Zum Hauptinhalt springen Zur Suche springen Zur Hauptnavigation springen
Beschreibung

An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent state-trait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion specificity, and reliability. Following a standard format, chapters review the theoretical underpinnings, strengths, and limitations of the various models; present data examples; and demonstrate each model's application and interpretation in Mplus, with numerous screen shots and output excerpts. Coverage encompasses both traditional models (autoregressive, change score, and growth curve models) and LST models for analyzing single- and multiple-indicator data. The book discusses measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, and provides strategies for model selection and reporting of results. User-friendly features include special-topic boxes, chapter summaries, and suggestions for further reading. The companion website features data sets, annotated syntax files, and output for all of the examples.

An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent state-trait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion specificity, and reliability. Following a standard format, chapters review the theoretical underpinnings, strengths, and limitations of the various models; present data examples; and demonstrate each model's application and interpretation in Mplus, with numerous screen shots and output excerpts. Coverage encompasses both traditional models (autoregressive, change score, and growth curve models) and LST models for analyzing single- and multiple-indicator data. The book discusses measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, and provides strategies for model selection and reporting of results. User-friendly features include special-topic boxes, chapter summaries, and suggestions for further reading. The companion website features data sets, annotated syntax files, and output for all of the examples.

Über den 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 [...].

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 -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

Details
Erscheinungsjahr: 2020
Fachbereich: Grundlagen (Methodik & Statistik)
Genre: Importe, Psychologie
Rubrik: Geisteswissenschaften
Medium: Taschenbuch
Reihe: Methodology in the Social Sciences
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9781462538782
ISBN-10: 1462538789
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Geiser, Christian
Hersteller: Guilford Publications
Methodology in the Social Sciences
Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 233 x 153 x 20 mm
Von/Mit: Christian Geiser
Erscheinungsdatum: 04.11.2020
Gewicht: 0,502 kg
Artikel-ID: 118016255

Ähnliche Produkte