is essential for replicability.

Effective data preparation lays the foundation for accurate and meaningful analysis. It allows you to trust the results that will follow in the subsequent stages.

Descriptive Statistics

Descriptive statistics help you summarize and make sense of your data by providing a clear overview of its key characteristics. These statistics are essential for understanding the central tendencies, variability, and distribution of your variables. Descriptive statistics include:

• Measures of Central Tendency : These include the mean (average), median (middle value), and mode (most frequent value). They help you understand the typical or central value of your data.
• Measures of Dispersion : Measures like the range, variance, and standard deviation provide insights into the spread or variability of your data points.
• Frequency Distributions : Creating frequency distributions or histograms allows you to visualize the distribution of your data across different values or categories.

Descriptive statistics provide the initial insights needed to understand your data's basic characteristics, which can inform further analysis.

Inferential Statistics

Inferential statistics take your analysis to the next level by allowing you to make inferences or predictions about a larger population based on your sample data. These methods help you test hypotheses and draw meaningful conclusions. Key concepts in inferential statistics include:

• Hypothesis Testing : Hypothesis tests (e.g., t-tests, chi-squared tests) help you determine whether observed differences or associations in your data are statistically significant or occurred by chance.
• Confidence Intervals : Confidence intervals provide a range within which population parameters (e.g., population mean) are likely to fall based on your sample data.
• Regression Analysis : Regression models (linear, logistic, etc.) help you explore relationships between variables and make predictions.
• Analysis of Variance (ANOVA) : ANOVA tests are used to compare means between multiple groups, allowing you to assess whether differences are statistically significant.

Inferential statistics are powerful tools for drawing conclusions from your data and assessing the generalizability of your findings to the broader population.

Qualitative Data Analysis

Qualitative data analysis is employed when working with non-numerical data, such as text, interviews, or open-ended survey responses. It focuses on understanding the underlying themes, patterns, and meanings within qualitative data. Qualitative analysis techniques include:

• Thematic Analysis : Identifying and analyzing recurring themes or patterns within textual data.
• Content Analysis : Categorizing and coding qualitative data to extract meaningful insights.
• Grounded Theory : Developing theories or frameworks based on emergent themes from the data.
• Narrative Analysis : Examining the structure and content of narratives to uncover meaning.

Qualitative data analysis provides a rich and nuanced understanding of complex phenomena and human experiences.

Data Visualization

Data visualization is the art of representing data graphically to make complex information more understandable and accessible. Effective data visualization can reveal patterns, trends, and outliers in your data. Common types of data visualization include:

• Bar Charts and Histograms : Used to display the distribution of categorical data or discrete data .
• Line Charts : Ideal for showing trends and changes in data over time.
• Scatter Plots : Visualize relationships and correlations between two variables.
• Pie Charts : Display the composition of a whole in terms of its parts.
• Heatmaps : Depict patterns and relationships in multidimensional data through color-coding.
• Box Plots : Provide a summary of the data distribution, including outliers.
• Interactive Dashboards : Create dynamic visualizations that allow users to explore data interactively.

Data visualization not only enhances your understanding of the data but also serves as a powerful communication tool to convey your findings to others.

As you embark on the data analysis phase of your empirical research, remember that the specific methods and techniques you choose will depend on your research questions, data type, and objectives. Effective data analysis transforms raw data into valuable insights, bringing you closer to the answers you seek.

How to Report Empirical Research Results?

At this stage, you get to share your empirical research findings with the world. Effective reporting and presentation of your results are crucial for communicating your research's impact and insights.

1. Write the Research Paper

Writing a research paper is the culmination of your empirical research journey. It's where you synthesize your findings, provide context, and contribute to the body of knowledge in your field.

• Title and Abstract : Craft a clear and concise title that reflects your research's essence. The abstract should provide a brief summary of your research objectives, methods, findings, and implications.
• Introduction : In the introduction, introduce your research topic, state your research questions or hypotheses, and explain the significance of your study. Provide context by discussing relevant literature.
• Methods : Describe your research design, data collection methods, and sampling procedures. Be precise and transparent, allowing readers to understand how you conducted your study.
• Results : Present your findings in a clear and organized manner. Use tables, graphs, and statistical analyses to support your results. Avoid interpreting your findings in this section; focus on the presentation of raw data.
• Discussion : Interpret your findings and discuss their implications. Relate your results to your research questions and the existing literature. Address any limitations of your study and suggest avenues for future research.
• Conclusion : Summarize the key points of your research and its significance. Restate your main findings and their implications.
• References : Cite all sources used in your research following a specific citation style (e.g., APA, MLA, Chicago). Ensure accuracy and consistency in your citations.
• Appendices : Include any supplementary material, such as questionnaires, data coding sheets, or additional analyses, in the appendices.

Writing a research paper is a skill that improves with practice. Ensure clarity, coherence, and conciseness in your writing to make your research accessible to a broader audience.

2. Create Visuals and Tables

Visuals and tables are powerful tools for presenting complex data in an accessible and understandable manner.

• Clarity : Ensure that your visuals and tables are clear and easy to interpret. Use descriptive titles and labels.
• Consistency : Maintain consistency in formatting, such as font size and style, across all visuals and tables.
• Appropriateness : Choose the most suitable visual representation for your data. Bar charts, line graphs, and scatter plots work well for different types of data.
• Simplicity : Avoid clutter and unnecessary details. Focus on conveying the main points.
• Accessibility : Make sure your visuals and tables are accessible to a broad audience, including those with visual impairments.
• Captions : Include informative captions that explain the significance of each visual or table.

Compelling visuals and tables enhance the reader's understanding of your research and can be the key to conveying complex information efficiently.

3. Interpret Findings

Interpreting your findings is where you bridge the gap between data and meaning. It's your opportunity to provide context, discuss implications, and offer insights. When interpreting your findings:

• Relate to Research Questions : Discuss how your findings directly address your research questions or hypotheses.
• Compare with Literature : Analyze how your results align with or deviate from previous research in your field. What insights can you draw from these comparisons?
• Discuss Limitations : Be transparent about the limitations of your study. Address any constraints, biases, or potential sources of error.
• Practical Implications : Explore the real-world implications of your findings. How can they be applied or inform decision-making?
• Future Research Directions : Suggest areas for future research based on the gaps or unanswered questions that emerged from your study.

Interpreting findings goes beyond simply presenting data; it's about weaving a narrative that helps readers grasp the significance of your research in the broader context.

With your research paper written, structured, and enriched with visuals, and your findings expertly interpreted, you are now prepared to communicate your research effectively. Sharing your insights and contributing to the body of knowledge in your field is a significant accomplishment in empirical research.

Examples of Empirical Research

To solidify your understanding of empirical research, let's delve into some real-world examples across different fields. These examples will illustrate how empirical research is applied to gather data, analyze findings, and draw conclusions.

Social Sciences

In the realm of social sciences, consider a sociological study exploring the impact of socioeconomic status on educational attainment. Researchers gather data from a diverse group of individuals, including their family backgrounds, income levels, and academic achievements.

Through statistical analysis, they can identify correlations and trends, revealing whether individuals from lower socioeconomic backgrounds are less likely to attain higher levels of education. This empirical research helps shed light on societal inequalities and informs policymakers on potential interventions to address disparities in educational access.

Environmental Science

Environmental scientists often employ empirical research to assess the effects of environmental changes. For instance, researchers studying the impact of climate change on wildlife might collect data on animal populations, weather patterns, and habitat conditions over an extended period.

By analyzing this empirical data, they can identify correlations between climate fluctuations and changes in wildlife behavior, migration patterns, or population sizes. This empirical research is crucial for understanding the ecological consequences of climate change and informing conservation efforts.

Business and Economics

In the business world, empirical research is essential for making data-driven decisions. Consider a market research study conducted by a business seeking to launch a new product. They collect data through surveys , focus groups , and consumer behavior analysis.

By examining this empirical data, the company can gauge consumer preferences, demand, and potential market size. Empirical research in business helps guide product development, pricing strategies, and marketing campaigns, increasing the likelihood of a successful product launch.

Psychological studies frequently rely on empirical research to understand human behavior and cognition. For instance, a psychologist interested in examining the impact of stress on memory might design an experiment. Participants are exposed to stress-inducing situations, and their memory performance is assessed through various tasks.

By analyzing the data collected, the psychologist can determine whether stress has a significant effect on memory recall. This empirical research contributes to our understanding of the complex interplay between psychological factors and cognitive processes.

These examples highlight the versatility and applicability of empirical research across diverse fields. Whether in medicine, social sciences, environmental science, business, or psychology, empirical research serves as a fundamental tool for gaining insights, testing hypotheses, and driving advancements in knowledge and practice.

Conclusion for Empirical Research

Empirical research is a powerful tool for gaining insights, testing hypotheses, and making informed decisions. By following the steps outlined in this guide, you've learned how to select research topics, collect data, analyze findings, and effectively communicate your research to the world. Remember, empirical research is a journey of discovery, and each step you take brings you closer to a deeper understanding of the world around you. Whether you're a scientist, a student, or someone curious about the process, the principles of empirical research empower you to explore, learn, and contribute to the ever-expanding realm of knowledge.

How to Collect Data for Empirical Research?

Introducing Appinio , the real-time market research platform revolutionizing how companies gather consumer insights for their empirical research endeavors. With Appinio, you can conduct your own market research in minutes, gaining valuable data to fuel your data-driven decisions.

Appinio is more than just a market research platform; it's a catalyst for transforming the way you approach empirical research, making it exciting, intuitive, and seamlessly integrated into your decision-making process.

Here's why Appinio is the go-to solution for empirical research:

• From Questions to Insights in Minutes : With Appinio's streamlined process, you can go from formulating your research questions to obtaining actionable insights in a matter of minutes, saving you time and effort.
• Intuitive Platform for Everyone : No need for a PhD in research; Appinio's platform is designed to be intuitive and user-friendly, ensuring that anyone can navigate and utilize it effectively.
• Rapid Response Times : With an average field time of under 23 minutes for 1,000 respondents, Appinio delivers rapid results, allowing you to gather data swiftly and efficiently.
• Global Reach with Targeted Precision : With access to over 90 countries and the ability to define target groups based on 1200+ characteristics, Appinio empowers you to reach your desired audience with precision and ease.

Get free access to the platform!

Join the loop 💌

Be the first to hear about new updates, product news, and data insights. We'll send it all straight to your inbox.

Get the latest market research news straight to your inbox! 💌

Wait, there's more

07.05.2024 | 28min read

Interval Scale: Definition, Characteristics, Examples

03.05.2024 | 29min read

What is Qualitative Observation? Definition, Types, Examples

02.05.2024 | 32min read

What is a Perceptual Map and How to Make One? (+ Template)

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

LEARN ABOUT:  Social Communication Questionnaire

Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

LEARN ABOUT: 12 Best Tools for Researchers

With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

Create a single source of real data with a built-for-insights platform. Store past data, add nuggets of insights, and import research data from various sources into a CRM for insights. Build on ever-growing research with a real-time dashboard in a unified research management platform to turn insights into knowledge.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

The Best Email Survey Tool to Boost Your Feedback Game

May 7, 2024

Top 10 Employee Engagement Survey Tools

Top 20 Employee Engagement Software Solutions

May 3, 2024

15 Best Customer Experience Software of 2024

May 2, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Penn State University Libraries

Empirical research in the social sciences and education.

• What is Empirical Research and How to Read It
• Finding Empirical Research in Library Databases
• Designing Empirical Research
• Ethics, Cultural Responsiveness, and Anti-Racism in Research
• Citing, Writing, and Presenting Your Work

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

• Specific research questions to be answered
• Definition of the population, behavior, or   phenomena being studied
• Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

• Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
• Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools used in the present study
• Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Reading and Evaluating Scholarly Materials

Reading research can be a challenge. However, the tutorials and videos below can help. They explain what scholarly articles look like, how to read them, and how to evaluate them:

• CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
• IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
• Credo Video: How to Read Scholarly Materials (4 min.)
• Credo Tutorial: How to Read Scholarly Materials
• Credo Tutorial: Evaluating Information
• Credo Video: Evaluating Statistics (4 min.)
• Next: Finding Empirical Research in Library Databases >>
• Last Updated: Feb 18, 2024 8:33 PM
• URL: https://guides.libraries.psu.edu/emp

• Law and Method

• Submission of articles
• Notifications
• Methods courses
• Editorial team
• E-mail alerts
• October, 2016
• Stumbling Blocks in Empirical Legal Research: Case Study Research
• October 2016
• Artikel Stumbling Blocks in Empirical Legal Research: Case Study Research
• Artikel Statistical Analyses of Court Decisions: An Example of Multilevel Models of Sentencing
• Redactioneel Introduction Special Issue Stumbling Blocks in Empirical Legal Research

Citeerwijze van dit artikel: Lisa Webley, ‘Stumbling Blocks in Empirical Legal Research: Case Study Research’, 2016, oktober-december, DOI: 10.5553/REM/.000020

Dit artikel wordt geciteerd in

• Introduction

Such legal research employs an empirical method to draw inferences from observations of phenomena extrinsic to the researcher. Putting it simply, legal researchers often collect and then analyse material (data) that they have read, heard or watched and subsequently make claims about how what they have learned may apply in similar situations that they have not observed (by inference). 1 x For insight into the extent to which legal researchers undertake empirical research and the lack of clarity around empirical methods in law see Epstein, L. and King, G. (2002) ‘The Rules of Inference’ Vol. 69 No. 1 The University of Chicago Law Review 1-133 at 3-6, Part I. One such form of empirical method is the case study, a methodological term which has been used by some researchers to describe studies that employ a combination of data sources to derive in-depth insight into a particular situation, by others to denote a particular ideological approach to research recognizing that the study is situated within its real-world context. 2 x Yin, R. K. (2014) Case Study Research Design and Methods (5th edn.) Sage Publications, 12-14. It is consequently a flexible definition encompassing approaches to the data and the stance of the researcher, 3 x See Hamel, J. with Dufour, S. and Fortin, D. (1993) Qualitative Research Methods Volume 2 , Sage Publications, ch 1. but its malleability has led some researchers incorrectly to stretch the term to encompass any study that focuses on one or a restricted number of situations. 4 x See Gerring, J. Case Study Research: Principles and Practices , Cambridge, Cambridge University Press, 2007 at 6. For a further discussion see Gerring, J. ‘What is a case study and what is it good for?’ (2004) Vol. 98 No 2 American Political Science Review 341-354. This looseness in definition in a legal context may perhaps be linked to confusion as between teaching and research case studies; some traditions in legal education employ a teaching method known also as ‘case study method’ which operates quite differently from its research counterpart. For a discussion of the differences between teaching and research case studies see Yin, 2014, 20 and for a discussion of teaching case studies see Ellet, W., (2007) The Case Study Handbook: How to Read, Discuss, and Write Persuasively About Cases , Boston MA, Harvard Business Review Press; Garvin, D. A. (2003) ‘Making the Case: Professional Education for the World of Practice.’ (Sept–Oct) Harvard Magazine 56-65. This has resulted in concerns that the definition has been co-opted as a means to explain any small n 5 x ‘n’ (number) is used to denote the number of observations in the study, N is used to describe the total number within the population when n denotes the sample observed. empirical study that has a focus on a particular subject, time-frame or location, and further that this has led to poor quality empirical research in law. 6 x For a discussion of the state of empirical research in law see Epstein and King, 2002. This is perhaps unsurprising, as law programmes tend to be very strong at teaching lawyers how to source, interrogate and then draw valid inferences from legal data sources such as cases and legislation, but less adept in the context of other types of data (for example survey data, interviews, non-legal documents and/or observation). 7 x See Webley, L. (2010) ‘Part III Doing Empirical Legal Studies Research Chapter 38 - Qualitative Approaches to ELS’ in Cane, P. and Kritzer, H. (eds.) Oxford Handbook of Empirical Legal Studies , Oxford, Oxford University Press. Case study method usually involves an array of research methods to generate a spectrum of numerical and non-numerical data that when triangulated provide a means through which to draw robust, reliable, valid inferences about law in the real world. 8 x For a discussion about the differences between numerical (quantitative) and non-numerical (qualitative) data see Webley, id; Epstein and King, 2002, at 2-3; King,G., Keohane, R. O., and Verba, S ., Designing Social Inquiry: Scientific Inference in Qualitative Research , Princeton NJ, Princeton University Press, 1994 at 6. It is relatively underused in empirical legal research. This article aims to make a contribution to those new to the case study method. It will examine the purpose of and why one may wish to undertake a case study, and work through the key elements of case study method including the main assumptions and theoretical underpinnings of this method. It will then turn to the importance of research design, including the crucial roles of the academic literature review, the research question and the use of rival theories to develop hypotheses in case study method. It will touch upon the relevance of identifying the observable implications of those hypotheses, and thus the selection of data sources and modes of analysis to allow for valid analytical inferences to be drawn in respect of them. In doing so it will consider, in brief, the importance of case study selection and variations like single or multi case approaches. Finally, it will conclude with some thoughts about the strengths and weaknesses associated with undertaking research via a case study method. It will address frequent stumbling blocks encountered by researchers, as well as ways so as to militate against common problems that researchers encounter. The discussion is necessarily cursory given the length of this article, but the footnotes provide much more detailed sources of guidance on each of the points raised here. This article is an introduction to a case study method rather than an analytical work on the method.

