Using design-based latent growth curve modeling with cluster-level predictor to address dependency

Jiun-Yu Wu*, Oi Man Kwok, Victor L. Willson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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.

Original languageEnglish
Pages (from-to)431-454
Number of pages24
JournalJournal of Experimental Education
Volume82
Issue number4
DOIs
StatePublished - 2 Oct 2014

Keywords

  • data dependency
  • design-based approach
  • latent growth curve models
  • longitudinal analysis
  • model-based approach
  • Monte Carlo simulation
  • multilevel models

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