Practical multilevel modeling using R
著者
書誌事項
Practical multilevel modeling using R
（Advanced quantitative techniques in the social sciences, 15）
Sage, c2023
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注記
Includes bibliographical references (p. 223-229) and index
内容説明・目次
内容説明
Practical Multilevel Modeling Using R provides students with a step-by-step guide for running their own multilevel analyses. Detailed examples illustrate the conceptual and statistical issues that multilevel modeling addresses in a way that is clear and relevant to students in applied disciplines. Clearly annotated R syntax illustrates how multilevel modeling (MLM) can be used, and real-world examples show why and how modeling decisions can affect results. The book covers all the basics but also important advanced topics such as diagnostics, detecting and handling heteroscedasticity, power analysis, and missing data handling methods. Unlike other detailed texts on MLM which are written at a very high level, this text with its applied focus and use of R software to run the analyses is much more suitable for students who have substantive research areas but are not training to be methodologists or statisticians. Each chapter concludes with a "Test Yourself" section, and solutions are available on the instructor website for the book. A companion R package is available for use with this text.
目次
1 Introduction
2 The unconditional means model
3 Adding predictors to a random intercepts model
4 Investigating cross-level interactions and random slope models
5 Understanding growth models
6 Centering in multilevel models
7 Multilevel modeling diagnostics
8 Multilevel logistic regression models
9 Modeling data structures with three (or more) levels
10 Missing data in multilevel models
11 Basic power analyses for multilevel models
12 Alternatives to multilevel models
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