• 1. Case Study Method: Purpose of a Case Study, Why Undertake One?

Case study method falls within the social science discipline and as such has scientific underpinnings. The case study examines phenomena in context, where context and findings cannot be separated. Case study design is also sometimes used to investigate how actors consider, interpret and understand phenomena (e.g., law, procedure, policy) and therefore allows the researcher to study perceptions of processes and how they influence behaviour, for example to understand judges’ sentencing choices in a Dutch police court. 9 x Mascini, P., van Oorschot, I., Weenink, D. and Schippers, G., (2016) ‘Understanding judges’ choices of sentence types as interpretative work: An explorative study in a Dutch police court’, (37) (1) Recht der Werkelijkheid 32-49. This may help to understand how laws are understood, and how and why they are applied and misapplied, subverted, complied with or rejected. This can flow back into the legal and policy making processes, court procedure, sentencing, punishment, diversion of offenders etc., and may have a high impact as a result. The conditions precedent for case study method have been succinctly explained by Yin as follows:

‘doing a case study would be the preferred method, compared to the others, in situations when (1) the main research questions are “how” or “why” questions; (2) a researcher has little or no control over behavioural events; and (3) the focus of study is a contemporary (as opposed to entirely historical) phenomenon.’ 10 x Yin, 2014: xxxi and further 16-17.

The key points to note here are that a case study is a real-world in-depth investigation of a current complex phenomenon. The research will take place in situ (rather than in the library or moot court room) where the researcher cannot control the behaviour of research participants.

The purpose of the study is to learn how or why something happens or is the way it is, and this is achieved by collecting and triangulating a range of data sources to test or explore hypotheses. 11 x Triangulation is the term used to explain that a research question is considered from as many different standpoints as possible, using as many different data types as possible to permit a holistic examination of the question to see which explanations, if any, remain consistent across all data sources. It caters for a wide range of modes of enquiry: the investigation may be exploratory (explore why or how something is the way it is), descriptive (describe why or how something is the way it is) or explanatory (determine which of a range of rival hypotheses, theories etc. explain why or how X is the way it is). 12 x Yin, 2014: 5-6. Some categorise case studies as those designed to be theory orientated, and those designed to be practice orientated. 13 x See Dul, J. and Hak, T. (2008) Case Study Methodology in Business Research , Oxford: Elsevier 8-11, 30-59. Thereafter the design scope is very broad; the data collected may be qualitative and/or quantitative, collected via a variety of methods, and the case study may be a single case or be made up of a small number of cases. The breadth of data collected may be illustrated by Latour’s ethnography of the Conseil d’Etat in France, which studied the connections between human and non-human actors to explore their relationship with ‘the legal’ and ‘the Law’ is assembled in that court context. 14 x Latour, B. (2010) The Making of Law: An Ethnography of the Conseil D’Etat , Cambridge: Polity Press. Case study method is a way of thinking about research and a process through which one seeks to produce reliable, fair findings. It can provide deep insight into a particular situation, whether particular in time, in location or in subject-matter. 15 x For a discussion of ethnomethodological aims to study practical life as experienced in context as an end in itself, as experience is subjective and situational, see Small, M.L. ‘‘How many cases do I need?’ On science and the logic of case selection in field-based research’ (2009) Vol. 10 (1) Ethnography 5, 18. It may allow for transferable findings in respect of the theoretical propositions/hypotheses being examined if not to a population as would often be the situation in much quantitative research. 16 x For greater insight on this point see Lipset, S. M., Trow, M. and Coleman, J.S. (1956) Union Democracy: The Internal Politics of the International Typographical Union , New York: New York Free Press at 419-420; Yin, 2014, 21. For a discussion of the problems inherent in aping quantitative terminology in qualitative work see, Small, 2009, 10, and at 19 for further reading on the logic of case study selection and further reading on extended case method. It aims to examine rival hypotheses, propositions, potential explanations previously advanced (exploratory study), or to test findings from a previous case study examining similar phenomena in a new instance (a replication or confirmation study). 17 x Gerring, 2007, 346.

As described so far it is a research method that appears to have a lot in common with experiments and tests of statistical significance. But case study method differs markedly from a big data survey or double-blind experiment in that it seeks explicitly a phenomenon in its natural environment and (in most instances) without means to control for variables, including the behaviour of any participants. 18 x Although note that there are some scholars who believe that case study method can include elements of experimental testing, for example, Gerring, J. and McDermott, R. (2007) ‘An Experimental Template for Case Study Research’ Vol. 51 No. 3 American Journal of Political Science 688-701. One such study in law that has been described by some, if not by the researchers themselves, as a case study did include an experimental design within the battery of methods employed see: Moorhead, R., Sherr, A., Webley, L., Rogers, S., Sherr, L., Paterson, A. & Domberger, S. (2001) Quality and Cost: Final Report on the Contracting of Civil Non-Family Advice and Assistance Pilot (Norwich: The Stationery Office). Experiments aim to control some factors so as to test hypotheses under different conditions, quantitative studies attempt to control for environmental factors through sampling techniques and data collection instrument design so as to minimise their biasing effects, but case study method does not involve control of the environment, or control for the environment, instead it aims to harness context and work within it. It examines in great detail one situation (referred to as a case or unit) or a very small number of situations, to use context as a means to particularise the findings. It also seeks to explain which elements of context may mean that some of the findings are applicable to other situations and if so under what conditions. A case study tells the researcher about the case and the extent to which previous explanations are sustained, in some instances it may also allow the researcher to make claims that some of the findings can be applied to another case or cases too, although this is heavily dependent on the research design and its execution. 19 x Campbell, D.T. Foreword in Yin, 2014 xviii. But it is rarely, if ever, a method that can be used by one to want to make universal claims. A case is not a proxy for a sample of a population in a survey, for example, it is a study of a phenomenon in itself rather than a means through which to view the whole world. Having said that, samples can be used to help select cases in a sound manner. 20 x Seawright, J. and Gerring, J. (2008) ‘Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options’ Vol. 61 No. 2 Political Research Quarterly 294-308.

Case studies are only one of a number of ways to undertake socio-legal or criminological research and it is important to give proper consideration to the full range of research methods prior to making a final decision to adopt a case study method. 21 x Yin, 2014: chapter 1. It may be better to employ a different one: legal history; doctrinal legal study (legal cases, legislation, regulatory documents); a policy study (policy documents, communiqués etc.); a statistical analysis (an analysis of the number of different types of legal cases that go before the courts, their key features and what role these play in chances of success for the plaintiff); a large-scale survey; stand-alone interviews; or an experiment in a simulated setting (asking lawyers to read through some scenarios and explain what advice they would give to a client in those situations). But a case study could employ a number of these methods in combination, so how then does one determine whether case study method is right for one’s study? It will largely depend on the nature of the research question to be answered and one’s appetite for undertaking in-depth research aimed at achieving thick description (detailed description of how or why something is as it is) 22 x For a discussion see: Ryle, G. (1949). The Concept of Mind . London: Hutchinson; Lincoln, Y.S. and Guba, E.G. (1985). Naturalistic Inquiry . Newbury Park, CA: Sage Publications; Holloway, I. (1997). Basic Concepts for Qualitative Research . London: Blackwell Science. and/or triangulated findings derived from a range of data sources that develop a new theory or test existing rival theories. It is an intensive study, it requires extremely good planning and design and a robust approach to data analysis too.

• 2. Case Study Method: Research Design

Research design is of paramount importance in achieving a successful case study, especially so given that the study focuses on one or a small number of situations and the researcher’s in-depth knowledge of and immersion within that case may lead more readily to confirmation bias than in some other forms of study. 23 x Confirmation or interpretive bias of data is something we all have grapple with, as the natural human tendency is to place more weight on evidence that confirms our view than on evidence that contradicts it. Strong research design can assist with counter-balancing this to some extent, including the transformation of any expected finding into a hypothesis that one then seeks to falsify rather than to confirm. Research design begins with the choice of research topic, usually then followed by a review of any relevant academic literatures (perhaps beyond the boundaries of one’s own discipline, for example sociology, criminology, political science) to determine an appropriate research question, noting all possible answers to the research question that are posited in the literature. 24 x See Yin, 2014, chapter 1 for more information on the role of the literature review. During this iterative phase the research question will be further refined, so that it may be articulated with precision, which is particularly important for much case study research as the link between the question and the chosen case or cases is usually explicit and explained, consequently a clear research question is considered by many to be an essential starting point to aid the selection of cases to be examined. The rival hypotheses, theories or propositions that may answer the question should, normally, be similarly delineated and clarified, those that remain plausible answers to the research question should be retained and be supplemented with any others that the researcher considers to be alternative viable explanations. Other approaches may be used that are more inductive than deductive, as in the case of many ethnographic case studies such as Latour’s, for example. This phase is an intellectually demanding one, but it sets the foundation for a strong study that is easier to execute at the point of data collection. The literature review also helps to ensure one is up-to-date, that one does not make the same mistakes that earlier researchers have reported as hazards, and to add a theoretical depth to one’s study that aids sophisticated analysis. It may also help to identify useful data collection methods and instruments too. And so time spent on the literature review may be very profitable. So far the discussion has been very general and therefore a little abstract. At this stage it may help to consider a hypothetical research proposal for case study research and work though it as the article progresses. The researcher in our hypothetical scenario is interested in undertaking research on recent reforms to the use of family mediation in the family justice system in England and Wales. She knows that it is now compulsory in most instances for the person who is initiating any court proceedings in a divorce to have participated in at least one mediation information and advice session with the aim of negotiating an outcome in relation to children, money and/or property prior to initiating proceedings in court. She is clear on the law and the procedural issues but not clear on how effective have been these changes, and this is her broad area of interest. After completing her literature review she understands that the key aims of the reforms were to reduce the number of cases going to court by increasing the number of cases that result in negotiated agreements between the divorcing spouses and in doing so to reduce the cost and the time involved in reaching outcomes in divorce cases, reduce the need for people to use lawyers in the negotiating process, to reduce acrimony between the divorcing spouses and to reduce the negative effects on children. Further, the reforms were intended to promote more durable outcomes between divorcing spouses that could be renegotiated effectively if the arrangements for the children needed to be updated to meet changing circumstances. But the researcher still needs to work these insights into a research question before making a final decision on whether a case study is the best method by which to conduct the research. The next sub-section will consider the framing of the research question, and will include examples of how our researcher may draft her question to maximise her chances of undertaking a great study on her area of interest.

A. The Research Question

The process of defining the research question may be a painful, frustrating one but it could also be creative too. It may be necessary to spend a considerable period of time reading the literature so as to narrow down the research topic or statement to a manageable, novel and/or important and scholarly question. 25 x For more assistance with legal research questions refer to Epstein, L. (1995) Studying Law and the Courts in Lee Epstein (ed) Contemplating Courts , Cong Q, 1, 3-5. Some argue it should also seek to address a real world problem, although that is a controversial component and suggests that knowledge for knowledge’s sake is not a legitimate aim. 26 x See King, Keohane and Verba, 1994, 15. The development of a research topic into a research question with reference to the academic literature is sometimes described as the phase in which the researcher has a conversation or dialogue with the literature. This dialogue grounds the study, it also informs the study design, including the case selection and data to be collected. The research question (a statement that ends with a question mark) is made up of two key elements: its substance, the topic or issue that you wish to address and the form of the question ‘who, what, where, how, why’. 27 x See Yin, 2014, at 11, and see further Campbell, J.P., Daft, R.L., Hulin, C. L. (1982) What to Study: Generating and Developing Research Questions (Studying Organizations) , Sage Publications, for further thoughts on research questions The substance of the research question is not simply the topic but the specifics of the topic – is your study to be a contemporary one or a historical one? In what context are you operating? What precisely are you endeavouring to study? The form of the question is also important: as indicated previously, case study method is considered to be better suited to research questions framed in ‘how’ or ‘why’ terms. Single case studies are considered to be an excellent means by which to uncover and understand the processes or mechanisms that influence particular variables (known as process tracing 28 x On process tracing see: Collier, D. ‘Understanding Process Tracing’ Vol. 44 Political Science and Politics 823-830 and George, A. L. and Bennett, A. Case Studies and Theory Development in the Social Sciences , Cambridge, MA, MIT Press, 2005. For an excellent insight into how this has been used in a legal and policy context with reference to changes in Georgia’s tax laws see Ulriksen, M.S, and Dadalauri, N. ‘Single Studies and Theory-testing: The Knots and Dots of the Process-tracing Method’ (2014) International Journal of Social Research Methodology 1- 17. ), why or how different variables are related to each other, for example what influences legislative change or policy formation on a given topic in a given country at a particular time. They are also a sophisticated means through which to test empirically and deductively the congruence of rival explanations (theories or propositions), ‘to what extent’ or ‘how’ and ‘why’ different theories are borne out by the data. 29 x See Blatter, J. and Haverland, M. Designing Case Studies , Basingstoke, Palgrave MacMillan, 2012 at 145 who consider process tracing involves inductive reasoning to build theory and congruence testing involves deductive reasoning to test theories. The form of the initial research question can confound some researchers who initially phrase their question as a ‘what’ question and as a result unnecessarily rule out case study method. Questions can often be reframed, for example: ‘what have the prosecutorial authorities in England and Wales done to integrate victims of domestic violence into the criminal justice process?’ may be rephrased as ‘how have the prosecutorial authorities in England and Wales integrated victims of domestic violence into the criminal justice process?’. At this stage our hypothetical researcher is faced with some choices: should she consider ‘how have the reforms to family mediation used in the divorce context affected the durability and suitability of post-divorce arrangements in England and Wales?’ This would focus on the agreements whether agreed or adjudicated, their longevity, the extent to which they could be made to work after the divorce and how any amendments to arrangements were sought. Alternatively, she could ask ‘how have the reforms to family mediation affected the way in which divorces are conducted in England and Wales?’ This would examine the steps people took so as to get divorced but may also consider the divorcing couples’ perceptions about the process to assist with examining the policy to reduce acrimony, it could also address how much time and money they spent in the process and it could also elicit data on how constructive was their relationship and negotiations subsequent to the initial agreement or adjudication. It could also bring in the role of lawyers and/or consider the children’s experience of the divorce process too. The research design would then follow the focus of the question. Case studies are much easier to design when the research question is expressed clearly, the theory is used to provide possible answers that may be explored or tested and the boundaries of the study are articulated. Some people find it helps to break down a draft research question into its substance and its form, and describe the purpose of the study in a couple of lines too, 30 x Epstein and King suggest a range of possible purposes, at 59, including: to explore something that has not previously been studied; to attempt to settle a debate that has been ongoing within the literature; to examine a well-considered question but in a new way; to collect and analyse new data to seek to confirm or refute previous findings; to analyse an existing data set in a new or better way to seek to confirm or refute previous findings or to develop new ones. and then compare the extent to which all three are congruent and precise before moving on to the next phase of the design process. We shall consider the important role of theory in the next sub-section.

