Hedging-based scoring rules for multiple-choice questions

This paper proposes two novel scoring rules for multiple-choice questions based on the test-takers’ propensity to hedge across possible answers. To examine these scoring rules, we randomly assign 2,986 participants in an IQ test into three conditions. In the control condition, participants choose one option, and receive one point for a correct response. In the treatment conditions, they can explicitly hedge by choosing k options: if the correct option is among the k chosen options, they receive 1/k point in the outcome-mixing treatment, and one point with probability 1/k in the probability-mixing treatment. We find that participants in both treatments hedge pervasively, score lower compared to those in the control, but do not improve their time allocation. While scores in the three conditions exhibit similar psychometric quality, we observe a significant correlation between academic performance and IQ score measured in the probability-mixing condition. These results highlight the potential of these two hedging-based scoring rules for practical applications in multiple-choice questions.