Data Analysis with Mplus

Christian Geiser

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November 14, 2012
ISBN 9781462502455
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305 Pages
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305 Pages
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A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats—for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips and solution strategies are presented in boxes in each chapter. The companion website ( features data sets, annotated syntax files, and output for all of the examples. Of special utility to instructors and students, many of the examples can be run with the free demo version of Mplus.

This title is part of the Methodology in the Social Sciences Series, edited by Todd D. Little, PhD.

“Mplus is arguably the most flexible commercially available software program for SEM and all of its special cases. Geiser has provided an admirable service to the community of researchers who use Mplus with this highly readable book. The book is an indispensable companion to more advanced SEM texts and is certainly an important supplementary text for graduate courses on SEM.”

—David Kaplan, PhD, Department of Educational Psychology, University of Wisconsin-Madison

“More and more researchers all over the world are using Mplus. I know of no other book that provides such a truly helpful tutorial on everything from the very first steps to how to run complicated SEM models like latent growth models. Beginners will very much appreciate how much attention the author pays to the basics. Many easy-to-make mistakes can be prevented by keeping this book within arm's reach. It is perfect for researchers at any career stage seeking an accessible, informative introduction to analyzing data with Mplus.”

—Rens van de Schoot, PhD, Department of Methods and Statistics, Utrecht University, The Netherlands

“This text combines an extensive tutorial in Mplus programming with clear descriptions of the statistical models being implemented. Coverage includes standard path and factor analytic models, as well as longitudinal, multilevel, and latent class models. Many real examples are analyzed throughout the book, with careful explanations of syntax, screen shots to help navigate the program, and thorough discussions of results. The companion website provides the data, input, output, and annotated syntax files for all examples. This book will be of great interest to students and researchers who want not only to learn about Mplus, but also to gain a better understanding of SEM.”

—Roger E. Millsap, PhD, Department of Psychology, Arizona State University

“Absolutely fantastic! I really wish I had had this book when I was a grad student. I will strongly recommend it to my own students, as well as to colleagues who ask for help with Mplus. The breadth of statistical techniques covered goes far beyond conventional SEM and makes this a valuable resource for both new and experienced Mplus users.”

—Alex Bierman, PhD, Department of Sociology, University of Calgary, Canada

Table of Contents

1. Data Management in SPSS

1.1 Coding Missing Values

1.2 Exporting an ASCII Data File for Mplus

2. Reading Data into Mplus

2.1 Importing and Analyzing Individual Data (Raw Data)

2.1.1 Basic Structure of the Mplus Syntax and Basic Analysis

2.1.2 Mplus Output for Basic Analysis

2.2 Importing and Analyzing Summary Data (Covariance or Correlation Matrices)

3. Linear Structural Equation Models

3.1 What are Linear SEMs?

3.2 Simple Linear Regression Analysis with Manifest Variables

3.3 Latent Regression Analysis

3.4 Confirmatory Factor Analysis

3.4.1 First-Order CFA

3.4.2 Second-Order CFA

3.5 Path Models and Mediator Analysis

3.5.1 Introduction and Manifest Path Analysis

3.5.2 Manifest Path Analysis in Mplus

3.5.3 Latent Path Analysis

3.5.4 Latent Path Analysis in Mplus

4. Structural Equation Models for Measuring Variability and Change

4.1 Latent State Analysis

4.1.1 LS versus LST Models

4.1.2 Analysis of LS Models in Mplus

4.1.3 Modeling Indicator-Specific Effects

4.1.4 Testing for Measurement Invariance across Time

4.2 LST Analysis

4.3 Autoregressive Models

4.3.1 Manifest Autoregressive Models

4.3.2 Latent Autoregressive Models

4.4 Latent Change Models

4.5 Latent Growth Curve Models

4.5.1 First-Order LGCMs

4.5.2 Second-Order LGCMs

5. Multilevel Regression Analysis

5.1 Introduction to Multilevel Analysis

5.2 Specification of Multilevel Models in Mplus

5.3 Option two level basic

5.4 Random Intercept Models

5.4.1 Null Model (Intercept-Only Model)

5.4.2 One-Way Random Effects of ANCOVA

5.4.3 Means-as-Outcomes Model

5.5 Random Intercept and Slope Models

5.5.1 Random Coefficient Regression Analysis

5.5.2 Intercepts-and-Slopes-as-Outcomes Model

6. Latent Class Analysis

6.1 Introduction to Latent Class Analysis

6.2 Specification of LCA Models in Mplus

6.3 Model Fit Assessment and Model Comparisons

6.3.1 Absolute Model Fit

6.3.2 Relative Model Fit

6.3.3 Interpretability

Appendix A: Summary of Key Mplus Commands Discussed in This Book

Appendix B: Common Mistakes in the Mplus Input Setup and Troubleshooting

Appendix C: Further Readings

About the Author

Christian Geiser, PhD, is CEO of Quantfish and former Professor of Psychology at Utah State University. His research interests are in psychometrics and structural equation modeling, particularly in longitudinal data analysis and multitrait–multimethod modeling. As part of his methodological work, he has presented new longitudinal structural equation modeling approaches for examining effects of situations and person–situation interactions, as well as models for integrating information from multiple reporters or other methods in longitudinal analyses. He offers Mplus workshops at


Graduate students, instructors, and researchers in psychology, education, human development and family studies, management, sociology, social work, nursing, public health, criminal justice, and communications.

Course Use

Serves as a supplemental text in graduate-level courses in multivariate statistics, factor analysis, structural equation modeling, multilevel modeling, or advanced quantitative methods.