Latent Growth Curve Modeling (Quantitative Applications in the Social Sciences),

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Book Title
Latent Growth Curve Modeling (Quantitative Applications in the
ISBN
9781412939553
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
SAGE Publications, Incorporated
ISBN-10
1412939550
ISBN-13
9781412939553
eBay Product ID (ePID)
64230413

Product Key Features

Number of Pages
112 Pages
Language
English
Publication Name
Latent Growth Curve Modeling
Subject
Probability & Statistics / Multivariate Analysis, Research, Statistics
Publication Year
2008
Type
Textbook
Subject Area
Mathematics, Social Science
Author
Nancy E. Briggs, Kristopher J. Preacher, Aaron Lee Wichman, Robert Charles Maccallum
Series
Quantitative Applications in the Social Sciences Ser.
Format
Trade Paperback

Dimensions

Item Height
0.4 in
Item Weight
5 Oz
Item Length
8.3 in
Item Width
5.3 in

Additional Product Features

Intended Audience
College Audience
LCCN
2008-006240
Dewey Edition
22
Series Volume Number
157
Illustrated
Yes
Dewey Decimal
519.5/35
Table Of Content
About the Authors Series Editor Introduction Acknowledgements 1. Introduction 2. Applying LGM to Empirical Data 3. Specialized Extensions 4. Relationships Between LGM and Multilevel Modeling 5. Summary Appendix References
Synopsis
Latent growth curve modeling (LGM)-a special case of confirmatory factor analysis designed to model change over time-is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This volume introduces LGM techniques to researchers, provides easy-to-follow, didactic examples of several common growth modeling approaches, and highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit. The book covers the basic linear LGM, and builds from there to describe more complex functional forms (e.g., polynomial latent curves), multivariate latent growth curves used to model simultaneous change in multiple variables, the inclusion of time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models. The authors also highlight approaches to dealing with missing data, different estimation methods, and incorporate discussion of model evaluation and comparison within the context of LGM. The models demonstrate how they may be applied to longitudinal data derived from the NICHD Study of Early Child Care and Youth Development (SECCYD).. Key Features * Provides easy-to-follow, didactic examples of several common growth modeling approaches * Highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit * Explains the commonalities and differences between latent growth model and multilevel modeling of repeated measures data * Covers the basic linear latent growth model, and builds from there to describe more complex functional forms such as polynomial latent curves, multivariate latent growth curves, time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models, Latent growth curve modeling (LGM)--a special case of confirmatory factor analysis designed to model change over time--is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This volume introduces LGM techniques to researchers, provides easy-to-follow, didactic examples of several common growth modeling approaches, and highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit. The book covers the basic linear LGM, and builds from there to describe more complex functional forms (e.g., polynomial latent curves), multivariate latent growth curves used to model simultaneous change in multiple variables, the inclusion of time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models. The authors also highlight approaches to dealing with missing data, different estimation methods, and incorporate discussion of model evaluation and comparison within the context of LGM. The models demonstrate how they may be applied to longitudinal data derived from the NICHD Study of Early Child Care and Youth Development (SECCYD).. Key Features - Provides easy-to-follow, didactic examples of several common growth modeling approaches - Highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit - Explains the commonalities and differences between latent growth model and multilevel modeling of repeated measures data - Covers the basic linear latent growth model, and builds from there to describe more complex functional forms such as polynomial latent curves, multivariate latent growth curves, time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models, Latent growth curve modeling (LGM)--a special case of confirmatory factor analysis designed to model change over time--is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This volume introduces LGM techniques to researchers, provides easy-to-follow, didactic examples of several common growth modeling approaches, and highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit. The book covers the basic linear LGM, and builds from there to describe more complex functional forms (e.g., polynomial latent curves), multivariate latent growth curves used to model simultaneous change in multiple variables, the inclusion of time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models. The authors also highlight approaches to dealing with missing data, different estimation methods, and incorporate discussion of model evaluation and comparison within the context of LGM. The models demonstrate how they may be applied to longitudinal data derived from the NICHD Study of Early Child Care and Youth Development (SECCYD).. Key Features - Provides easy-to-follow, didactic examples of several common growth modeling approaches - Highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit - Explains the commonalities and differences between latent growth model and multilevel modeling of repeated measures data - Covers the basic linear latent growth model, and builds from there to describe more complex functional forms such as polynomial latent curves, multivariate latent growth curves, time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models Learn more about "The Little Green Book" - QASS Series Click Here, Latent growth curve modeling (LGM)--a special case of confirmatory factor analysis designed to model change over time--is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This volume introduces LGM techniques to researchers, provides easy-to-follow, didactic examples of several common growth modeling approaches, and highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit. The book covers the basic linear LGM, and builds from there to describe more complex functional forms (e.g., polynomial latent curves), multivariate latent growth curves used to model simultaneous change in multiple variables, the inclusion of time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models. The authors also highlight approaches to dealing with missing data, different estimation methods, and incorporate discussion of model evaluation and comparison within the context of LGM. The models demonstrate how they may be applied to longitudinal data derived from the NICHD Study of Early Child Care and Youth Development (SECCYD).. Key Features · Provides easy-to-follow, didactic examples of several common growth modeling approaches · Highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit · Explains the commonalities and differences between latent growth model and multilevel modeling of repeated measures data · Covers the basic linear latent growth model, and builds from there to describe more complex functional forms such as polynomial latent curves, multivariate latent growth curves, time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models
LC Classification Number
QA278.6.L32 2008

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