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
SAGE, c2020
大学図書館所蔵 全9件
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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