Item Details

Longitudinal Data Analysis Using Structural Equation Models [electronic resource]

John J. McArdle and John R. Nesselroade
Format
EBook; Book; Online
Published
Washington, D.C. : American Psychological Association, [2014].
Language
English
ISBN
9781433817151, 1433817152
Contents
  • 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.
Description
Mode of access: World wide Web.
Notes
Includes bibliographical references (pages 373-400) and index.
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Technical Details
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    a| 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.
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