Categorical Data Analysis with Structural Equation Models Applications in Mplus and lavaan

by Grimm, Kevin J.

• ISBN13: 9781462558315
• ISBN10: 1462558313
• Format: Hardcover
• Copyright: 2025-09-10
• Publisher: The Guilford Press

Summary

Multivariate categorical outcomes, such as Likert scale responses and disease diagnoses, require specialized structural equation modeling (SEM) software to be analyzed properly. Providing needed skills for applied researchers and graduate students, this book leads readers from regression analysis with categorical outcomes to complex SEMs with latent variables for categorical indicators. The initial section sets the stage by demonstrating regression analyses for binary, ordered, or count outcomes using R, with comparable SAS code at the companion website. Chapters then reanalyze the same data using Mplus and R lavaan to show how univariate models for categorical outcomes can be estimated and interpreted with SEM programs. Subsequently, the book turns to multivariate models, discussing path models, confirmatory factor models, and latent variable path models with categorical outcomes. Concluding chapters cover advanced SEM with categorical outcomes, including growth models, latent class models, and survival models. Worked-through examples and annotated Mplus and lavaan code are featured throughout.

Author Biography

Kevin J. Grimm, PhD, is Professor of Psychology at Arizona State University. His research interests include multivariate methods for the analysis of change, multiple group and latent class models for understanding divergent developmental processes, categorical data analysis, machine learning techniques for psychological data, and cognitive/achievement development. Dr. Grimm teaches graduate quantitative courses, including Longitudinal Growth Modeling, Machine Learning in Psychology, Structural Equation Modeling, Advanced Categorical Data Analysis, and Intermediate Statistics. He has also taught workshops sponsored by the American Psychological Association's Advanced Training Institute, Statistical Horizons, Instats, Stats Camp, and various departments and schools across the country. 

Table of Contents

1. Regression, Structural Equation Modeling, Mplus, and lavaan
I. Regression Analysis with Categorical Outcomes in R
2. Regression Models with Binary Outcomes in R
3. Regression Models with Ordinal Outcomes in R
4. Regression Models with Count Outcomes in R
II. Regression Analysis with Structural Equation Modeling Programs
5. Structural Equation Modeling with Categorical Outcomes in Mplus and lavaan
6. Binary Regression Models in Mplus and lavaan
7. Ordered and Nominal Regression Models in Mplus and lavaan
8. Count Regression Models in Mplus
III. Structural Equation Models and Applications
9. Path Analysis with Categorical Outcomes in Mplus and lavaan
10. Confirmatory Factor Models with Categorical Indicators in Mplus and lavaan
11. Latent Variable Path Models with Categorical Outcomes in Mplus and lavaan
IV. Advanced Structural Equation Models and Applications
12. Growth Models with Ordered Categorical Outcomes in Mplus and lavaan
13. Multiple Group Confirmatory Factor Models in Mplus and lavaan
14. Finite Mixture and Latent Class Models in Mplus
15. Zero-Inflated Count Outcomes in Mplus
16. Survival Analysis in Mplus
References
Author Index
Subject Index
About the Author