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Longitudinal data analysis using structural equation models /
"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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Washington, D.C. :
American Psychological Association,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a McArdle, John J., |e author. | |
| 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 1 online resource (xi, 426 pages) : |b illustrations | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |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. | |
| 520 | |a "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) | ||
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