Linear Mixed Models: A Practical Guide Using Statistical Software

摘要: INTRODUCTION What Are Linear Mixed Models (LMMs)? A Brief History of LINEAR MIXED MODELS: AN OVERVIEW Introduction Specification LMMs The Marginal Model Estimation in Computational Issues Tools for Selection Model-Building Strategies Checking Assumptions (Diagnostics) Other Aspects Power Analysis Chapter Summary TWO-LEVEL MODELS FOR CLUSTERED DATA: THE RAT PUP EXAMPLE Rat Pup Study Overview the Data Steps Software Procedures Results Hypothesis Tests Comparing across Interpreting Parameter Estimates Final Estimating Intraclass Correlation Coefficients (ICCs) Calculating Predicted Values Diagnostics Notes and Recommendations THREE-LEVEL DATA CLASSROOM Classroom REPEATED-MEASURES BRAIN Brain Implied Variance-Covariance Matrix Analytic Approaches RANDOM COEFFICIENT LONGITUDINAL AUTISM Autism Note: Problems with D An Alternative Approach: Fitting an Unstructured Covariance DENTAL VENEER Dental Veneer WITH CROSSED FACTORS: SAT SCORE Score Recommended Additional APPENDIX A: STATISTICAL SOFTWARE RESOURCES B: CALCULATION OF MARGINAL VARIANCE-COVARIANCE MATRIX C: ACRONYMS/ABBREVIATIONS BIBLIOGRAPHY INDEX