The performance of multilevel growth curve models under an autoregressive moving average process

Daniel Murphy, Keenan Pituch

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The authors examined the robustness of multilevel linear growth curve modeling to misspecification of an autoregressive moving average process. As previous research has shown (J. Ferron, R. Dailey, & Q. Yi, 2002; O. Kwok, S. G. West, & S. B. Green, 2007; S. Sivo, X. Fan, & L. Witta, 2005), estimates of the fixed effects were unbiased, and Type I error rates for the tests of the fixed effects were generally accurate when the present authors correctly specified or underspecified the model. However, random effects were poorly estimated under many conditions, even under correct model specification. Further, fit criteria performed inconsistently and were especially inaccurate when small sample sizes and short series lengths were combined. With the exception of elevated Type I error rates that occurred under some conditions, the best performance was obtained by use of an unstructured covariance matrix at the first level of the growth curve model.

Original languageEnglish (US)
Pages (from-to)255-284
Number of pages30
JournalJournal of Experimental Education
Volume77
Issue number3
DOIs
StatePublished - Apr 1 2009
Externally publishedYes

Keywords

  • Covariance misspecification
  • Growth curve analysis
  • Multilevel model
  • Performance assessment

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology

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