A comprehensive and accessible guide to learning and successfully applying QCA

Social phenomena can rarely be attributed to single causes—instead, they typically stem from a myriad of interwoven factors that are often difficult to untangle. Drawing on set theory and the language of necessary and sufficient conditions, qualitative comparative analysis (QCA) is ideally suited to capturing this causal complexity. A case-based research method, QCA regards cases as combinations of conditions and compares the conditions of each case in a structured way to identify the necessary and sufficient conditions for an outcome.

Qualitative Comparative Analysis: An Introduction to Research Design and Application is a comprehensive guide to QCA. As QCA becomes increasingly popular across the social sciences, this textbook teaches students, scholars, and self-learners the fundamentals of the method, research design, interpretation of results, and how to communicate findings.

Following an ideal typical research cycle, the book’s ten chapters cover the methodological basis and analytical routine of QCA, as well as matters of research design, causation and causal complexity, QCA variants, and the method’s reception in the social sciences. A comprehensive glossary helps to clarify the meaning of frequently used terms. The book is complemented by an accessible online R manual to help new users to practice QCA’s analytical steps on sample data and then implement with their own findings. This hands-on textbook is an essential resource for students and researchers looking for a complete and up-to-date introduction to QCA.

In this modern era of mathematical modeling, applications have become increasingly complicated. As the complexity grows, it becomes more and more difficult to draw meaningful conclusions about the behavior of theoretical models and their relations to reality. Alongside methods that emphasize quantitative properties and the testing of scientific details, there is a need for approaches that are more qualitative. These techniques attempt to cover whole families of models in one bold stroke, in a manner that allows robust conclusions to be drawn about them.

Loop analysis and time averaging provide a means of interpreting the properties of systems from the network of interactions within the system. The authors' methodology concentrates on graphical representation to guide experimental design, to identify sources of external variability from the statistical pattern of variables, and to make management decisions.

Although most of the examples are drawn from ecology, the methods are relevant to all of the pure and applied sciences. This relevance is enhanced by case studies from such diverse areas as physiology, resource management, the behavioral sciences, and social epidemiology. The book will be useful to a broad readership from the biological and social sciences as well as the physical sciences and technology. It will interest undergraduate and graduate students along with researchers active in these disciplines. Here the reader will find a strong rationale for maintaining a holistic approach, revealing what insights and advantages are retained by the broader perspective and, more explicitly, by the synergistic effects that cannot be discerned by reducing systems to their smallest parts.

A concise, yet engaging introduction to the field of ethics, this volume offers a systematic study of the foundations of moral responsibility. Montague Brown guides the reader on an examination of a wide range of ethical positions, including relativism, emotivism, egoism, utilitarianism, Kantian formalism, and natural law.

Brown explains not only the history behind the development of each position, but also the roles science, democracy, and religion play in moral thinking today.

Students and teachers of philosophy, ethics, and religion, as well as the general reader, will find that this book tackles the serious issues and offers an insightful, accessible introduction to major ethical positions and the great moral philosophers.