The origins, development, and application of Qualitative Comparative Analysis: the first 25 years

2013 ◽  
Vol 6 (1) ◽  
pp. 115-142 ◽  
Author(s):  
Axel Marx ◽  
Benoît Rihoux ◽  
Charles Ragin
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Quantitative Research ◽  
Comparative Method ◽  
Qualitative Comparative Analysis ◽  
Research Approach ◽  
Quarter Century ◽  
Quantitative Research Methods ◽  
Qualitative And Quantitative ◽  
The Social

A quarter century ago, in 1987, Charles C. Ragin published The Comparative Method, introducing a new method to the social sciences called Qualitative Comparative Analysis (QCA). QCA is a comparative case-oriented research approach and collection of techniques based on set theory and Boolean algebra, which aims to combine some of the strengths of qualitative and quantitative research methods. Since its launch in 1987, QCA has been applied extensively in the social sciences. This review essay first sketches the origins of the ideas behind QCA. Next, the main features of the method, as presented in The Comparative Method, are introduced. A third part focuses on the early applications. A fourth part presents early criticisms and subsequent innovations. A fifth part then focuses on an era of further expansion in political science and presents some of the main applications in the discipline. In doing so, this paper seeks to provide insights and references into the origin and development of QCA, a non-technical introduction to its main features, the path travelled so far, and the diversification of applications.

A Tale of Two Cultures

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Social Sciences ◽  
Quantitative Research ◽  
Social Science Research ◽  
Measurement Data ◽  
Science Research ◽  
Quantitative Research Methods ◽  
Qualitative And Quantitative ◽  
The Social ◽  
Scientific Worldview ◽  
Different Cultures

Some in the social sciences argue that the same logic applies to both qualitative and quantitative research methods. This book demonstrates that these two paradigms constitute different cultures, each internally coherent yet marked by contrasting norms, practices, and toolkits. The book identifies and discusses major differences between these two traditions that touch nearly every aspect of social science research, including design, goals, causal effects and models, concepts and measurement, data analysis, and case selection. Although focused on the differences between qualitative and quantitative research, the book also seeks to promote toleration, exchange, and learning by enabling scholars to think beyond their own culture and see an alternative scientific worldview. The book is written in an easily accessible style and features a host of real-world examples to illustrate methodological points.

Research Methods in the Social Sciences: An A-Z of key concepts

2021 ◽  
Keyword(s):  
Social Sciences ◽  
Research Methods ◽  
Quantitative Research ◽  
Academic Disciplines ◽  
Current Status ◽  
Quantitative Research Methods ◽  
Qualitative And Quantitative ◽  
The Social ◽  
Wide Range ◽  
Key Concepts

Research Methods in the Social Sciences features chapters that cover a wide range of concepts, methods, and theories. Each chapter begins with an introduction to a method, using real-world examples from a wide range of academic disciplines, before discussing the benefits and limitations of the approach, its current status in academic practice, and finally providing tips and advice on when and how to apply the method in research. The text covers both well-established concepts and emerging ideas, such as big data and network analysis, for qualitative and quantitative research methods.

Stirring the Frequentist Pot with a Dash of Bayes

2006 ◽  
Vol 14 (3) ◽  
pp. 339-344 ◽  
Author(s):  
Andrew Bennett
Keyword(s):  
Social Sciences ◽  
Research Methods ◽  
Quantitative Research ◽  
Turning Point ◽  
Quantitative Research Methods ◽  
Qualitative And Quantitative ◽  
Social Inquiry ◽  
Long Run ◽  
The Social ◽  
Good Research

Rethinking Social Inquiry (RSI) is a key turning point in a long arc of development and contestation within and between qualitative and quantitative research methods in the social sciences. It builds on and further advances three important trends in these research methods: a renaissance in qualitative methods in the last decade, the continuing refinement of statistical and formal methods, and a nascent convergence of methodologists of all kinds behind a more pluralistic vision of methodology that includes growing interest in multimethod work. RSI achieves these contributions not just substantively but symbolically, bringing together leading methodologists in the quantitative and qualitative traditions, most notably the editors themselves, to address the tough issue of what would constitute shared standards for good research regardless of method. Although much of the initial commentary on RSI will no doubt focus on its critiques of Designing Social Inquiry, I suspect that in the long run the subtitle of RSI (“Diverse Tools, Shared Standards”) better captures what will be its lasting contribution to the social sciences.

