Typically in calibration research, subjects perform a task and make a judgment about the success of the task. Accurate findings help subjects improve self-calibration. In addition, researchers rely on the accuracy of findings to make inferences about underlying metacognitive processes. Consequently, it is important that the measures used to assess monitoring accuracy are as free of bias as possible. Bias indicates whether an observed value of monitoring accuracy over- or underestimates the true value. This study examined gamma and three other viable statistics, d', C, and the G Index, currently used to measure monitoring accuracy. Using Monte Carlo simulation techniques, I sought to determine whether bias occurred in the use of any of the four measures, and if so, which was least biased. I found substantial bias in gamma, d', and C, especially in shorter and less difficult experimental conditions. Bias was caused primarily by the presence of empty cells in a 2 x 2 contingency table. Empty cells, in turn, were caused by a small sample size that was often insufficient to populate all the cells in the table with at least one response. The fourth statistic, the G Index, was not affected by the same computational issues as the other statistics and showed no bias. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]