Abstract
Introduction
In their recent book Qualitative Comparative Analysis Using R: A Beginner’s Guide, Ioana-Elena Oana, Carsten Q. Schneider, and Eva Thomann provide a step-by-step guide on how to implement the latest QCA protocol in R. Published by Cambridge University Press in 2021, the book aims to “facilitate the efficient teaching, use, and independent learning” of QCA with advanced software (Oana et al., 2021, p. 21). This book is a must-read for beginners as well as experienced scholars. Starting with the basics, it ultimately represents an excellent manual on how to apply the most recent developments in QCA using R software.
Summary of the Media Content
The book is structured into three parts that correspond to the steps of QCA before, during, and after the analysis. Chapter 1 starts with an introduction of QCA as a research approach and its core assumptions. It also outlines essentials of a research design for a typical QCA study. Chapter 2 focuses on the calibration process, which is highly important, given that QCA does not operate on existing data and instead uses the membership scores of cases in sets. The authors present different approaches to the assignment of set membership scores to cases and demonstrate major diagnostic tools that can be used during calibration.
Chapters 3 and 4 guide the reader through the analyses of necessity and sufficiency, respectively. They first outline the logic behind these analyses and then explain the interpretation of their parameters of fit. In Chapter 3, the authors also consider two sources of trivialness for necessity and highlight that any detected necessary conditions must be meaningful conceptually. In Chapter 4, Oana, Schneider and Thomann provide practical advice about how to deal with model ambiguity as well as suggest several strategies to manage the problem of limited empirical diversity.
Chapters 5 and 6 present advanced analytic tools that can be used following the main analysis of the data. In particular, Chapter 5 outlines sophisticated ways to assess robustness of QCA results. It also explains the cluster diagnostics and how to integrate sequences and casual chains into QCA. Chapter 6 is devoted to set-theoretic theory evaluation as well as set-theoretic multi-method research that allows scholars to identify typical and deviant cases for in-depth case studies. Finally, Chapter 7 provides a more general discussion of how to interpret QCA results, presents standards of good practice, and outlines the major developments that are likely to shape the application of QCA in the future.
Value of the Media
The book will be definitely of interest not only for beginners but also for advanced users of QCA and mixed methods researchers. In particular, scholars doing mixed methods research largely benefit from reading Chapter 6 because it focuses on how to “bring cases back into the picture after a QCA result has been produced” (Oana et al., 2021, p. 180). The authors explicitly point out that by studying individual cases researchers are able to make their QCA-based inferences stronger as well as to identify specific mechanisms linking sufficient combinations of conditions to the outcome. In Chapter 6, Oana, Schneider and Thomann also discuss how to select best available cases for within-case analysis depending on distinctive analytical goals.
Contribution to Literature and Minor Criticisms
Overall, the book successfully reaches its objective to introduce a new method and new software. Each chapter contains numerous empirical examples and illustrations that help the reader to grasp the main concepts presented by the authors. In addition, each chapter includes executable R code with a detailed explanation. Following this step-by-step guide, readers will be able to implement not only the up-to-date QCA protocol but also various ways of visualizing QCA results and to perform such advanced diagnostics as robustness checks, set-theoretic theory evaluation and set-theoretic multi-method research.
This book also makes a significant contribution to the literature promoting transparency and replicability in qualitative and mixed methods research. The authors provide explicit guidance regarding how “to establish the highest possible levels of transparency about the analysis” so the results are replicable and traceable by readers (Oana et al., 2021, p. 219). Furthermore, the analyses in the book are done in a transparent way. All materials including data sets, R scripts, template R code are freely available online. These resources will be very helpful for both learning and teaching QCA.
However, since QCA is still an emerging methodology, the book might have benefited from a section discussing more general debates about strengths and limitations of QCA as a research approach and a technique to address doubts and concerns that novice users might have regarding this method. In addition, while the authors provide extensive number of good empirical examples to facilitate the learning process, examples of not so good applications of QCA could have been also informative and educating.
Conclusion
All in all, Qualitative Comparative Analysis Using R: A Beginner’s Guide offers a gentle introduction to QCA using R software. Starting with the basics, the authors eventually provide an overview of the most advanced developments in QCA and explain how to apply them in R. This book is an essential reading for a wide audience including teachers, students, as well as practitioners and researchers interested in case-based methodology and mixed methods research.