B. The Theory

Case studies afford the opportunity to observe a sequence of events or factors, to evaluate which produce an outcome and why, 31 x Peters, B.G. Comparative Politics: Theory and Method , Basingstoke, Palgrave, 1998 at 14. and to do so in their natural environment. One of the challenges for legal researchers, less so for criminological or sociological researchers who are often trained more fully in this regard, is the need to engage with theory before moving on to the next stage of research design. By theory I mean the explanations that have been posited in the academic literature for why or how something is the way that it is, or claims that suggest relationships between certain things. 32 x But interestingly, stance, or more accurately epistemology is of less significance to this research method than to many others. Case study method links the research question, research design, analysis and logic of inference to such an extent that is can accommodate a range of epistemological traditions from the realist to relativist/interpretivist. And thus scholars who consider that there are facts independent of our interpretation of them (in essence, hard facts operating in an objective reality) and scholars who consider all ‘facts’ to be local interpretations constructed through our own lenses, are able to operate within a case study framework. Having said that their choice of data sources, and their approach to data generation and analysis may well vary considerably. In this context a theory is a relatively precise speculative answer to a research question, which may have been developed by undertaking a study or by analysing others’ studies (a meta-analysis). And theories can be converted into hypotheses when considered in the light of a new research question. The use of theory is exemplified by Uriksen and Dadalauri’s case study on tax policy reform in Georgia which aimed to answer why and how Georgia initiated and managed to implement quite radical and substantial tax reforms between 1991 and 2005 and in doing so sought to interrogate theoretical explanations about the nature of policy reform in developing countries and further to develop a model that could be tested in other post-Soviet states. 33 x Ulriksen and Dadalauri, ibid. See further Dadalauri, N. Tax Policy Formation and the Transnationalizationof the Public Policy Arena; A Case Study of Georgia , Aarhus, Politica, 2011) Georgia was selected as a crucial case. The reasoning for this and also for the methods employed in this study are elegantly set out in the article cited above. In our hypothetical case study it may be possible, for example, to test the theory that mediated agreements lead to less acrimonious relationships between the divorced couple than do lawyer negotiated agreements. One could examine the theory that family mediation is a cheaper and faster alternative to lawyer negotiated settlements and that those mediated agreements are more durable and better suited to family circumstances. In doing so, one may test existing theories and/or to develop a new theory. Or one could test in the chosen context a single theory that is dominant or particularly novel. Our researcher could undertake further reading of the literature to add to these hypotheses and to refine them and eliminate those that are no longer plausible in the light of more detailed investigation. This is known as setting out ‘priors’, prior explanations raised in the academic literature. 34 x On the importance of the identification of priors see Beach, D. and Pedersen, R.B., ‘What is process tracing actually tracing? The three variants of process tracing methods and their uses and limitations’ Sept 1-4 2011 The American Political Science Association Annual Meeting , Seattle, WA as cited by Ulriksen and Dadalauri, ibid; and further Gerring, 2007 ibid. The researcher’s next task would be to consider what she would expect to observe in the study, were any of these hypotheses true (the observable implications of the hypotheses), for example, our researcher would expect to see that divorces conducted using family mediation would be settled through mediation, that the settlement process would cost less and be concluded quicker than in lawyer-led divorces, and that those that used family mediation would be more able to engage in constructive dialogue post-divorce and to renegotiate arrangements in respect of children without the need to resort to lawyers or to the courts. The researcher may also draw up a hypothesis that the agreements would be more durable and the outcomes for children more positive. This pre-emptive delineation of as many possible observable implications, and how they could be measured, would allow the researcher to plan how to conduct the study and to adopt an appropriate design more likely to lead to a robust answer to the research question. 35 x For more assistance on extracting observable implications and considering their measurement see Epstein and King, 2002, 70-76. But one needs to be able to articulate the theory converted into hypotheses with clarity, in order for an observation protocol to be developed. Further, it helps the research design if the researcher is able to pose rival theories or explanations so as to design the study to test for plausible alternative explanations too. For example, one of the rival theories in the family mediation study is that the kind of people who use mediation through to conclusion and the kind of people who either refuse to do so or who drop out without reaching a settlement are different, and those drawn to mediation are more consensus driven and better able to communicate with their spouse than are those who do not. This rival hypothesis would alert the researcher to the need to design the study to examine those who do conclude mediated agreements and those who do not in order to analyse this rival claim. A case study allows for the examination of complex interrelationships between variables in situ, and the theory helps to identify what those variables are.

C. The Selection of the ‘Case’ or ‘Cases’

Definitionally this is when it gets somewhat complicated, as ‘case’ can easily become confused with a ‘legal case’ and further a ‘case‘ can sometimes be confused with the same word used in a different context in quantitative research, a case meaning a single observation or a single data point. This has led some, such as Gerring, to suggest that it may be more accurate to refer to a case study as a unit study so as to underline that this type of study examines multiple things within one unit rather than examining one data source in one context. 36 x See Gerring, 2004, 342. See further King, Keohane, and Verba, 1994, 76-77. The selection of the case or cases is a profound one in any study of this kind. A case may be selected because it is critical to the research question, it is typical, atypical, it provides a longitudinal opportunity (study over time), or it is revelatory meaning that it allows insight where previously this has not be possible. 37 x See Yin, 2014, 51. The nature of the case, its boundaries and features and why it was selected should be set out clearly. 38 x Gerring, 2004, 344. There are often difficulties in establishing the boundaries of the case, the phenomenon under study, and the context that provides a background to the phenomenon but is not itself the object of enquiry. Gerring articulates this as the formal case (the phenomenon) and the informal cases (the penumbra of phenomena which are the context but which will need to be explored in a less formal way so as to distinguish the boundaries of the formal case). The informal units are peripheral, but may have bearing on the formal unit or case, and by considering these informal units at the beginning of the study, and close to its conclusion it will help the researcher to work out what is particular about the unit, and what is transferable to other units. The selection of the case should be guided by the extent to which this location in space, focus and/or time lends itself to construct validity, internal validity, external validity and reliability of design in respect to the research question. 39 x See Yin, 2014, chapter 2. Case studies are particularly prone to selection bias, meaning that the case is selected on the basis of the dependent variable rather than on the basis of the independent variable – selected because of an effect that has been noted rather than its cause when the nature of the cause is the real object of many ‘how’ or ‘why’ research study questions. 40 x See Geddes, B. Paradigms and Sand Castle: Theory Building and Research Design in Comparative Politics , Ann Arbor, University of Michigan Press, 2005. For example, in a legal context if we wanted to examine rival explanations for how a particular legislative reform, for example the introduction of same sex marriage in England and Wales, has had an impact on community cohesion between different religious and community groups, it may seem, on the face of it, a good idea to select a town like Brighton with a vibrant LGBT community as the case to be studied. After-all, the uptake of same-sex marriage has been very high in Brighton and so it could be considered to be a key site of study. However, Brighton is well known as a LGBT friendly town and people drawn to live there would tend to be very positive about the introduction of same-sex marriage. If the reason for the study was to consider whether tensions have emerged between community and religious groups with different views on marriage, then Brighton would not likely give much opportunity to examine these issues. It was LGBT friendly before the reform and it continues to be. And community and religious groups have worked well together before and after the change in the law. By selecting the case on the basis of the effect of the changed legal landscape, the high numbers of gay and lesbian marriages, we may have selected a case that is atypical or simply a poor unit within which to view the causes or the influences that led to the legislative reform. Researchers are prone to make this mistake when undertaking a deductive study to test the congruence of rival hypotheses in a context where they have insufficient knowledge about the independent variable (the causes) that gave rise to, say, the change in the law. There may be other factors to consider too: in our family mediation study, the researcher may choose to steer away from London as the case study location, if she is interested in ‘typical’ divorces given that London has a much greater than average number of high net worth divorces that include very large sums of money and property portfolios, in addition to many divorces involving non-British couples who married abroad. A solid grasp of the literature can help to alleviate this possibility of incorrect case selection along with detailed consideration of the relevant features of a range of possible case studies prior to final selection. Does selecting a multiple-case study limit the likelihood of such problems, further should the study be at one point in time or a repeated measure at different periods of time? A case study may be designed so as to allow for cross sectional analysis between two or more cases, and further a temporal variation may be introduced into this form of analysis too. Our researcher could study family mediation over time: the same divorces pre, during and post settlement and then later again to examine durability. Single, multiple or cross-sectional case studies often serve different purposes. Multiple case studies are more likely to be used when the causal relationship between an independent and dependent variables are being analysed, so that the interaction of the variables in different environments can be examined comparatively in different contexts (in an experimental protocol one would be able to manipulate the conditions so as to test the variables and thus the different hypotheses). For example, if the study was examining the relationship between violent crime rates and criminal justice sentencing policy to examine whether tougher criminal penalties for violent crime lead to a reduction in violent crime rates, and if tougher criminal penalties for lesser offences led to greater imprisonment levels and greater recidivism including an escalation of violent crime, then a multi-jurisdictional case study may allow for a better assessment of those by permitting different combinations of variables to be compared as against each other. In our family mediation study, the researcher may choose to use London as a crucial case study (with its unusual profile of divorcing spouses with a very wide range of asset values) alongside a more typical rural and a more typical urban location to consider the hypotheses under different conditions. However, it may also be possible to test the hypotheses in a single case study by charting the relationship between the variables over time, with particular attention being paid to the points in time when sentencing policy changed or crime rates dropped or raised, or when family mediation was first introduced, when it became established as a compulsory part of the system. 41 x But the difficulty with multi case studies is that specified conditions or features within individual cases may have more influence on the variables being studied than the variables that one are analysing across the studies. This may lead one to draw erroneous conclusions about causality. This single case study also illustrates the independent variable problem: to what extent are violent crime rates and/or sentencing policy more likely attributable to other societal changes evident at different points in time than each other? Without knowledge of this it is difficult to proceed. Sometimes the extent to which a case study is referred to as single or multiple is a matter of nomenclature, for example, Elliott and Kling’s study 42 x Elliott, M. and Kling, R. ‘Organizational Usability of Digital Libraries: Case Study of Legal Research in Civil and Criminal Courts’ (1997) Vol 48 (11) Journal of the American Society for Information Science 1023-1035. on the organisational usability of digital libraries, a case study of legal research in civil and criminal courts, could be described as a single case study (as in their study) because it addresses digital libraries in one context – legal research in courts – it is also geographically bounded to the Los Angeles County, but data is collected from a number of courts and thus it could be argued to be a multiple case study if each court were considered to be a case. The important distinction, however, is how that data are treated: if the data are pooled and analysed as a single unit then the case study is generally considered to be a single unit or single case study, if the data are analysed comparatively as between the sites of collection then it would generally be considered to be a multiple unit or multiple case study. Where comparisons are being made over time but within a unit then the terminology is often that of a single unit as data is both compared and pooled too. Single case study research is considered to be an excellent vehicle for exploratory and developmental research (as evidenced by the Georgia tax policy study and Dnes analysis of the nature of a particular type of contract – franchise contracts in the UK 43 x See Dnes, A.W. ‘A Case-Study Analysis of Franchise Contracts’ (1993) Vol. 28 Journal of Legal Studies 367-393. or Latour’s Conseil d’Etat study mentioned above 44 x See Latour, B. (2010) above. ), confirmatory research necessitates a design that allows the researcher robustly to test a small number of hypotheses forensically and it may be advisable to consider a multiple case study method to achieve this aim. 45 x Although note that Gerring, 2004, at 347 indicates that a single case study may credibly make causal claims, if, for example, the case has been selected as it is particularly representative of others or it is a critical or crucial case, see further: Eckstein, H. (1975) ‘Case Studies and Theory in Political Science’ in Regarding Politics: Essays on Political Theory, Stability, and Change, Berkeley: University of California Press, 1992). The family mediation case study conducted in London and a more typical urban and rural area is a good example of this, given that the types of divorce cases are likely to be quite different, the context is also different too, and so cost, duration, durability and acrimony could be tested under different conditions to see whether they held true in all conditions or were context or divorce type dependent. Sometimes researchers are inclined to use a multiple case study approach in the expectation that more cases (units) will provide more data and more comparable data that can be used to derive robust findings. However, data collected across multiple case studies is less rather than more likely to be comparable as the conditions within the case study cannot be manipulated or controlled by the researcher and yet environment is expected to have an impact on the data. 46 x Gerring indicates that researchers tend to face the choice between knowing more about less or less about more, 2014, 348. Where more than one case is selected, each subsequent addition should provide a more complete and accurate picture in respect of the research question, instead of attempting to provide greater representativeness (as indicated above, this is not the purpose of case study method). 47 x See Small, 2009, 24-26. If one wishes to make comparisons between case studies it is important to adhere closely to comparative methodology in the definition of the cases to be selected, the analysis of the relevant similarities and differences between those cases, the data to be collected, compared and why, and the likely limitations of the cross unit comparison. 48 x See Gerring, 2004 at 348. For guidance on comparative methodology in a legal context see: Van Hoecke, M. (2004) Epistemology and Methodology of Comparative Law , Portland, Oregon: Hart Publishing. Multiple cases studies are more difficult to accomplish successfully, and it is advisable to work out clearly what each of the case studies will contribute to answering the research question before finalising those to be the subject of the enquiry. The most appropriate design will be dependent on the research question selected and the hypotheses or propositions under investigation through the case study method. 49 x For a detailed discussion see Gerring, 2004 at 343.

