Multilevel modeling : applications in Stata, IBM SPSS, SAS, R, & HLM

著者

書誌事項

Multilevel modeling : applications in Stata, IBM SPSS, SAS, R, & HLM

G. David Garson

SAGE, c2020

大学図書館所蔵 件 / 全9件

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注記

Includes bibliographical references (p. 493-502) and index

内容説明・目次

内容説明

Multilevel Modeling: Applications in STATA (R), IBM (R) SPSS (R), SAS (R), R & HLM (TM) provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences. Author G. David Garson's step-by-step instructions for software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource for their students and a foundation for their teaching alike.

目次

Preface Acknowledgments About the Author Chapter 1 * Introduction to Multilevel Modeling Overview What Multilevel Modeling Does The Importance of Multilevel Theory Types of Multilevel Data Common Types of Multilevel Model Mediation and Moderation Models in Multilevel Analysis Alternative Statistical Packages Multilevel Modeling Versus GEE Summary Glossary Challenge Questions With Answers Chapter 2 * Assumptions of Multilevel Modeling About This Chapter Overview Model Specification Construct Operationalization and Validation Random Sampling Sample Size Balanced and Unbalanced Designs Data Level Linearity and Nonlinearity Independence Recursivity Missing Data Outliers Centered and Standardized Data Longitudinal Time Values Multicollinearity Homogeneity of Error Variance Normally Distributed Residuals Normal Distribution of Variables Normal Distribution of Random Effects Convergence Covariance Structure Assumptions Summary Glossary Challenge Questions With Answers Chapter 3 * The Null Model Overview Testing the Need for Multilevel Modeling Likelihood Ratio Tests Partition of Variance Components Examples Summary Glossary Challenge Questions With Answers Chapter 4 * Estimating Multilevel Models Fixed and Random Effects Why Not Just Use OLS Regression? Why Not Just Use GLM (ANOVA)? Types of Estimation Robust and Cluster-Robust Standard Errors Summary Glossary Challenge Questions With Answers Chapter 5 * Goodness of Fit and Effect Size in Multilevel Models Overview Goodness of Fit Measures and Tests Effect Size Measures Effect Size and Endogeneity Summary Glossary Challenge Questions With Answers Chapter 6 * The Two-Level Random Intercept Model Overview SPSS Stata SAS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 7 * The Two-Level Random Coefficients Model Overview SPSS Stata SAS HLM 7 R Significance (p) Values for Variance Components Summary Glossary Challenge Questions With Answers Chapter 8 * The Three-Level Unconditional Random Intercept Model with Longitudinal Data Overview SPSS Stata SAS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 9 * Repeated Measures and Heterogeneous Variance Models Overview SPSS SAS Stata R HLM 7 Summary Glossary Challenge Questions With Answers Chapter 10 * Residual and Influence Analysis for a Three-Level RC Model About This Chapter Overview Why Residual Analysis? Data Model Model Diagnostics SAS Stata SPSS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 11 * Cross-Classified Linear Mixed Models Overview Data Model Research Purpose Stata SPSS SAS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 12 * Generalized Linear Mixed Models Overview Estimation Methods Data Model Stata SAS SPSS HLM 7 R Summary Glossary Challenge Questions With Answers Appendix 1: Data Used in Examples. Refers to Student Companion Website Appendix 2: Reporting Multilevel Results References Index

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