Analytic Standard Errors for Exploratory Process Factor Analysis

Guangjian Zhang, Michael W. Browne, Anthony D. Ong, Sy Miin Chow

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Exploratory process factor analysis (EPFA) is a data-driven latent variable model for multivariate time series. This article presents analytic standard errors for EPFA. Unlike standard errors for exploratory factor analysis with independent data, the analytic standard errors for EPFA take into account the time dependency in time series data. In addition, factor rotation is treated as the imposition of equality constraints on model parameters. Properties of the analytic standard errors are demonstrated using empirical and simulated data.

Original languageEnglish (US)
Pages (from-to)444-469
Number of pages26
JournalPsychometrika
Volume79
Issue number3
DOIs
StatePublished - Jul 1 2014

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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