Qualitative Comparative Analysis
Wendy Olsen, CCSR/Social Statistics
Qualitative Comparative Analysis (QCA) offers a new, systematic way of studying configurations of cases. QCA is used in comparative research and when using case-study research methods. The QCA analysts interprets the data qualitatively whilst also looking at causality between the variables. Thus the two-stage approach to studying causality has a qualitative first stage and a systematic second stage using QCA.
QCA is truly a mixed-methods approach to research. The basic data-handling mechanism is a simple qualitative table of data. This matrix is made up of rows and columns. Its column elements can be binary (yes/no), ordinal, or scaled index variates. QCA is best suited to small- to medium-N case-study projects with between 3 and 250 cases.
Crisp-set QCA uses only binary variates for its truth table. Fuzzy-set QCA also uses ordinal variates. A variate is a column of numbers representing real, not hypothetical, cases. In implementing QCA, one can code up the case-study data using NVIVO 7 software to create substantive case attributes. Multiple-level nested or non-nested cases can be handled. Fuzzy-set analysis is an optional extra stage, which also uses Boolean logic, but which is not necessary for QCA and tends not to be as qualitative as crisp-set QCA (csQCA) itself.
Experts at Manchester
- Dr Wendy Olsen , Senior Lecturer in Social Science Research Methods (SED) and in Socio-Economic Research (SOSS)
- Matthias Vom Hau, Brooks World Poverty Institute (now joined with the Institute for Development Policy and Management to become the Global Development Institute)
A research grant from the British Academy allowed Manchester University to host an Expert Roundtable on the Study of Strategies of Social Change using the Method of Qualitative Comparative Analysis (QCA) in 2008. Experts from Manchester University and the UK then visited Japan to hold a second roundtable there in 2009. A mixed-methods research training workshop took place on 15 June, 2010.
- Rihoux, B., & Ragin, C. C. (2009). Configurational comparative methods. Qualitative Comparative Analysis (QCA) and related techniques (Applied Social Research Methods). Thousand Oaks and London: Sage.
- Rihoux, B., and M. Grimm, eds. (2006). Innovative Comparative Methods For Policy Analysis: Beyond the quantitative-qualitative divide. New York, NY, Springer.
- Ragin, C.C. (2008). Redesigning social inquiry: Set relations in social research. Chicago: Chicago University Press.
- Ragin, C. C. (2000). Fuzzy-set social science. Chicago; London, University of Chicago Press. (One only needs to read the first half to cover QCA; the second half covers Fuzzy Set Analysis.)
- Byrne, D., and C. Ragin, eds. (2009), Handbook of Case-Centred Research Methods, London: Sage.
- QUAL-COMPARE email discussion list - JISC email list about Qualitative Comparative Analysis
- Compasss - International network promoting small-N and medium-N comparative methods
Wendy Olsen offers software support and advice via both these web sites.
Staff interested in Qualitative software Nvivo
- Dr. Rudolf Sinkovics - Alliance Manchester Business School (AMBS), International Business
- Dr. Yanuar Nugroho - formerly Alliance Manchester Business School (AMBS), Technological innovation
- Prof Cathy Cassell - formerly Alliance Manchester Business School (AMBS), Qualitative methods in organisational research
- Dr. Richard Kyle - Nursing, Midwifery and Social Work, Health and social geographer
- Dr. Sarah Kendal - Nursing, Midwifery and Social Work, Emotional wellbeing interventions
- Dr. Ziv Amir - Nursing, Midwifery and Social Work, Survivorship and cancer
- Dr Linda McGowan - Nursing, Midwifery and Social Work, Women's health
- Prof. Alys Young - Nursing, Midwifery and Social Work, Social work research
- Wendy Olsen - School of Social Sciences, Sociology of economic life
- Prof. Jennifer Mason - Social Sciences, Kinship and family
Staff interested in Qualitative Software Atlas-TI
- Dr. Jane Griffiths - Nursing, Midwifery and Social Work, Supportive and Palliative Care in Community Nursing
- Dr. Malcolm Campbell - Nursing, Midwifery and Social Work, Statistics
- Prof. Christi Deaton - Nursing, Midwifery and Social Work, Structural equation modelling and multi-level modelling
- Prof. Peter Callery - Nursing, Midwifery and Social Work, Self care of long term conditions in childhood and young people
Download PDF slides of the presentation ' What is QCA? '
Qualitative Comparative Analysis
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What is qualitative comparative analysis?
Qualitative Comparative Analysis (QCA) is a research methodology used in analyzing multiple cases in complex situations. This methodology can help in explaining why change occurs in some cases and why it doesn't happen in others. It is primarily a quantitative analysis method used to analyze qualities, though qualitative data can be used to derive those qualities. QCA should not be confused with qualitative analysis methods or constant comparison method which are forms of qualitative analysis
QCA was developed in 1987s by Charles Ragin, a social scientist. Qualitative comparative analysis is a set-based theory that seeks to explain the relationship between causal conditions and outcomes through the concept of sets and their relations.
QCA models causal conditions as superset or subset relationships between an outcome and explanatory factors. Using this approach, a researcher can determine whether a causal condition is neither sufficient nor necessary, sufficient but not necessary, necessary but not sufficient or sufficient to produce an outcome.
Qualitative comparative analysis approach draws strength from both quantitative and qualitative research methods. It combines the mathematical approaches used in quantitative research with the inductive and comparative case-based techniques employed in qualitative research.
Assumptions of qualitative comparative analysis
QCA is based on two main assumptions. Firstly, it assumes that one factor is rarely sufficient to produce a change. Instead, change is often caused by different combinations of factors. Secondly, Qualitative comparative analysis also recognizes that different combinations of factors can produce the same result.
When do researchers use QCA?
QCA is handy when cases are too small to apply statistical analysis techniques like linear regression and too large for qualitative case-study methodology. It is usually employed for analyzing an intermediate number of cases, usually between 10 and 50.
In addition, researchers use QCA when they predict that the causal structure of an outcome would likely be equifinal, conjunctural, and complex. Equifinal implies that there are different ways to achieve an outcome. Conjunctural means that outcome can only be obtained through a combination of conditions.
Lastly, researchers use qualitative comparative analysis when they are interested in knowing whether the causal conditions are sufficient, necessary, or both for predicting an outcome
How to use qualitative comparative analysis
Generally, there are six steps involved in qualitative comparative analysis. Usually, the steps are iterative, so you may find yourself moving back and forth when using QCA. Below is the summary of how to do QCA:
The first step is to identify the change you are interested in studying and the factors (in theory) that bring these changes. In other words, you determine the outcome you want to explain and define the target set.
The next step is to identify the set of causal conditions expected to contribute to the outcome under study. You can make your selections based on theory, knowledge of the cases, and prior research about the outcome. To use QCA, you are expected to select some cases in which the 'outcome' happened, and some other similar cases which didn't produce the same result.
The third step in QCA is to develop a set of factors commonly known as conditions. These involve listing out outcomes and key factors whose presence or absence may produce those outcomes
After identifying your cases and factors, you are to develop criteria for scoring each of the factors. There are two scoring methods you can adopt — crisp set and fuzzy set. In a crisp set, the score is either '0' or '1', while in the fuzzy set, the score is set at any level between '0' and '1'. For instance, the scores in the fuzzy set can be '0', 0.22, 055, 0.88, or '1'. When using a crisp set, '0' means an absence while '1' means a presence. If the factors can't be classified as present or absent, you should adopt the fuzzy set scoring approach.
After scoring all your factors, the next action is to analyze your dataset. This is usually done using computer software like Tosmana and fs/QCA or a spreadsheet formula/macro. However, if you are working on a small number of cases, you can scan the scores and identify patterns by eye.
Proceed to interpret your findings once the computer software has analyzed your dataset and identified the possible combinations of factors. This involves going to the individual cases you identified and asking whether your findings make sense or not.
Sometimes, it may be necessary to collect more data on the cases, run another dataset analysis using the computer software or even go back to examine if the theories you adopted in step one are still valid. Your study is ready for publishing when you arrive at a satisfactory solution, i.e., an explanation for the outcome under study.
Strengths of qualitative comparative analysis
QCA takes an in-depth look at each case and enables the researcher to draw patterns across different cases. It helps in understanding and explaining why change happens across an intermediary number of cases. Additionally, QCA can be applied in situations where the cases are too few to use traditional statistical analysis. Unlike qualitative approaches, which often do not aim to replicate findings, QCA makes it possible for others to test and replicate your findings.
Weaknesses of qualitative comparative analysis
The first weakness of QCA is that a minimum number of cases is required before you can use it. Secondly, missing information in one factor in any of the cases will render the affected case unusable. Critics have argued that this may lead to situations in which the researcher ignores an essential factor. Lastly, QCA is an iterative process, so it is hard to predict how much time is needed for the study.
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Devers, J; Lallemand, N; Burton, R; Kahwati, L; McCall, N; Zuckerman, S. (2013). Using Qualitative Comparative Analysis (QCA) to Study Patient-Centered Medical Homes . Urban Institute.
Ragin, C. (1998). The Logic of Qualitative Comparative Analysis . International Review of Social History.
Sehring, J; Korhonen-Kurki, K; Brockhaus, M. (2013). Q ualitative Comparative Analysis (QCA). An application to compare national REDD+policy processes . CIFOR
Simister, N; Scholz, V. (2017). Qualitative Comparative Analysis (QCA) . M&E Universer.
Pattern coding groups qualitative data into sets, themes, and constructs.
Simultaneous coding is a method of qualitative coding where a single excerpt of data is coded with multiple codes.
Decide if you want to start off with a set of codes and stick with them (deductive coding), come up with the codes as you read what you see in your data (inductive), or take a combination approach.
Values coding deals with labeling the values, attitudes and belief systems that are expressed by participants.
Initial coding is where you break down your qualitative data into discrete excerpts and create codes to label them with.
Process coding is helpful when you want to understand actions in the data. You can catalogue observable activities or conceptual actions.
Descriptive coding is where you code passages according to topic in order to summarize the topic of the data.
Structural coding when you to take a large set of semi-structured data, and structure it into smaller pieces for further analysis.
In vivo codes utilize the language and terminology used by the participants rather than alternative methods where codes are researcher-derived.
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Comparative Case Studies
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Comparison in qualitative research.
- Lesley Bartlett Lesley Bartlett University of Wisconsin–Madison
- and Frances Vavrus Frances Vavrus University of Minnesota
- Published online: 30 June 2020
Comparison is a valuable and widely touted analytical technique in social research, but different disciplines and fields have markedly different notions of comparison. There are at least two important logics for comparison. The first, the logic of juxtaposition, is guided by a neopositivist orientation. It uses a regularity theory of causation; it structures the study by defining cases, variables, and units of analysis a priori ; and it decontextualizes knowledge. The second, the logic of tracing, engages a realist theory of causation and examines how processes unfold, influenced by actors and the meanings they make, over time, in different locations, and at different scales. These two logics of comparison lead to distinct methodological techniques. However, with either logic of comparison, three dangers merit attention: decontextualization, commensurability, and ethnocentrism. One promising research heuristic that attends to different logics of comparison while avoiding these dangers is the comparative case study (CCS) approach. CCS entails three axes of comparison. The horizontal axis encourages comparison of how similar policies and practices unfold across sites at roughly the same level or scale, for example across a set of schools or across home, school, religious institution, and community organization. The vertical axis urges comparison across micro-, meso-, and macro-levels or scales. For example, a study of bilingual education in the United States should attend not only to homes, communities, classroom, and school dynamics (the micro-level), but also to meso-level district, state, and federal policies, as well as to factors influencing international mobility at the macro-level. Finally, the transversal axis, which emphasizes change over time, urges scholars to situate historically the processes or relations under consideration.
- qualitative methods
- comparative case studies
- horizontal comparison
- vertical comparison
- transversal comparison
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- What Is a Case Study? | Definition, Examples & Methods
What Is a Case Study? | Definition, Examples & Methods
Published on May 8, 2019 by Shona McCombes . Revised on January 30, 2023.
A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.
A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .
Table of contents
When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case.
A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.
Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.
You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.
Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:
- Provide new or unexpected insights into the subject
- Challenge or complicate existing assumptions and theories
- Propose practical courses of action to resolve a problem
- Open up new directions for future research
Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.
However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.
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While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:
- Exemplify a theory by showing how it explains the case under investigation
- Expand on a theory by uncovering new concepts and ideas that need to be incorporated
- Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions
To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.
There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.
The aim is to gain as thorough an understanding as possible of the case and its context.
In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.
How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .
Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).
In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.
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Qualitative comparative analysis
Qualitative Comparative Analysis (QCA) is a means of analysing the causal contribution of different conditions (e.g. aspects of an intervention and the wider context) to an outcome of interest.
QCA starts with the documentation of the different configurations of conditions associated with each case of an observed outcome. These are then subject to a minimisation procedure that identifies the simplest set of conditions that can account for all the observed outcomes, as well as their absence.
The results are typically expressed in statements expressed in ordinary language or as Boolean algebra. For example:
- A combination of Condition A and condition B or a combination of condition C and condition D will lead to outcome E.
- In Boolean notation this is expressed more succinctly as A*B + C*D→E
QCA results are able to distinguish various complex forms of causation, including:
- Configurations of causal conditions, not just single causes. In the example above, there are two different causal configurations, each made up of two conditions.
- Equifinality, where there is more than one way in which an outcome can happen. In the above example, each additional configuration represents a different causal pathway
- Causal conditions which are necessary, sufficient, both or neither, plus more complex combinations (known as INUS causes – insufficient but necessary parts of a configuration that is unnecessary but sufficient), which tend to be more common in everyday life. In the example above, no one condition was sufficient or necessary. But each condition is an INUS type cause
- Asymmetric causes – where the causes of failure may not simply be the absence of the cause of success. In the example above, the configuration associated with the absence of E might have been one like this: A*B*X + C*D*X →e Here X condition was a sufficient and necessary blocking condition.
