About this product

Product Identifiers

PublisherSAGE Publications, Incorporated

ISBN-101412939550

ISBN-139781412939553

eBay Product ID (ePID)64230413

Product Key Features

Number of Pages112 Pages

LanguageEnglish

Publication NameLatent Growth Curve Modeling

SubjectProbability & Statistics / Multivariate Analysis, Research, Statistics

Publication Year2008

TypeTextbook

Subject AreaMathematics, Social Science

AuthorNancy E. Briggs, Kristopher J. Preacher, Aaron Lee Wichman, Robert Charles Maccallum

SeriesQuantitative Applications in the Social Sciences Ser.

FormatTrade Paperback

Dimensions

Item Height0.4 in

Item Weight5 Oz

Item Length8.3 in

Item Width5.3 in

Additional Product Features

Intended AudienceCollege Audience

LCCN2008-006240

Dewey Edition22

Series Volume Number157

IllustratedYes

Dewey Decimal519.5/35

Table Of ContentAbout 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

SynopsisLatent 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 NumberQA278.6.L32 2008