monotone likelihood ratio, nonparametric IRT models, parametric IRT models, polytomous IRT models, stochastic ordering,

An automated item selection procedure for selecting unidimensional scales of polytomous items from multi dimensional datasets is developed for use in the context of the Mokken item response theory model of monotone homogeneity (Mokken & Lewis, 1982). The selection procedure is directly based on the selection procedure proposed by Mokken (1971, p. 187) and relies heavily on the scalability coefficient H (Loevinger, 1948; Molenaar, 1991). New theoretical results relating the latent model structure to H are provided. The item selec tion procedure requires selection of a lower bound for H. A simulation study determined ranges of H for which the unidimensional item sets were retrieved from multidimensional datasets. If multidimensionality is suspected in an empirical dataset, well-chosen lower bound values can be used effectively to detect the unidi mensional scales.

No abstract

invariant item ordering, item response theory, nonparametric polytomous IRT models, parametric polytomous IRT models,

IRT models, monotone likelihood ratio, nonparametric IRT models, parametric IRT models, polytomous items,

acceleration model, adjacent category models, continuation ratio models, cumulative probability models, hierarchical relationships between IRT models, invariant item ordering, monotone likelihood ratio, polytomous IRT models, sequential model, stochastic ordering,

In the present study, a procedure that has been used to select dichotomous items in computerized adaptive testing was applied to polytomous items. This procedure was designed to select the item with maximum weighted information. In a simulation study, the item information function was integrated over a fixed interval of ability values and the item with the maximum area was selected. This maximum interval information item selection procedure was compared to a maximum point information item selection procedure. Substantial differences between the two item selection procedures were not found when computerized adaptive tests were evaluated on bias and the root mean square of the ability estimate.

No abstract