- The relative influence of different individual conditions and causal configurations in a set of cases being examined. In the example above, the first configuration may have been associated with 10 cases where the outcome was E, whereas the second might have been associated with only 5 cases. Configurations can be evaluated in terms of coverage (the percentage of cases they explain) and consistency (the extent to which a configuration is always associated with a given outcome).
QCA is able to use relatively small and simple data sets. There is no requirement to have enough cases to achieve statistical significance, although ideally there should be enough cases to potentially exhibit all the possible configurations. The latter depends on the number of conditions present. In a 2012 survey of QCA uses the median number of cases was 22 and the median number of conditions was 6. For each case, the presence or absence of a condition is recorded using nominal data i.e. a 1 or 0. More sophisticated forms of QCA allow the use of “fuzzy sets” i.e. where a condition may be partly present or partly absent, represented by a value of 0.8 or 0.2 for example. Or there may be more than one kind of presence, represented by values of 0, 1, 2 or more for example. Data for a QCA analysis is collated in a simple matrix form, where rows = cases and columns = conditions, with the rightmost column listing the associated outcome for each case, also described in binary form.
QCA is a theory-driven approach, in that the choice of conditions being examined needs to be driven by a prior theory about what matters. The list of conditions may also be revised in the light of the results of the QCA analysis if some configurations are still shown as being associated with a mixture of outcomes. The coding of the presence/absence of a condition also requires an explicit view of that condition and when and where it can be considered present. Dichotomisation of quantitative measures about the incidence of a condition also needs to be carried out with an explicit rationale, and not on an arbitrary basis.
Although QCA was originally developed by Charles Ragin some decades ago it is only in the last decade that its use has become more common amongst evaluators. Articles on its use have appeared in Evaluation and the American Journal of Evaluation.
For a worked example, see Charles Ragin’s What is Qualitative Comparative Analysis (QCA)? , slides 6 to 15 on The bare-bones basics of crisp-set QCA.
[A crude summary of the example is presented here]
In his presentation Ragin provides data on 65 countries and their reactions to austerity measures imposed by the IMF. This has been condensed into a Truth Table (shown below), which shows all possible configurations of four different conditions that were thought to affect countries’ responses: the presence or absence of severe austerity, prior mobilisation, corrupt government, rapid price rises. Next to each configuration is data on the outcome associated with that configuration – the numbers of countries experiencing mass protest or not. There are 16 configurations in all, one per row. The rightmost column describes the consistency of each configuration: whether all cases with that configuration have one type of outcome, or a mixed outcome (i.e. some protests and some no protests). Notice that there are also some configurations with no known cases.
Ragin’s next step is to improve the consistency of the configurations with mixed consistency. This is done either by rejecting cases within an inconsistent configuration because they are outliers (with exceptional circumstances unlikely to be repeated elsewhere) or by introducing an additional condition (column) that distinguishes between those configurations which did lead to protest and those which did not. In this example, a new condition was introduced that removed the inconsistency, which was described as “not having a repressive regime”.
The next step involves reducing the number of configurations needed to explain all the outcomes, known as minimisation. Because this is a time-consuming process, this is done by an automated algorithm (aka a computer program) This algorithm takes two configurations at a time and examines if they have the same outcome. If so, and if their configurations are only different in respect to one condition this is deemed to not be an important causal factor and the two configurations are collapsed into one. This process of comparisons is continued, looking at all configurations, including newly collapsed ones, until no further reductions are possible.
[Jumping a few more specific steps] The final result from the minimisation of the above truth table is this configuration:
SA*(PR + PM*GC*NR)
The expression indicates that IMF protest erupts when severe austerity (SA) is combined with either (1) rapid price increases (PR) or (2) the combination of prior mobilization (PM), government corruption (GC), and non-repressive regime (NR).
This slide show from Charles C Ragin, provides a detailed explanation, including examples, that clearly demonstrates the question, 'What is QCA?'
This book, by Schneider and Wagemann, provides a comprehensive overview of the basic principles of set theory to model causality and applications of Qualitative Comparative Analysis (QCA), the most developed form of set-theoretic method, for research ac
This article by Nicolas Legewie provides an introduction to Qualitative Comparative Analysis (QCA). It discusses the method's main principles and advantages including its concepts.
COMPASSS (Comparative methods for systematic cross-case analysis) is a website that has been designed to develop the use of systematic comparative case analysis as a research strategy by bringing together scholars and practitioners who share its use as
This paper from Patrick A. Mello focuses on reviewing current applications for use in Qualitative Comparative Analysis (QCA) in order to take stock of what is available and highlight best practice in this area.
Marshall, G. (1998). Qualitative comparative analysis. In A Dictionary of Sociology Retrieved from https://www.encyclopedia.com/social-sciences/dictionaries-thesauruses-pictures-and-press-releases/qualitative-comparative-analysis
'Qualitative comparative analysis' is referenced in:
- 52 weeks of BetterEvaluation: Week 34 Generalisations from case studies?
- Week 18: is there a "right" approach to establishing causation in advocacy evaluation?
- Rainbow Framework : Check the results are consistent with causal contribution
- Data mining
- Qualitative comparative analysis: A valuable approach to add to the evaluator’s ‘toolbox’? Lessons from recent applications
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- > The Research Imagination
- > COMPARATIVE RESEARCH METHODS
- 1 RESEARCH PROCESS
- 2 THEORY AND METHOD
- 3 RESEARCH DESIGN
- 4 MEASUREMENT
- 5 ETHICAL AND POLITICAL ISSUES
- 7 SURVEY RESEARCH
- 8 INTENSIVE INTERVIEWING
- 9 OBSERVATIONAL FIELD RESEARCH
- 10 FEMINIST METHODS
- 11 HISTORICAL ANALYSIS
- 12 EXPERIMENTAL RESEARCH
- 13 CONTENT ANALYSIS
- 14 AGGREGATE DATA ANALYSIS
- 15 COMPARATIVE RESEARCH METHODS
- 16 EVALUATION RESEARCH
- 17 INDEXES AND SCALES
- 18 BASIC STATISTICAL ANALYSIS
- 19 MULTIVARIATE ANALYSIS AND STATISTICAL SIGNIFICANCE
- EPILOGUE: THE VALUE AND LIMITS OF SOCIAL SCIENCE KNOWLEDGE
- Appendix A A Precoded Questionnaire
- Appendix B Excerpt from a Codebook
- Author Index
- Subject Index
15 - COMPARATIVE RESEARCH METHODS
Published online by Cambridge University Press: 05 June 2012
In contrast to the chapters on survey research, experimentation, or content analysis that described a distinct set of skills, in this chapter, a variety of comparative research techniques are discussed. What makes a study comparative is not the particular techniques employed but the theoretical orientation and the sources of data. All the tools of the social scientist, including historical analysis, fieldwork, surveys, and aggregate data analysis, can be used to achieve the goals of comparative research. So, there is plenty of room for the research imagination in the choice of data collection strategies. There is a wide divide between quantitative and qualitative approaches in comparative work. Most studies are either exclusively qualitative (e.g., individual case studies of a small number of countries) or exclusively quantitative, most often using many cases and a cross-national focus (Ragin, 1991:7). Ideally, increasing numbers of studies in the future will use both traditions, as the skills, tools, and quality of data in comparative research continue to improve.
In almost all social research, we look at how social processes vary and are experienced in different settings to develop our knowledge of the causes and effects of human behavior. This holds true if we are trying to explain the behavior of nations or individuals. So, it may then seem redundant to include a chapter in this book specifically dedicated to comparative research methods when all the other methods discussed are ultimately comparative.
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- COMPARATIVE RESEARCH METHODS
- Paul S. Gray , Boston College, Massachusetts , John B. Williamson , Boston College, Massachusetts , David A. Karp , Boston College, Massachusetts , John R. Dalphin
- Book: The Research Imagination
- Online publication: 05 June 2012
- Chapter DOI: https://doi.org/10.1017/CBO9780511819391.016
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Books about case studies, resources on case studies, case studies in the literature.
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According to the book Understanding Case Study Research , case studies are "small scale research with meaning" that generally involve the following:
- The study of a particular case, or a number of cases.
- That the case will be complex and bounded.
- That it will be studied in its context.
- That the analysis undertaken will seek to be holistic.
The following books are available as ebooks through the UCSF Library unless otherwise noted
- Case Studies A tutorial on case study research from Colorado State University.
- Case Study: A Strategic Research Methodology An article from the American Journal of Applied Sciences by Khairul Baharein Mohd Noor. This article defends case study methodology as an appropriate methodology, giving a description, the process and its strengths and weaknesses.
- The Case Study Approach This article by Crowe et al gives a nice overview of case studies and includes several examples from health science research
- Five Misunderstandings About Case-Study Research This article examines five common misunderstandings about case-study research
- How to do Case Study Research This article from Donna Zucker at the University of Massachusetts College of Nursing describes how to design and write about case study research
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Violence on the front line: a qualitative comparative analysis of the causes of patient violence towards medical staff in China during the COVID-19 pandemic
- Mengmeng Yin 1 ,
- Wei Zhang ORCID: orcid.org/0000-0003-0178-0750 1 ,
- Richard Evans 2 ,
- Chengyan Zhu 3 ,
- Longwen Wang 4 &
- Jun Song 5
Current Psychology ( 2023 ) Cite this article
COVID-19, reduced funding and a shortage of healthcare workers has led to growing international concern about patient violence towards medical staff in medical settings. As the number of reported physical and verbal assaults increases, many medical staff are considering leaving their positions due to the resulting impact on their mental and physical wellbeing, creating a critical need to understand the causes for violence towards medical staff working on the front line. This study aims to examine the causes for patient violence towards medical staff in China during the COVID-19 pandemic. A case library was created containing twenty reported incidents of patient violence towards medical staff during the pandemic in China. Based on the Triadic Reciprocal Determinism (TRD) theory, we identify the personal, environmental, and behavioral factors, that cause incidents of violence towards medical staff. The outcome was set as ‘Medical Staff Casualties’, referring to whether, due to the violence experienced, the medical staff member was injured or died, or only experienced threatening or insulting behavior. Data was analyzed using Qualitative Comparative Analysis (QCA) to clarify the relationship between the different conditions and their relationship with the outcome. The study’s results reveal that Relationship Closeness is a necessary condition for patient violence in the presence of outcome. Secondly, four distinct types of causes for patient violence towards medical staff were identified: Strong Relationship Oriented Violence, Healthcare Resources and Services Mismatched Violence, Violence caused by Ineffective Patient-Physician Communication, and Ineffective Communication Superimposed Low Patient Compliance Violence. Scientific guidance is provided for the creation of measures to prevent future violence towards medical staff from occurring. Strict precautions should be taken for preventing violence to protect a healthy society and harmonious medical environment, emphasizing the need for joint governance of multiple participants.
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Incidents of patient violence towards medical staff has increased significantly in recent years. This is often attributed to reduced healthcare funding, a shortage of healthcare workers and, more recently, an increase in the volume of patients due to COVID-19. Workplace hazards, such as falls and exposure to infectious diseases, is common in the medical sector, however violence towards medical staff, including physical and verbal abuse from patients and their relatives, is becoming more commonplace, creating long-term effects on staff psychological and physical well-being. Levels of exposure to violence is increasing in medical settings, resulting in more frequent patient-physician conflicts and a deterioration of the patient-physician relationship. The International Labor Organization (ILO) and the World Health Organization (WHO) have defined violence towards medical staff as those workers that experience verbal and physical attacks and threats towards them in the workplace. Despite duty of care provisions provided by healthcare providers, these experiences pose a clear or implicit challenge to staff safety, health and physical wellbeing (Wang et al., 2020 ). The common types of violence towards medical staff include physical violence, psychological violence, and sexual violence (Spector et al., 2014 ), which mainly include verbal attacks, threats and insults; sexual harassment and rape; and physical abuse of medical workers. In China, physical and psychological violence imparted by patients or their relatives is most common (Pich & Roche, 2020 ; Wang et al., 2020 ; Yesilbas & Baykal, 2021 ). Violence towards medical staff has serious adverse consequences for patients, physicians, hospitals and the general society. Firstly, violence can often result in increased hazards to medical staff, such as a reduction in their safety, feeling of disrespect and being unsafe. These experiences can have a long-term negative impact on their psychology and working state (Elamin et al., 2021 ; Yang et al., 2021a ). Secondly, patient violence can negatively impact their subsequent treatment and overall healthcare (Roche et al., 2010 ). Thirdly, for the medical industry, it not only drives local medical talent away, but also destroys the order of public places (Du et al., 2020 ; Yang et al., 2021b ). In this study, violence towards medical staff in China is defined as any violence behaviors medical staff experienced in their workplace, including but not limited to insults, threats or physical attacks by patients or their companions, resulting in physical or mental health harm, as well as disturbing normal medical work. The prevalence of violence towards medical staff and corresponding huge negative social impacts make explorations on the cause of patient violence worthwhile. It contributes to patient violence reduction, workplace environment improvement and good patient-physician relationship maintenance.
