Educational and Psychological Measurement, Volume 83, Issue 5, Page 861-884, October 2023.
Even though the impact of the position of response options on answers to multiple-choice items has been investigated for decades, it remains debated. Research on this topic is inconclusive, perhaps because too few studies have obtained experimental data from large-sized samples in a real-world context and have manipulated the position of both correct response and distractors. Since multiple-choice tests’ outcomes can be strikingly consequential and option position effects constitute a potential source of measurement error, these effects should be clarified. In this study, two experiments in which the position of correct response and distractors was carefully manipulated were performed within a Chilean national high-stakes standardized test, responded by 195,715 examinees. Results show small but clear and systematic effects of options position on examinees’ responses in both experiments. They consistently indicate that a five-option item is slightly easier when the correct response is in A rather than E and when the most attractive distractor is after and far away from the correct response. They clarify and extend previous findings, showing that the appeal of all options is influenced by position. The existence and nature of a potential interference phenomenon between the options’ processing are discussed, and implications for test development are considered.

Educational and Psychological Measurement, Volume 83, Issue 5, Page 1033-1058, October 2023.
Recent approaches to the detection of cheaters in tests employ detectors from the field of machine learning. Detectors based on supervised learning algorithms achieve high accuracy but require labeled data sets with identified cheaters for training. Labeled data sets are usually not available at an early stage of the assessment period. In this article, we discuss the approach of adapting a detector that was trained previously with a labeled training data set to a new unlabeled data set. The training and the new data set may contain data from different tests. The adaptation of detectors to new data or tasks is denominated as transfer learning in the field of machine learning. We first discuss the conditions under which a detector of cheating can be transferred. We then investigate whether the conditions are met in a real data set. We finally evaluate the benefits of transferring a detector of cheating. We find that a transferred detector has higher accuracy than an unsupervised detector of cheating. A naive transfer that consists of a simple reuse of the detector increases the accuracy considerably. A transfer via a self-labeling (SETRED) algorithm increases the accuracy slightly more than the naive transfer. The findings suggest that the detection of cheating might be improved by using existing detectors of cheating at an early stage of an assessment period.