Score conversion methods with modern test theory approach: Ability, difficulty, and guessing justice methods

The one-parameter logistic (1-PL) model is widely used in Item Response Theory (IRT) to estimate student ability; however, ability-based scoring disregards item difficulty and guessing behavior, which can bias proficiency interpretations. This study evaluates three scoring alternatives derived from IRT: an ability-based conversion, a difficulty-weighted conversion, and a proposed guessing-justice method. Dichotomous responses from 400 students were analyzed using the Rasch (1-PL) model in the R environment with the ltm package. The 1-PL specification was retained to support a parsimonious and interpretable calibration framework consistent with the comparative scoring purpose of the study. Rasch estimation produced item difficulty values ranging from −1.03 to 0.18 and identified 268 unique response patterns. Ability-based scoring yielded only eight score distinctions, demonstrating limited discriminatory capacity. In contrast, the guessing-justice method produced a substantially more differentiated distribution, with approximately 70 percent of patterns consistent with knowledge-based responding and 30 percent indicative of guessing. The findings indicate that scoring models incorporating item difficulty and guessing behaviour provide a more equitable and accurate representation of student proficiency than traditional ability-based conversions. The proposed approach offers a practical and implementable alternative for classroom assessment and can be applied using widely accessible spreadsheet software such as Microsoft Excel.