Bias of exploratory and cross-validated DETECT index under unidimensionality

Patrick O. Monahan, Timothy E. Stump, Holmes Finch, Ronald K. Hambleton

Research output: Contribution to journalArticle

8 Scopus citations


DETECT is a nonparametric ''full'' dimensionality assessment procedure that clusters dichotomously scored items into dimensions and provides a DETECT index of magnitude of multidimensionality. Four factors (test length, sample size, item response theory [IRT] model, and DETECT index) were manipulated in a Monte Carlo study of bias, standard error, and root mean square error (RMSE) under the condition of unidimensionality. Bias, standard error, and RMSE of both DETECT indices increased as test length and sample size decreased. Results suggest that the cross-validated index should always be preferred over the exploratory index, even for 100 examinees and five items. Bias, standard error, and RMSE may be problematic for both indices under certain conditions of small samples or short tests. A Monte Carlo procedure could be built into DETECT to estimate and adjust for potential bias.

Original languageEnglish (US)
Pages (from-to)483-503
Number of pages21
JournalApplied Psychological Measurement
Issue number6
StatePublished - Nov 1 2007


  • Bias
  • Dimensionality
  • Monte Carlo

ASJC Scopus subject areas

  • Psychology(all)
  • Psychology (miscellaneous)
  • Social Sciences (miscellaneous)

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