Using Design-Based Latent Growth Curve Modeling With Cluster-Level Predictor to Address Dependency Academic Article uri icon

abstract

  • The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the higher-level predictor was not included and that standard errors of the regression coefficients from the higher-level were underestimated when a regular LGCM was used. Nevertheless, random effect estimates, regression coefficients, and standard error estimates were consistent with those from the true MLGCM when the design-based LGCM included the higher-level predictor. They discussed implication for the study with empirical data illustration. Copyright © Taylor & Francis Group, LLC.

author list (cited authors)

  • Wu, J., Kwok, O., & Willson, V. L.

citation count

  • 8

publication date

  • March 2014