No abstract

There is re-emerging interest in adopting forced-choice items to address the issue of response bias in Likert-type items for noncognitive latent traits. Multidimensional pairwise comparison (MPC) items are commonly used forced-choice items. However, few studies have been aimed at developing item response theory models for MPC items owing to the challenges associated with ipsativity. Acknowledging that the absolute scales of latent traits are not identifiable in ipsative tests, this study developed a Rasch ipsative model for MPC items that has desirable measurement properties, yields a single utility value for each statement, and allows for comparing psychological differentiation between and within individuals. The simulation results showed a good parameter recovery for the new model with existing computer programs. This article provides an empirical example of an ipsative test on work style and behaviors.

The Rasch facets model was developed to account for facet data, such as student essays graded by raters, but it accounts for only one kind of rater effect (severity). In practice, raters may exhibit various tendencies such as using middle or extreme scores in their ratings, which is referred to as the rater centrality/extremity response style. To achieve better measurement quality in rater data, it is desirable to simultaneously consider both rater severity and rater centrality/extremity. A new facets model is thus developed by adding to the Rasch facets model a weight parameter for the item thresholds for each rater. The parameters of the new facets model can be estimated with the JAGS freeware. An empirical example is provided to illustrate the implications and applications of the new model. Two simulation studies were conducted. The first simulation was to evaluate the parameter recovery of the new facets model and the consequences of ignoring the effects of rater centrality/extremity on parameter estimation. The second simulation was to illustrate how rater severity affects the relationship between rater centrality and the standard deviation of raw rating scores.

In some tests, examinees are required to choose a fixed number of items from a set of given items to answer. This practice creates a challenge to standard item response models, because more capable examinees may have an advantage by making wiser choices. In this study, we developed a new class of item response models to account for the choice effect of examinee-selected items. The results of a series of simulation studies showed: (1) that the parameters of the new models were recovered well, (2) the parameter estimates were almost unbiased when the new models were fit to data that were simulated from standard item response models, (3) failing to consider the choice effect yielded shrunken parameter estimates for examinee-selected items, and (4) even when the missingness mechanism in examinee-selected items did not follow the item response functions specified in the new models, the new models still yielded a better fit than did standard item response models. An empirical example of a college entrance examination supported the use of the new models: in general, the higher the examinee's ability, the better his or her choice of items.