Longitudinal data analysis using structural equation models /

"We have led a workshop on longitudinal data analysis for the past decade, and participants at this workshop have asked many questions. Our first motive in writing this book is to answer these questions in an organized and complete way. Second, the important advances in longitudinal methodology...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: McArdle, John J. (Autor), Nesselroade, John R. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Washington, D.C. : American Psychological Association, 2014.
Temas:
Longitudinal method.
Psychology > Research.
Psychology.
Research.
Psychology
Research
Longitudinal Studies
Méthode longitudinale.
Psychologie > Recherche.
Psychologie.
Recherche.
psychology.
research (function)
PSYCHOLOGY > Reference.
PSYCHOLOGY > Statistics.
Longitudinal method
Psychology > Research
Forschungsmethode
Strukturgleichungsmodell
Längsschnittuntersuchung
Psychologie
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Preface
  • Overview
  • Foundations
  • Background and goals of longitudinal research
  • Basics of structural equation modeling
  • Some technical details on structural equation modeling
  • Using the simplified ram notation
  • Benefits and problems of longitudinal structure modeling
  • The first purpose of LSEM : direct identification of intra-individual changes
  • Alternative definitions of individual changes
  • Analyses based on latent curve models (LCM)
  • Analyses based on time series regression (TSR)
  • Analyses based on latent change score (LCS) models
  • Analyses based on advanced latent change score models
  • The second purpose of LSEM : identification of inter-individual differences in intra-individual changes
  • Studying inter-individual differences in intra-individual changes
  • Repeated measures analysis of variance as a structural model
  • Multi-level structural equation modeling approaches to group differences
  • Multi-group structural equation modeling approaches to group differences
  • Incomplete data with multiple group modeling of changes
  • The third purpose of LSEM : identification of inter-relationships in growth
  • Considering common factors/latent variables in models
  • Considering factorial invariance in longitudinal SEM
  • Alternative common factors with multiple longitudinal observations
  • More alternative factorial solutions for longitudinal data
  • Extensions to longitudinal categorical factors
  • The fourth purpose of LSEM : identification of causes (determinants) of intra-individual changes
  • Analyses based on cross-lagged regression and changes
  • Analyses based on cross-lagged regression in changes of factors
  • Current models for multiple longitudinal outcome scores
  • The bivariate latent change score model for multiple occasions
  • Plotting bivariate latent change score results
  • The fifth purpose of lsem : identification of inter-individual differences in causes (determinants) of intra-individual changes
  • Dynamic processes over groups
  • Dynamic influences over groups
  • Applying a bivariate change model with multiple groups
  • Notes on the inclusion of randomization in longitudinal studies
  • The popular repeated measures analysis of variance
  • Summary and discussion
  • Contemporary data analyses based on planned incompleteness
  • Factor invariance in longitudinal research
  • Variance components for longitudinal factor models
  • Models for intensively repeated measures
  • CODA : the future is yours!
  • References.

Ejemplares similares