D. The Selection of Data Sources, Data Generation and Collection

Strong research design logically links the research question(s) with the hypotheses, with the data generation and collection methods, which in turn should be logically linked with the data analysis methods employed too. By now the researcher is likely to have a very good idea of the types of data that may be relevant to the study (derived from documents, people, extant statistics, other artefacts like images), 50 x Yin sets out six sources of evidence: documents, archival records, interviews, direct observations, participant observation, physical artefacts, Yin, 2014, 105-118, and four principles of data collection: multiple sources of data; creation of a case study database; maintain the chain of evidence; exercise care when using data from electronic sources, at 118-129. having identified the substance, form and purpose of the question, the rival hypotheses that may contribute to answering the question, and the observable implications of those hypotheses and how they may be measured. Our researcher having drawn her broad question as ‘how have the reforms to family mediation affected the way in which divorces are conducted?’ and narrowed down the case study to a geographic location(s) or a type of divorcing couples or divorce context, will have considered the possible data sources as including the spouses, their children, the family mediators and lawyers, court files, mediation and lawyer negotiated agreements, official statistics and more. And so it should be possible to chart how the data collection and data analysis methods all fit together so as to allow the observable implications to be explored, the hypotheses proved, amended, or disproved and a rounded, reliable answer to the research question be achieved. But this design phase may also go beyond the identification and selection of data sources, it requires choices to be made about how data will be collected and measured. 51 x See Yin, 2014, chapter 3 for more information on what one needs to do before data collection begins, and chapter 4 on data collection itself. Each choice that is made, consciously, or unconsciously, will have an effect on the data that is captured and the reliability and validity of that data. And this in turn will affect the outcome of the study. Consequently, the design should be scrutinised to uncover the biases that may become entrenched within it, the study redesigned where necessary to eliminate or limit bias and any remaining biases be taken into account during the later analysis and reporting phases. 52 x See Small, 2009, 12-15. This will require a degree of reflection on data type (strengths and weaknesses in allowing observable implications to be explored), data selection (all data, if not all then what process is being used to select it and how may that skew the findings, known as selection bias); data collection (how is the data being derived, and is it raw data or is it material that requires a judgement to be made, for example how will we measure ‘satisfaction or ‘acrimony’ or ‘durability of an agreement’, how reliable and valid is the data collection instrument 53 x For a discussion of reliability and validity in measurement see Webley, 2010 and further Epstein and King, 2002, 80-99. ) and later too data analysis. It is also worth piloting each data collection exercise with a small number of observations so as to allow defects to be worked out, and experience in the field to allow for redesign too. And then one should collect as much data on each of the possible observable implications as is practicable, including data of different types generated or collected via different methods so as to allow for triangulation in respect of each hypothesis. For example, in Elliott and King’s study they collected data via observation, participant observation and interviews, analysed court documents and legal technology documents; 54 x Elliott and Kling, ibid at 1025. in Dnes’ study the data included franchise agreements and contracts, financial accounts and other financial data, interviews; 55 x Dnes, ibid at 369-370. and in Dadalauri’s study data were derived from primary sources (policy proposals, experts’ recommendations and the minutes of parliamentary sessions) and secondary sources (reports, media briefs, statistical sources) plus from semi-structured interviews with key actors in the policy process. 56 x Ulrikesn and Dadalauri, ibid at 13. Case study method necessitates a measure of flexibility in research design to allow for new knowledge to shape and improve the starting design, but that does not reduce the need for a robust design plan at the inception of the study. The design needs to be scrupulously documented, including challenges faced and amendments made so as to aid others to analyse the validity of the research design and to assess the extent to which the study findings are reliable and robust. 57 x For a discussion see King, Keohane and Verba, 1994, 12. Epstein and King suggest that legal scholars give the same attention to the recording, storage and analysis of data as they would expect of the police and prosecutors when securing the chain of evidence in a criminal case. 58 x Epstein and King, 2002, 24. And it is to data analysis that we now turn.

• 3. Case Study Method: Use of the Data, Inferences and Finding Meaning

Case study findings are reached through a process of logical valid inferences regardless of whether the data collected and analysed is qualitative, quantitative or both. 59 x For a discussion on this point see King, Keohane and Verba, 1994, chapter 1 ‘The Science in Social Science’. But first the data must usually be described in summary form, before being subjected to further analysis to consider what the data indicates about the various hypotheses and their observable implications in this case context. Subsequently, it is possible to attempt to derive descriptive inferences that suggest what these data on observable instances indicate about non-observables ones, in other words what findings one considers to be transferable to a non-observed context. The analysis may also allow for causal inferences to be made, that explain what effects would be expected to occur if certain conditions were fulfilled in this or another context. This is not dissimilar to data analysis in other types of empirical legal research and therefore it is considered only briefly here. However, case study method is structured with triangulation of data at the fore, allowing the researcher to reach robust findings reached by integrating analysis from multiple data points gathered using different methods. This section will briefly address data analysis, the drawing of inferences and the importance of demonstrating one’s working out, in turn.

A. Data Analysis

The first stage of data analysis is often validly to summarise the data collected in the light of the research question and hypotheses and anticipated observable implications, to summarise the numbers (mean, median, mode, standard deviation, range) and to summarise the text (for example, categorise and consider relationships between categories, or code and consider the frequency or codes). 60 x See Epstein and King, 2002, 25-29 for more information on quantitative data description. Different types of data will often be analysed using different methods or traditions, as illustrated by the way in which legal cases are analysed according to traditions accepted by lawyers, which is distinct from legal analysis of legislation, and policy analysis of policy documents: survey data would be analysed statistically, text based data (interviews, documents etc.) via the mode of analysis selected to interrogate and derive meaning from language, for example via grounded theory method, thematic coding, content analysis, hermeneutics etc. 61 x For a discussion of the different methods of text based data analysis see Yin, 2014, chapter 5 or Webley, 2010. There are a range of general strategies open to the researchers, some of which focus on the theoretical propositions, others aim to develop thick description, others still examine plausible rival explanations. 62 x See Yin, 2014, 136-142. Findings are considered robust where they are evidenced via multiple stands of data and its analysis. The use of multiple data sources to test each hypothesis allows the researcher to build up a thoroughly nuanced picture of the extent to which each hypothesis is sustained, needs to be refined, or rejected. The analysis will be conducted in the light of the research question parameters and also the hypotheses being examined by the research, as exemplified by the discussion in the Georgia tax policy case study. 63 x Ulriksen and Dadalauri, ibid. This process is likely to be iterative, in that data will often be analysed as one phase of data collection is complete and any lessons learned from that may lead to some reframing of the research question, reconsideration of the hypotheses, and amendments to the next phase of data collection yet to begin. The key is that, as with all social science methods, amendments to the question and methods, the analysis of the data and the inferences drawn from the data should be publicly explained and in sufficient detail so that they are replicable by others on the basis of the information provided in the write-up of the study; King, Keohane and Verba remind us that inferences lead to uncertain conclusions – inferences are not proven facts, they are propositions being advanced that are available to be tested by others. 64 x King, Keohane and Verba, 1994, 8. Conclusions remain tentative until replicated validly and consistently. The science and the rules of inference are important in allowing us to judge the validity and reliability of the findings, and these are closely interwoven with the research design and execution of the study.

B. Inferences

Case studies are often considered to be more useful when seeking to derive descriptive rather than causal inferences, as the researcher is not able to manipulate the environment so as to test propositions in such a way as to be sure that causal relationships have satisfactorily been established. Descriptive inferences are ‘the process of using the facts we know to learn about facts we do not know’ , by describing something that has been observed and inferring under what circumstances a similar pattern or occurrence may occur in a carefully defined unobserved situation. 65 x Epstein and King, 2002, 29. For example, if in our hypothetical family mediation study we learned that greater numbers of the divorcing clients who we interviewed/observed before the introduction of the compulsory mediation information and assessment meetings were aware that there was state funding available for family mediation, compared with the divorcing clients who we interviewed/observed after the introduction of these meetings then we may infer that this finding was likely to apply to divorcing clients outside our observed group too (all other things being equal). We do not know for certain that is accurate, as we only have data from our study participants, but our description of our findings has led us to infer something about those outside our observation group. Many doctoral candidates and early career academics baulk at the suggestion that descriptive inference is a valuable mode of analysis, as they associate ‘descriptive’ with the less positive feedback that they may have received in earlier work. But the pursuit of descriptive inferences is not a low-level aspiration in a context in which little is known about the case under scrutiny. Descriptive inferences allow for categorisation of findings which may lead on to further theory building and theory testing, categorisation goes to the heart of analysis development. So our finding above begs the question ‘why is this so?’ and we could either extend our study to answer this sub-question, or leave that for a later study. In some instances the inferences a researcher wishes to draw may be causal ones that infer an effect that will be caused by a set of defined factors occurring together. As an example, in our family mediation study we may wish to examine whether family mediation is more likely to be successful for couples with relatively similar educational backgrounds, medium to high incomes with both spouses in full-time employment, when compared with those who have unequal educational backgrounds, incomes and job-statutes and with low incomes. Where causal inferences are the point of the study, it may be possible to develop these with a well-chosen cross-case multi-case case study design. However, a causal inference first requires the identification of a causal mechanism (the process by which dependent variable A is affected by independent variable B, for example the causal mechanism for a defendant in the UK to be released from pre-trial detention (variable A) is a bail hearing in court (variable B)). 66 x See Epstein and King, 2002, 34-37 for help distinguishing between causal mechanisms and variables and causal effects. Case studies are often a really good means by which these mechanisms, or processes, may be uncovered – known as ‘process tracing’ whereby the researcher charts in detail the relationships between two or more variables and explores these connections to deduce those that are causal and those more likely to be coincidental. 67 x See Gerring, 2004, 348 and further Roberts, C., The Logic of Historical Explanation, Pennsylvania State University Press 1996, 66. Further, a single case study may allow a researcher to interrogate extant explanations that suggest causal implications, in other words to test predictions about what will happen in particular situations (assuming those situations are observable as part of the case study). This is known as ‘pattern-matching’. 68 x See Gerring, 2004, 348 and further Campbell, D. T. [1975] “‘Degrees of Freedom’ and the Case Study” in E. Samuel Overnman (ed) Methodology and Epistemology in Social Science , Chicago, University of Chicago Press: 1988, 380. This is where clarity about the purpose of the study becomes particularly important, as certain conditions will need to have been built into the research design for some analytical techniques. 69 x Yin, 2014, 142-168, provides five different analytical techniques: pattern-matching; explanation building; time-series analysis; logic models; and cross-case synthesis and suggests that after this phase the researcher will likely move on to work through all plausible alternative conclusions to examine whether the most likely conclusion is the only conclusion. The study will need to be designed with a very clear and narrow focus to achieve its aims. As indicated above, case studies may be entirely self-contained studies that provide in-depth knowledge of a single unit of analysis, but more often than not the researcher will wish for those findings to be considered applicable to situations that she/he has not observed. The challenge is to explain which findings are particular to the case study and which elements of the findings are relevant beyond the case study’s boundaries. 70 x Gerring, 2004, 345. It may be difficult to define this with precision, but where there is ambiguity it is safer to over explain and to over report the ambiguity and the possible range of inferences and their limitations rather than to over simplify and obfuscate the difficulty in reaching definitive findings. 71 x See Gerring, 2004, 346. Legal researchers are sometimes criticised for being vague in their explanations of the target of their inferences (to which other unobserved situations do these findings apply, and why?), or worse still their claims in the absence of evidence to prove that their inference is generalizable to a wide variety of situations. 72 x Epstein and King, 2002, 31. This may be a function of lawyers’ professional training as advocates, who in presentation would seek to persuade others to accept their position and who would gloss over inconvenient precedents. But lawyers are also trained to be forensic in seeking out the weaknesses of their arguments as well as those of their opponents and by harnessing these skills in the presentation of their case study findings; they should be able to display the highest standards of scientific reporting. Some of the ambiguities associated with inferences may be avoided if, as Gerring suggests, the scholar specifies clearly which propositions apply to which novel circumstances and exhibit and explain the evidence upon which this contention is based. 73 x Gerring, J. (2001) Social Science Methodology: A Criterial Framework , Cambridge, Cambridge University Press, 90-99. In other words, do the findings relate only to this case, are they intended to relate more broadly to similar cases and if so what marks out other situations as similar? Is similarity about time frame, location, a certain set of markers such types of participants, socio-economic, legal or political factors? And what is one’s evidence in support of this? The burden of proof always rests with the researcher. We shall turn to this in the next sub-section.

C. Reporting Findings

It can be challenging to know how to report one’s findings in an article or thesis, which is unsurprising when one considers that little attention is paid to this aspect of scholarship on doctoral legal programmes in many jurisdictions. 74 x See further Epstein and King, 2002. For assistance in how to report on cases studies and writing up and presentational considerations, including audience and purpose, see Yin, 2014, chapter 6. The rule is that one must provide as much detail as possible, at least enough to allow someone else to be able to replicate the study using only the information provided. Further, there needs to be sufficient discussion of the decisions taken, challenges faced and the consequent limitations of the findings so as to allow others to evaluate the reliability of one’s findings. As King et al. note: report uncertainty, be sceptical about causality, and consider rival hypotheses. 75 x King et al, 1996, 30-33. The process of interrogating one’s own decisions and inferences and reporting on them in full in the article or thesis may allow one to avoid the invidious charge made of many other legal scholars’ work. 76 x Epstein and King, 2002, 6-7. One suggestion is that legal scholars may wish to look for the weakest link in their chain of reasoning, something which lawyers are trained to do in a legal context, and then estimate how certain they are of their findings taking that weakness into account. 77 x Epstein and King, 2002, 50. However, other aspects of our professional training sometimes come into conflict with this approach: a research study is not an act of advocacy, and training as a lawyer may derail the process of empirical enquiry when lawyers unconsciously act for the client in their head and seek to persuade the outside world that their client’s view is a valid one, rather than to act as a legal social-scientist and demonstrate to other social scientists the extent to which their findings are valid, robust, reliable, and subject to limitation. 78 x See further Miller, A. S. ‘The Myth of Objectivity in Legal Research and Writing’ (1969) Vol. 18 Catholic University Law Review 290. For example, a researcher who is more in favour or less in favour of family mediation may inadvertently confirm their stance and steps need to be taken to lessen this risk. This role conflict is particularly problematic given that empirical legal research may lead to legal reform affecting large sections of the population and the findings confidently exhorted in the literature may be used to justify policy changes. 79 x Epstein and King, 2002, 8-10. Further, even if the research were not to be read outside of an academic environment, it is incumbent on all academics to produce research that is reliable and robust, lawyers are quick to critique legal scholarship that had been poorly executed and socio-legal scholarship should be treated no differently.

• 4. Conclusions: Why (Not to) Use Case Study Method?