Planning for Knowledge Management Implementation in Academic Libraries: Empirical Evidence From Federal University Libraries in Nigeria

2018 ◽  
Vol 17 (04) ◽  
pp. 1850039 ◽  
Author(s):  
Cyprian. I. Ugwu ◽  
I. J. Ezema
Keyword(s):  
Social Sciences ◽  
Knowledge Management ◽  
Standard Deviation ◽  
Quantitative Research ◽  
Implementation Process ◽  
Research Approach ◽  
University Libraries ◽  
The Social ◽  
The University ◽  
Descriptive Survey

The main reason for the failure of many knowledge management (KM) projects is the absence of a well-defined framework or strategy to guide KM implementation. This paper is an attempt to determine the planning needs of the KM deployment process and propose a framework that could be used specifically by the federal university libraries in Nigeria to guide the KM implementation process. Quantitative research approach was adopted in this study and the design was a descriptive survey. A total of 300 librarians responded to the survey that sought their opinions on the planning needs for KM implementation process. The survey instrument was a questionnaire, and it was used to collect data from the respondents. Data collected were analysed using mean, standard deviation, ranks and percentages obtained with the aid of the Statistical Package for the Social Sciences (SPSS). The results of the study revealed the planning needs for KM implementation as consisting of the goals which the university library intends to achieve through KM, the KM process, skills and tools required as well as the type of partnerships needed. Based on these needs, this study proposes a KM framework made up of strategies and tactical moves to guide the KM implementation process.

Data as representations: contextualizing qualitative and quantitative research strategies

2000 ◽  
Vol 39 (1) ◽  
pp. 99-113 ◽  
Author(s):  
Jaan Valsiner
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Quantitative Research ◽  
Quantitative Methods ◽  
Main Issue ◽  
Research Strategies ◽  
Qualitative And Quantitative ◽  
Qualitative And Quantitative Methods ◽  
The Social ◽  
New States

The opposition between “quantitative” and “qualitative” perspectives in contemporary social science is an organizational limitation that directs discussions of the topic away from the main issue - the adequacy of any kind of data in respect to the phenomena they represent. This is particularly complicated if the phenomena are known to include inherent dynamics, are modifiable by the research encounter, or develop towards new states of existence. It is often assumed that qualitative and quantitative methods are mutually exclusive alternatives within a methodological process that is itself unified. The article shows that quantitative methods are derivates of a qualitative process of investigation, which itself can lead to the construction of inadequate data. The issue of the representativeness of the data - qualitative or quantitative - remains the central unresolved question for the methodology of the social sciences. Errors in representation can be diminished by correction of methods through direct (experiential) access to the phenomena, guided by the researcher's educated intuition.

scholarly journals Systematische kwalitatieve vergelijkende analyse

KWALON ◽  
2012 ◽  
Vol 17 (3) ◽  
Author(s):  
Stefan Verweij ◽  
Lasse M. Gerrits
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Spatial Planning ◽  
Qualitative Data ◽  
Qualitative Comparative Analysis ◽  
The Social

Systematic Qualitative Comparative Analysis Systematic Qualitative Comparative Analysis Qualitative Comparative Analysis (QCA) was introduced in the social sciences by Charles Ragin in 1987. Literature on and applications of QCA show the method as a way to systematically organize, summarize and compare qualitative data to discover and analyze patterns occurring over cases. Although the literature stresses the importance of iterating between theory and data in its procedures, its grounded nature remains relatively underexposed. In this article we illustrate the principles of QCA by means of a qualitative comparative analysis of fourteen Dutch spatial planning projects, thereby also articulating the method’s grounded nature.

Aggregation Bias and the Analysis of Necessary and Sufficient Conditions in fsQCA

2016 ◽  
Vol 46 (2) ◽  
pp. 242-251 ◽  
Author(s):  
Bear F. Braumoeller
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Qualitative Comparative Analysis ◽  
Sufficient Conditions ◽  
Necessary And Sufficient Conditions ◽  
Aggregation Bias ◽  
Data Sets ◽  
The Social ◽  
Necessary And Sufficient ◽  
Causal Complexity

Fuzzy-set qualitative comparative analysis (fsQCA) has become one of the most prominent methods in the social sciences for capturing causal complexity, especially for scholars with small- and medium- N data sets. This research note explores two key assumptions in fsQCA’s methodology for testing for necessary and sufficient conditions—the cumulation assumption and the triangular data assumption—and argues that, in combination, they produce a form of aggregation bias that has not been recognized in the fsQCA literature. It also offers a straightforward test to help researchers answer the question of whether their findings are plausibly the result of aggregation bias.

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