Nonparametric Regression and the Parametric Bootstrap for Local Dependence Assessment

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Abstract
Ideas underlying nonparametric regression and the parametric bootstrap are discussed. An overview is provided of their application to item response theory and, in particular, local dependence assessment. The resulting nonparametric item response theory parametric bootstrap can remove the need to specify a particular parametric form for the item response functions and correct for the statistical bias caused by conditioning on observed test scores. The method is applied to the problem of assessing local dependence that varies with examinee trait levels. This is done by using pointwise testing bands to examine the item pair conditional covariance at each examinee trait level. The pointwise bands are used to diagnose speededness in a testing situation in which unanswered items are scored as incorrect.

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