Confirmatory Factor Analysis

by J. Micah Roos, Shawn Bauldry

Overview

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 in R, Stata, and Mplus.

Product Details

ISBN-13:	9781544375137
Publisher:	SAGE Publications
Publication date:	11/05/2021
Series:	Quantitative Applications in the Social Sciences , #189
Pages:	144
Product dimensions:	5.50(w) x 8.50(h) x (d)

About the Author

J. Micah Roos is an associate professor of sociology at Virginia Polytechnic Institute and State University. His research interests include knowledge, science, religion, culture, stratification, measurement, racial attitudes, and quantitative methods. He ties these interests together through a quantitative, measurement approach to the sociology of knowledge and culture, with a focus on stratification along the early life course. Another strand of his work involves applying techniques in confirmatory factor analysis to the problem of differential item functioning or item-level bias. He holds a Ph D in Sociology from the University of North Carolina at Chapel Hill.

Shawn Bauldry is an Associate Professor in the Department of Sociology at Purdue University. He received a Ph.D. in Sociology and an M.S. in Statistics from the University of North Carolina at Chapel Hill in 2012 and has previously taught at the University of Alabama at Birmingham. His research in applied statistics primarily focuses on the development of structural equation models. His research in sociology explores interrelationships between socioeconomic resources and health over the life course and across generations. His work has appeared in a variety of outlets including Sociological Methodology, Sociological Methods & Research, Psychological Methods, Social Forces, and Journal of Health and Social Behavior.

Table of Contents

Chapter 1: Introduction
Chapter 2: Model Specification
Chapter 3: Identification and Estimation
Chapter 4: Model Evaluation and Respecification
Chapter 5: Measurement Invariance
Chapter 6: Categorical Indicators
Chapter 7: Conclusion
Appendix: Reliability of Scales
Glossary
Bibliography

Editorial Reviews

This is a well-written and comprehensive text.

Michael D. Biderman

Confirmatory Factor Analysis is well written and easy to read, The book covers the essentials necessary for understanding and using CFA. It is appropriate for graduate students and professors new to this analysis approach.

Jerry J. Vaske

Roos and Bauldry lucidly set out foundations of confirmatory factor analysis (CFA) as applied in the assessment and construction of scales. Beginning with model specification, they discuss identification, estimation, and assessment of CFA models, before developing extensions to assessing measurement invariance and categorical (rather than quantitative) indicators.

Peter V. Marsden

The authors provide a masterful and fluid overview of confirmatory factor analysis that will guide readers to the best practices whether conducting their own research or evaluating the research of others.

John Hoffmann