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On Fitting a Multivariate Two-Part Latent Growth Model.

Shu Xu1, Shelley A Blozis2, Elizabeth A Vandewater3

  • 1New York University.

Structural Equation Modeling : a Multidisciplinary Journal
|January 16, 2018
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Summary
This summary is machine-generated.

This study introduces a 2-part latent growth model for analyzing semicontinuous longitudinal data. It explores interrelationships between growth factors using Monte Carlo integration for accurate behavioral change analysis.

Keywords:
Monte Carlo integrationlongitudinal semicontinuous variablesmultivariate two-part latent growth curve model

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Area of Science:

  • Quantitative Psychology
  • Behavioral Statistics
  • Longitudinal Data Analysis

Background:

  • Semicontinuous data, common in behavioral research, presents unique analytical challenges.
  • Traditional models may not adequately capture both the initiation and intensity of behaviors over time.
  • Understanding simultaneous changes in behavioral probability and magnitude is crucial.

Purpose of the Study:

  • To develop and validate a 2-part latent growth model for semicontinuous longitudinal data.
  • To investigate the interrelationships between growth factors of two simultaneously modeled variables.
  • To implement a robust estimation method accounting for simulation-based computational uncertainty.

Main Methods:

  • Application of a 2-part latent growth model to semicontinuous longitudinal data.
  • Utilizing a Monte Carlo (MC) integration algorithm for estimating interrelationships between growth factors.
  • Development of a SAS macro leveraging Mplus for model estimation and handling sampling uncertainty.
  • Comparison of maximum likelihood estimates using rectangular numerical integration and MC integration methods.

Main Results:

  • The proposed 2-part latent growth model effectively analyzes semicontinuous data, capturing changes in both behavioral probability and magnitude.
  • The Monte Carlo integration algorithm provides a reliable method for studying interrelationships between latent growth factors.
  • The developed SAS macro successfully estimates the model, incorporating sampling uncertainty from the simulation-based approach.
  • Demonstration of obtaining maximum likelihood estimates via both rectangular and MC integration methods.

Conclusions:

  • The 2-part latent growth model offers a powerful framework for analyzing complex longitudinal behavioral data.
  • Monte Carlo integration is a valuable tool for enhancing the accuracy of latent growth models with semicontinuous outcomes.
  • The SAS macro provides a practical implementation for researchers using this advanced statistical approach.