Residual-Based Person Fit Statistics over Test Sections

Most tests are composed of multiple sections (each section has group of items) such as different item formats, different content category, competencies, different difficulty levels, test dimensions, testlets, and interpretive exercise items. Students could show unexpected and unacceptable responses across these sections. Studying person fit over item level cannot detect aberrant response over test sections. The study proposes a residual-based person fit statistic over test sections with a dichotomous IRT model. The paper demonstrates the new section-level person fit statistic and investigates its distributional properties and power of detecting aberrance in person responses with comparison to Wright s between person fit statistic. The proposed section-level person fit statistic shows superior distributional properties with both true and real ability and item parameters. Moreover, the performance of the proposed person fit statistic is also examined with real data.