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The Resource Longitudinal data analysis using structural equation models, John J. McArdle and John R. Nesselroade, (electronic resource)
Longitudinal data analysis using structural equation models, John J. McArdle and John R. Nesselroade, (electronic resource)
Resource Information
The item Longitudinal data analysis using structural equation models, John J. McArdle and John R. Nesselroade, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item Longitudinal data analysis using structural equation models, John J. McArdle and John R. Nesselroade, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.
 Summary
 "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 are too often overlooked in favor of simpler but inferior alternatives. That is, certainly researchers have their own ideas about the importance of longitudinal structural equation modeling (LSEM), including concepts of multiple factorial invariance over time (MFIT), but we think these are essential ingredients of useful longitudinal analyses. Also, the use of what we term latent change scores, which we emphasize here, is not the common approach currently being used by many other researchers in the field. Thus, a second motive is to distribute knowledge about MFIT and the latent change score approach. Most of the instruction in this book pertains to using computer programs effectively. A third reason for writing this book is that we are enthusiastic about the possibilities for good uses of the longitudinal methods described here, some described for the first time and most never used in situations where we think they could be most useful. In essence, we write to offer some hope to the next generation of researchers in this area. Our general approach to scientific discourse is not one of castigation and critique of previous work; rather than attack the useful attempts of others, we have decided to applaud all the prior efforts and simply lay out our basic theory of longitudinal data analysis. We hope our efforts will spawn improved longitudinal research"Preface. (PsycINFO Database Record (c) 2014 APA, all rights reserved)
 Language
 eng
 Extent
 xi, 426 p.
 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 intraindividual 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 interindividual differences in intraindividual changes
 Studying interindividual differences in intraindividual changes
 Repeated measures analysis of variance as a structural model
 Multilevel structural equation modeling approaches to group differences
 Multigroup structural equation modeling approaches to group differences
 Incomplete data with multiple group modeling of changes
 The third purpose of LSEM : identification of interrelationships 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 intraindividual changes
 Analyses based on crosslagged regression and changes
 Analyses based on crosslagged 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 interindividual differences in causes (determinants) of intraindividual 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
 Isbn
 9781433817151
 Label
 Longitudinal data analysis using structural equation models
 Title
 Longitudinal data analysis using structural equation models
 Statement of responsibility
 John J. McArdle and John R. Nesselroade
 Language
 eng
 Summary
 "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 are too often overlooked in favor of simpler but inferior alternatives. That is, certainly researchers have their own ideas about the importance of longitudinal structural equation modeling (LSEM), including concepts of multiple factorial invariance over time (MFIT), but we think these are essential ingredients of useful longitudinal analyses. Also, the use of what we term latent change scores, which we emphasize here, is not the common approach currently being used by many other researchers in the field. Thus, a second motive is to distribute knowledge about MFIT and the latent change score approach. Most of the instruction in this book pertains to using computer programs effectively. A third reason for writing this book is that we are enthusiastic about the possibilities for good uses of the longitudinal methods described here, some described for the first time and most never used in situations where we think they could be most useful. In essence, we write to offer some hope to the next generation of researchers in this area. Our general approach to scientific discourse is not one of castigation and critique of previous work; rather than attack the useful attempts of others, we have decided to applaud all the prior efforts and simply lay out our basic theory of longitudinal data analysis. We hope our efforts will spawn improved longitudinal research"Preface. (PsycINFO Database Record (c) 2014 APA, all rights reserved)
 Cataloging source
 DcWaAPA
 http://library.link/vocab/creatorName
 McArdle, John J
 Index
 index present
 LC call number
 BF76.6.L65
 LC item number
 M33 2014
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 http://library.link/vocab/relatedWorkOrContributorName

 Nesselroade, John R
 EBSCOhost
 http://library.link/vocab/subjectName

 Longitudinal method
 Psychology
 Psychology
 Research
 Label
 Longitudinal data analysis using structural equation models, John J. McArdle and John R. Nesselroade, (electronic resource)
 Bibliography note
 Includes bibliographical references and index
 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 intraindividual 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 interindividual differences in intraindividual changes  Studying interindividual differences in intraindividual changes  Repeated measures analysis of variance as a structural model  Multilevel structural equation modeling approaches to group differences  Multigroup structural equation modeling approaches to group differences  Incomplete data with multiple group modeling of changes  The third purpose of LSEM : identification of interrelationships 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 intraindividual changes  Analyses based on crosslagged regression and changes  Analyses based on crosslagged 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 interindividual differences in causes (determinants) of intraindividual 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
 Dimensions
 cm
 Extent
 xi, 426 p.
 Form of item
 online
 Isbn
 9781433817151
 Reproduction note
 Electronic reproduction.
 Specific material designation
 remote
 System control number
 (DcWaAPA)apa09246388
 Label
 Longitudinal data analysis using structural equation models, John J. McArdle and John R. Nesselroade, (electronic resource)
 Bibliography note
 Includes bibliographical references and index
 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 intraindividual 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 interindividual differences in intraindividual changes  Studying interindividual differences in intraindividual changes  Repeated measures analysis of variance as a structural model  Multilevel structural equation modeling approaches to group differences  Multigroup structural equation modeling approaches to group differences  Incomplete data with multiple group modeling of changes  The third purpose of LSEM : identification of interrelationships 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 intraindividual changes  Analyses based on crosslagged regression and changes  Analyses based on crosslagged 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 interindividual differences in causes (determinants) of intraindividual 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
 Dimensions
 cm
 Extent
 xi, 426 p.
 Form of item
 online
 Isbn
 9781433817151
 Reproduction note
 Electronic reproduction.
 Specific material designation
 remote
 System control number
 (DcWaAPA)apa09246388
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