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Longitudinal data analysis using structural equation models

Longitudinal data analysis using structural equation models

Material type
단행본
Personal Author
McArdle, John J. Nesselroade, John R.
Title Statement
Longitudinal data analysis using structural equation models / John J. McArdle and John R. Nesselroade.
Publication, Distribution, etc
Washington, D.C. :   American Psychological Association,   2014.  
Physical Medium
xi, 426 p. : ill. ; 26 cm.
ISBN
9781433817151 1433817152
Content Notes
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.
Bibliography, Etc. Note
Includes bibliographical references (p. 373-400) and index.
Subject Added Entry-Topical Term
Longitudinal method. Psychology -- Research.
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010 ▼a 2013046896
020 ▼a 9781433817151
020 ▼a 1433817152
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050 0 0 ▼a BF76.6.L65 ▼b M33 2014
082 0 0 ▼a 150.72/1 ▼2 23
084 ▼a 150.721 ▼2 DDCK
090 ▼a 150.721 ▼b M115L
100 1 ▼a McArdle, John J.
245 1 0 ▼a Longitudinal data analysis using structural equation models / ▼c John J. McArdle and John R. Nesselroade.
260 ▼a Washington, D.C. : ▼b American Psychological Association, ▼c 2014.
300 ▼a xi, 426 p. : ▼b ill. ; ▼c 26 cm.
504 ▼a Includes bibliographical references (p. 373-400) and index.
505 0 0 ▼g Preface -- Overview -- Foundations -- Background and goals of longitudinal research -- ▼t Basics of structural equation modeling -- ▼t Some technical details on structural equation modeling -- ▼t Using the simplified ram notation -- ▼t Benefits and problems of longitudinal structure modeling -- ▼t The first purpose of LSEM : direct identification of intra-individual changes -- ▼t Alternative definitions of individual changes -- ▼t Analyses based on latent curve models (LCM) -- ▼t Analyses based on time series regression (TSR) -- ▼t Analyses based on latent change score (LCS) models -- ▼t Analyses based on advanced latent change score models -- ▼t The second purpose of LSEM : identification of inter-individual differences in intra-individual changes -- ▼t Studying inter-individual differences in intra-individual changes -- ▼t Repeated measures analysis of variance as a structural model -- ▼t Multi-level structural equation modeling approaches to group differences -- ▼t Multi-group structural equation modeling approaches to group differences -- ▼t Incomplete data with multiple group modeling of changes -- ▼t The third purpose of LSEM : identification of inter-relationships in growth -- ▼t Considering common factors/latent variables in models -- ▼t Considering factorial invariance in longitudinal SEM -- ▼t Alternative common factors with multiple longitudinal observations -- ▼t More alternative factorial solutions for longitudinal data -- ▼t Extensions to longitudinal categorical factors -- ▼t The fourth purpose of LSEM : identification of causes (determinants) of intra-individual changes -- ▼t Analyses based on cross-lagged regression and changes -- ▼t Analyses based on cross-lagged regression in changes of factors -- ▼t Current models for multiple longitudinal outcome scores -- ▼t The bivariate latent change score model for multiple occasions -- ▼t Plotting bivariate latent change score results -- ▼t The fifth purpose of lsem : identification of inter-individual differences in causes (determinants) of intra-individual changes -- ▼t Dynamic processes over groups -- ▼t Dynamic influences over groups -- ▼t Applying a bivariate change model with multiple groups -- ▼t Notes on the inclusion of randomization in longitudinal studies -- ▼t The popular repeated measures analysis of variance -- ▼t Summary and discussion -- ▼t Contemporary data analyses based on planned incompleteness -- ▼t Factor invariance in longitudinal research -- ▼t Variance components for longitudinal factor models -- ▼t Models for intensively repeated measures -- ▼t CODA : the future is yours! -- References.
650 0 ▼a Longitudinal method.
650 0 ▼a Psychology ▼x Research.
700 1 ▼a Nesselroade, John R.
740 0 2 ▼a First purpose of LSEM : direct identification of intra-individual changes.
740 0 2 ▼a Second purpose of LSEM : identification of inter-individual differences in intra.
740 0 2 ▼a Third purpose of LSEM : identification of inter-relationships in growth.
740 0 2 ▼a Fourth purpose of LSEM : identification of causes (determinants) of intra-individual changes.
740 0 2 ▼a Bivariate latent change score model for multiple occasions.
740 0 2 ▼a Fifth purpose of lsem : identification of inter-individual differences in causes (determinants) of intra-individual changes.
740 0 2 ▼a Popular repeated measures analysis of variance.
945 ▼a KLPA

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