Latent Growth Curve Modeling

  title={Latent Growth Curve Modeling},
  author={Kristopher J Preacher},
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 

Number of Factors in Growth Curve Modeling

Simple calculations are proposed to estimate the order of the polynomial factors in basic growth curve models, which parameterize the mean and covariance structure of a set of repeated measures by latent factors.

Second-Order Latent Growth Models with Shifting Indicators

Second-order latent growth models assess longitudinal change in a latent construct, typically employing identical manifest variables as indicators across time. However, the same indicators may be

Modeling Longitudinal Data from Research on Adolescence

Advantages of Longitudinal Data Design and Data Considerations Missing Data Analysis Techniques Panel Models Growth Curve Models Time Series and Related Models Mediation and Moderation in

The ABC's of LGM: An Introductory Guide to Latent Variable Growth Curve Modeling.

This paper presents a basic introduction to a latent growth modeling approach for analyzing repeated measures data, including the specification and interpretation of the growth factors, primary extensions such as the analysis of growth in multiple populations, and structural models including both precursors of growth, and subsequent outcomes hypothesized to be influenced by the growth functions.

Introduction to Confirmatory Factor Analysis and Structural Equation Modeling

Confirmatory factor analysis (CFA) is a powerful and flexible statistical technique that has become an increasingly popular tool in all areas of psychology including educational research. CFA focuses

Latent Growth Modeling in IS Research: Basic Tenets, Illustration, and Practical Guidelines

The basic tenets of LGM are described and guidelines for using LGM in IS research are offered, including framing hypotheses with time as a central component and implementing LGM models to test these hypotheses.

Latent Growth Modeling for Information Systems: Theoretical Extensions and Practical Applications

The basic tenets of LGM are described and guidelines for applying LGM to Information Systems IS research are offered, and a model validation criterion, namely "d-separation," is proposed to evaluate why and when LGM works and test its fundamental properties and assumptions are proposed.

Logistic Growth Modeling with Markov Chain Monte Carlo Estimation

A new growth modeling approach is proposed to can fit inherently nonlinear (i.e., logistic) function without constraint nor reparameterization. A simulation study is employed to investigate the

Latent Growth Modeling for Communication Research: Opportunities and Perspectives

This chapter presents the specific advantages of conceptualizing and analyzing change in a LGM framework and offers a methodologically sound and useful presentation of the procedure from a structural equation modeling perspective.

Building Latent Growth Models Using PROC CALIS: A Structural Equation Modeling Approach

This paper illustrates the structural equation modeling approach of building latent growth models (LGMs) using PROC CALIS and includes a new conceptual idea of combining simplex approach and classical LGM to include autoregressive terms and moving average terms in order to improve the way the authors can conduct longitudinal analysis.