A novel approach for calculating the item discrimination for Likert type of scales

Abstract

Item analysis is performed by developers as an integral part of the scale development process. Thus, items are excluded from the scale depending on the item analysis prior to the factor analysis. Existing item discrimination indices are calculated based on correlation, yet items with different response patterns are likely to have a similar item discrimination index. This study proposed a new item discrimination index that can be used in Likert type of scales and examined its effect on factor analysis results. For this purpose, simulative datasets were generated, and items were excluded from the analysis according to the .20, .30 and .35 item discrimination index criteria, and exploratory factor analysis was performed for a single factor. Accordingly, it was found that more variance could be explained by a single factor with fewer items compared to other discrimination indices when the .20 criterion of the slope coefficient was used as suggested in this study. Similar findings were obtained using the .35 criterion with other discrimination indices. In this context, it is recommended to use the slope coefficient as an additional discrimination index calculation method in the scale development process.Item analysis is performed by developers as an integral part of the scale development process. Thus, items are excluded from the scale depending on the item analysis prior to the factor analysis. Existing item discrimination indices are calculated based on correlation, yet items with different response patterns are likely to have a similar item discrimination index. This study proposed a new item discrimination index that can be used in Likert type of scales and examined its effect on factor analysis results. For this purpose, simulative datasets were generated, and items were excluded from the analysis according to the .20, .30 and .35 item discrimination index criteria, and exploratory factor analysis was performed for a single factor. Accordingly, it was found that more variance could be explained by a single factor with fewer items compared to other discrimination indices when the .20 criterion of the slope coefficient was used as suggested in this study. Similar findings were obtained using the .35 criterion with other discrimination indices. In this context, it is recommended to use the slope coefficient as an additional discrimination index calculation method in the scale development process.