The lowest-priced, brand-new, unused, unopened, undamaged item in its original packaging (where packaging is applicable).Packaging should be the same as what is found in a retail store, unless the item is handmade or was packaged by the manufacturer in non-retail packaging, such as an unprinted box or plastic bag.See details for additional description.
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
Christian Geiser, PhD, 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.