Violence towards medical staff is a complex and dynamic process, and the cause of patient violence is multi-folded. Scholars have identified various factors of violence towards medical staff. For example, Levin et al. used Ecological Occupational Health Framework to explain violence towards emergency department nurses, and found nurse factors, hospital factors, and social environment factors (Levin et al., 1998 ). Ramacciati et al. categorized the causes of violence towards emergency nurses into nurse factors, patient factors, and hospital organizational environment factors based on the Global Approach to Violence towards Emergency Nurses (GAVEN) (Ramacciati et al., 2018 ). Chapman et al. developed the STAMPEDAR (note: acronyms for the nine behavioral manifestations) violence assessment framework, which includes the patient’s behavioral presentation such as mumbling and pacing, as well as the patient’s mood and disease process, to help medical staff quickly identify patients with violent tendency and reduce violence in the workplace (Chapman et al., 2009 ). Generally, causes of patient violence towards medical staff can be divided into the following three aspects. First, at the patient aspect, individual’s characteristics are crucial. For example, patient’s disease severity (Cai et al., 2019 ; Liu et al., 2019 ), mental stability (Crilly et al., 2004 ; Vezyridis et al., 2015 ), and emotions (Ma et al., 2021 ). Patient’s poor education and low ethical standards (Li et al., 2017 ), and increased patients’ rights awareness (Yu et al., 2015 ) are identified as important factors contributing to patient violence. In addition, patient behavioral factors, including uncooperative treatment behavior (Zhang et al., 2021 ), refusal to communicate (Khan et al., 2021 ), and refusal to accept treatment results (Ma et al., 2021 ) are frequently mentioned. Second, at the physician perspective, physician’s characteristics, including inadequate knowledge of professional skills and unfriendly service attitudes (Cai et al., 2019 ), as well as physician’s inappropriate behavior, such as disrespect for patients (Shafran-Tikva et al., 2017 ) during healthcare service delivery are common factors. Third, the environmental factors include internal environment of patient-physician interaction, hospital environment, and social environment. The internal environment of interaction refers to information asymmetry (Kesavan et al., 2020 ). The hospital environment includes crowded conditions (He, 2014 ; Thomas et al., 2019 ), excessively long queues (Abdellah & Salama, 2017 ; Alhamad et al., 2021 ), and inadequate healthcare resources (Shafran-Tikva et al., 2017 ; Shaikh et al., 2020 ), which are highly likely to bring multiple adverse stimuli to patients in such noisy and dangerous situations. The social environment related factors, including expensive costs (Kesavan et al., 2020 ), negative media coverage (Jiao et al., 2015 ; Yu et al., 2015 ), and distorted social norms (Du et al., 2020 ), which lead to patients’ prejudice towards medical staff, eroded patient-physician trust (Qi, 2020 ; Tucker et al., 2015 ), and induces patient violence.
The COVID-19 pandemic has added significant strain to healthcare resources, including costs associated with healthcare provision. As a result, however, a new sphere of research has emerged for scholars to explore the determinants of violence towards medical staff during a major public health emergency. A study conducted in Egypt revealed that violence towards medical staff mainly occurred due to patients’ panic about the epidemic, shortage of healthcare resources, long waiting times, and poor communication (Arafa et al., 2021 ). Another study from Peru attributed frequent patient violence to the lack of healthcare service options during the pandemic (Del Carpio-Toia et al., 2021 ). In Pakistan, a systematic analysis of reports written during the first six months after the COVID-19 outbreak found that the reasons for violence towards medical staff is mainly due to patients’ distrust in physicians, inability to accept patient deaths, and resistance to epidemic prevention measures (Bhatti et al., 2021 ). Some scholars have also compared the underlying reasons that cause violence towards medical staff during the normal period (i.e., prior to COVID-19) and the COVID-19 pandemic. For instance, Garg et al. ( 2020 ) compared the causes of violence towards medical staff in Western India before and during the COVID-19 pandemic. Their results showed similar reasons in both instances, such as shortages in security personnel, poor construction of hospital facilities, and the poor ability of medical staff. The main differences, however, were attributed to overcrowding of hospital environments, long wait times, deterioration of patients’ condition, and poor quality of food received during hospital stay. In China, some studies have shown that patient-physician relationships have improved since the outbreak of COVID-19 (Hu et al., 2021 ) while the level of trust between patients and physicians has also enhanced (Chi et al., 2021 ; Gao et al., 2020 ; Zhou et al., 2021a ). Zhou et al. ( 2021a ) attributed these changes to Chinese citizens’ united fight against COVID-19 and the critical role played by medical staff on the front line. Similarly, positive reports from news outlets, as well as new medical policies, benefited citizens (Zhou et al., 2021b ). However, as China’s government has attempted to control the spread of COVID-19, incidents of violence towards medical staff has continued (Devi, 2020 ; Xie et al., 2021 ). In this sense, further research is expected to identify the influencing mechanism of patient violence towards medical staff during a major public health crisis.
To sum up, firstly, most extant research has featured on patients (Du et al., 2020 ; Ma et al., 2019 ) or healthcare workers (Naveen Kumar et al., 2020 ; Seema et al., 2019 ) to determine the factors that cause patient violence towards healthcare workers by cross-sectional survey design (Garg et al., 2020 ; Shafran-Tikva et al., 2017 ; Spelten et al., 2020 ). However, the data collected is subjective and may not be universally accepted in reality. Secondly, many extant studies relied structural modeling approach to determine the factors and their influencing path on patient violence, as well as risk factors of healthcare workers’ exposure to patient violence (Cheung et al., 2017 ; Tian et al., 2020 ; Zhu et al., 2022 ). Although the complexity of violence towards medical staff requires significant further research into the interactions between influencing factors, extant research has focused predominantly on the roles of the independent factors rather than the combination of different conditions. Traditional approaches have failed to satisfy the need of coupling possible influencing factors to explain the causes of patient violence. This leads to the existing coping strategies for patient violence are much the same but slightly different, and poor guidance for practice. Additionally, the particular context of global COVID-19 pandemic provides new research potential. Therefore, this study aims to identify the determinants of patient violence towards medical staff from multiple cases collected during the COVID-19 pandemic in China. Specifically, building upon Triadic Reciprocal Determinism theory, this study explores the role of possible combinations of determinants in predicting violence using the QCA approach, and the proposed targeted strategies for patient violence reduction. The research questions are as follows. What are the coupling determinants leading to the increase in patient violence during the COVID-19 pandemic? and, how do they differ compared to the normal state, prior to the outbreak of COVID-19 periods?
Theoretical framework: triadic reciprocal determinism
This study uses the Triadic Reciprocal Determinism theory, proposed by Albert Bandura in 1986, to explain the causes of patient violence towards medical staff in China during the COVID-19 pandemic. The TRD theory emphasizes the impact of personal factors, environmental factors, and behavioral factors, on human behavior, and posits that the three elements have relatively independent but interactive change relationships (Bandura, 1986 ). Personal factors include individuals’ perception of the environment and cognition of behavior, as well as other internal characteristics, such as thinking, self-evaluation, physiological response-ability, and cognitive ability (Guo & Jiang, 2008 ). Environmental factors mainly refer to external environmental factors and the internal objective environment between patients and physicians which affects the personal cognition and behavior of patients. The behaviors of individuals are an observable social activity and are expressed through action and language during communication. The TRD theory highlights the interaction mechanism among personal internal characteristic factors, external environmental factors, and personal behavioral factors which is the important theoretical basis for exploring the causes of violence towards medical staff and the relationship between the various factors (Fig. 1 ).
Triadic reciprocal determinism theory model
According to the TRD theory model, violence towards medical staff is essentially the result of the interaction between the patients’ personal factors, the environment, and the behavior during the patient-physician interaction. Throughout the medical treatment, any activities may contribute to the occurrence of violence behavior. In this study, we refer to personal factors as those pertaining to patients, including their relatives, friends, colleagues, or other companions. Personal factors include both attributes of individual characteristics such as severity of disease and stability of mental state, attributes of intimate relationships established between patients and other individuals in the current healthcare settings, and individual moral standards expressed through their certain behaviors. Environmental factors refer to the organizational factors within the healthcare settings, as well as factors within the patient-physician interaction, such as the information asymmetry within patient and physician during the healthcare service process. Behavioral factors represent both the patients’ and physicians’ behavior throughout the process of healthcare service delivery, i.e., the behavioral feedback that individuals provide in response to perceived environmental stimuli in a complex and dynamic healthcare delivery environment. As shown in Fig. 2 , P represents personal factors, E represents environmental factors, and B represents behavioral factors. The two-way arrow between every two factors indicates their mutual connections. By analyzing the combined effects between different variables to investigate and explain the causes of patient violence behavior.
Patients’ experiences, perceptions, expectations, and evaluation of the healthcare services they receive are closely linked to their violence towards medical staff. Their behavior is always triggered by external stimuli (Bulle & Rode, 2018 ; Piraianu et al., 2021 ) which are workplace hazards, similar to risk of infection or slips and falls. In addition, external stimuli can be triggered by human interaction, such as verbal communication, physical contact, and conflicts. Due to differences in perceptions of the environment and patients’ cognitive behavior, their resulting behavior can be different. Under the various conditions experienced in medial settings, a minor issue can evolve into a violent action towards medical staff. For example, in a noisy overcrowded hospital environment, patients can experience extreme anxiety and tension, and fear for their health. If they see busy medical staff or experience long wait times, they are more likely to develop dissatisfaction. Once a small external stimulus is triggered, their emotions are likely enhanced. Consequently, the medical staff (i.e., the direct contact with patients) will bear most of the anger and violence that ensues. In return, the unfriendly behavior will exacerbate already intense patient-physician conflicts and erode the established trust between patients and physicians.
TRD-based analytical framework for patients’ violence
- Qualitative comparative analysis
This study employs a qualitative comparative analysis (QCA) approach, proposed by Charles Ragin in 1987, to examine the determinants of patient violence towards medical staff during the COVID-19 pandemic in China. The QCA approach, widely used in social sciences research, contributes to exploring multiple concurrent causalities through a small or medium-sized case comparison, usually with 10–40 samples to meet research needs (Rihoux & Ragin, 2008 ). QCA expands the analytical framework of causality (Furnari et al., 2021 ; Rihoux & Ragin, 2008 ). First, the assumption of concurrent causality replaces the ideology of a single factor acting independently. Second, multiple combinations of antecedents have equivalent effects on the emergence of a particular outcome. Again, causal effects are no longer consistent, the antecedent variable exerts positive or negative utility depending on its combination with other conditions. Finally, QCA emphasizes the asymmetry of causes, i.e., the emergence or non-emergence of an outcome may require different combinations of causes to explain it separately. In sum, QCA can better explain the heterogeneity across cases, as well as the complex configuration effect between conditions (Huo & Li, 2022 ), which is the highlight of this study in exploring the causes of violence towards medical staff. By using QCA, it allows us to assess the influence of possible factor combinations for the exploration of the mechanism of multiple factors regarding a specific research question (Ragin, 1987 ).
The more widely used QCA analysis techniques include crisp-set QCA (csQCA), multi-value set QCA (mvQCA), and fuzzy-set QCA (fsQCA). The csQCA calibrates variables as dichotomous variables, assigned 0 or 1. While, mvQCA is an extension of csQCA, allowing multi-valued variables, and fsQCA introduces the concept of set affiliation, representing the degree to which different cases belong to a certain set through fuzzy set scores. This study adopted csQCA method for analysis to elucidate the mechanisms of patient violence towards medical staff. It can reduce the complexity of the phenomenon by measuring the presence or absence of variables and has a greater advantage in studies dealing with small to medium-sized sample sizes (Kogut & Ragin, 2006 ; Roig-Tierno et al., 2017 ).
As part of this study, we created a case library containing incidences of patient violence towards medical staff during the COVID-19 pandemic in China. The time period in which cases were collected was from 1 December, 2019, to 1 July, 2020. This period was chosen as it captures the entire first wave of the epidemic in China. Meanwhile, incidents of patient violence towards medical staff received significant attention from media outlets and the public during COVID-19 which can help in providing sufficient information and case novelty. Since the causes of patient violence may vary between private and public hospitals, we limit our search to public hospitals in China. To ensure the completeness and accuracy of each case, we collected the case information across different channels, including Sina Weibo, WeChat, Toutiao.com, and other websites. The search strategy employed used the following key phrases: patient’s violence , medical disputes , violence against medical staff , and violence in hospitals . In the process of searching for the cases, we also cross-checked the case reports, comments, and all other relevant information from different sources. In addition, efforts were made to ensure the heterogeneity of cases by searching for as many potential cases as possible.
A case library containing twenty-six cases was initially created after retrieving information pertaining to violent incidents that were widely reported in the media. After this, further selection criteria were applied, including: (1) the patient violence towards medical staff occurred in China; (2) the patients and physicians exposed to the violent incidents have an established connection i.e., they are engaged in the delivery or receiving of healthcare services; (3) the case information collected is sufficient for analysis purposes, including a storyline, causes, development process, and consequences. As a result, six cases were removed following this screening process (see Table 9 in the Appendix for details). In one case, the patient and physician did not commence the delivery of the healthcare service and, therefore, we determined that they did not establish a real patient-physician relationship. In addition, the other five cases did not report sufficient information, such as the causes and process of violence.
In total, twenty cases were included in the case library (see Table 10 in the Appendix for details). The time period for these was from 24 December, 2019, to 22 June, 2020. The cases occurred across 15 cities and 12 provinces in China, of which Beijing had the most cases (30%), followed by Hubei Province (15%) and Sichuan Province (10%). The result of all incidents can be divided into two types: medical staff casualties and no casualties. With regards the identity of those committing violence towards medical staff, 19 cases involved patients or their relatives, of which the patient or their immediate relatives accounted for 63%. In terms of the rank of hospital where violence occurred, cases included not only first-level hospitals in small villages, but also tertiary level hospitals in first-tier Chinese cities. Specifically, the case library included one first-level hospital, two secondary hospitals, and seventeen tertiary level hospitals, including twelve tertiary first-class hospitals.
