Comparison Generalized Item Location Indices for Polytomous Items: A Monte Carlo Simulation Study

Generalized Item Location Indices (GILI) is a method that deals with the process of developing or selecting polytomous items based on a single value. This method converts multiple location indices obtained for polytomous items into a single indices. The purpose of this research is to examine the performance of GILI under different simulation conditions. For the study, how three different GILI (LImean -LImedian-LIIRF) change according to the number of categories (3, 5 and 7), location parameter (-2, -1, 0, 1 and 2) and sample size (200, 500 and 1000) were compared with a monte carlo simulation. According to the results, the LImedian was estimated with the highest error in all conditions. On the other hand, LImean and LIIRF produce similar error amounts for all conditions. Although LImean and LIIRF produce similar results at -1, 0 and +1 location levels, LImean makes more accurate predictions at -2 and +2 location levels. It was concluded that as the number of categories increases, the amount of error calculated in small samples increases. LImean -LImedian-LIIRF values, which are matched with the individual's ability in tests developed for different purposes and CAT applications, can be a good parameter for determining which item to choose next during the administration of the test. As a result, the fact that the proposed method is easier and faster will facilitate the practitioners in the item selection process.