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Einband grossLongitudinal Structural Equation Modeling with Mplus
ISBN/GTIN

Longitudinal Structural Equation Modeling with Mplus

E-BookEPUBDRM AdobeE-Book
344 Seiten
Englisch
APA Publicationserschienen am07.10.2020
Verfügbare Formate
TaschenbuchKartoniert, Paperback
EUR70,00
E-BookEPUBDRM AdobeE-Book
EUR66,99

Produkt

Details
Weitere ISBN/GTIN9781462546411
ProduktartE-Book
EinbandartE-Book
FormatEPUB
Format HinweisDRM Adobe
FormatE101
Erscheinungsjahr2020
Erscheinungsdatum07.10.2020
Seiten344 Seiten
SpracheEnglisch
Dateigrösse7121 Kbytes
Artikel-Nr.5412580
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
List of Abbreviations
Guide to Statistical Symbols
1. A Measurement Theoretical Framework for Longitudinal Data: Introduction to Latent State-Trait Theory
1.1 Introduction
1.2 Latent State-Trait Theory
1.3 Chapter Summary
1.4 Recommended Readings
2. Single-Factor Longitudinal Models for Single-Indicator Data
2.1 Introduction
2.2 The Random Intercept Model
2.3 The Random and Fixed Intercepts Model
2.4 The ξ-Congeneric Model
2.5 Chapter Summary
2.6 Recommended Reading
3. Multifactor Longitudinal Models for Single-Indicator Data
3.1 Introduction
3.2 The Simplex Model
3.3 The Latent Change Score Model
3.4 The Trait-State-Error Model
3.5 Latent Growth Curve Models
3.6 Chapter Summary
3.7 Recommended Readings
4. Testing Measurement Equivalence in Longitudinal Studies
4.1 Introduction
4.2 The Latent State (LS) Model
4.3 The Latent State Model with Indicator-Specific Residual Factors (LS-IS Model)
4.4 Chapter Summary
4.5 Recommended Readings
5. Multiple-Indicator Longitudinal Models
5.1 Introduction
5.2 Latent State Change (LSC) Models
5.3 The Latent Autoregressive/Cross-Lagged States (LACS) Model
5.4 Latent State-Trait (LST) Models
5.5 Latent Trait Change (LTC) Models
5.6 Chapter Summary
5.7 Recommended Readings
6. Modeling Intensive Longitudinal Data
6.1 Introduction
6.2 Special features of Intensive Longitudinal Data
6.3 Specifying Longitudinal SEMs for Intensive Longitudinal Data
6.4 Chapter Summary
6.5 Recommended Readings
7. Missing Data Handling
7.1 Introduction
7.2 Missing Data Mechanisms
7.3 Maximum Likelihood Missing Data Handling
7.4 Multiple Imputation (MI)
7.5 Planned Missing Data Designs
7.6 Chapter Summary
7.7 Recommended Readings
8. How to Choose between Models and Report the Results
8.1 Model Selection
8.2 Reporting Results
8.3 Chapter Summary
8.4 Recommended Readings
References
Author Index
Subject Index
mehr

Autor

Christian Geiser, PhD, is a former professor of quantitative psychology. He currently works as an instructor and statistical consultant. His areas of expertise are in structural equation modeling, longitudinal data analysis, latent class modeling, multitrait-multimethod analysis, and measurement. His website is https://christiangeiser.com/.