The study’s outcome was set to Medical Staff Casualties . Violence reported in the cases included injuries or deaths of medical staff, as well as verbal threats or insults. Eighteen cases reported casualties while two cases reported no casualties. In considering the outcome of the violence with casualties, we set those cases that resulted in the injury or death of medical staff as 1 and those that reported no casualties as 0. Building on the theoretical framework proposed in this study, we identified the conditions associated with personal factors (i.e., Disease Severity, Mental Stability, Relationship Closeness, and Moral Standard), environmental factors (i.e., Insufficient Resources, Hospital Rank, Information Asymmetry, and Treatment Experience), and behavioral factors (i.e., Inappropriate Service Behavior, Psychological Deviant Behavior, Patient Cooperativeness, and Patient-Physician Communication).
Personal factors refer to individuals’ characteristics and the internal driving force for their responsive behavior. These include the Disease Severity, Mental Stability, Relationship Closeness, and Moral Standard of the person committing the violence towards medical staff.
The health condition of the patient has long been regarded as a crucial determinant for violence towards medical staff (Ma et al., 2019 ). It is believed that hospital emergency departments have a much higher risk of experiencing patient violence towards their medical staff as patients are more likely to experience critical healthcare issues (Cai et al., 2019 ; Liu et al., 2019 ). In general, patients with acute and severe conditions attach more significance to their diagnosis and therapeutic effect. In addition, violence is more likely to occur during uncertain events, such as when physicians relay upsetting news. In this study, if the patient’s disease is critical (i.e., the case reported that the patient has a severe disease , the patient requires emergency rescue or surgical treatment ), it was coded as 1, otherwise 0.
Mental stability refers to the mental condition of the person committing the violence towards medical staff and highlights the individual’s capacity to remain rational when solving conflicts (Shafran-Tikva et al., 2017 ). In general, if patients or their companions maintain a calm state of mind, they are less likely to assault medical staff. On the contrary, if they are in a poor mental state (e.g., mental disorder, drunk, drug abuse, or suffer from long-term illness), they are likely to have less tolerance and can be easily irritated and become violence (Crilly et al., 2004 ; Vezyridis et al., 2015 ). In this study, if the mental stability of the perpetrator is unreliable, as demonstrated by drunkenness, drug abuse, or mental disorder , it was coded as 1, otherwise 0.
Relationship closeness describes the relationship between the patient and their companions during healthcare delivery. Evidence shows that the perpetrators of violence against medical staff are mainly the patients and their relatives, for instance, parent-child relationships and brother-sister relationships (Aljohani et al., 2021 ; Byon et al., 2021 ). Compared to other forms of relationship, a close relationship with the patient is more likely to develop much stronger empathy and attack medical staff with unpleasant treatment experiences. In this study, if the perpetrators are the patients and / or their relatives , it was coded as 1, otherwise 0.
An individual’s moral standards refer to their ability to follow social values, including respecting others and being compliant with hospital rules. For those being violent towards medical staff, they may demonstrate early signs of unfriendly behavior due to low moral standards. This behavior is frequently reported in previous literature on patient violence, including e.g., patients ignoring hospital rules regarding smoking or queuing (Li et al., 2017 ). Once their requests are refused, they are more likely to develop violent behavior. In this study, if patients or their companions cut in line, accuse medical staff for no reason, smoke in public places, or make unreasonable demands , it was coded as 1, otherwise 0.
Environmental factors include both the hospital environment, in terms of the medical facilities and resources, and the internal environment patients experience during the healthcare service process. They are demonstrated in Insufficient Resources, Hospital Rank, Information Asymmetry, and Treatment Experience.
Insufficient resources refer to the division between the healthcare demands of patients and their corresponding service providers e.g., shortages in healthcare professionals (Singh et al., 2019 ). Due to the imbalanced economic development level across eastern and western cities in China, the total amount of high-quality healthcare resources is relatively insufficient in less-developed western China which leads to maladjustment and mismatch between healthcare service provision and public demand (Chai et al., 2020 ). In addition, the siphon effect of large hospitals in large cities has further widened the imbalance of healthcare resources. Qualified medical staff, advanced medical equipment and facilities, high-quality healthcare resources, and drugs are relatively concentrated (Yu et al., 2021 ). However, this misallocation of resources means that hospitals with insufficient resources are less likely to satisfy the demands of patients and are more likely, therefore, to experience patient violence towards medical staff. In this study, if the hospital is described as facing a shortage of medical staff, holding patients exceeding capacity, or low efficiency in the case , it was coded as 1, otherwise 0.
In China, hospitals are classified into different ranks considering their amount of healthcare resources, capacity, and service population size. In general, the higher the rank of hospital, the better the healthcare service received by patients. However, excessive medical treatment from physicians in high-rank hospitals happens frequently (He, 2014 ). If patients suffer unfair treatment, they may demonstrate bad emotions, leading to conflicts or violent attacks. In this study, we assign violence occurred in tertiary hospitals as 1, otherwise 0.
The healthcare knowledge gap between patients and physicians is widely acknowledged (Kesavan et al., 2020 ). During diagnosis and treatment of patients, it is difficult for physicians to answer all patient’s concerns. In addition, as most patients use the Internet frequently, they may find it difficult to distinguish between misleading health information and information provided by their physician. The inability to appropriately understand the physician’s explanation and their blind trust in alternative health information found online make patients less confident in physicians. In this study, if the cases have emphasized the sharp differences in the healthcare knowledge gap , it was coded as 1, otherwise 0.
Treatment experience is directly connected to the hospital environment, including the hospital’s facilities and the attitude of medical staff encountered (Li et al., 2017 ). It is demonstrated as a hospital’s noisy environment and crowded space (Darawad et al., 2015 ; Thomas et al., 2019 ), long wait times (Abdellah & Salama, 2017 ; Raveel & Schoenmakers, 2019 ), and poor guidance provided at reception. All these factors may cause patients to experience negative emotions towards physicians with possible violence ensuing. In this study, if the cases state the reason as long waiting time or dissatisfaction with the physician’s treatment, experiencing conflicts and quarrels with medical staff , it was coded as 1, otherwise 0.
Behavioral factors concern the interactive behavior and consequences experienced by the patient. They are demonstrated in Inappropriate Service Behavior, Psychological Deviant Behavior, Patient Cooperativeness, and Patient-Physician Communication.
Inappropriate service behavior
In the process of receiving healthcare services, some inappropriate behavior of physicians may be experienced by patients e.g., an unfriendly behavior may lead patients to become violent (Shafran-Tikva et al., 2017 ). Specifically, it can be the physician’s avoidance of patients, refusal of patient registration, ironic language usage and others. In this study, if the report emphasized an existence of physician’s inappropriate service behavior , it was coded as 1, otherwise 0.
Psychological deviant behavior
Psychological deviant behavior is deeply rooted in the culture that physicians portray when solving patient problems (Ma et al., 2021 ). It is caused by the unrealistic expectation of patients towards healthcare services, such as expecting a higher quality of physician or hospital. If the treatment effect fails to satisfy the patient’s expectations, a psychological deviation tendency is likely to be present. In a typical case, patients develop moderate or high resistance to the treatment effect and demand a detailed explanation which can easily induce violent behavior. In this study, if the report indicates that the patient refused the treatment result , it was coded as 1, otherwise 0.
Patient cooperativeness refers to a patient’s compliance with physicians and their cooperation with the physicians’ treatment plan. If the patient demonstrates a low willingness to cooperate with the physician, patient-physician conflict is likely to occur and lead to violence (Zhang et al., 2021 ). In this study, if the report demonstrates that the patient did not obey the treatment plan , it was coded as 1, otherwise 0.
Patient-physician communication refers to the two-way interaction between patients and physicians, which requires time and energy to support. Insufficient or unsuccessful communication between the two parties is often observed in healthcare settings (Khan et al., 2021 ; Zhang et al., 2021 ). Failure to effectively establish patient-physician communication may result in patients becoming violent. In this study, if the case highlighted failed or insufficient patient-physician communication , it was coded as 1, otherwise 0.
By following the instructions of the csQCA approach, each condition was created into a dichotomized variable. Table 1 provides the details.
Analysis of normal necessity and sufficiency
Firstly, the normal analysis of necessity and sufficiency was performed for each condition in the presence of Medical Staff Casualties . The sufficient condition refers to the presence of a condition that can predict the expected outcome, but is not the only cause of the outcome. The necessary condition predicts the outcome in combination with other conditions, and the necessary condition appears in all such combinations (Heidrich & Bandelow, 2019 ; Schneider & Wagemann, 2012 ). Consistency and coverage are two indicators for the judgment of necessity or sufficiency between conditions and outcome (Ragin, 2006 ). Superscript (*) indicates that the condition meets or exceeds the consistency requirement of 0.90, which can be considered a necessary condition for the outcome in QCA analyses (Ragin, 2000 ). Table 2 shows that Relationship Closeness is a necessary condition for patients’ violence to cause physicians to be injured or die, which can be explained by the existence of the Relationship Closeness in all conditional configurations leading to the violence. However, this does not mean that the stronger relationship, the more likely violence is to occur. Since patients’ violence towards medical staff is the result of the interaction of numerous factors, it is necessary to further examine the configurations of all conditions at the same time. This study, therefore, explores the configurations of conditions to reveal the causes and mechanisms of patients’ violence towards medical staff during the COVID-19 pandemic in China.
For QCA, the number of condition combinations increases exponentially with the addition of condition variables (Rihoux & Ragin, 2008 ). This study identified three condition variables, and the number of possible logical condition combinations exceeds the number of existing cases. Direct QCA analysis of 12 conditions may yield complex combinations, and fail in effective theoretical refinement. Followed the practice on addressing the problems (Zhu & Wang, 2020 ), this study investigates critical factors causing patient violence in terms of personal factors, environmental factors, and behavioral factors, as well as their possible combinations, building upon TRD theory.
Standard analysis was completed in the presence of Medical Staff Casualties . Intermediate solutions were created and selected that were superior to both parsimonious solutions and complex solutions (Rihoux & Ragin, 2008 ) to complete subsequent configurations. Ragin ( 1987 ) proposed that the results are good when the consistency value of the intermediate solution is above 0.8 and the coverage value is above 0.5. We also built truth tables that are critical to QCA to explain the presence of patients’ violent behavior. Truth tables are an analysis tool based on the principle of formal logic (Smela & Sejkora, 2022 ), which can demonstrate all possible condition combinations and states in the result set, as well as the number of cases in each configuration (Kahwati & Kane, 2018 ). QCA requires that the minimum case frequency threshold should be set to 1 or 2 when the sample size is relatively small (Ragin, 1995 ). Considering the number of cases in this study and avoiding extreme case configurations, we set the minimum case frequency threshold to 2, meaning that we only included configurations with 2 or more cases. The consistency threshold value was set to the default value of 0.8 (Rihoux & Ragin, 2008 ), which means that configurations with PRI (Proportional Reduction in Inconsistency) consistency less than 0.8 in the outcome column were coded as 0.
With regards personal factors, two configurations can lead to medical staff being injured or dying (coverage: 0.89, consistency: 0.89). Configuration A1 indicates that strong relationship closeness and reliable mental stability leads to the injury of medical staff. In configuration A2, the combination of low disease severity , strong relationship closeness , and low moral standard , stimulates violence from patients.
Configurations A1 and A2 can be combined as MSC=A1+A2=~MTS*RC+~DS*RC*MRS=RC*(~ MTS + ~ DS*MRS) . The Relationship Closeness can be regarded as the necessary condition for personal factors to affect the outcome. The Disease Severity , Mental Stability , and Moral Standard can influence the outcome combined with Relationship Closeness . From the twenty cases reviewed, nineteen incidents of violence were committed by the patients themselves and / or their relatives. Therefore, timely detection and soothing of patients’ and their companions’ emotions plays a pivotal role in avoiding contradictions and violence towards medical staff (Tables 3 and 4 ).
With regards environmental factors, two configurations were identified as being able to describe the conditions that cause medical staff injury or death (coverage: 0.56, consistency: 1). Configuration B1 indicates that the combination of sufficient resources , high- rank hospital , and unsatisfactory treatment experience leads to patient violence towards medical staff. Configuration B2 indicates that the combination of sufficient resources , high- rank hospital , and high information asymmetry stimulates violent behavior.
Configurations B1 and B2 can be combined as MSC = B1 + B2 = ~ IR*HR*TE + ~ IR*HR*IA = HR*~IR *(TE + IA) . The combination of HR and ~ IR appears as the necessary condition for environmental factors to affect the outcome, just as we know that the higher the rank of a hospital usually has sufficient medical supplies in our real life. From the twenty cases reviewed, fourteen had the necessary combination (Tables 5 and 6 ).
Regarding behavioral factors, three configurations can explain the conditions that lead to medical staff being injured or dying (coverage: 0.72, consistency: 0.93). Configuration C1 indicates that the combination of appropriate service behavior , no psychological deviant behavior , and ineffective patient-physician communication results in medical staff casualties. Configurations C2 and C3 demonstrate that the combinations of low patient cooperativeness and ineffective patient-physician communication , with appropriate service behavior or no rejection of the treatment results, brings about the patient’s violent behavior. Based on configuration C1, configurations C2 and C3 superimpose the situation that patients do not cooperate with physicians during the treatment process, which means that the situation and likelihood of violent behavior towards medical staff is more detailed.
