Factor Analysis

Comprehensive and comprehensible, this classic text covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. Emphasizing the usefulness of the techniques, it presents sufficient mathematical background for understanding and applying its use. This includes the theory as well as the empirical evaluations. The overall goal is to show readers how to use factor analysis in their substantive research by highlighting when the differences in mathematical procedures have a major impact on the substantive conclusions, when the differences are not relevant, and when factor analysis might not be the best procedure to use. Although the original version was written years ago, the book maintains its relevance today by providing readers with a thorough understanding of the basic mathematical models so they can easily apply these models to their own research. Readers are presented with a very complete picture of the "inner workings" of these methods. The new Introduction highlights the remarkably few changes that the author would make if he were writing the book today. An ideal text for courses on factor analysis or as a supplement for multivariate analysis, structural equation modeling, or advanced quantitative techniques taught in psychology, education, and other social and behavioral sciences, researchers who use these techniques also appreciate this book’s thorough review of the basic models. Prerequisites include a graduate level course on statistics and a basic understanding of algebra. Sections with an asterisk can be skipped entirely if preferred.

Measurement connects theoretical concepts to what is observable in the empirical world, and is fundamental to all social and behavioral research. In this volume, J. Micah Roos and Shawn Bauldry introduce a popular approach to measurement: Confirmatory Factor Analysis (CFA). As the authors explain, CFA is a theoretically informed statistical framework for linking multiple observed variables to latent variables that are not directly measurable. The authors begin by defining terms, introducing notation, and illustrating a wide variety of measurement models with different relationships between latent and observed variables. They proceed to a thorough treatment of model estimation, followed by a discussion of model fit. Most of the volume focuses on measures that approximate continuous variables, but the authors also devote a chapter to categorical indicators. Each chapter develops a different example (sometimes two) covering topics as diverse as racist attitudes, theological conservatism, leadership qualities, psychological distress, self-efficacy, beliefs about democracy, and Christian nationalism drawn mainly from national surveys. Data to replicate the examples are available on a companion website, along with code for R, Stata, and Mplus.

This accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA) for its emphasis on practical and conceptual aspects rather than mathematics or formulas. Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities ...

Describes various commonly used methods of initial factoring and factor rotation. In addition to a full discussion of exploratory factor analysis, confirmatory factor analysis and various methods of constructing factor scales are also presented.

Providing a practical, thorough understanding of how factor analysis works, Foundations of Factor Analysis, Second Edition discusses the assumptions underlying the equations and procedures of this method. It also explains the options in commercial computer programs for performing factor analysis and structural equation modeling. This long-awaited e

A firm knowledge of factor analysis is key to understanding much published research in the social and behavioral sciences. Exploratory Factor Analysis by W. Holmes Finch provides a solid foundation in exploratory factor analysis (EFA), which along with confirmatory factor analysis, represents one of the two major strands in this field. The book lays out the mathematical foundations of EFA; explores the range of methods for extracting the initial factor structure; explains factor rotation; and outlines the methods for determining the number of factors to retain in EFA. The concluding chapter addresses a number of other key issues in EFA, such as determining the appropriate sample size for a given research problem, and the handling of missing data. It also offers brief introductions to exploratory structural equation modeling, and multilevel models for EFA. Example computer code, and the annotated output for all of the examples included in the text are available on an accompanying website.

This is a concise, easy to use, step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using the open source software R. In this book, Dr. Watkins systematically reviews each decision step in EFA with screen shots of R and RStudio code, and recommends evidence-based best practice procedures. This is an eminently applied, practical approach with few or no formulas and is aimed at readers with little to no mathematical background. Dr. Watkins maintains an accessible tone throughout and uses minimal jargon and formula to help facilitate grasp of the key issues users will face while applying EFA, along with how to implement, interpret, and report results. Copious scholarly references and quotations are included to support the reader in responding to editorial reviews. This is a valuable resource for upper-level undergraduate and postgraduate students, as well as for more experienced researchers undertaking multivariate or structure equation modeling courses across the behavioral, medical, and social sciences.