Modeling Longitudinal and Multilevel Data - Info and Reading Options
Practical Issues, Applied Approaches, and Specific Examples
By Todd D. Little and Jürgen Baumert
"Modeling Longitudinal and Multilevel Data" was published by Lawrence Erlbaum in January 1, 2000, the book is classified in Computers genre, it has 304 pages and the language of the book is English.
“Modeling Longitudinal and Multilevel Data” Metadata:
- Title: ➤ Modeling Longitudinal and Multilevel Data
- Authors: Todd D. LittleJürgen Baumert
- Language: English
- Number of Pages: 304
- Is Family Friendly: Yes - No Mature Content
- Publisher: Lawrence Erlbaum
- Publish Date: January 1, 2000
- Genres: Computers
“Modeling Longitudinal and Multilevel Data” Subjects and Themes:
- Subjects: ➤ Statistical methods - Longitudinal method - Social sciences - Education, data processing - Sciences sociales - Méthodes statistiques - Méthode longitudinale - SOCIAL SCIENCE - Methodology
Edition Specifications:
- Format: Hardcover
- Weight: 1.4 pounds
- Dimensions: 9 x 6 x 1 inches
Edition Identifiers:
- Google Books ID: MeZSAQAACAAJ
- The Open Library ID: OL7937244M - OL23707706W
- Online Computer Library Center (OCLC) ID: 47008933 - 42719912
- Library of Congress Control Number (LCCN): 99052417
- ISBN-13: 9780805830545
- ISBN-10: 0805830545
- All ISBNs: 0805830545 - 9780805830545
AI-generated Review of “Modeling Longitudinal and Multilevel Data”:
Snippets and Summary:
This book focuses on the practical issues and approaches to handling longitudinal and multilevel data. All data sets and the corresponding command files are available via the Web.
"Modeling Longitudinal and Multilevel Data" Description:
Google Books:
This book focuses on the practical issues and approaches to handling longitudinal and multilevel data. All data sets and the corresponding command files are available via the Web. The working examples are available in the four major SEM packages--LISREL, EQS, MX, and AMOS--and two Multi-level packages--HLM and MLn. All equations and figural conventions are standardized across each contribution. The material is accessible to practicing researchers and students. Users can compare and contrast various analytic approaches to longitudinal and multiple-group data including SEM, Multi-level, LTA, and standard GLM techniques. Ideal for graduate students and practicing researchers in social and behavioral sciences.
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