Can IRT solve the missing data problem in test equating?

In this paper test equating is considered as a missing data problem. The unobserved responses of the reference population to the new test must be imputed to specify a new cutscore. The proportion of students from the reference population that would have failed the new exam and those having failed the reference exam are made approximately the same. We investigate whether item response theory (IRT) makes it possible to identify the distribution of these missing responses and the distribution of test scores from the observed data without parametric assumptions for the ability distribution. We show that while the score distribution is not fully identifiable, the uncertainty about the score distribution on the new test due to non-identifiability is very small. Moreover, ignoring the non-identifiability issue and assuming a normal distribution for ability may lead to bias in test equating, which we illustrate in simulated and empirical data examples

Response Mixture Modeling of Intraindividual Differences in Responses and Response Times to the Hungarian WISC-IV Block Design Test

Response times may constitute an important additional source of information about cognitive ability as it enables to distinguishing between different intraindividual response processes. In this paper, we present a method to disentangle interindividual variation from intraindividual variation in the responses and response times of 978 subjects to the 14 items of the Hungarian WISC-IV Block Design test. It is found that faster and slower responses differ in their measurement properties suggesting that there are intraindivual differences in the response processes adopted by the subjects