In this issue
“Research Methods in Clinical Psychology: An Introduction for Students and Practitioners” (3rd edition)
By Chris Barker, Nancy Pistrang, & Robert Elliott
Published in December 2015 by Wiley ($49.95 paperback; $39.99 e-book)
This is the third edition of a well-respected research methods textbook for clinical psychologists and allied mental health professionals. It takes readers through the complete sequence of a research project, from start to finish, addressing both the practical and the theoretical issues raised by each method. It takes a methodological pluralist stance, covering both quantitative approaches (e.g., psychometric theory, experimental design and small-N methods) and also qualitative approaches (such as choosing between qualitative approaches, their underlying philosophies, conducting semi-structured interviews and analyzing qualitative data). There is a companion website with material for instructors, including PowerPoint slides for each chapter. The authors aimed to make the text reader-friendly, with frequent summaries, illustrative studies, end-of-chapter suggestions for further reading and questions for reader reflection.
“Meta-Analysis: A Structural Equation Modeling Approach”
By Mike W.-L. Cheung
Published in May 2015 by Wiley ($75 hardback; $60.99 e-book)
Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. Examples in R and in Mplus are included. It will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses and will also be useful to researchers and statisticians using SEM in biostatistics.
“Statistical Rethinking: A Bayesian Course with Examples in R and Stan”
By Richard McElreath
Published in December 2015 by Chapman and Hall/CRC ($89.95 hardback; $62.97 e-book)
This book builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. Topics range from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, the book prepares them for more advanced or specialized statistical modeling.
“Quantifying the Qualitative: Information Theory for Comparative Case Analysis”
By Katya Drozdova and Kurt Taylor Gaubatz
Published in January 2016 by Sage ($47 paperback)
This book presents a systematic approach to comparative case analysis based on insights from information theory. This new method, which requires minimal quantitative skills, helps students, policymakers, professionals and scholars learn more from comparative cases. The approach avoids the limitations of traditional statistics in the small-n context and allows analysts to systematically assess and compare the impact of a set of factors on case outcomes with easy-to-use analytics. Rigorous tools reduce bias, improve the knowledge gained from case studies and provide straightforward metrics for effectively communicating results to a range of readers and leaders.