Latent growth curve modeling

Cover of: Latent growth curve modeling |

Published by Sage Publications in Thousand Oaks, Calif .

Written in English

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Subjects:

  • Latent structure analysis,
  • Latent variables,
  • Longitudinal method,
  • Social sciences -- Statistical methods

Edition Notes

Includes bibliographical references and index.

Book details

StatementKristopher J. Preacher ... [et al.].
SeriesQuantitative applications in the social sciences -- 157
ContributionsPreacher, Kristopher J.
Classifications
LC ClassificationsQA278.6 .L32 2008
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL16522932M
ISBN 109781412939553
LC Control Number2008006240

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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.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its by: Latent Growth Curve Modeling (Quantitative Applications in the Social Sciences Book ) - Kindle edition by Preacher, Kristopher J., Wichman, Aaron Lee, MacCallum, Robert Charles, Briggs, Nancy E.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Latent Growth Curve Modeling (Quantitative /5(8).

Latent growth modeling is a statistical technique used in the structural equation modeling (SEM) framework to estimate growth trajectories. It is a longitudinal analysis technique to estimate growth over a period of time.

It is widely used in the field of behavioral science, education and social science. It is also called latent growth curve. Latent growth curve models (LGCM) are a versatile tool to model change in individual units over time. The most common application in communication research is the analysis of panel data.

Latent curve analysis; Latent trajectory models; Structural equation models Definition A method for modeling repeated measures as latent variables is composed of a random intercept and random slope(s) that permit individual cases to have unique trajectories of change over time.

Latent growth curve modeling (LGM) is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This book 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/5(3).

Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). this is an excellent book for graduate courses in latent trajectory models as well as a supplemental text for courses in structural modeling. This book is an excellent aid and reference for researchers in.

Download This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures.

It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM).

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. Latent growth curve modeling book It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use.

This video is a lecture that covers latent growth (curve) modeling - including the steps for random intercepts and slopes taken from the Beaujean SEM lavaan book.

Lecture materials and. From there, each chapter is dedicated to a different latent variable model including exploratory and confirmatory factor analysis (CFA), structural equation modeling (SEM), multiple groups CFA/SEM, least squares estimation, growth curve models, mixture models, item response theory (both dichotomous and polytomous items), differential item.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures.

It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage o. Latent growth curve modeling (LGM) is an indispensable and increasingly ubiquitous approach for modeling longitudinal data.

This book introduces LGMtechnique. An Introduction to Latent Variable Growth Curve Modeling by Terry E. Duncan,available at Book Depository with free delivery worldwide/5(6).

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.

The book covers the basic linear LGM, and builds from there to describe more complex functional forms (e.g., polynomial latent curves. Get this from a library. Latent growth curve modeling. [Kristopher J Preacher; Sage Publications.;] -- "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.

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, Price: $ variable modeling techniques such as latent growth curve models (cf. MacCallum & Austin, ). The objective of these approaches is to capture information about interindividual differences in intraindividual change over time (Nesselroade, ).

However, conventional growth modeling approaches assume that. The goals of the current study were addressed with latent growth curve analysis (LGCA) in a structural equation modeling framework using Mplus (Muthén & Muthén, In this book, we have provided an introduction to latent growth curve modeling, illustrated many potential applications with data, and described several extensions and advanced applications that lie outside the basic LGM demonstrated how models more advanced than simple linear models may.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use.

It is designed to take advantage of the reader's familiarity with analysis of variance and structural equation modeling (SEM) in 3/5(1). This is an excellent book for anyone who wishes to not only understand the theory behind latent growth curve modeling but also seeing how it is directly applied in a number of situations.

For a reader like me who depends upon the literature to help understand newer statistical approaches, a book like this is a breath of fresh air.5/5(2). Latent growth curve modeling as an integrative approach to the analysis of change MANUEL C. VOELKLE 1 Abstract Latent Growth Curve Models (LGCM) are discussed as a general data-analytic approach to the analysis of change.

Conventional, but popular, File Size: 1MB. Latent growth curve modeling (LGM) is an indispensable and increasingly ubiquitous approach for modeling longitudinal data.

This book 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. Latent Growth Curve Modeling. by Aaron Lee Wichman,Dr.

Kristopher J. Preacher,Dr. Nancy E. Briggs,Robert Charles MacCallum. Quantitative Applications in the Social Sciences (Book ) Thanks for Sharing. You submitted the following rating and review.

Brand: SAGE Publications. Growth Curve Models with Categorical Outcomes Katherine E. Masyn1, Hanno Petras2 and Weiwei Liu3 1Harvard Graduate School of Education, Cambridge, MA, USA 2Research and Development, JBS International, North Bethesda, MD, USA 3NORC at the University of Chicago, Bethesda, MD, USA Overview Motivated by the limited available literature on.

Latent Growth Curve Modeling: A Brief History and Overview Historically, growth curve models(e.g., Potthoff & Roy, ) have been used to model longitudinal data in which repeated measurements are observed for some outcome variable at a number of occasions. The latent growth curve approach is rooted in the exploratory factor analysis(EFA)File Size: KB.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use/5(6).

Read "An Introduction to Latent Variable Growth Curve Modeling Concepts, Issues, and Application, Second Edition" by Terry E.

Duncan available from Rakuten Kobo. This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated me Brand: Taylor And Francis.

The advantages of the structural equation modeling framework to growth curve modeling is its tremendous flexibility in specifying models of substantive interest.

Developments in this area have allowed the merging of growth curve models and latent class models for the purpose of isolating unique populations defined by unique patterns of growth. This document focuses on structural equation modeling. It is conceptually based, and tries to generalize beyond the standard SEM treatment.

It includes special emphasis on the lavaan package. Topics include: graphical models, including path analysis, bayesian networks, and network analysis, mediation, moderation, latent variable models, including principal components analysis and ‘factor. Latent growth curve modeling (LGM) is an increasingly ubiquitous approach for modeling longitudinal data.

This book introduces LGM techniques to researchers, provides didactic examples of common growth modeling approaches and highlights advancements regarding the treatment of missing data, parameter estimation and model fit.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use.

It is designed to take Pages: A comprehensive introduction to confirmatory factor analysis, latent growth curve modeling, and growth mixture modeling is provided so the book can be used by readers of various skill levels.

The book’s datasets are available on the web. Latent Growth Curve Modeling T hus far, the examples used to motivate the utility of structural equation modeling have been based on cross-se ctional data. Specifically, it has been assumed that the data have been obtained from a sample of individuals mea-sured at one point in time.

Although it may be argued that most applications. In recent years, latent growth curve (LGC) modeling has become one of the most promising statistical techniques for modeling longitudinal data.

The CALIS procedure in SAS® could be used to fit an LGC model. As one application of structural equation modeling (SEM), LGC modeling relies on indices to evaluate model fit. However, it has beenFile Size: KB. The latent growth curve model (LGCM) is built on the pioneering work by Rao (), Tucker (), Meredith and Tisak (), McArdle (), and McArdle and Epstein ().The model is best depicted graphically as shown in Figure 1.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use.

It is designed to take advantage of the reader’s familiarity with analysis of 3/5(5). (). An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (2nd ed.) Structural Equation Modeling: A Multidisciplinary Author: Karen E.

Stamm, Lisa L. Harlow, Theodore A. Walls.Mplus is a powerful statistical package used for the analysis of latent variables. Among the kinds of analysis it can perform are exploratory factor analysis, confirmatory factor analysis, latent class analysis, latent growth curve modeling, structural equation modeling and multilevel modeling.

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