This series provides applied researchers and students with analysis and research design books that emphasize the use of methods to answer research questions. Rather than emphasizing statistical theory, each volume in the series illustrates when a technique should (and should not) be used and how the output from available software programs should (and should not) be interpreted. Common pitfalls as well as areas of further development are clearly articulated.
Most titles in the series include extensive downloadable files and online resources.
By Christian Geiser
November 04, 2020
An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent state–trait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion specificity, and ...
By James Jaccard, Jacob Jacoby
June 09, 2020
This accessible, hands-on text has now been revised and updated, with expanded coverage of topics including how theory may emerge from exploratory data analysis. The book prepares graduate students, new researchers, and even seasoned investigators to develop their own theories and build on existing...
By Charles S. Reichardt
September 26, 2019
Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings. Foremost expert Charles S. Reichardt provides an in-depth examination ...
By Christopher McCarty, Miranda J. Lubbers, Raffaele Vacca, José Luis Molina
May 03, 2019
Written at an introductory level, and featuring engaging case examples, this book reviews the theory and practice of personal and egocentric network research. This approach offers powerful tools for capturing the impact of overlapping, changing social relationships and contexts on individuals' ...
By Deborah L. Bandalos
March 22, 2018
Which types of validity evidence should be considered when determining whether a scale is appropriate for a given measurement situation? What about reliability evidence? Using clear explanations illustrated by examples from across the social and behavioral sciences, this engaging text prepares ...
By Larry R. Price
January 11, 2017
Grounded in current knowledge and professional practice, this book provides up-to-date coverage of psychometric theory, methods, and interpretation of results. Essential topics include measurement and statistical concepts, scaling models, test design and development, reliability, validity, factor ...
By Richard B. Darlington, Andrew F. Hayes
October 21, 2016
Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of ...
By David L. Weakliem
May 17, 2016
Examining the major approaches to hypothesis testing and model selection, this book blends statistical theory with recommendations for practice, illustrated with real-world social science examples. It systematically compares classical (frequentist) and Bayesian approaches, showing how they are ...
By Rex B. Kline
December 24, 2015
Emphasizing concepts and rationale over mathematical minutiae, this is the most widely used, complete, and accessible structural equation modeling (SEM) text. Continuing the tradition of using real data examples from a variety of disciplines, the significantly revised fourth edition incorporates ...
By Noel A. Card
November 27, 2015
Offering pragmatic guidance for planning and conducting a meta-analytic review, this book is written in an engaging, nontechnical style that makes it ideal for graduate course use or self-study. The author shows how to identify questions that can be answered using meta-analysis, retrieve both ...
By Timothy A. Brown
January 29, 2015
With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate ...
By David Kaplan
September 17, 2014
Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including ...
