Slika naslovnice

Longitudinal data analysis using structural equation models

Permalink: http://skupni.nsk.hr/Record/ffzg.KOHA-OAI-FFZG:330358/TOC
Glavni autor: McArdle, John J. (-)
Ostali autori: Nesselroade, John R. (-)
Vrsta građe: Knjiga
Jezik: eng
Impresum: Washington : American Psychological Association, 2014
Predmet:
Longitudinal method.
Psychology > Research.
Sadržaj:
  • 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.

Slični primjerci