Case study method is a powerful and engaging approach to research that has real utility in socio-legal and criminological research even if it has to-date been relatively little used. Our reticence to use it may be explained by the need for a researcher to be sufficiently adept with a range of social science research methods; (non-legal) empirical methods have historically had little treatment within undergraduate legal courses and relatively little attention even at a postgraduate level. 80 x Genn, H., Partington, M, and Wheeler, S. (2006) Law in the Real World: Improving Our Understanding of How Law Works Final Report and Recommendations , London: The Nuffield Foundation. Further, doctoral supervisors may feel inadequate to the task of supervising doctoral students proposing to undertake research through case study method and steer them towards a more standard mixed method approach such as a survey coupled with some interviews, or away from non-legal empirical methods altogether. But with some training, and a high degree of planning it is perfectly possible to undertake a good quality case study in a legal context and we can learn much from them. They are also an ideal means to focus on the particular and yet to draw analytical inferences to similar contexts too, something which lawyers are trained to do throughout their studies and a skill which they can bring to bear on a broader range of data than they otherwise often do. However, case study method is far more than focusing on a single situation, or ‘case’, it is far more than providing a temporal or physical boundary to our research endeavour. It requires us to adopt a structured and reflective approach to research design in many instances, to consider pre-emptively possible explanations (hypotheses) and rival propositions and to engage with theory at an early stage in a study. In a legal context case studies are generally, if not exclusively, more effective when: seeking to make descriptive rather than causal inferences; examining issues in depth rather than broadly and when the researcher is seeking to examine multiple sources of data so as to make comparisons within a case rather than between multiple cases. Further, they are also often more effective for seeking causal mechanisms rather than causal effects; for research that is exploratory rather than confirmatory; and when variations within the case selected are important for the study of the phenomenon. 81 x Gerring, 2004, 352. They are extremely useful when analysing how those involved in law and policy-making, the application of legal rules and procedures perceive these processes, how they react to them and how this influences the effectiveness of those rules, processes and procedures. The research process is an iterative and creative one that engages lawyers’ considerable analytical skills. As such case study method is worthy of a larger presence within the legal academic empirical tool-kit.

1 For insight into the extent to which legal researchers undertake empirical research and the lack of clarity around empirical methods in law see Epstein, L. and King, G. (2002) ‘The Rules of Inference’ Vol. 69 No. 1 The University of Chicago Law Review 1-133 at 3-6, Part I.

2 Yin, R. K. (2014) Case Study Research Design and Methods (5th edn.) Sage Publications, 12-14.

3 See Hamel, J. with Dufour, S. and Fortin, D. (1993) Qualitative Research Methods Volume 2 , Sage Publications, ch 1.

4 See Gerring, J. Case Study Research: Principles and Practices , Cambridge, Cambridge University Press, 2007 at 6. For a further discussion see Gerring, J. ‘What is a case study and what is it good for?’ (2004) Vol. 98 No 2 American Political Science Review 341-354. This looseness in definition in a legal context may perhaps be linked to confusion as between teaching and research case studies; some traditions in legal education employ a teaching method known also as ‘case study method’ which operates quite differently from its research counterpart. For a discussion of the differences between teaching and research case studies see Yin, 2014, 20 and for a discussion of teaching case studies see Ellet, W., (2007) The Case Study Handbook: How to Read, Discuss, and Write Persuasively About Cases , Boston MA, Harvard Business Review Press; Garvin, D. A. (2003) ‘Making the Case: Professional Education for the World of Practice.’ (Sept–Oct) Harvard Magazine 56-65.

5 ‘n’ (number) is used to denote the number of observations in the study, N is used to describe the total number within the population when n denotes the sample observed.

6 For a discussion of the state of empirical research in law see Epstein and King, 2002.

7 See Webley, L. (2010) ‘Part III Doing Empirical Legal Studies Research Chapter 38 - Qualitative Approaches to ELS’ in Cane, P. and Kritzer, H. (eds.) Oxford Handbook of Empirical Legal Studies , Oxford, Oxford University Press.

8 For a discussion about the differences between numerical (quantitative) and non-numerical (qualitative) data see Webley, id; Epstein and King, 2002, at 2-3; King,G., Keohane, R. O., and Verba, S ., Designing Social Inquiry: Scientific Inference in Qualitative Research , Princeton NJ, Princeton University Press, 1994 at 6.

9 Mascini, P., van Oorschot, I., Weenink, D. and Schippers, G., (2016) ‘Understanding judges’ choices of sentence types as interpretative work: An explorative study in a Dutch police court’, (37) (1) Recht der Werkelijkheid 32-49.

10 Yin, 2014: xxxi and further 16-17.

11 Triangulation is the term used to explain that a research question is considered from as many different standpoints as possible, using as many different data types as possible to permit a holistic examination of the question to see which explanations, if any, remain consistent across all data sources.

12 Yin, 2014: 5-6.

13 See Dul, J. and Hak, T. (2008) Case Study Methodology in Business Research , Oxford: Elsevier 8-11, 30-59.

14 Latour, B. (2010) The Making of Law: An Ethnography of the Conseil D’Etat , Cambridge: Polity Press.

15 For a discussion of ethnomethodological aims to study practical life as experienced in context as an end in itself, as experience is subjective and situational, see Small, M.L. ‘‘How many cases do I need?’ On science and the logic of case selection in field-based research’ (2009) Vol. 10 (1) Ethnography 5, 18.

16 For greater insight on this point see Lipset, S. M., Trow, M. and Coleman, J.S. (1956) Union Democracy: The Internal Politics of the International Typographical Union , New York: New York Free Press at 419-420; Yin, 2014, 21. For a discussion of the problems inherent in aping quantitative terminology in qualitative work see, Small, 2009, 10, and at 19 for further reading on the logic of case study selection and further reading on extended case method.

17 Gerring, 2007, 346.

18 Although note that there are some scholars who believe that case study method can include elements of experimental testing, for example, Gerring, J. and McDermott, R. (2007) ‘An Experimental Template for Case Study Research’ Vol. 51 No. 3 American Journal of Political Science 688-701. One such study in law that has been described by some, if not by the researchers themselves, as a case study did include an experimental design within the battery of methods employed see: Moorhead, R., Sherr, A., Webley, L., Rogers, S., Sherr, L., Paterson, A. & Domberger, S. (2001) Quality and Cost: Final Report on the Contracting of Civil Non-Family Advice and Assistance Pilot (Norwich: The Stationery Office).

19 Campbell, D.T. Foreword in Yin, 2014 xviii.

20 Seawright, J. and Gerring, J. (2008) ‘Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options’ Vol. 61 No. 2 Political Research Quarterly 294-308.

21 Yin, 2014: chapter 1.

22 For a discussion see: Ryle, G. (1949). The Concept of Mind . London: Hutchinson; Lincoln, Y.S. and Guba, E.G. (1985). Naturalistic Inquiry . Newbury Park, CA: Sage Publications; Holloway, I. (1997). Basic Concepts for Qualitative Research . London: Blackwell Science.

23 Confirmation or interpretive bias of data is something we all have grapple with, as the natural human tendency is to place more weight on evidence that confirms our view than on evidence that contradicts it. Strong research design can assist with counter-balancing this to some extent, including the transformation of any expected finding into a hypothesis that one then seeks to falsify rather than to confirm.

24 See Yin, 2014, chapter 1 for more information on the role of the literature review.

25 For more assistance with legal research questions refer to Epstein, L. (1995) Studying Law and the Courts in Lee Epstein (ed) Contemplating Courts , Cong Q, 1, 3-5.

26 See King, Keohane and Verba, 1994, 15.

27 See Yin, 2014, at 11, and see further Campbell, J.P., Daft, R.L., Hulin, C. L. (1982) What to Study: Generating and Developing Research Questions (Studying Organizations) , Sage Publications, for further thoughts on research questions

28 On process tracing see: Collier, D. ‘Understanding Process Tracing’ Vol. 44 Political Science and Politics 823-830 and George, A. L. and Bennett, A. Case Studies and Theory Development in the Social Sciences , Cambridge, MA, MIT Press, 2005. For an excellent insight into how this has been used in a legal and policy context with reference to changes in Georgia’s tax laws see Ulriksen, M.S, and Dadalauri, N. ‘Single Studies and Theory-testing: The Knots and Dots of the Process-tracing Method’ (2014) International Journal of Social Research Methodology 1- 17.

29 See Blatter, J. and Haverland, M. Designing Case Studies , Basingstoke, Palgrave MacMillan, 2012 at 145 who consider process tracing involves inductive reasoning to build theory and congruence testing involves deductive reasoning to test theories.

30 Epstein and King suggest a range of possible purposes, at 59, including: to explore something that has not previously been studied; to attempt to settle a debate that has been ongoing within the literature; to examine a well-considered question but in a new way; to collect and analyse new data to seek to confirm or refute previous findings; to analyse an existing data set in a new or better way to seek to confirm or refute previous findings or to develop new ones.

31 Peters, B.G. Comparative Politics: Theory and Method , Basingstoke, Palgrave, 1998 at 14.

32 But interestingly, stance, or more accurately epistemology is of less significance to this research method than to many others. Case study method links the research question, research design, analysis and logic of inference to such an extent that is can accommodate a range of epistemological traditions from the realist to relativist/interpretivist. And thus scholars who consider that there are facts independent of our interpretation of them (in essence, hard facts operating in an objective reality) and scholars who consider all ‘facts’ to be local interpretations constructed through our own lenses, are able to operate within a case study framework. Having said that their choice of data sources, and their approach to data generation and analysis may well vary considerably.

33 Ulriksen and Dadalauri, ibid. See further Dadalauri, N. Tax Policy Formation and the Transnationalizationof the Public Policy Arena; A Case Study of Georgia , Aarhus, Politica, 2011) Georgia was selected as a crucial case. The reasoning for this and also for the methods employed in this study are elegantly set out in the article cited above.

34 On the importance of the identification of priors see Beach, D. and Pedersen, R.B., ‘What is process tracing actually tracing? The three variants of process tracing methods and their uses and limitations’ Sept 1-4 2011 The American Political Science Association Annual Meeting , Seattle, WA as cited by Ulriksen and Dadalauri, ibid; and further Gerring, 2007 ibid.

35 For more assistance on extracting observable implications and considering their measurement see Epstein and King, 2002, 70-76.

36 See Gerring, 2004, 342. See further King, Keohane, and Verba, 1994, 76-77.

37 See Yin, 2014, 51.

38 Gerring, 2004, 344. There are often difficulties in establishing the boundaries of the case, the phenomenon under study, and the context that provides a background to the phenomenon but is not itself the object of enquiry. Gerring articulates this as the formal case (the phenomenon) and the informal cases (the penumbra of phenomena which are the context but which will need to be explored in a less formal way so as to distinguish the boundaries of the formal case). The informal units are peripheral, but may have bearing on the formal unit or case, and by considering these informal units at the beginning of the study, and close to its conclusion it will help the researcher to work out what is particular about the unit, and what is transferable to other units.

39 See Yin, 2014, chapter 2.

40 See Geddes, B. Paradigms and Sand Castle: Theory Building and Research Design in Comparative Politics , Ann Arbor, University of Michigan Press, 2005.

41 But the difficulty with multi case studies is that specified conditions or features within individual cases may have more influence on the variables being studied than the variables that one are analysing across the studies. This may lead one to draw erroneous conclusions about causality. This single case study also illustrates the independent variable problem: to what extent are violent crime rates and/or sentencing policy more likely attributable to other societal changes evident at different points in time than each other? Without knowledge of this it is difficult to proceed.

42 Elliott, M. and Kling, R. ‘Organizational Usability of Digital Libraries: Case Study of Legal Research in Civil and Criminal Courts’ (1997) Vol 48 (11) Journal of the American Society for Information Science 1023-1035.

43 See Dnes, A.W. ‘A Case-Study Analysis of Franchise Contracts’ (1993) Vol. 28 Journal of Legal Studies 367-393.

44 See Latour, B. (2010) above.

45 Although note that Gerring, 2004, at 347 indicates that a single case study may credibly make causal claims, if, for example, the case has been selected as it is particularly representative of others or it is a critical or crucial case, see further: Eckstein, H. (1975) ‘Case Studies and Theory in Political Science’ in Regarding Politics: Essays on Political Theory, Stability, and Change, Berkeley: University of California Press, 1992).

46 Gerring indicates that researchers tend to face the choice between knowing more about less or less about more, 2014, 348.

47 See Small, 2009, 24-26.

48 See Gerring, 2004 at 348. For guidance on comparative methodology in a legal context see: Van Hoecke, M. (2004) Epistemology and Methodology of Comparative Law , Portland, Oregon: Hart Publishing.

49 For a detailed discussion see Gerring, 2004 at 343.

50 Yin sets out six sources of evidence: documents, archival records, interviews, direct observations, participant observation, physical artefacts, Yin, 2014, 105-118, and four principles of data collection: multiple sources of data; creation of a case study database; maintain the chain of evidence; exercise care when using data from electronic sources, at 118-129.

51 See Yin, 2014, chapter 3 for more information on what one needs to do before data collection begins, and chapter 4 on data collection itself.

52 See Small, 2009, 12-15.

53 For a discussion of reliability and validity in measurement see Webley, 2010 and further Epstein and King, 2002, 80-99.

54 Elliott and Kling, ibid at 1025.

55 Dnes, ibid at 369-370.

56 Ulrikesn and Dadalauri, ibid at 13.

57 For a discussion see King, Keohane and Verba, 1994, 12.

58 Epstein and King, 2002, 24.

59 For a discussion on this point see King, Keohane and Verba, 1994, chapter 1 ‘The Science in Social Science’.

60 See Epstein and King, 2002, 25-29 for more information on quantitative data description.

61 For a discussion of the different methods of text based data analysis see Yin, 2014, chapter 5 or Webley, 2010.

62 See Yin, 2014, 136-142.

63 Ulriksen and Dadalauri, ibid.

64 King, Keohane and Verba, 1994, 8.

65 Epstein and King, 2002, 29.

66 See Epstein and King, 2002, 34-37 for help distinguishing between causal mechanisms and variables and causal effects.

67 See Gerring, 2004, 348 and further Roberts, C., The Logic of Historical Explanation, Pennsylvania State University Press 1996, 66.

68 See Gerring, 2004, 348 and further Campbell, D. T. [1975] “‘Degrees of Freedom’ and the Case Study” in E. Samuel Overnman (ed) Methodology and Epistemology in Social Science , Chicago, University of Chicago Press: 1988, 380.

69 Yin, 2014, 142-168, provides five different analytical techniques: pattern-matching; explanation building; time-series analysis; logic models; and cross-case synthesis and suggests that after this phase the researcher will likely move on to work through all plausible alternative conclusions to examine whether the most likely conclusion is the only conclusion.

70 Gerring, 2004, 345.

71 See Gerring, 2004, 346.

72 Epstein and King, 2002, 31.

73 Gerring, J. (2001) Social Science Methodology: A Criterial Framework , Cambridge, Cambridge University Press, 90-99.

74 See further Epstein and King, 2002. For assistance in how to report on cases studies and writing up and presentational considerations, including audience and purpose, see Yin, 2014, chapter 6.

75 King et al, 1996, 30-33.

76 Epstein and King, 2002, 6-7.

77 Epstein and King, 2002, 50.

78 See further Miller, A. S. ‘The Myth of Objectivity in Legal Research and Writing’ (1969) Vol. 18 Catholic University Law Review 290.

