A Comparison of pseudo-Maximum Likelihood and Asymptotically Distribution-Free dynamic factor analysis parameter estimation in fitting covariance-structure models to block-Toeplitz matrices representing single-subject multivariate time-series

Peter Molenaar, John R. Nesselroade

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

The study of intraindividual variability pervades empirical inquiry in virtually all subdisciplines of psychology. The statistical analysis of multivariate time-series data - a central product of intraindividual investigations - requires special modeling techniques. The dynamic factor model (DFM), which is a generalization of the traditional common factor model, has been proposed by Molenaar (1985) for systematically extracting information from multivariate time-series via latent variable modeling. Implementation of the DFM model has taken several forms, one of which involves specifying it as a covariance-structure model and estimating its parameters from a block-Toeplitz matrix derived from the multivariate time-series. We compare two methods for estimating DFM parameters within a covariance-structure framework - pseudo-Maximum Likelihood (p-ML) and Asymptotically Distribution Free (ADF) estimation - by means of a Monte Carlo simulation. Both methods appear to give consistent model parameter estimates of comparable precision, but only the ADF method gives standard errors and chi-square statistics that appear to be consistent. The relative ordering of the values of all estimates appears to be very similar across methods. When the manifest time-series is relatively short, the two methods appear to perform about equally well.

Original languageEnglish (US)
Pages (from-to)313-342
Number of pages30
JournalMultivariate Behavioral Research
Volume33
Issue number3
DOIs
StatePublished - Jan 1 1998

All Science Journal Classification (ASJC) codes

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

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