The impact of misspecifying the within-subject covariance structure in multiwave longitudinal multilevel models: A Monte Carlo study

Oi Man Kwok, Stephen West, Samuel B. Green

Research output: Contribution to journalArticle

68 Scopus citations

Abstract

This Monte Carlo study examined the impact of misspecifying the σ matrix in longitudinal data analysis under both the multilevel model and mixed model frameworks. Under the multilevel model approach, under-specification and generalmisspecification of the σ matrix usually resulted in overestimation of the variances of the random effects (e.g., τ00, ττ11) and standard errors of the corresponding growth parameter estimates (e.g., SEβ0, SEβ1). Overestimates of the standard errors led to lower statistical power in tests of the growth parameters. An unstructured σ matrix under the mixed model framework generally led to underestimates of standard errors of the growth parameter estimates. Underestimates of the standard errors led to inflation of me type I error rate in tests of the growth parameters. Implications of the compensatory relationship between the random effects of the growth parameters and the longitudinal error structure for model specification were discussed.

Original languageEnglish (US)
Pages (from-to)557-592
Number of pages36
JournalMultivariate Behavioral Research
Volume42
Issue number3
DOIs
StatePublished - Jan 1 2007

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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