Moderated multiple regression, spurious interaction effects, and IRT

Sun Mee Kang, Niels G. Waller

Research output: Contribution to journalReview articlepeer-review

35 Scopus citations

Abstract

Two Monte Carlo studies were conducted to explore the Type I error rates in moderated multiple regression (MMR) of observed scores and estimated latent trait scores from a two-parameter logistic item response theory (IRT) model. The results of both studies showed that MMR Type I error rates were substantially higher than the nominal alpha levels when scale scores were composed of summed binary item responses (e.g., true/false, yes/no, disagree/agree items). Performing the regression analyses on estimated trait scores (θ) from a two-parameter logistic model improved the error detection rates considerably. That is, the Type I error rates for spurious interaction effects were similar to the nominal alpha levels under most conditions. These findings suggest that IRT provides a viable means of controlling an important source of spurious interactions in data sets that are well characterized by IRT models.

Original languageEnglish (US)
Pages (from-to)87-105
Number of pages19
JournalApplied Psychological Measurement
Volume29
Issue number2
DOIs
StatePublished - Mar 1 2005

Keywords

  • Item response theory
  • Moderated multiple regression
  • Spurious interaction
  • Type I error

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