Configurations C1, C2 and C3 can be combined as MSC = C1 + C2 + C3 = ~ ISB*~PDB*PPC + ~ PDB*PC*PPC + ~ ISB*PC*PPC = PPC*(~ ISB*~PDB + ~ PDB*PC + ~ ISB*PC) . Obviously, Patient-Physician Communication can be regarded as an important condition for behavioral factors affecting the outcome. It forms three paths that ultimately led to medical staff casualties by combining any two conditions. In this study, 75% of cases showed ineffective communication between patients and physicians. When receiving healthcare services, patients have the right to choose their diagnosis and treatment plan on the premise that they receive clear guidance about the associated risks. However, this cannot be used as an excuse for patients not to cooperate with physicians. If the patients question the physician’s advice or refuses to cooperate, and the communication and mediation with the physician are ineffective, the unpredictable conditions can cause unnecessary trouble (Tables 7 and 8 ).
Robustness check is crucial in QCA analysis (De Marchi et al., 2022 ; Wu et al., 2021 ). The most common testing method is to adjust relevant parameters, such as consistency threshold value and minimum case frequency threshold (Waldkirch et al., 2021 ; White et al., 2021 ). In this study, the robustness check is performed by comparing the combination of variables and the differences in parameters of the configurations in the before and after results (Schneider & Wagemann, 2012 ). Following previous studies, this study conducted the robustness check by adjusting the consistency threshold value from 0.8 to 0.82 (Huo & Li, 2022 ). Results showed that there was no difference in the before-after comparison between the configurations of personal and environmental factors, and the configuration of environmental factors changed, but the important roles of two variables, Patient Cooperativeness and Patient-Physician Communication, remained prominent. Therefore, our results are robust.
Patient violence towards medical staff has become an increasingly common occurrence in healthcare settings worldwide (Ozdamar Unal et al., 2022 ). The COVID-19 global pandemic exposed this situation which poses serious challenges to the safety, and mental and physical wellbeing of medical staff (Bellman et al., 2022 ; Catton, 2020 ; Romate & Rajkumar, 2022 ). Throughout the pandemic, medical staff have experienced threats and challenges related to e.g., extreme mental pressure, shortages in personal protective equipment, pressures to diagnose and treat patients suffering from COVID-19, communication problems between patients, their companions and physicians, and the unknown fear of death surrounding the disease (Huang & Zhao, 2020 ; Kang et al., 2020 ).
As previous studies have demonstrated, violence is not caused by a single factor but is a result of a combination of many factors (Al-Shaban et al., 2021 ). We outlined four types of patient violence built upon TRD-based analytical framework for patients’ violence, and further explained the interaction and determinism relationship across factors. First, the interactive determination between personal factors and environmental factors. When exposed to stressful and crowded hospital environment, patients are prone to have more anxiety and intense about their health condition. In turn, the negativity spread by individuals continues to fuel unrest in the hospital environment. Meanwhile, if the patient companions had close intimate relationships, they are more sensitive to the negative stimulus of the environment. Second, the interactive determination between environmental factors and behavioral factors. Hospital visits increase greatly because of the pandemic. As a result, the healthcare environment is fully occupied, and fail to satisfy the patient healthcare need in terms of waiting time and treatment time. The psychological imbalance may trigger violence towards medical staff, and cause medical resources lost. Third, the interactive determination between personal factors and behavioral factors. Although patient participation increases during healthcare consultations, healthcare knowledge gap persists. Some patients may reluctant to follow physician advices and refuse to communicate when disagreement arises. This, in return, leads to poor treatment outcomes for patients.
Strong relationship oriented violence
We summarized configurations A1 and A2 as Strong Relationship Oriented Violence. In medical settings, compared to the friends or colleagues of patients, relatives are more likely to have stronger empathy for patients’ diseases and have greater opportunity for contact with the physicians (Lafta & Falah, 2019 ). They are more sensitive to negative “stimuli” in the limited healthcare service environment, and to give behavioral feedback (e.g., violence behavior that harms healthcare professionals).
Relationship Closeness is a necessary condition for inciting patient violence towards medical staff resulting in casualties. The majority of perpetrators are patients themselves and / or their relatives which is a prominent feature of violence towards medical staff both before (AbuAlRub & Al-Asmar, 2011 ; Albashtawy et al., 2015 ; Shea et al., 2017 ) and during the COVID-19 pandemic (Ghareeb et al., 2021 ; Naseem et al., 2022 ). A study conducted in Iraq during the pandemic found that 93.9% of medical staff experienced violence towards them from patients or their families (Lafta et al., 2021 ). Another study in Peru reached a similar conclusion (Del Carpio-Toia et al., 2021 ). The reasons may be as follows: First, patients and their families face numerous mental pressures when receiving healthcare services, such as possibility of fatal disease (Muzembo et al., 2015 ). Their emotions can become unstable and fluctuate. Physicians, as the object of patients’ emotional expression, bear most of the brunt of patients’ negative emotions. Ultimately, patients may choose extreme ways to vent their frustrations if their problem is not solved in a perceived satisfactory manner, such as being violent towards medical staff. Second, some relatives may protest or become violent towards medical staff to demonstrate their affection towards the patient (Liu & Tan, 2021 ). To some extent, relatives may show their concern for their loved ones to self-satisfy their performance needs for family affection or simply to obtain economic benefits, such as higher compensation. Third, strict prevention policies enforced during COVID-19 increased the complexity and risks associated with receiving medical treatment, leading to difficulties in patients’ families being able to accompany them or visit them during their hospital stay. The restricted visits to patients (Dopelt et al., 2022 ; Xie et al., 2021 ) and dissatisfaction with hospitals’ prevention policies (Freytag et al., 2021 ) have been confirmed as two of the causes for patient violence towards medical staff. In an analysis by Arafa et al. (Arafa et al., 2021 ), they found that patients’ extreme panic and anxiety about COVID-19 fueled conflict with physicians and escalated patient violence towards medical staff during the pandemic. During the prevention and control stages of the COVID-19 pandemic, patients and their companions were situated in hospitals with a high risk of infection and poorly protected equipment. Hospitals in heavily affected areas took measures to limit the number of caregivers on duty at any onetime and implemented more strict administration at the hospital entrance.
To prevent Strong Relationship Oriented Violence, firstly, physicians should realize the importance of recognizing and caring for the emotional changes of patients and their companions. Physicians should detect and alleviate patients’ medical anxiety in a timely manner rather than simply collecting disease-related information for diagnosis purposes. Secondly, patients are expected to improve their moral cultivation and standards, reasonably control their negative emotions, and strictly abide by hospital regulations, including disease prevention policies. Thirdly, hospitals must strengthen education surrounding moral standards for both physicians and patients by combining traditional and new media approaches. Prior research has suggested that social media and other communication channels are critical to combating violence towards medical staff (Bellizzi et al., 2022 ). Furthermore, hospitals should effectively promote that any violence towards medical staff will result in legal action.
Healthcare resources and service mismatch violence
Configurations B1 and B2 were summarized as the Healthcare Resources and Services Mismatched Violence. During the pandemic, highly ranked hospitals were often overcrowded due to the significant flow of people demanding medical attention. This meant that medical settings became more complex and contained risk factors that may result in violent incidents towards medical staff (Yang et al., 2021a ). With the implementation of the Healthy China Strategy, people’s health awareness has been raised and the demand for health and disease management has expanded, surge in proactive patients causing overcrowding in hospitals. In noisy hospital environments, patients may quickly become discomforted which breeds conflict because seeing a doctor is a highly private behavior. In tertiary hospitals, patients have higher expectations about the healthcare services and treatment they will receive. The imbalance between patients’ expectations and actual feelings (Wu et al., 2015 ) may eventually lead to violence towards medical staff. In addition, the professional quality of physicians in higher-rank hospitals is stronger, and the difference in healthcare knowledge with patients is more obvious, which plays a leading role in patient-physician cultural conflict and further stimulates conflict.
The problem of insufficient healthcare resources is not too prominent in China. Previous studies have emphasized that a lack of resources is a significant factor which leads to patient violence towards medical staff. Specifically, insufficient resources can include long wait times, but short consultation times (Arafa et al., 2021 ), declining medical environment (Basis et al., 2021 ), and the shortage of healthcare resources, such as ICU wards and ventilators (Chamsi-Pasha et al., 2020 ; McKay et al., 2020 ). During the severe stage of the COVID-19 pandemic, the Chinese government tried its hardest to ensure the supply of medical materials to all levels of hospitals. Similarly, hospitals in China also received medical supplies and assistance from other countries. We emphasize that based on the relative adequacy of hardware environmental conditions, the soft skills of medical services are extraordinarily important to the patient’s experience and satisfaction. In this study, one of the most common causes for violence towards medical staff was patients becoming dissatisfied about the long wait times. During the pandemic, hospitals made flexible adjustments according to the current pandemic situation. For example, some hospitals stopped receiving patients and created designated hospitals for fever clinics. The serious imbalance in the number of patients and physicians due to a great number of patients flocking to a few hospitals directly led to the extension of waiting times of patients.
To prevent Healthcare Resources and Services Mismatched Violence, first, the physicians should strengthen their professional capabilities. When delivering healthcare to patients, physicians should timely synchronize the relay of disease information to patients in an easy-to-understand language to fully respect patients’ right to know. For patients, the most important action is to establish correct medical cognition and have reasonable psychological expectations for treatment. For hospitals, as providers of healthcare services, they should realize the need for improving healthcare resources and services. On the one hand, it is necessary to focus on improving the overall quality of healthcare delivered, which is the core task of a hospitals, but they should also actively cultivate physicians’ professional ability and good service awareness, and organize health education and charity activities to promote healthcare knowledge which shortens the distance between patients and physicians. On the other hand, the COVID-19 pandemic reminds us that hospitals should ensure the reserve of medical materials and respond to public health emergencies with sufficient strength. For governments, they should allocate high-quality healthcare resources in a fair manner and speed-up the cultivation and retention of medical talent to alleviate the problem of the great disparity in the number of patients and physicians.
Violence caused by ineffective patient-physician communication
We named configuration C1 as the Violence caused by Ineffective Patient-Physician Communication. When delivering healthcare services, the decision-making involved in diagnosis and treatment, and the follow-up care requires effective patient-physician communication. In this situation, the patients are in a weak position with regards communication with physicians. In most incidents of violence, patients’ verbal expressions show an irrational state, which can lead to a failure in patient-physician communication.
Patient-Physician Communication is a critical element of delivering healthcare services successfully (Tai-Seale et al., 2021 ). Ineffective or failed communication between patients and physicians is considered a common cause of violence towards medical staff (Boafo, 2016 ; Ramacciati et al., 2018 ; Wu et al., 2012 ). Recent research indicates that a friendly physician attitude and behavior improved the patient-physician relationship during the COVID-19 pandemic, which provides a new research perspective to clarify the importance of patient-physician communication (Basis et al., 2021 ; Hu et al., 2021 ; Zhou et al., 2021a ). At the same time, it also provides a direction for future research in terms of improving the relationship between patients and physicians (Gao et al., 2020 ). The friendly communication style of physicians causes a series of positive chain reactions which not only improves the patient-physician relationship but also reduces the chances of violence towards medical staff. In this study, we did not find the significant effect of physicians’ inappropriate behavior on violence towards medical staff. A study conducted in Turkey found that 58.2% of physicians developed excessive medical accident anxiety during the pandemic (Buran & Altin, 2021 ), which shows that physicians are becoming more aware of if they are portraying inappropriate behavior when delivering services. This suggests that when physicians pay attention to their behavior, it can prevent violence to a certain extent.
To prevent Violence due to Ineffective Patient-Physician Communication, both patients and physicians must trust and respect each other. The patients and physicians should strive to learn and develop communication skills and improve communication efficiency to create an effective dialogue with a friendly attitude. For physicians, they should develop their empathy ability to be healthcare workers with sound temperament and they should try to think from the standpoint of patients, which is the entry point of a good patient-physician relationship. For patients, it is necessary for them to actively communicate with physicians and be able to rationally express their views without becoming frustrated. Even if a dispute inevitably occurs, patients should remain objective and calm and use legal means to solve the dispute to safeguard their rights and interests. Hospitals should intervene in times of dispute and deal with patients’ complaints fairly, which is an effective way to curb future disputes turning into violent incidents.
Ineffective communication superimposed low patient compliance violence
We summarized configurations C2 and C3 as the Ineffective Communication Superimposed Low Patient Compliance Violence, which emphasizes the combination of ineffective patient-physician communication and low patient cooperativeness. The results of the communication and cooperation between patients and physicians are directly related to patients’ health and wellbeing. The low patient compliance and ineffective communication will aggravate the patient’s condition which may become a potential reason for contradiction and future acts of violence.
Many studies have shown that communication skills, as well as attitudes and modes between patients and physicians, play an important role in the continuous and normal promotion of healthcare services (Kwon & Noh, 2013 ). The tension between patients and physicians has been eased, which not only benefits from the positive improvement of patient-physician communication but is also inseparable from the active cooperation of patients with physicians’ medical plans. The positive feedback demonstrates that communication and cooperation are extremely important during patient-physician interactions. However, the impact of patient compliance with physicians on violence towards medical staff has not garnered significant attention in current research. In this study, patient compliance mainly refers to patients’ cooperation with physicians’ advice, arrangements, and decisions. Patients act as participatory decision-makers in the collaborative model of healthcare services (Taube, 2016 ). The diagnosis and treatment of diseases are based on a great deal of interaction and communication between patients and physicians. Patient compliance and acceptance of treatment results have been confirmed to affect the patient-physician relationship (de Waard et al., 2018 ), and a poor patient-physician relationship may lead to violence towards medical staff (Zhou et al., 2021a ).