79 Epstein and King, 2002, 8-10.

80 Genn, H., Partington, M, and Wheeler, S. (2006) Law in the Real World: Improving Our Understanding of How Law Works Final Report and Recommendations , London: The Nuffield Foundation.

81 Gerring, 2004, 352.

• ↑ Back to top

Journal code: 23527927

Journal title: Law and Method

Journal id: 114364

Report an error Suggestions? Disclaimer © Boom juridisch

Experimental (Empirical) Research Articles

• Library vs. Google
• Background Reading
• Keyword Searching
• Evaluating Sources
• Citing Sources
• Professional Organizations
• Need more help?

How Can I Find Experimental (Empirical) Articles?

Many of the recommended databases in this research guide contain scholarly experimental articles (also known as empirical articles or research studies or primary research). Search in databases like: 

• APA PsycInfo ​
• ScienceDirect

Because those databases are rich in scholarly experimental articles, any well-structured search that you enter will retrieve experimental/empirical articles. These searches, for example, will retrieve many experimental/empirical articles:

• caffeine AND "reaction time"
• aging AND ("cognitive function" OR "cognitive ability")
• "child development" AND play

Experimental (Empirical) Articles: How Will I Know One When I See One?

Scholarly experimental articles  to conduct and publish an experiment, an author or team of authors designs an experiment, gathers data, then analyzes the data and discusses the results of the experiment. a published experiment or research study will therefore  look  very different from other types of articles (newspaper stories, magazine articles, essays, etc.) found in our library databases..

In fact, newspapers, magazines, and websites written by journalists report on psychology research all the time, summarizing published experiments in non-technical language for the general public. Although that kind of article can be interesting to read (and can even lead you to look up the original experiment published by the researchers themselves),  to write a research paper about a psychology topic, you should, generally, use experimental articles written by researchers. The following guidelines will help you recognize an experimental article, written by the researchers themselves and published in a scholarly journal.

Structure of a Experimental Article Typically, an experimental article has the following sections:

• The author summarizes her article
• The author discusses the general background of her research topic; often, she will present a literature review, that is, summarize what other experts have written on this particular research topic
• The author describes the experiment she designed and conducted
• The author presents the data she gathered during her experiment
• The author offers ideas about the importance and implications of her research findings, and speculates on future directions that similar research might take
• The author gives a References list of sources she used in her paper

Look for articles structured in that way--they will be experimental/empirical articles. ​

Also, experimental/empirical articles are written in very formal, technical language (even the titles of the articles sound complicated!) and will usually contain numerical data presented in tables. 

As noted above, when you search in a database like APA PsycInfo, it's really easy to find experimental/empirical articles, once you know what you're looking for. Just in case, though, here is a shortcut that might help:

First, do your keyword search, for example:

In the results screen, on the left-hand side, scroll down until you see "Methodology." You can use that menu to refine your search by limiting the articles to empirical studies only:

You can learn learn more about advanced search techniques in APA PsycInfo here . 

• << Previous: Resources
• Next: Research Tips >>
• Last Updated: May 1, 2024 3:14 PM
• URL: https://libguides.umgc.edu/counseling

• Open access
• Published: 09 May 2024

Analysis of factors influencing hospitalization cost of patients with distal radius fractures: an empirical study based on public traditional Chinese medicine hospitals in two cities, China

• Mengen Chen 1 , 2   na1 ,
• Jingyu Yang 3 , 4   na1 ,
• Haojia Hou 5   na1 ,
• Baozhu Zheng 6 ,
• Shiji Xia 2 ,
• Yuhan Wang 2 ,
• Jing Yu 2 ,
• Guoping Wu 2 ,
• Henong Sun 2 ,
• Xuan Jia 2 ,
• Hao Ning 2 ,
• Hui Chang 2 ,
• Xiaoxi Zhang 1 , 2 , 7 ,
• Youshu Yuan 1 , 2 &
• Zhiwei Wang 2 , 8  

BMC Health Services Research volume  24 , Article number:  605 ( 2024 ) Cite this article

34 Accesses

Metrics details

Distal radius fractures (DRFs) have become a public health problem for all countries, bringing a heavier economic burden of disease globally, with China’s disease economic burden being even more acute due to the trend of an aging population. This study aimed to explore the influencing factors of hospitalization cost of patients with DRFs in traditional Chinese medicine (TCM a ) hospitals to provide a scientific basis for controlling hospitalization cost.

With 1306 cases of DRFs patients hospitalized in 15 public TCM a hospitals in two cities of Gansu Province in China from January 2017 to 2022 as the study object, the influencing factors of hospitalization cost were studied in depth gradually through univariate analysis, multiple linear regression, and path model.

Hospitalization cost of patients with DRFs is mainly affected by the length of stay, surgery and operation, hospital levels, payment methods of medical insurance, use of TCM a preparations, complications and comorbidities, and clinical pathways. The length of stay is the most critical factor influencing the hospitalization cost, and the longer the length of stay, the higher the hospitalization cost.

Conclusions

TCM a hospitals should actively take advantage of TCM b diagnostic modalities and therapeutic methods to ensure the efficacy of treatment and effectively reduce the length of stay at the same time, to lower hospitalization cost. It is also necessary to further deepen the reform of the medical insurance payment methods and strengthen the construction of the hierarchical diagnosis and treatment system, to make the patients receive reasonable reimbursement for medical expenses, thus effectively alleviating the economic burden of the disease in the patients with DRFs.

Peer Review reports

Introduction

DRFs are defined as a fracture within 3 cm from the articular surface of the distal radius [ 1 ], which is a relatively common type of fracture, most commonly seen in elderly women and children, whereas the occurrence of young adults is traumatic violence greater [ 2 , 3 ]. A fracture of the distal radius may be described as a Colles, Smith, Barton, or Hutchinson fracture depending on the characteristics of the injury [ 2 , 4 ]. In addition to these four commonly used fracture names, some specially named fractures have been deferred in clinical work, such as chauffeur’s fracture, die-punch fracture, and so on [ 5 , 6 ].

Patients with DRFs account for approximately one-sixth of all fractures in the United States emergency departments, with an annual incidence of more than 640,000 cases [ 7 ], costing roughly $170 million in 2007 in Medicare reimbursement alone [ 8 ], and the incidence of DRFs in the United States is expected to continue to rise based on the evidence from existing studies [ 9 , 10 ]. In addition to the U.S., available studies also show that the incidence of DRFs in countries and regions such as the United Kingdom, Finland, Sweden, and Norway will continue to increase over the coming period [ 11 , 12 , 13 , 14 ]. In China, DRFs account for about 20% of emergency fractures and 75% of forearm fractures, and the number of distal radius fracture patients may exceed 200 million by 2025 with the aging of China’s population [ 15 , 16 , 17 ]. DRFs have become a public health problem that places a heavy economic burden on people around the world, with fewer studies of DRFs in TCM a hospitals being conducted to alleviate this problem.

TCM a is the main component of Chinese medicine with a long history of development and is characterized by Chinese cultural connotations and local characteristics. In the process of its development, TCM b has gradually formed characteristic therapies and methods for the treatment of some diseases, and these diseases are named ‘TCM b advantageous diseases’. DRFs is one of the advantageous diseases in TCM b , treated as one of the key specialties in TCM a hospitals. The treatment of DRFs in TCM a hospitals can be broadly divided into two categories, one is the non-surgical treatment through reduction maneuvers and splinting, which is also the conservative treatment actively adopted by TCM a hospitals, and the other is the surgical treatment through the fixation of the bone position using plate or stent to gradually achieve the healing of the injury [ 1 , 18 , 19 , 20 , 21 ]. When the patient’s fracture condition is not serious, the TCM a hospitals tend to promote the conservative treatment without secondary surgery of removing the plate, which generates less consumption of medical resources, supplementing with Chinese medicine to cooperate with the treatment can significantly improve the speed of recovery, reduce the length of stay, and ultimately reduce the hospitalization cost of the patient effectively.

The Chinese government is currently pushing forward the reform of medical insurance payment methods to improve the quality of medical services as well as to control medical costs, and the TCM b advantageous diseases will be taken as the main target and direction of the preliminary reform in the exploration of the payment reform of TCM a hospitals. The TCM b advantageous diseases of DRFs is a common type of fracture in various countries with a high incidence of disease, analyzing and researching the factors affecting its hospitalization cost has great significance for health economics and public health, especially in the context of the Chinese government’s policy of comprehensively implementing the reform of the diagnosis related groups (DRG) or diagnosis-intervention packet (DIP) medical insurance payment methods with the TCM b advantageous disease of DRFs as a pioneer of the medical insurance reform field [ 22 ]. Exploring the influencing factors of hospitalization cost of TCM b advantageous diseases such as DRFs in TCM a hospitals, can provide thoughts for the Chinese health insurance authorities to promote the reform of the payment methods for controlling medical costs in TCM a hospitals, and at the same time inspire cost control of general hospitals (Western medicine) with optimization of their treatment modalities.

Study design and population

The study data was obtained from the Health Statistics and Information Center of the Gansu Province Health and Wellness Commission. All information on patients hospitalized in 15 TCM a hospitals in Qingyang City and Tianshui City from January 2017 to June 2022 was extracted from the center’s province-wide big data platform for universal health and was cleaned and screened by the corresponding inclusion and exclusion criteria. Our inclusion criteria was western medical diagnosis code S52.500x001 (ICD-10), exclusion criteria were logical errors or missing data that could not be adjusted or supplemented based on the data, as well as patients with the length of stay greater than 90, and 1306 valid cases were included finally (Fig. S 1 ).

Data processing

The endogenous variables in this study were length of stay and hospitalization cost, mainly because existing studies have shown a significant correlation between hospitalization cost and length of stay [ 23 , 24 , 25 , 26 ], which also facilitated the subsequent comprehensive and systematic analysis of the influencing factors of hospitalization cost. The exogenous variables included patients’ basic information, medical situation, and treatment modality. Patients’ basic information included gender, ethnicity, age, marital status, complications and comorbidities, etc., indicators of medical situation included visit times, payment methods of medical insurance, hospital levels, route of admission, and treatment modalities included clinical pathways, types of treatment, use of TCM b preparations, use of TCM b diagnostic and therapeutic equipment, use of TCM b diagnostic and therapeutic techniques, diagnosis and treatment based on TCM b evidence, and surgeries and operations. In particular, since the raw data of length of stay and hospitalization cost did not obey normal distribution, the logarithm of the two data was used as the dependent variable in the regression analyses with the log-transformed data approximated a normal distribution. Further clarification, the analysis using log-transformed data aimed to explore correlations of variables theoretically, whereas the actual comparison of variances used the raw data transformed by the EXP [log(x)] function. The details of the coding and assignment processing of each variable are shown in Table S 1 .

Statistical analysis

Before formal statistical analysis, hospitalization cost was adjusted according to the CPI (Consumer Price Index, CPI) of Healthcare in Gansu Province from 2017 to 2022 to reduce study bias, with 2016 as the base period. Statistical analysis methods in our study mainly involve univariate analysis, multiple linear regression, and path model. Mann–Whitney U rank sum test was used in the univariate analysis when the independent variable is dichotomous, and the Kruskal–Wallis H test for a multi-categorical variable. The independent variables for multiple linear regression were selected from statistically significant variables in the univariate analysis, and regression models were built using the logarithm of length of stay and hospitalization cost as the dependent variables. It is worth mentioning that the covariate “Cities” was included in the regression analysis to minimize the bias caused by the differences in economic and social development between Qingyang City and Tianshui City. Path analysis used the logarithm of hospitalization cost as the dependent variable, the length of stay as the mediator variable, and significant independent variables from the multiple linear regression models as the input variables to comprehensively analyze the factors affecting hospitalization cost. The univariate analysis and multiple linear regression models were performed using SPSS 26.0, and the path model was developed using AMOS 24.0. The test level for the above statistical analysis was α = 0.05.

Univariate analysis

In univariate analysis of length of stay and hospitalization cost in patients with DRFs, we found the patient’s length of stay is associated with gender, age, marital status, visit times, payment methods of medical insurance, hospital levels, admission routes, types of treatment, clinical pathways, use of TCM a preparations, use of TCM b diagnostic and therapeutic equipment, diagnosis and treatment based on TCM b evidence, complications and comorbidities, and surgeries and operations ( P  < 0.05), and the patient’s hospitalization cost is associated with age, marital status, visit times, payment methods of medical insurance, hospital levels, admission routes, types of treatment, clinical pathways, use of TCM a preparations, diagnosis and treatment based on TCM b evidence, complications and comorbidities, surgeries and operations, and length of stay ( P  < 0.05) (Table  1 ).

Multiple linear regression

Multiple linear regression models were established with the log-transformed values of length of stay and hospitalization cost as the dependent variables, with statistically significant in the univariate analysis as the independent variables ( P  < 0.05) (Table  2 ).

From the results of multiple linear regression, we found the length of stay is mainly affected by the patient’s gender, age (45–60), marital status (married), payment methods of medical insurance (UEBMI, others), hospital levels, TCM b and Western medical treatment, diagnosis and treatment based on TCM b evidence, complications and comorbidities, and surgeries and operations, with the regression equation of the patient’s length of stay: Y 1  = 0.816–0.058*X 1  + 0.052*X 3–1  + 0.047*X 4–1 –0.117*X 6–1 –0.115*X 6–3  + 0.140*X 7  + 0.075*X 9–1 –0.047*X 15 –0.176*X 16 ( F  = 19.437, P  < 0.001, R 2  = 0.250). Hospitalization cost is mainly affected by the patient’s marital status (married, others), hospital levels, clinical pathways (Western medicine pathway, no pathway), use of TCM a preparations, diagnosis and treatment based on TCM b evidence, complications and comorbidities, surgeries and operations, and length of stay, with the regression equation of the patient’s hospitalization cost: Y 2  = 2.852 + 0.086*X 4–1  + 0.111*X 4–2  + 0.230*X 7 –0.235*X 10–1 –0.081*X 10–2 –0.092 *X 11  + 0.055*X 14 –0.045*X 15 –0.283*X 16  + 0.823* Y 1 ( F  = 113.156, P  < 0.001, R 2  = 0.649). The VIF (Variance inflation factor, VIF) values for each variable in the regressions analysis of length of stay and hospitalization cost are close to or less than 10, meaning there is no collinearity in either model. Moreover, the residual statistical coefficient of hospitalization cost is \(0.592\left({P}_{e}=\sqrt{{1-R}^{2}}\right)\) , less than the standardized coefficient of Y 1 , indicating there may be other factors indirectly affecting hospitalization cost, and a comprehensive analysis of the impact of hospitalization cost should be developed by establishing a path model.

Based on the multiple linear regression results of length of stay and hospitalization cost, statistically significant independent variables were included as input variables, and a path model was developed with length of stay as the mediator variable and hospitalization cost as the dependent variable (Fig.  1 ).

Path diagram of influencing factors of hospitalization cost of DRFs patients

From the path model analysis results, we could get the specific decomposition effect of factors affecting the hospitalization cost of patients with DRFs, and we also could further quantitatively rank the influencing factors, the specific results are shown in Table  3 . It should be stated in advance that the direct path coefficient of the independent variable on the dependent variable is equal to the standardized regression coefficient, and the indirect path coefficient of the independent variable on the dependent variable through the mediator is equal to the product of direct path coefficient of the independent variable on the mediator and direct path coefficient of the mediator on the dependent variable, and the total path coefficient is the sum of the direct path coefficient and the indirect path coefficient.

