Using Full-information Item Analysis to Improve Item Quality

Abstract

ABSTRACT Full-information item analysis provides item developers and reviewers comprehensive empirical evidence of item quality, including option response frequency, point-biserial index (PBI) for distractors, mean-scores of respondents selecting each option, and option trace lines. The multi-serial index (MSI) is introduced as a more informative item-total correlation, accounting for variable distractor performance. The overall item PBI is empirically compared to the MSI. For items from an operational mathematics and reading test, poorly performing distractors are systematically removed to recompute the MSI, indicating improvements in item quality. Case studies for specific items with different characteristics are described to illustrate a variety of outcomes, focused on improving item discrimination. Full-information item analyses are presented for each case study item, providing clear examples of interpretation and use of item analyses. A summary of recommendations for item analysts is provided.