Increasing the Precision of Subscale Scores by Using Out-of-Scale Information

Abstract

In this study, the precision of subscale score estimates was evaluated when out-of-scale information was incorporated. Procedures that incorporated out-of-scale information and only information within a subscale were compared through a series of simulations. It was revealed that more information (i.e., more precision) was always provided for subscale score estimates when out-of-scale information was used. The degree of the information gain depended on the number of out-of-scale items, the magnitude of item discrimination power, and the magnitude of subscale-trait correlation. Also, the accuracy of subscale score estimates was evaluated. Contrary to precision, subscale score estimates were somewhat more biased with out-of-scale information when there were more out-of-scale items and/or when out-of-scale items had high item discrimination power. This tendency was more apparent when the correlation between subscale traits was low. It was concluded that subscale-trait correlation is an important factor to be considered when out-of-scale information is used.