By using the above calculation method, the effect size of the factors affecting the hospitalization cost of DRFs patients could be derived, and the ranking results of the degree of influence for each factor on the hospitalization cost as follows: length of stay, surgeries and operations, hospital levels, use of TCM a preparations, marital status (married), payment methods of medical insurance (others), complications and comorbidities, marital status (others), clinical pathway (no pathway), diagnosis and treatment based on TCM b evidence, TCM b and Western medical treatment, gender, age (45–60), clinical pathway (Western medicine), and payment methods of medical insurance (URBMI).

As shown by univariate analysis, the hospitalization cost of inpatients with DRFs, an advantageous disease of Chinese medicine in TCM a hospitals, was mainly related to inpatients’ age, marital status, visit times, payment methods of medical insurance, hospital levels, admission routes, types of treatment, clinical pathways, use of TCM a preparations, diagnosis and treatment based on TCM b evidence, complications and comorbidities, surgeries and operations, and length of stay. The hospitalization cost of patients of age (45–60) with DRFs was higher than age (< 45 or > 60), and the hospitalization cost of unmarried patients was lower than the married or other marital status. Besides, different hospitalized patients with different methods of payment for health insurance will also have an impact on their hospitalization cost, and the UEBMI was the highest, followed by the UEBMI and other health insurance, and the lowest was UEBMI, a key point to consider is that China’s township peasants’ income is lower than urban workers, and their level of health care consumption and ability are also weaker. Furthermore, the hospitalization cost through other admission routes was higher than emergency and outpatient care because patients admitted through others may have a more severe disease profile, resulting in higher consumption of medical services and resources. For example, patients admitted in the form of transfer may be transferred to higher-level hospitals because their conditions are too severe to be effectively treated in lower-level hospitals, and the medical costs the patients face in higher-level hospitals for the same diseases will be higher, as verified in our and others’ studies [ 27 , 28 , 29 ]. What’s more, different types of treatment for patients in TCM a hospitals also led to different hospitalization cost, with the cost of pure TCM b treatment being significantly higher than combined TCM b and Western medicine treatment or independent Western medicine treatment, inconsistent with the results of some studies [ 30 , 31 ], probably because the therapeutic effect of pure TCM b treatment is relatively slow to appear, and the long treatment course leads to high cost, and the sample hospitals are TCM a hospitals with mostly predominantly TCM b treatment programs, making TCM a cost higher. Of note, the hospitalization cost of patients without diagnosis and treatment based on TCM b evidence was higher than those had, mainly because diagnosis and treatment based on TCM b evidence can reduce the patient’s rehabilitation course by improving treatment efficacy and optimizing the treatment plan, resulting in relatively less hospitalization cost, consistent with the studies conducted by Shou Wujing [ 32 ], Wang Shihua [ 33 ], et al. In addition, hospitalization cost was lower for patients using TCM a preparations than for those who did not, higher for patients with complications and comorbidities than for those without, and higher for patients undergoing surgery and operations than for non-surgical patients, mostly in correlation with the content of healthcare services and the consumption of healthcare resources, specifically, the use of TCM a preparations speeds up the process of recovery and reduces the length of stay, and the complications and comorbidities, as well as surgeries and operations, increase the difficulty in treating the disease and generate more healthcare resources to be used.

In our study, by further combining the results of multiple linear regression and path model analysis, we found the inpatient hospitalization cost of DRFs with TCM b advantageous diseases in TCM a hospitals is related to length of stay, surgeries and operations, hospital levels, use of TCM a preparations, payment methods of medical insurance (others), marital status (married), complications and comorbidities, marital status (other), clinical pathways (no pathway), payment methods of medical insurance (URBMI), age (45–60), clinical pathways (Western medicine), gender, and diagnosis and treatment based on TCM b evidence, and the length of stay was the key influencing factor, similar to some scholars’ studies [ 34 , 35 , 36 , 37 ]. Simply put, the longer the length of stay, the relatively more healthcare resources are used by the hospitals, resulting in higher hospitalization cost. Additionally, hospitalization cost and length of stay were lower for female patients than for males, patients’ age (45–60) and marital status (married, other) were associated with higher hospitalization cost and length of stay, and patients would have lower hospitalization cost and length of stay if their payment methods of medical insurance are URBMI and others. Through the analysis of our models, it can also be concluded that the higher the level of the hospital, the more serious the complications and comorbidities with surgeries and operations performed, the higher hospitalization cost and the longer length of stay will be for DRFs patients, and the patients who are adopting TCM b pathway, using TCM a preparations, and not undergoing diagnosis and treatment based on TCM b evidence would face a greater economic burden of the disease.

From the point of cost control for dominant diseases (TCM b advantageous diseases) in TCM a hospitals, firstly, the length of stay of patients should be minimized on the premise of ensuring the efficacy of life-saving treatment. Secondly, the rate of hospital surgery should be controlled, and the fractures that can be treated conservatively with Chinese medicine should be actively adopted [ 38 , 39 ]. Thirdly, the levels of TCM a hospitals, as one of the main factors influencing hospitalization cost, should receive further attention. Accordingly, the local authorities should continue to promote the construction of a hierarchical diagnosis and treatment system for TCM a medical institutions, and regulate the conditions and severity of patients that should be treated in TCM a hospitals of all levels reasonably, to avoid the admission of patients with lower levels of illnesses into higher-level hospitals as much as possible, and to alleviate the financial burden of illness on both the patients and the health insurance fund [ 40 , 41 ]. As the main body of medical cost control, TCM a hospitals should actively guide patients to use TCM a preparations and carry out diagnosis and treatment based on TCM b evidence, the use of TCM a preparations can enable patients to get higher-value rehabilitation, and evidence-based care will enable patients to get higher-quality diagnostic and therapeutic services, both of which are conducive to the reduction of the length of stay and the realization of lower cost control. Of greater concern, the selection of clinical pathways for DRFs patients hospitalized in TCM a hospitals should be based on the actual situation of the patient’s condition, and not be considered unilaterally only from the perspective of cost control, but be combined with the comprehensive consideration of patients’ treatment needs and treatment cost, with the main principle of the patients’ effective medical treatment and relatively low cost being adhered to.

Limitations

Our study was based on hospitalized patients with DRFs in TCM a hospitals in Tianshui City and Qingyang City. On the one hand, the valid samples obtained were relatively small due to the quality of the cases and other reasons, so the study was not broadly representative. On the other hand, our study mainly focused on the TCM a hospitals themselves and did not incorporate the Western medicine hospitals for comparative study, making the study object too homogeneous, and it will be necessary to further optimize the content and form of the study and expand the study object and topic. In addition, the database lacks complete information on the occupations and household incomes of patients with DRFs, which may have an impact on the deeper refinement of our study.

Our study indicates the main influencing factors of hospitalization cost are the length of stay, surgeries and operations, hospital levels, use of TCM a preparations, payment methods of medical insurance, and complications and comorbidities, with the length of stay being the primary influencing factor. China’s government medical and health reform should pay particular attention to the length of stay of patients with TCM b advantageous diseases, encourage TCM a hospitals to try to take DRG or DIP as the main health insurance payment method, and advocate TCM a doctors to adopt non-surgical TCM b specialty therapies under the circumstance ensuring the efficacy of the treatment, to reduce the length of stay, increase health insurance reimbursement and lower the hospitalization cost as much as possible.

Availability of data and materials

Datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Distal radius fractures

Traditional Chinese medicine (TCM a for ‘Traditional Chinese Medicine’, TCM b for ‘diagnosis and treatment-based evidence’)

Diagnosis related groups

Diagnosis-intervention packet

Urban employee basic medical insurance

Urban residents’ basic medical insurance

New cooperative medical scheme

Variance inflation factor

Traumatology and Orthopaedics Group of the Chinese Orthopaedic Association (CMA), Chinese Orthopaedic Association of Orthopaedic Surgery, External Fixation and Limb Reconstruction Group. Guidelines for the diagnosis and treatment of distal radius fractures in Chinese adults(2023). Chin J Traumatol Orthop. 2023;25(1):6–13.

Google Scholar  

MacIntyre NJ, Dewan N. Epidemiology of distal radius fractures and factors predicting risk and prognosis. J Hand Ther. 2016;29(2):136–45.

Article   PubMed   Google Scholar  

Shapiro LM, Kamal RN. Distal radius fracture clinical practice guidelines-updates and clinical implications. J Hand Surg Am. 2021;46(9):807–11.

Porrino JA Jr, Maloney E, Scherer K, Mulcahy H, Ha AS, Allan C, et al. Fracture of the distal radius: epidemiology and premanagement radiographic characterization. AJR Am J Roentgenol. 2014;203(3):551–9.

Esakki S, MacDermid J, Vajravelu S. Linking of the american academy of orthopaedic surgeons distal radius fracture clinical practice guidelines to the international classification of functioning, disability, and health; international classification of diseases; and icf core sets for hand conditions. Hand (N Y). 2016;11(3):314–21.

Buxton SJ. Colles and Carr: some history of the wrist fracture. Ann R Coll Surg Engl. 1966;38(4):253–7.

CAS   PubMed   PubMed Central   Google Scholar  

Chung KC, Spilson SV. The frequency and epidemiology of hand and forearm fractures in the United States. J Hand Surg Am. 2001;26(5):908–15.

Article   CAS   PubMed   Google Scholar  

Shauver MJ, Clapham PJ, Chung KC. An economic analysis of outcomes and complications of treating distal radius fractures in the elderly. J Hand Surg Am. 2011;36(12):1912–8. e1911-1913.

Nellans KW, Kowalski E, Chung KC. The epidemiology of distal radius fractures. Hand Clin. 2012;28(2):113–25.

Article   PubMed   PubMed Central   Google Scholar  

Kamal RN, Shapiro LM. American academy of orthopaedic surgeons/American society for surgery of the hand clinical practice guideline summary management of distal radius fractures. J Am Acad Orthop Surg. 2022;30(4):e480–6.

Stirling ERB, Johnson NA, Dias JJ. Epidemiology of distal radius fractures in a geographically defined adult population. J Hand Surg Eur. 2018;43(9):974–82.

Article   Google Scholar  

Flinkkilä T, Sirniö K, Hippi M, Hartonen S, Ruuhela R, Ohtonen P, et al. Epidemiology and seasonal variation of distal radius fractures in Oulu, Finland. Osteoporos Int. 2011;22(8):2307–12.

Brogren E, Petranek M, Atroshi I. Incidence and characteristics of distal radius fractures in a southern Swedish region. BMC Musculoskelet Disord. 2007;8:48.

Lofthus CM, Frihagen F, Meyer HE, Nordsletten L, Melhuus K, Falch JA, et al. Epidemiology of distal forearm fractures in Oslo, Norway. Osteoporos Int. 2008;19(6):781–6.

Lu QY, Hao YX. Advances in the treatment of distal radius fractures. Int J Orthop. 2017;38(3):154–7. 165.

Zheng ST, W D, Hao HH. Advances in the treatment of distal radius fractures. Chin J Orthop. 2016;36(5):314–20.

Mu HZ, Han ZB. China’s economic growth expectations and paths to realization in a period of rapidly growing elderly population. Popul Econ. 2021(6):1-21.

Zhao K, Yin QD, Li X, Lu Y, Yang HX, Wei CB, et al. Comparison of three methods of treating displaced distal radius fractures. Chin J Orthop Surg. 2023;31(19):1748–54.

Zhu XR, Li DY, Zheng X, Li HW, Guo KJ. Advances in the treatment of distal radius fractures combined with ulnar stem fractures. Chin J Bone Joint Surg. 2019;12(01):62–7.

Wong TC, Chiu Y, Tsang WL, Leung WY, Yam SK, Yeung SH, et al. Casting versus percutaneous pinning for extra-articular fractures of the distal radius in an elderly Chinese population: a prospective randomised controlled trial. J Hand Surg Eur. 2010;35(3):202–8.

Article   CAS   Google Scholar  

Chan YH, Foo TL, Yeo CJ, Chew WY. Comparison between cast immobilization versus volar locking plate fixation of distal radius fractures in active elderly patients, the Asian perspective. Hand Surg. 2014;19(1):19–23.

China National Health Insurance Administration (CNHIA). Notice of three-year action plan for the reform of DRG/DIP payment methods. Bulletin of the China National Medical Security Bureau; 2021.

Shrestha SS, Zhang P, Hora I, Geiss LS, Luman ET, Gregg EW, et al. Factors contributing to increases in diabetes-related preventable hospitalization costs among U.S. adults during 2001–2014. Diabetes Care. 2019;42(1):77–84.

Chen ME, Yang JY, Kong FX, Su B. Analysis of factors influencing hospitalization costs of diabetic patients - based on three models: multiple linear regression, decision tree, and through-trail analysis. Health Econ Res. 2023;40(04):84–9+93.

Kowalsky SJ, Zenati MS, Steve J, Esper SA, Lee KK, Hogg ME, et al. A combination of robotic approach and ERAS pathway optimizes outcomes and cost for pancreatoduodenectomy. Ann Surg. 2019;269(6):1138–45.

Di Fusco M, Shea KM, Lin J, Nguyen JL, Angulo FJ, Benigno M, et al. Health outcomes and economic burden of hospitalized COVID-19 patients in the United States. J Med Econ. 2021;24(1):308–17.

Yang P, Yang R, Luo Y, Zhang Y, Hu M. Hospitalization costs of road traffic injuries in Hunan, China: a quantile regression analysis. Accid Anal Prev. 2023;194:107368.

Chen ME, Kong FX, Su B, Wei XX, Yang JY. Factors influencing hospitalization cost for diabetic patients in traditional Chinese medicine hospitals in Qingyang City, China. Int J Environ Res Public Health. 2022;19(21):13859.

Liu X, Kong D, Lian H, Zhao X, Zhao Y, Xu Q, et al. Distribution and predictors of hospital charges for haemorrhagic stroke patients in Beijing, China, March 2012 to February 2015: a retrospective study. BMJ Open. 2018;8(3):e017693.

Liu LM, Qiu JY, Xuan TH, Yang YR, Li SL, Man XW, et al. Comparative analysis of hospitalization costs of Chinese medicine advantageous diseases in general hospitals and hospitals of Chinese medicine types. China Health Econ. 2022;41(07):24–7.

Gu SW, Xu XM, Yue QY, Wan XW. Impact of different treatment pathways on hospitalization costs of patients with acute cerebral infarction. J Jiangxi Univ Tradit Chin Med. 2020;32(03):10–4.

Shou WJ. A study of hospitalization costs and influencing factors of stroke patients in Chinese medicine hospitals. Master’s Thesis. 2018.

Wang SH. Evaluation study on the composition of hospitalization cost and its influencing factors for lung cancer patients treated with traditional Chinese medicine in Northeast China. Master’s Thesis. 2019.

