Longitudinal data analysis using structural equation models / John J. McArdle, John R. Nesselroade.

Format:

Book

Author/Creator:

McArdle, John J., author.

Nesselroade, John R., author.

Language:

English

Subjects (All):

Longitudinal method.

Psychology--Research.

Psychology.

Physical Description:

xi, 426 pages ; 27 cm

Edition:

First edition.

Place of Publication:

Washington, DC: American Psychological Association, [2014]

Summary:

When determining the most appropriate method for analyzing longitudinal data, you must first consider what research question you want to answer. McArdle and Nesselroade identify five basic purposes of longitudinal structural equation modeling. For each purpose, they present the most useful strategies and models. Two important but underused approaches are emphasized: multiple factorial invariance over time and latent change scores. This volume covers a wealth of models in a straightforward, understandable manner. Rather than overwhelm the reader with an extensive amount of algebra, the authors use path diagrams and emphasize methods that are appropriate for many uses. Book jacket.

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.

Notes:

Includes bibliographical references and index.

ISBN:

9781433817151

1433817152

OCLC:

866857468