To prevent the Ineffective Communication Superimposed Low Patient Compliance Violence, first, patients should respect and cooperate with the physicians’ treatment plan based on friendly communication. The patients should fully understand the advantages and disadvantages of different treatment plans and choose carefully in combination with the professional advice received from physicians. Second, physicians should improve their professionalism when delivering healthcare services and guidance, and should give more personalized treatment plans to obtain the full trust of patients. Third, governments should actively publicize positive stories and gradually cultivate the trust of patient groups in physicians. It is believed that the incidents of violence can be reduced at the source through the joint efforts of patients and physicians and the improvement of a social environment.
Theoretically, this study not only extends the application of TRD theory in explaining causes of violence towards medical staff, but also provides a novel perspective to group the influencing factors of patient violence. Compared with previous studies with extensive efforts in identifying different factors among stakeholders, this study regards the causes of patient violence as mutually affected based on TRD theory. Benefited from the advantages of QCA analysis for explaining multiple concurrent causal relationships, this study adopted flexible configurational combinations to reveal the neglected interactions and substitutions among factors of patient violence towards medical staff. Practically, since the combined effect among factors specifies types and characteristics of violence towards medical staff, it provides important guidance for formulating targeted violence prevention and control measures. Meanwhile, this study also emphasizes the importance of relationships, healthcare service quality, and patient-physician communication and collaboration in patient violence prevention during public health emergencies. In addition, patient compliance is much more important in precipitating patient violence during COVID-19 pandemic than that of normal times. This may help in understanding of the patient-physician relationship in the Asian cultural context.
Specifically, this study has implications for healthcare service delivery, hospital management, and the policy-making. For healthcare service delivery, medical professionals need to strengthen the emotional care of both patients and their companions, especially those who have low patient compliance. They are expected to master more patient and empathetic communication skills to relieve the negative emotions in time, and keep patient well informed of possible disease consequences and the necessity to cooperate with treatment. For hospital management, in addition to sufficient supply of staff resources and medical materials, the hospital should protect personal safety of medical staff throughout the offline consultation environment including the pre-consultation, consultation and post-consultation stages. For example, strict security checks for patients in the pre-consultation stage, warm-hearted hospital guide and emotional care during the consultation stage, efficient medical insurance reimbursement and medical record review in the post-consultation stage. For major public health emergencies like COVID-19 pandemic, as patients surge in the hospital, many threatening factors emerge. The hospital should increase security checks to maintain stable medical order, and respond quickly to adverse events and requests for help in time. Meanwhile, medical administrators are required to be fully prepared to spot potential conflicts and resolve the disputes in a timely manner. For policymakers, violence towards medical staff during COVID-19 is not only about the personal safety of healthcare workers, but also about epidemic prevention and control. Although the conduct is minor during normal times, the same conduct may jeopardize the public during emergencies. It is necessary to emphasize the harsh of penalties during public health emergencies, and promote zero tolerance of patient violence atmosphere in the society.
Although this study has contributed significantly to current understanding about the impact of COVID-19 on the causes of patient violence towards medical staff, it does have numerous limitations. First, the actual impact may be different across geographical regions. Future studies can develop the existing case library to further compare the differentiated impacts of the pandemic. Secondly, we identified the mechanism solely on selected cases and this might neglect potential determinants from unreported information. Future research can explore more critically the causes of patient violence using a qualitative approach with patients, physicians, hospital managers, and healthcare administrators. In addition, the forms of violence against medical staff can be more than verbal and physical violence in the emerging digitalized world. Future study can look up to the Internet-based violence towards medical staff, for example the characters, causes and counter measurements for cyberbullying towards medical staff (Zhu et al., 2021 ).
Building on the TRD theory, this study identified twelve conditions from three elements (i.e., personal, environmental and behavioral factors) to systematically analyze and characterize the causes of patient violence towards medical staff during the COVID-19 pandemic in China. We adopted csQCA to analyze the formation mechanism of patients’ violence from the combined effect of conditions perspective. First, this study classified four types of causes of violence, including Strong Relationship Oriented Violence, Healthcare Resources and Services Mismatched Violence, Violence caused by Ineffective Patient-Physician Communication, and Ineffective Communication Superimposed Low Patient Compliance Violence, to characterize the differentiated mechanism of patients’ violence during a major public health crisis. Second, similar to pre-COVID-19 outbreak studies, we reconfirmed the important role of kinship, resources and services inequality, and patient-physician communication in precipitating patient violence. While, the difference highlighted is that patient compliance with treatment is also a strong predictor of patient violence towards medical staff, which has not been emphasized in relevant studies prior to the outbreak of COVID-19. This study also contributes to the creation of anti-violence strategies that target various types of patient violence for physicians, hospitals, and health administrators.
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This paper is supported by National Natural Science Foundation of China (Project No. 72104087), National Social Science Foundation of China (Project No. 18CZZ001), and China Association for Science and Technology High-end Science and Technology Innovation New Think Tank Youth Fund（Project No. 2021ZZZLFZB1207041).
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MY, WZ, RE, CZ, LW and JS conceptualized the paper, MY, WZ, CZ and LW contributed in the data collection and analysis, MY and WZ wrote the draft, RE, CZ, LW and JS contributed to the Discussion and revision of the paper. All authors have agreed the submission.
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Yin, M., Zhang, W., Evans, R. et al. Violence on the front line: a qualitative comparative analysis of the causes of patient violence towards medical staff in China during the COVID-19 pandemic. Curr Psychol (2023). https://doi.org/10.1007/s12144-023-04456-w
Accepted : 22 February 2023
Published : 02 March 2023
DOI : https://doi.org/10.1007/s12144-023-04456-w
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The SAGE Handbook of Case-Based Methods
- By: David Byrne & Charles C. Ragin
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- Online pub date: January 01, 2012
- Discipline: Anthropology , Business and Management , Criminology and Criminal Justice , Communication and Media Studies , Counseling and Psychotherapy , Economics , Education , Geography , Health , Marketing , Nursing , Political Science and International Relations , Psychology , Social Policy and Public Policy , Social Work , Sociology
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- DOI: https:// doi. org/10.4135/9781446249413
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‘This book provides a fresh and stimulating approach to causal analysis in the social sciences. International experts provide not just the philosophical arguments for a case-based approach to research but also detailed chapters on: ‘why-to’, ‘when-to’ and ‘how-to’. Traditional distinctions between qualitative and quantitative are rejected in favour of a case-based approach which is applicable across the social sciences and beyond’ - Professor Angela Dale, The University of Manchester
- Notes on Contributors
- Introduction: Case-Based Methods: Why We Need Them; What They Are; How to Do Them
- Chapter 1 | Complexity and Case
- Chapter 2 | The Contextualist Approach to Social Science Methodology
- Chapter 3 | Reflexivity, Realism and the Process of Casing
- Chapter 4 | Single-Case Probabilities
- Chapter 5 | Complex Realist and Configurational Approaches to Cases: A Radical Synthesis
- Typologies–Ways of Sorting Things Out
- Chapter 6 | Explanatory Typologies in Qualitative Analysis1
- Chapter 7 | Introducing Cluster Analysis: What Can It Teach Us about the Case?
- Chapter 8 | Visualising Types: The Potential of Correspondence Analysis
- Chapter 9 | How Classification Works, or Doesn't: The Case of Chronic Pain
- Quantitative Approaches to Case-Based Method
- Chapter 10 | Case-Centred Methods and Quantitative Analysis
- Chapter 11 | The Logic and Assumptions of MDSO–MSDO Designs
- Chapter 12 | The Case for Qualitative Comparative Analysis (QCA): Adding Leverage for Thick Cross-Case Comparison
- Chapter 13 | On the Duality of Cases and Variables: Correspondence Analysis (CA) and Qualitative Comparative Analysis (QCA)
- Chapter 14 | Using Cluster Analysis, Qualitative Comparative Analysis and NVivo in Relation to the Establishment of Causal Configurations with Pre-existing Large-N Datasets: Machining Hermeneutics
- Qualitative Approaches to Case-Based Research
- Chapter 15 | Computer-Based Qualitative Methods in Case Study Research
- Chapter 16 | Extending the Ethnographic Case Study
- Chapter 17 | Scope in Case Study Research
- Chapter 18 | Small-N Access Cases to Refine Theories of Social Exclusion and Access to Socially Excluded Individuals and Groups
- Chapter 19 | Using Comparative Data: A Systems Approach to a Multiple Case Study1
- Chapter 20 | Making the Most of an Historical Case Study: Configuration, Sequence, Casing, and the US Old-age Pension Movement
- Chapter 21 | Poetry and History: The Case for Literary Evidence1
- Chapter 22 | Cultural Case Studies
- Chapter 23 | Social Interactions and the Demand for Sport: Cluster Analysis in Economics
- Chapter 24 | The Proper Relationship of Comparative-Historical Analysis to Statistical Analysis: Subordination, Integration, or Separation?
- Chapter 25 | Case Studies and the Configurational Analysis of Organizational Phenomena
- Chapter 26 | The Case in Medicine
- Chapter 27 | Team-Based Aggregation of Qualitative Case Study Data in Health Care Contexts: Challenges and Learning
- Chapter 28 | Working with Cases in Development Contexts: Some Insights from an Outlier
- Chapter 29 | Non-nested and Nested Cases in a Socioeconomic Village Study
- Chapter 30 | Causality and Interpretation in Qualitative Policy-Related Research
- Chapter 31 | Reflections on Casing and Case-Oriented Research
- Author Index
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Comparative Case Study
- Software Verification
- Information Technology
- Software Testing
- Software Engineering
- System Development
- User Involvement
- User Participation
Human–Environment Relationship: Comparative Case Studies
C.G. Knight , in International Encyclopedia of the Social & Behavioral Sciences , 2001
A comparative case study is a research approach to formulate or assess generalizations that extend across multiple cases. The nature of comparative case studies may be explored from the intersection of comparative and case study approaches. Case studies and qualitative comparative analyses share a common explanatory limitation with quantitative models—they are fundamentally heuristic rather than definitive . Strategies in comparative case studies include research on the basis of community, place, perspective, structure, system, scale, transects, gradients, time, events, analogs, or theory as organizing principles. However, the major organizing principle of a comparative case study often includes others that are subsidiary but inherent in the nature of the cases selected. There are no formal rules for evaluating comparative case studies, but reasonable criteria against which studies can be assessed do exist. The intriguing variety of strategies for organizing comparison means that this approach to understanding will continue to be a vital part of human–environment scholarship.
Qualitative Analysis, Political Science
Kevin G. Barnhurst , in Encyclopedia of Social Measurement , 2005
Comparative Case Studies
Another early genre of qualitative political science techniques, comparative case studies, developed along with historical and theoretical approaches and came into its own by the middle 20th century, dedicated primarily to comparing the governments either of different countries, or especially in the United States, of different states. Although debates went on between those who preferred a more theoretical political science and those who preferred a more professional, government- and citizen-focused political science, the central methodological assumption of both parties emphasized measurement, with natural science providing the ideal model. This quantitative current advanced among those conducting case studies by the 1970s, such as Arend Lijphart (and later Ada Finifter). Quantification spread so extensively that recent case-study-methods books focus on quantitative procedures rather than on qualitative processes or interpretation.
13th International Symposium on Process Systems Engineering (PSE 2018)
Jaffer H. Ghouse , ... David C. Miller , in Computer Aided Chemical Engineering , 2018
This work presents a tray-by-tray distillation column model using MESH equations within the IDAES modeling framework that is suitable for both simulation and deterministic optimization. For the conceptual design of distillation columns, both NLP and GDP frameworks are available, and a comparative case study was presented in this work. In the case study, the NLP and GDP frameworks yield similar solutions; however, the NLP framework using bypass streams requires fewer nonlinear function evaluations compared to the GDP solution, but a robust initialization scheme was necessary. On the other hand, the main limitation of the GDP framework was the quality of the linear approximation, leading to slower convergence. This may be addressed by adding support for stronger GDP to MILP reformulation techniques in GDPopt, improving the bounds via automatic bound-strengthening tools and adding logical propositions to screen out structurally redundant configurations. At the same time, the impact of the reduced space sub-problems in LOA can be seen, as only a few function evaluations are required for each sub-problem, even without a complex initialization scheme. A thorough comparison between the two frameworks will be considered in the future and will incorporate more rigorous property models such as the cubic equation of state.
Heather J. Hether , Christopher Calabrese , in Technology and Health , 2020
Social media provide an innovative platform for health promotion, yet research suggests these platforms are not leveraged effectively. Instead, campaigns struggle to actively engage with participants beyond simple interactions. Inspiring user-generated content (UGC), wherein individuals create and post their own content, is a strategy that has potential to improve health promotion, especially among young people who are heavy users of social media. However, inspiring UGC is difficult. This chapter presents a comparative case study analysis of three campaigns that were highly successful in leveraging social media, and UGC in particular, in support of health-related goals. Through this analysis, four similarities were identified across all three campaigns. These similarities include the facilitation of online community, the cultivation of positive affect, the support of celebrity influencers, and the opportunity for creative self-expression. While these campaigns also received criticism, they each were, nonetheless, effective at making an impact on social media and achieving campaign goals. Through these case study analyses and a review of current research, a holistic assessment of the theory and practice of social media for health promotion is presented. Moreover, future directions for both the research and practice of health promotion on social media are identified.
Joseph W. Elder , in Encyclopedia of Social Measurement , 2005
As American sociologists generally define it, qualitative research differs from quantitative research in its greater dependence on the subjects of study (rather than on the sociologists studying the subjects) to define variables, historical sequences, and causal relationships. Qualitative research tends to focus on human agency (and hence widespread individual variability) rather than on structural agency (and hence recurrent comparability). Qualitative research is often used in case studies, including comparative case studies and extended case studies. Qualitative research is also sometimes used in pilot studies and the early stages of inquiry, when research problems and relevant variables are being initially identified.