Ariza JG, Walton SM, Suarez AM, Sanabria M, Vesga JI. An initial evaluation of expanded hemodialysis on hospitalizations, drug utilization, costs, and patient utility in Colombia. Ther Apher Dial. 2021;25:621–7.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ou W, Zhang Q, He J, Shao X, Yang Y, Wang X. Hospitalization costs of injury in elderly population in China: a quantile regression analysis. BMC Geriatr. 2023;23(1):143.

Xu L, Jan S, Chen M, Si L. Sociodemographic and health system factors associated with variations in hospitalization costs for fractures in adults aged 45 years and over a cross-sectional study of provincial health accounts in Jiangsu Province, China. Arch Osteoporos. 2021;16(1):147.

Yu C, Xia Q, Li Q, Wu J, Wang X, Wu J. Hospitalization costs of COPD cases and its associated factors: an observational study at two large public tertiary hospitals in Henan Province, China. BMC Geriatr. 2023;23(1):457.

Ju JH, Jin GZ, Li GX, Hu HY, Hou RX. Comparison of treatment outcomes between nonsurgical and surgical treatment of distal radius fracture in elderly: a systematic review and meta-analysis. Langenbecks Arch Surg. 2015;400(7):767–79.

Li HB, Ma BT. Progress in the conservative treatment of distal radius fractures in the elderly. Chin J Integr Chin Western Med Surg. 2023;29(03):406–10.

Hu H, Wang R, Li H, Han S, Shen P, Lin H, et al. Effectiveness of hierarchical medical system policy: an interrupted time series analysis of a pilot scheme in China. Health Policy Plan. 2023;38(5):609–19.

Shao H, Jin C, Xu J, Zhong Y, Xu B. Supply-demand matching of medical services at a city level under the background of hierarchical diagnosis and treatment - based on Didi Chuxing Data in Haikou, China. BMC Health Serv Res. 2022;22(1):354.

Download references

Acknowledgements

The authors would like to acknowledge the Gansu Provincial Health and Wellness Commission for data support for our study.

This study was supported by the State Administration of Traditional Chinese Medicine (SACM) (Grant NO. ZYZB-2023-435 and NO. GZY-FJS-2022-045).

Author information

Mengen Chen, Jingyu Yang and Haojia Hou contributed equally to this work.

Authors and Affiliations

School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102400, China

Mengen Chen, Xiaoxi Zhang & Youshu Yuan

School of Management, Beijing University of Chinese Medicine, Beijing, 102400, China

Mengen Chen, Shiji Xia, Yuhan Wang, Jing Yu, Guoping Wu, Henong Sun, Xuan Jia, Hao Ning, Hui Chang, Xiaoxi Zhang, Youshu Yuan & Zhiwei Wang

School of Health Management, Gansu University of Chinese Medicine, Lanzhou, 730000, China

Jingyu Yang

School of Public Health, Lanzhou University, Lanzhou, 730000, China

School of Public Health, Gansu University of Chinese Medicine, Lanzhou, 730000, China

School of Stomatology, Capital Medical University, Beijing, 100050, China

Baozhu Zheng

Guang’anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China

Xiaoxi Zhang

National Institute of Chinese Medicine Development and Strategy, Beijing, 102400, China

Zhiwei Wang

You can also search for this author in PubMed   Google Scholar

Contributions

MC HH, BZ, and JYY participated in data collection and collation. MC, XZ, YY, HN, HC, and ZW participated in the method design, analyzed data, and drafted the initial manuscript. GW, HS, XJ, SX, JY and YW participated in text-checking correction and helped to draft the manuscript. ZW and JYY oversaw and provided input on all aspects of manuscript writing and the final analytical plan. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhiwei Wang .

Ethics declarations

Ethics approval and consent to participate.

The need for informed consent was waived by Beijing University of Chinese Medicine Ethics Committee (BUCM-1002600). All methods were carried out in accordance with relevant guidelines and regulations. Since research data comes from a government agency database without critical private information about individuals for our study, we believed that no official ethics approval should be sought for this study according to Measures for the Ethical Review of Biomedical Research Involving Humans by China’s National Health and Family Planning Commission in 2016.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

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

Supplementary Information

Additional file 1: supplemental fig. s1..

Flowchart illustrating patients selection. Supplemental Table S1. Classification and assignment of variables.

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.

Chen, M., Yang, J., Hou, H. et al. Analysis of factors influencing hospitalization cost of patients with distal radius fractures: an empirical study based on public traditional Chinese medicine hospitals in two cities, China. BMC Health Serv Res 24 , 605 (2024). https://doi.org/10.1186/s12913-024-10953-w

Download citation

Received : 29 December 2023

Accepted : 04 April 2024

Published : 09 May 2024

DOI : https://doi.org/10.1186/s12913-024-10953-w

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

• TCM a hospitals
• TCM b advantageous diseases
• Hospitalization cost

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

Can green finance reduce carbon emission? A theoretical analysis and empirical evidence from China

• Research Article
• Published: 10 May 2024

Cite this article

• Peifeng Jiang 1 ,
• Chaomin Xu 2 &
• Yizhi Chen   ORCID: orcid.org/0009-0007-7399-5378 3  

As an important way for China to achieve its dual-carbon goal, green finance has become the foundation for promoting high-quality economic development in China. In order to clarify the mechanism of green finance on carbon emissions, this paper puts green finance into the economic model and deduces the relationship between green finance and carbon emission reduction. This paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2008 to 2019, using the individual fixed effect model, dynamical model, mediator model, and SDM model to study the impact of green finance on carbon emissions and its impact path of upgrading of the industrial structure and the development of science and technology based on the measurement of the green finance development index of each province by the entropy method. The findings show that the development of green finance can reduce carbon emission significantly, which can be sustained until at least the third phase and generates spatial spillover effects; regional heterogeneity analysis finds that the development of green finance shows geographical discrepancies: compared with the eastern and western regions, the development of green finance in central region can reduce carbon emissions more significantly; not only can the development of green finance directly reduce carbon emission, but also through the upgrading of industrial structure and technological innovation. The research not only provides a new perspective and supplementary empirical evidence for understanding the carbon emission reduction effect of green finance, but also offers some useful references for green finance to contribute to carbon emission reduction.

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

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data availability statement

Not applicable.

Baron RM, Kenny DA (1986) The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51(6):1173–1182

Article   CAS   Google Scholar  

Chen XY (2020) Financial structure, technological innovation and carbon emission: on the development of green financial system. Guangdong Soc Sci 04:41–50

Google Scholar  

Chen X, Chen Z (2021) Can green finance development reduce carbon emissions? Empirical evidence from 30 Chinese provinces. Sustainability-Basel 13(21):12137

Deng Y (2022) Research on the Impact of Agricultural Green Technology Progress on Carbon Emissions. Tongfang CNKI (Beijing) Technology Co., Ltd. https://doi.org/10.27409/d.cnki.gxbnu.2022.000093

Book   Google Scholar  

Ding J, Li ZF, Huang JB (2022) Can the green credit policy promote the green innovation in enterprises?—— Based on the perspective of policy effect differentiation. Financ Res 12:55–73

Gan C, Voda M (2023) Can green finance reduce carbon emission intensity? Mechanism and threshold effect. Environ Sci Pollut Res 30:640–653

Article   Google Scholar  

Guo XY (2022) The influence mechanism and empirical test of green finance in boosting the transformation of low-carbon economy. South Finan 01:52–67

Huang Z (2023) Does green investment reduce carbon emissions? New evidence from partially linear functional-coefficient models. Heliyon 9:e19838

Khan MA, Riaz H, Ahmed M, Saeed A (2022) Does green finance really deliver what is expected? An Empirical Perspective. Borsa Istanb Rev 22(3):586–593

Krogstrup S, Oman W (2019) Macroeconomic and financial policies for climate change mitigation: A review of the literature.

Lee CC, Lee CC (2022) How does green finance affect green total factor productivity? Evidence from, China. Energy Econ 107:105863

Lei XD (2022) Research on the impact of green finance on economic low-carbon transformation and development. Tongfang CNKI (Beijing) Technology Co., Ltd. https://doi.org/10.27623/d.cnki.gzkyu.2022.000687

Li LY (2017) Analysis of inter-provincial total carbon emission efficiency and its influencing factors in China. Dongbei University of Finance and Economics

Lin BQ (2022) High-quality economic growth in China in the process of carbon neutrality. Econ Res 57(01):56–71

Lu J, Yan Y, Wang TX (2021) Research on the micro effect of green credit policy —— based on the perspective of technological innovation and resource reallocation. China’s Ind Econ 01:174–192

Lu H, Xu Y, Wang W, Zhao J, Li G, Tian M (2023) Can China reach the CO2 peak by 2030? A forecast perspective. Environ Sci Pollut Res 30(59):123497–123506

Nepal R, Zhao XM, Liu Y, Dong K (2024) Can green finance strengthen energy resilience? The case of China. Technol Forecast Soc Chang 202:123302

Pan DY, Chen CQ, Michael G (2021) Financial Policy and Economic Low-carbon Transformation —— Research based on the perspective of growth. Financ Res 12:1–19

Peng, Yan W, Elahi E (2022) Does the green credit policy affect the scale of corporate debt financing? Evidence from listed companies in heavy pollution industries in China. Environ Sci Pollut Res 29:755–767

Puspanjali B, Anasuya H, Narayan S (2023) Achieving carbon neutrality target in the emerging economies: role of renewable energy and green technology. Gondwana Res 121:16–32

Ran Q, Liu L, Razzaq A et al (2023) Does green finance improve carbon emission efficiency? Experimental evidence from China. Environ Sci Pollut Res 30:48288–48299

Rasoulinezhad E, Taghizadeh-Hesary F (2022) Role of green finance in improving energy efficiency and renewable energy development. Energ Effi 15:14

Shi DM, Shi XY (2022) Green finance and high-quality economic development: mechanism, characteristics and empirical research. Stat Study 39(01):31–48

Su DW, Lian L (2018) Does green credit affect the investment and financing behavior of heavily polluting enterprises? Financ Res 12:123–137

Sun SY, Wang XY, Gao CY (2023) Can green credit play a carbon emission reduction effect?. China’s Population. Resour Environ 33(08):37–47

Wang J, Ma Y (2022) How does green finance affect CO2 emissions? Heterogeneous and mediation effects analysis. Front Environ Sci 10:931086

Wang X, Wang Y (2021) Green credit policy promotes green innovation research. Manag World 37(06):173–188

Wang Y, Pan DY, Peng Y (2019) Research on green credit incentive Policy based on DSGE model. Financ Res 11:1–18

Wang QJ, Wang HJ, Chang CP (2022) Environmental performance, green finance and green innovation: what’s the long-run relationships among variables? Energ Econ 110:106004

Wang S, Xu J, Zhang W (2023) Research on the impact of green finance on China’s carbon neutralization capacity. Environ Sci Pollut Res 30:108330–108345

Wang LL, Yang XY, Cai QH (2024) Influence mechanism of green finance on regional emission reduction. Heliyon 10(1):e23861

Wen SY, Shi HM, Guo J (2022a) Research on the emission reduction effect of green finance from the perspective of general equilibrium theory: from model construction to empirical test. China Manag Sci 30(12):173–184

Wen WY, Liu H, Wang H (2022b) Green finance, green innovation, and high-quality economic development. Financ Res 08:1–17

CAS   Google Scholar  

Wen ZL, Ye BJ (2014) Mediating effect analysis: methods and model development. Adv Psychol Sci 22(05):731–745

Wu GL, Liu X, Cai YL (2024) The impact of green finance on carbon emission efficiency. Heliyon 10:e23803

Xu J, Chen F, Zhang W, Liu Y, Li T (2023) Analysis of the carbon emission reduction effect of Fintech and the transmission channel of green finance. Financ Res Lett 56:104127

Yan CL, Li T, Lan W (2016) Financial development, innovation, and carbon dioxide emissions. Financ Res 01:14–30

Yin ZM, Sun XQ, Xing MY (2021) Study on the influence of green finance development on green total factor productivity. Stat Decis-Making 37(03):139–144

Yu CH, Wu X, Zhang D, Chen S, Zhao J (2021) Demand for green finance: resolving financing constraints on green innovation in China. Energy Policy 153:112255

Zhang J, Jiang HQ, Liu GY, Zeng WH (2018) A study on the contribution of industrial restructuring to reduction of carbon emissions in China during the five Five-Year Plan periods. J Clean Prod 176:629–635

Zhang W, Zhu Z, Liu X (2022) Can green finance improve carbon emission efficiency? Environ Sci Pollut Res 29:68976–68989

Zhu B, Zhang TL (2021) The impact of cross-region industrial structure optimization on economy, carbon emissions and energy consumption: a case of the Yangtze River Delta. Sci Total Environ 778:146089

Download references

This paper is funded in accordance with the China Association for Science and Technology for the 2022 Graduate Science Popularization Ability Improvement Project (KXYJS2022064) Jiangsu Provincial Postgraduate Practice Innovation Program (KYCX23_1097).

Yizhi Chen thanks the China Association for Science and Technology for the 2022 Graduate Science Popularization Ability Improvement Project (KXYJS2022064) Jiangsu Provincial Postgraduate Practice Innovation Program (KYCX23_1097) for its support.

Author information

Authors and affiliations.

School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangdong, 510320, China

Peifeng Jiang

School of Economics, Guangdong University of Finance and Economics, Guangdong, 510320, China

School of Economics, Jinan University, Guangdong, 510632, China

You can also search for this author in PubMed   Google Scholar

Contributions

Author 1 (first author): Peifeng Jiang. PF contributed to the conceptualization, methodology, design, theoretical model derivation, and data analysis of the study and was a major contributor in writing the manuscript.

Author 2: Chaomin Xu. CX contributed to the investigation, former analysis, data analysis, validation, and writing of the study.

Author 3 (corresponding author): Yizhi Chen. YC contributed to the project administration, supervision, revising, and editing of the study.

All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yizhi Chen .

Ethics declarations

Ethics statement.

No conflict of ethics.

Conceptualization

Under the background of the increasingly serious problem of carbon emissions, how China achieves “carbon peak” and “carbon neutrality” shows its pursuit of high-quality economic development and its responsibility of a major country. In addition, whether China can achieve “carbon peak” and “carbon neutrality” will significantly affect the global carbon reduction action. But there are few theoretical research on carbon emission and green finance. So this paper attempts to construct an economic model of green finance and carbon emission.

Methodology

This paper uses the individual fixed effect model, dynamical model, mediator model, and SDM model to study the impact of green finance on carbon emissions and its impact path of upgrading of the industrial structure and the development of science and technology.

The data processing, modeling analysis, and plotting in this paper were carried out using Excel and Stata.

Consent to participate

Consent for publication, author statement.

The manuscript describes original work and is not under consideration by any other journal. All authors have approved the manuscript that is enclosed.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Eyup Dogan

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Jiang, P., Xu, C. & Chen, Y. Can green finance reduce carbon emission? A theoretical analysis and empirical evidence from China. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33572-8

Download citation

Received : 01 January 2024

Accepted : 30 April 2024

Published : 10 May 2024

DOI : https://doi.org/10.1007/s11356-024-33572-8

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

• Green finance
• Carbon emission reduction
• Theoretical analysis
• Mediating effect model
• Dynamical model
• Spatial Durbin model
• Find a journal
• Publish with us
• Track your research