Using multilevel logistic regression to evaluate person-fit in IRT models

I describe how multilevel logistic regression can be used to assess the con sistency of an individual's response pattern with an item response theory m easurement model. Specifically, by treating item responses as being nested within individuals, multilevel logistic regression is used to estimate a pe rson-response curve that models how an individual's item endorsement rate d ecreases as a function of item difficulty. The slope of an individual's per son-response curve is used as an indicator of the degree of response consis tency or person-fit. I argue that the proposed multilevel modeling approach to person-fit assessment has several potential advantages over traditional techniques. The most important advantage being that the multilevel modelin g approach allows explanatory variables to be entered into the model so tha t the causes of response inconsistency or differential test functioning can be investigated.