Interviewing is one of the principal methodologies of qualitative research. Like anthropologists and psychologists, sociologists engaged in interviewing are generally trained to be sensitive to response bias and to be aware of how their rapport with their subjects, their ways of phrasing initial and follow-up questions, and their own verbal and nonverbal cues can distort the information they receive. Other methodologies used in qualitative research involve collecting oral histories, other kinds of oral productions, written materials, and visual productions. Further methodologies can include phrase-completion exercises, recording “natural” conversations, forming focus groups, and conducting various types of observations (structured and unstructured, participant and nonparticipant, obtrusive and unobtrusive) of individuals' behavior. Because of potentially sensitive information being obtained from individuals through such qualitative methodologies, sociologists engaged in primary research are often required to conform to certain ethical standards, including the informed consent of the participants and their own guarantees of record destruction to protect the anonymity of individuals.
Data gathered through qualitative research methodologies often require special forms of storing and retrieving, as well as of processing. Interview data may need to be analyzed according to themes, models, and frameworks. Unspoken materials, as well as spoken materials, from interviews should be reviewed. Premises and logical structures of narrative and performance data may require content analyses as well as cultural and historical contextualizations and criticisms of sources. Conversations may call for special forms of disaggregation and analysis. The skills required for dealing with qualitative data correspond in many ways to the skills of verstehen and ausdruck mentioned by Max Weber in his essay The Meaning of “Ethical Neutrality” in Sociology and Economics . The perception is shared among many sociologists that qualitative researchers are more likely than quantitative researchers to question, as Max Weber and the phenomenologists did, the applicability of physical/natural science methods to the study of social behavior.
Pattern Matching: Methodology
W.N. Dunn , in International Encyclopedia of the Social & Behavioral Sciences , 2001
3.4 Pattern-Matching Case Studies
When quasi-experimental designs are unfeasible or undesirable, several forms of case study analysis are available. Each of these involves pattern matching.
Theory-Directed Case Study Analysis . When a well-specified theory is available, a researcher can construct a pattern of testable implications of the theory and match it to a pattern of observations in a single case (Campbell 1975 ). Using statistical ‘degrees of freedom’ as a metaphor, the theory-directed case study is based on the concept of ‘implications space,’ which is similar to ‘sampling space.’ Testable implications are functional equivalents of degrees of freedom, such that the more implications (like a larger sample) the more confident we are in the validity of the conclusions drawn. But because theories are almost inevitably affected by the culturally acquired frames of reference of researchers, the process of testing implications should be done by at least two ‘ethnographers’ who are foreign to and native to the culture in which the case occurs. The process of triangulation among observers (ethnographers) can be expanded to include two (or more) cases.
Qualitative Comparative Case Study Analysis . Two or more cases are compared by first creating a list of conditions that are believed to affect a common outcome of interest (see Ragin 1999 , 2000 ). The multiplication rule, r m , is used to calculate the number of possible ordered configurations of r categories, given m conditions. When r =2 and m =4, there are 2 4 =16 configurations, each of which may involve causal order. These configurations become the rows in a ‘truth table,’ and each row configuration is sequentially applied to an outcome with r categories (e.g., successful vs. unsuccessful outcome). Because this method examines possible configurations, and two or more different configurations may explain the same outcome in different cases, the qualitative comparative method should be contrasted with traditional (tabular) multivariate analysis. The qualitative comparative method matches configurable patterns that have been formally structured by means of set theory and Boolean algebra against patterns of observations in case materials.
Modus Operandi Analysis . When quasi-experimental research is not possible, modus operandi methods (see Scriven 1975 ) may be appropriate for making causal inferences in specific contexts. Modus operandi methods are based on the analogy of a coroner who must distinguish symptoms and properties of causes from the causes themselves. The first step is to assemble a list of probable causes, preferably one that is quasi-exhaustive. The second is to recognize the pattern of causes that constitutes a modus operandi —modus refers to the pattern, while operandi refers to specific and ‘real’ causes. The modus operandi of a particular cause is its characteristic causal chain, which represents a configuration of events, properties, and processes. Modus operandi analysis has been formalized, partially axiomatized, and advanced as a way to change the orientation of the social and behavioral sciences away from abstract, quantitative, predictive theories toward specific, qualitative, explanatory analyses of causal patterns (Scriven 1974, p. 108).
Quality Software Development
Douglass E. Post , ... Robert F. Lucas , in Advances in Computers , 2006
6.1 Quantitative Estimation
These “lessons learned” were based on a qualitative and a quantitative analysis of the histories of the different code projects and comparison with the Information Technology industry and conventional project management and scientific research. The quantitative analysis was a key element in establishing that these code projects had not been given a consistent set of requirements, resources and schedules. While our analysis [ 69 ] was relatively simple compared to the methods often employed in the Information Technology (IT) community [ 74 ], the conclusions are very clear. We found that the key predictor of success was the age of the code project and the amount of time allocated to complete the project and meet milestones. Our analysis of the historical data indicated that it takes about 8 years to develop a code with the initial level of capability needed to meet the requirements. The projects that had 8 years of development often succeeded, and all those that did not have 8 years of development time failed to meet their initial milestones. This result emphasized the crucial need for a consistent set of requirements, resources and schedule.
The case studies included metrics (code size, team size, age, etc.). To see if this experience was consistent with the Information Technology (IT) community experience, an analysis was performed on the case studies using a generic “function point” model [ 74 ] widely used by the IT industry. We calibrated this model for scientific code projects using the comparative case study data. Function points are a weighted total of inputs, outputs, inquiries, logical files and interfaces [ 74 , 78 ]. Functions points were not developed for technical software, but were the best measure available
Equation (5) converts the single lines of code, available for all of the projects, to Function points (FP). T. Capers Jones lists the equivalent single lines of code (SLOC) per function point (FP) for the common computer languages [ 74 ] since computer languages have different information densities.
In this model, the required schedule and average team size are determined by the Function Point (FP) count (Eqs. (6)–(8) ). These general scalings were modified to account for the added complexity and viscosity associated with developing scientific codes specifically for the nuclear weapons complex. The schedule was lengthened by 1.5 years to account for the additional time it takes to recruit, hire, train and obtain security clearances for code development staff (the last step has no analogue in the commercial IT world). Using a methodology developed by the Lawrence Livermore National Laboratory Engineering Department [ 71 ], a contingency factor of 1.6 was calculated to account for the additional risks, uncertainties, and complexities for the restrictive computing environments that these projects shared (Eq. (7) ). The standard FP scaling for the size of the code team (Eq. (8) ) [ 74 ] was modified to match the comparative case study data. This included a correction for small code teams.
Seven code projects were analyzed ( Table VIII ). For reasons of anonymity, we have identified the projects with birds [ 43 ]. Table VIII lists the size of the code in function points, the time estimated by Eq. (4) to develop the initial capability of the code project, the actual age of the code at the point it was expected to accomplish its first milestone, whether or not the project succeeded, the optimal code team size estimated from Eq. (8) and the actual size of the team. The sizes of the codes (e.g., lines of code, loc) were approximate estimates by the code teams. Establishing the size of the code teams was challenging. In general, good records were not available. Thus the code team sizes were generally estimated by the code team leaders. Because good records were not kept, it was also difficult to account for staff who worked on the code project but were part of other organizations. More than one-half of the Gull code project team, for instance, was part of other organizations. Where this was an issue, we used conservative estimates. For example, the Gull code project staff probably had a staffing level of about 50 for the first 4 or 5 years of its life instead of the 35 we assumed. We used a smaller number based on the actual number of people we could definitely identify as having worked on the project.
Table VIII . Software Resource Estimates for the Comparative Case Study Projects *
The case histories and the estimation procedures indicate that it generally takes a minimum of 8 years for a code team to develop an initial capability for a weapons code project. The requirements for a weapons code are determined by the physics necessary to simulate a nuclear weapon. The contractors had over 50 years of experience in this area, and know these requirements in detail. Codes of this type have between 3000 and 6000 function points ( Fig. 6 ).
Fig. 6 . Time required to complete a project and average code team size as a function of code capability measured in function points.
Some of these codes were started well before the federal program began in 1996 (the Egret, Jabiru, Gull and Tern projects). The Egret project was started roughly in 1992 and had a working prototype in 1994. The Jabiru code project was started before 1992. The Kite, Puffin and Finch code projects were started around early 1997. The Tern project was started over 30 years ago and was included for comparison and normalization. Since the history of these code project can be matched with scalings derived from the experience of the commercial software industry, it is reasonable to conclude that the constraints, computer science practices and management issues that generally apply to the IT industry generally apply to the development of weapons codes as well (i.e., there is no “Silver Bullet” that can radically reduce the development time [ 79 ]).
The dominant factor for success is the age of the code project (see line 5 of Table VIII ). The code projects that did not have sufficient time (8 years—see Fig. 7 ) failed to meet their milestones. The two projects (Egret and Jabiru) that successfully met the initial milestone were at least 8 years old. Three other projects (Kite, Finch and Puffin) were less than 8 years old and didn’t meet their initial milestone. The Gull project was eight years old but didn’t meet its initial milestone. Two of the projects eventually were successful in meeting later milestones after they had more development time. The other two did not meet their milestones and were eventually abandoned. The Gull project was successful in meeting a different set of milestones, but not the initial set. This is clear evidence that schedules and requirements must be consistent. The schedule cannot be fixed independently of the requirements, a fact long appreciated by the IT industry [ 48 , 74 ] but not adequately taken into account in the early planning for the whole program. The program set the milestone for demonstrating the capability of each code project to be three and a half years (December 1999) after the beginning of the program (~ mid 1996) and three years after the date that many of the code projects were launched (~ January 1997).
Fig. 7 . Project schedule for six large-scale computational science code projects.
Adequate development time is necessary—but not sufficient—for success. Several code projects failed in spite of having adequate time. Poor practices and inadequate support—implicitly included in the contingency factor—hurt some of the projects as well. The Gull code project failed to meet its milestones even with adequate time and ample resources.
Another point is that it is clear from the function point scaling relations (Eqs. (5)–(8) ) is that the code requirements determine both the schedule and resources needed for success. This estimating analysis indicates the importance of a realistic set of requirements, schedule and resources. Without them, projects will fail and the needed applications will not be developed.
These case studies helped persuade the program’s senior management that the “younger” code teams (those started less than 8 years before the milestone) deserved a second chance. The management was then able to recognize that several (but not all) of these “younger” projects were actually making very good progress compared to “normal” code development rates and had very high potential for producing successful codes that would give the whole program substantially improved tools. Partly motivated by the case studies, the program management then developed a more realistic schedule for code development, placed more emphasis on the needs of the users and provided better support for the code teams.
Three issues identified as “lessons learned” are expanded on in the following two sections: verification and validation and software quality. Both areas are crucial for success for technical software projects, and have special—and often not well understood—requirements.
Data Archives: International
R.C. Rockwell , in International Encyclopedia of the Social & Behavioral Sciences , 2001
2 The Establishment of Data Archives
The intellectual leaders of what came to be known as the ‘data archives movement’ came from both the academy and the private sector. From the academy came the founder of modern comparative politics, Stein Rokkan (Norway); two of the pioneers of the empirical study of political behavior (see Databases, Core: Political Science and Political Behavior ), Philip E. Converse and Warren E. Miller (United States); and a leader of comparative research on social structure (see Human–Environment Relationship: Comparative Case Studies ), Erwin K. Scheuch (West Germany). Converse and Miller sought to share with their colleagues the election data they were collecting and also to use the new model of the National Election Studies to foster the collection of data on the re-emerging European democracies. Rokkan and Scheuch sought to describe and understand the movement toward a united Europe. All recognized the great potential of data archives to facilitate comparative and time-series (see Time Series: General ) research as well as to aid the social and behavioral sciences to become more transparent and self-correcting.
The private sector saw its polling data as integral to the functioning of modern democracies and held that understanding public opinion was crucial for a free society. One of the founders of modern polling (see Polling ), Elmo B. Roper, organized the first of the social science data archives before the 1950s when he decided to share with social scientists and others the data from his polls. These collections of survey data were under development by 1947 at Williams College (Massachusetts), based upon holdings that Roper had been accumulating for 10 years. Williams College formally established the Roper Center for Public Opinion Research in 1957. Roper was joined in contributions by two other leaders of public opinion polling in the US, George H. Gallup and Archibald Crossley, and by many of the other leading survey firms.
A succession of archives followed. The Central Archive for Empirical Social Research (ZA), based at the University of Cologne in the former West Germany, was next in 1960, followed in 1962 by the Steinmetz Archive in Amsterdam, the Netherlands, and the Inter-university Consortium for Political Research (ICPR), at the University of Michigan, Ann Arbor. By the beginning of the 1970s several more European countries had data archives. By 1964 the International Social Science Council (ISSC) had sponsored a second conference on Social Science Data Archives and had a standing Committee on Social Science Data, both of which stimulated the data archives movement. By the beginning of the twenty-first century, most developed countries and some developing countries had organized formal and well-functioning national data archives. In addition, college and university campuses often have ‘data libraries’ that make data available to their faculty, staff, and students; most of these bear minimal archival responsibility, relying for that function on a national institution.
