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 (www.guilford.com/geiser-materials) 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.
Mục lục
1. Data Management in SPSS1.1 Coding Missing Values1.2 Exporting an ASCII Data File for Mplus2. Reading Data into Mplus2.1 Importing and Analyzing Individual Data (Raw Data)2.1.1 Basic Structure of the Mplus Syntax and Basic Analysis2.1.2 Mplus Output for Basic Analysis2.2 Importing and Analyzing Summary Data (Covariance or Correlation Matrices)3. Linear Structural Equation Models3.1 What are Linear SEMs?3.2 Simple Linear Regression Analysis with Manifest Variables3.3 Latent Regression Analysis3.4 Confirmatory Factor Analysis3.4.1 First-Order CFA3.4.2 Second-Order CFA3.5 Path Models and Mediator Analysis3.5.1 Introduction and Manifest Path Analysis3.5.2 Manifest Path Analysis in Mplus3.5.3 Latent Path Analysis3.5.4 Latent Path Analysis in Mplus4. Structural Equation Models for Measuring Variability and Change4.1 Latent State Analysis4.1.1 LS versus LST Models4.1.2 Analysis of LS Models in Mplus4.1.3 Modeling Indicator-Specific Effects4.1.4 Testing for Measurement Invariance across Time4.2 LST Analysis4.3 Autoregressive Models4.3.1 Manifest Autoregressive Models4.3.2 Latent Autoregressive Models4.4 Latent Change Models4.5 Latent Growth Curve Models4.5.1 First-Order LGCMs4.5.2 Second-Order LGCMs5. Multilevel Regression Analysis5.1 Introduction to Multilevel Analysis5.2 Specification of Multilevel Models in Mplus5.3 Option two level basic5.4 Random Intercept Models5.4.1 Null Model (Intercept-Only Model)5.4.2 One-Way Random Effects of ANCOVA5.4.3 Means-as-Outcomes Model5.5 Random Intercept and Slope Models5.5.1 Random Coefficient Regression Analysis5.5.2 Intercepts-and-Slopes-as-Outcomes Model6. Latent Class Analysis6.1 Introduction to Latent Class Analysis6.2 Specification of LCA Models in Mplus6.3 Model Fit Assessment and Model Comparisons6.3.1 Absolute Model Fit6.3.2 Relative Model Fit6.3.3 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
Giới thiệu về tác giả
Christian Geiser, Ph D, is Assistant Professor in the Department of Psychology at Utah State University in Logan. His methodological research focuses on the development, evaluation, and application of latent variable psychometric models for longitudinal and multimethod data. In his substantive research, he focuses on individual differences in spatial abilities and how they can be explained.
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