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Multilevel and Longitudinal Modeling with IBM SPSS. 2nd ed. IBM SPSSを利用した多水準及び縦断的モデリング 第2版
・ISBN 978-0-415-81710-3 hard GB£ 150.00
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・ISBN 978-0-415-81711-0 paper GB£ 44.99
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電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 978-0-203-70124-9
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| 著者・編者 | Heck, Ronald H. / Thomas, Scott L. / Tabata, Lynn N., |
|---|---|
| シリーズ | Quantitative Methodology Series |
| 出版社 | (Routledge, UK) |
| 出版年月 | 2014 |
| ページ数 | 443 pp. |
| 言語 | ENG |
| ニュース番号 | ＜604-378＞ |
解説
This book demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS mixed-effects program (MIXED). Annotated screen shots provide readers with a step-by-step understanding of each technique and navigating the program. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of model development to show readers the rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at www.routledge.com/9780415817110.
Highlights of the new edition include:
- Updated throughout to reflect IBM SPSS Version 21.
- Further coverage of growth trajectories, coding time-related variables, covariance structures, individual change and longitudinal experimental designs (Ch.5).
- Extended discussion of other types of research designs for examining change (e.g., regression discontinuity, quasi-experimental) over time (Ch.6).
- New examples specifying multiple latent constructs and parallel growth processes (Ch. 7).
- Discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures (Ch.1).
The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and cross-classified data sets. Chapters 3 and 4 introduce the basics of multilevel modeling: developing a multilevel model, interpreting output, and trouble-shooting common programming and modeling problems. Models for investigating individual and organizational change are presented in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 provides an illustration of multilevel models with cross-classified data structures. The book concludes with ways to expand on the various multilevel and longitudinal modeling techniques and issues when conducting multilevel analyses. It's ideal for courses on multilevel and longitudinal modeling, multivariate statistics, and research design taught in education, psychology, business, and sociology.