Data archives usually are administratively housed in research universities or in government science or information agencies. Funding for these operations varies widely in scale and stability. The financially largest of the data archives, the Inter-university Consortium for Political and Social Research (formerly ICPR), had in 2000 an annual budget of almost $7 million that was about equally divided between (a) income from grants and contracts and (b) income from tuition and annual dues paid by more than 350 institutions. Many archives have both long-term governmental support and income from user fees; others subsist on small annual grants or support from individual universities. Since 1977 the archives have been organized loosely under the International Federation of Data Organizations (IFDO), which is an associate member of the ISSC. IFDO provides for cooperation among the archives and has been of material assistance in assisting nascent archives, particularly by providing technical and organizational assistance to new archivists in developing countries. European archives are organized more closely under the Council of European Social Science Data Archives (CESSDA), which has provided a basis for cooperative planning and execution of projects, technical assistance, and training. There is no comparable North American organization, although in the 1960s there was the Council of Social Science Data Archives. The archives are largely national in funding, organization, and substantive scope; there is no single international archive of social science data to which all social and behavioral scientists can turn, nor is there as yet a complete ‘union catalog.’ There was discussion in the late 1950s of organizing central data archives for all of Europe, but the present nationally based system emerged in its stead.
Because archives' holdings have now accumulated to encompass decades of observation in some societies, it may be anticipated that future historians will be among the heaviest users of data archives. So also will sociologists and political scientists involved in comparative and time-series research. Urban anthropologists and humanists are finding in data archives information about values, knowledge, and mass culture that was previously unavailable to them. There is also a substantial level of usage by journalists, policy analysts, and citizens. Usage can be expected to increase substantially as broader categories of researchers realize the value of archived resources and as technological progress eases access to data.
International and Transboundary Accords, Environmental
M.J. Peterson , in International Encyclopedia of the Social & Behavioral Sciences , 2001
3 Factors Influencing the Success of International Environmental Accords
Efforts to identify the conditions facilitating or inhibiting the negotiation and implementation of international environmental accords have focused on three broad sets of factors: institutions, actor beliefs and material conditions. There is as yet little consensus on how these factors, singly or in combination, contribute to success or failure. This stems partly from the scarcity, until very recently, of carefully designed comparative case studies, but even more from the use of two very different conceptions about how actors think and choose: a rational choice account, regarding actors as utility maximizers with stable interests focused primarily on material costs and benefits, and a social practices account, regarding actors as developing their understandings of themselves and their interests from membership in a wider society and socialization into its norms, discourses and collective learning processes.
Rational choice approaches treat actor interests and motivations as stable and inquire into how material conditions and institutions affect actor choice. Insights into the impact of material conditions and the possible roles of institutions in facilitating negotiation and implementation of agreements have been developed in two ways, by looking at particular features of activity and by classifying problems into broad types posing distinct cooperative challenges because of the way in which actor calculations of self-interest intersect in interaction. The first stream of work has highlighted such considerations as the greater difficulty of regulating large numbers of actors, each creating a small part of the problem, than a small number, each creating a significant part of it; the different opportunities for particular governments to regulate depending on whether those involved in an activity move from place to place (as in shipping) or remain in one place (as in mining); the impact of linkage or separation between costs and benefits of better environmental practice (for instance, the great difficulty of securing cooperation from upstream or upwind polluters); and the influence of changing cost–benefit ratios of environmentally superior action as new technology is developed or regulations alter the relative cost of continuing old rather than adopting new forms of activity.
The second stream, drawing strongly on economic theories of property rights regimes, provision of collective goods and government regulation of markets (e.g., Sandler 1997 , Symes 1998 ), has identified the distinct regulatory challenges posed by different types of situations. In this view, the management of common pool resources and common sinks poses the toughest challenges to successful cooperation because they involve collaboration situations in which actors remain tempted to maximize short-term gain by violating their commitments. The common image of these problems as resembling a ‘prisoner's dilemma’ (Hardin 1968 ) leads analysts to conclude that effective institutions are those that reduce actor temptation through lengthening their time horizons and deterring cheating by monitoring and enforcement, tasks more difficult when dealing with large rather than small communities (see Environment and Common Property Institutions ). Transboundary environmental problems, in this analysis, are international level expression of the familiar problem of negative externalities, complicated by the fact more than one government is involved (see Environmental Policy: Protection and Regulation ). The range of policy solutions suggested for single countries, ranging from central government ‘command and control’ measures to ‘market-based measures’ resting on conducive property rights, taxation and regulatory regimes, can work across borders only if governments cooperate quite closely. Yet, as rational choice analysts have noticed, this cooperation is hindered because governments (and other actors) have a strong stake in continuing to use the regulatory scheme with which they are already familiar. Rational choice analysts treat regulatory harmonization as a coordination problem in which firms and governments have different preferences about regulation but little incentive to depart from an agreed regulatory scheme once it is established. Here, institutions can be used to increase the likelihood of cooperative action by augmenting the flow of information about actor preferences and behavior, providing possibilities of reducing discontent about differences in costs or benefits through concessions on related issues, or reducing transaction costs by substituting group discussions for one on one bargaining.
Rational choice analysts anticipate that the substantive provisions of international environmental accords will have a strong focus on enforcement mechanisms when addressing common pool resources or common sinks, and a strong focus on information flows, compensation mechanisms and reducing transactions costs when addressing regulatory harmonization. However, the actual development of international environmental accords has not conformed to these expectations. Through addressing these matters current international environmental accords reflect greater concern for developing common information about the environmental problem and fostering joint deliberation regarding standards for proper conduct. This has provided openings for the social practices theorists who claim that actor choices are guided by logics of appropriateness derived from shared definitions of social roles and proper conduct by role-holders, rather than by utilitarian calculation of material interests. In this view, institutions serve primarily to define legitimate and illegitimate courses of conduct, facilitate socialization of actors into their roles, facilitate actor engagement in learning about particular situations, and promote joint deliberation about the content of the shared social beliefs and norms.
Social practices analysts expect greater cooperation on environmental problems when governments (and other actors) adopt an ecological worldview, accept protection of the biosphere as a distinct policy goal, redefine government responsibility and sound business practice to include fostering ecologically sustainable forms of human activity, and include effects on the biosphere among the criteria for judging the merits of alternate economic and social policy measures. For social practices theorists successful international environmental cooperation is not a matter of altering incentives while leaving interests unchanged, but of altering beliefs so definitions of interest will be changed. In their view, institutions are important primarily as mechanisms for facilitating redefinition of social roles and norms, socialization into and internalization of new norms, and continued actor learning.
This orientation has led social practices theorists to focus on the conditions under which learning occurs and new beliefs become disseminated from small groups of initial advocates to wider segments of society (e.g., Clark et al. 2001 ). These analyses highlight the importance of visible negative effects in triggering reconsideration, of scientific consensus on the extent and material causes of environmental degradation in facilitating acceptance of new information, and of activity by transnational sets of policy entrepreneurs and social movements in disseminating new worldviews, beliefs and information more widely.
Though rational choice and social practices approaches are often treated as irreconcilable theoretical rivals, they are actually complementary because each illuminates particular aspects of human behavior. Social practices analysis is strongest on the importance of causal and normative beliefs in guiding actor choices, and in beginning to identify the mechanisms of deliberation and learning through which those beliefs are maintained or changed. Rational choice analysis is strongest in indicating how material calculations factor into actor choice within the areas of discretion permitted by all but the most determinate social norms, and how different patterns of individual actor positions affect the likelihood of different compromises and the stability of various coalitions that might arise. The complementarity of the two approaches has been recognized implicitly in the stream of policy analysis inquiring into how each of the mechanisms of persuasion, alteration of information, shift in the availability of opportunities to engage in particular behaviors, reward and punishment can be used to bring about desired behavioral change. It is also acknowledged by the growing attention paid among both rational choice and social practices theorists to the ways in which international institutions are affected by and affect domestic realignments that create new constituencies for environmental policies or alter the balance between existing constituencies. Though many social scientists will continue to prefer one approach over the other, a growing number will explore integrating the two more explicitly in more careful empirical tracings of actual processes of establishing, implementing and revising international environmental accords.
The impact of clinical leadership on health information technology adoption: Systematic review
Tor Ingebrigtsen , ... Jeffrey Braithwaite , in International Journal of Medical Informatics , 2014
4.3 Risk of bias
All studies were observational. Eight (25%) were comparative case studies and three of these (9%) used a longitudinal observational design. Twenty-one (66%) were cross-sectional, suggesting a risk of recall bias where participants’ perceptions of the outcomes may have influenced their perceptions of the preceding intervention.
With regards to the GRADE ratings (supplementary file 3), four comparative qualitative studies [35–38] were rated up to moderate quality because they showed large effect sizes or very different outcomes. Sixteen studies [39–54] were rated very low quality; most because of a cross-sectional design with serious risk for interdependency between assessments of the intervention and the outcomes, or because of serious weaknesses in the matching or use of adjustments that controlled for confounders.
We did not search “grey literature” and the snowball search yielded few new references. We planned to include studies published in German and Scandinavian languages, but identified none that met our criteria. The included studies represented a range of countries and settings that reported on different levels of success or failure. Accordingly, no publication bias was detected, but selective reporting favouring studies with positive findings could not be excluded.
Psychological Research Methods: Types and Tips
Psychological research methods are the techniques used by scientists and researchers to study human behavior and mental processes. These methods are used to gather empirical evidence. The goal of these methods is to obtain objective and verifiable data collected through scientific experimentation and observation.
One of the key goals of psychological research is to make sure that the data collected is reliable and valid. Reliability means that the data is consistent and can be replicated, while validity refers to the accuracy of the data collected. Researchers must take great care to ensure that their research methods are reliable and valid, as this is essential for drawing accurate conclusions and making valid claims about human behavior.
Quantitative vs. Qualitative Psychological Research Methods
Psychological research methods can be broadly divided into two main types: quantitative and qualitative. These two methods differ in their approach to data collection and analysis.
While quantitative and qualitative research methods differ in their approach to data collection and analysis, they are often used together to gain a more complete understanding of complex phenomena. For example, a researcher studying the impact of social media on mental health might use a quantitative survey to gather numerical data on social media use and a qualitative interview to gain insight into participants’ subjective experiences with social media.
Types of Psychological Research Methods
Surveys are a commonly used research method in psychology that involve gathering data from a large number of people about their thoughts, feelings, behaviors, and attitudes. Surveys can be conducted in a variety of ways, including in-person interviews, online questionnaires, and paper-and-pencil surveys. Surveys are particularly useful when researchers want to study attitudes or behaviors that are difficult to observe directly or when they want to generalize their findings to a larger population.
Experimental Psychological Research Methods
Correlational psychological research methods.
Correlational research is a research method used in psychology to investigate the relationship between two or more variables without manipulating them. The goal of correlational research is to determine the extent to which changes in one variable are associated with changes in another variable. In other words, correlational research aims to establish the direction and strength of the relationship between two or more variables.
A meta-analysis is a research method commonly used in psychology to combine and analyze the results of multiple studies on a particular topic. The goal of a meta-analysis is to provide a comprehensive and quantitative summary of the existing research on a topic, in order to identify patterns and relationships that may not be apparent in individual studies.
Tips for Using Psychological Research Methods
Understand the different types of research methods: , develop a clear research question: , use proper sampling techniques: .
Sampling is the process of selecting participants for a study. It is important to use proper sampling techniques to ensure that the sample is representative of the population being studied. Random sampling is considered the gold standard for sampling, but other techniques, such as convenience sampling, may also be used depending on the research question.
Use reliable and valid measures:
Consider ethical issues:, analyze and interpret the data appropriately : .
After collecting the data, it is important to analyze and interpret the data appropriately. This may involve using statistical techniques to identify patterns and relationships between variables, and using appropriate software tools for analysis.
Communicate findings clearly:
Frequently asked questions, what are the 5 methods of psychological research, what is the most commonly used psychological research method.
Experimental research is also a widely used method in psychology, particularly in the areas of cognitive psychology , social psychology , and developmental psychology . Other methods, such as survey research, case study research, and naturalistic observation, are also commonly used in psychology research, depending on the research question and the variables being studied.
How do you know which research method to use?
Palinkas LA. Qualitative and mixed methods in mental health services and implementation research. J Clin Child Adolesc Psychol . 2014;43(6):851-861. doi:10.1080/15374416.2014.910791
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Comparative analysis of animal-powered waterwheels in mediterranean alluvial plains: medjerda (tunisia) and jucar rivers (spain).
1. Introduction and Objectives
2. materials and methods, 3. historical context, 3.1. testour, 3.2. ribera alta, 4. selected study areas, 4.1. medjerda–testour, 4.1.1. geology and geomorphology, 4.1.2. climate and hydrology, 4.2. júcar–ribera alta, 4.2.1. geology and geomorphology, 4.2.2. climate and hydrology, 5.1. analysis of animal-powered waterwheels in testour, 5.1.1. location, 5.1.2. characteristics and current status of waterwheel systems in testour, 5.2. analysis of the animal-powered waterwheels on the meanders of the júcar, 5.2.1. location, 5.2.2. characteristics and current situation, 6. discussion and conclusions, author contributions, data availability statement, conflicts of interest.
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Share and Cite
Fansa Saleh, G.; Iranzo García, E.; Pérez Cueva, A.J. Comparative Analysis of Animal-Powered Waterwheels in Mediterranean Alluvial Plains: Medjerda (Tunisia) and Jucar Rivers (Spain). Land 2023 , 12 , 594. https://doi.org/10.3390/land12030594
Fansa Saleh G, Iranzo García E, Pérez Cueva AJ. Comparative Analysis of Animal-Powered Waterwheels in Mediterranean Alluvial Plains: Medjerda (Tunisia) and Jucar Rivers (Spain). Land . 2023; 12(3):594. https://doi.org/10.3390/land12030594
Fansa Saleh, Ghaleb, Emilio Iranzo García, and Alejandro J. Pérez Cueva. 2023. "Comparative Analysis of Animal-Powered Waterwheels in Mediterranean Alluvial Plains: Medjerda (Tunisia) and Jucar Rivers (Spain)" Land 12, no. 3: 594. https://doi.org/10.3390/land12030594
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