Estimating the Probability of Traditional Copying, Conditional on Answer-Copying Statistics
Allen, J.; Ghattas, A.
Applied Psychological Measurement 40(4): 258-273
ISSN/ISBN: 1552-3497 PMID: 29881052 DOI: 10.1177/0146621615622780
Statistics for detecting copying on multiple-choice tests produce p values measuring the probability of a value at least as large as that observed, under the null hypothesis of no copying. The posterior probability of copying is arguably more relevant than the p value, but cannot be derived from Bayes' theorem unless the population probability of copying and probability distribution of the answer-copying statistic under copying are known. In this article, the authors develop an estimator for the posterior probability of copying that is based on estimable quantities and can be used with any answer-copying statistic. The performance of the estimator is evaluated via simulation, and the authors demonstrate how to apply the formula using actual data. Potential uses, generalizability to other types of cheating, and limitations of the approach are discussed.