A novel alternative to analyzing multiple choice questions via discrimination index.

The value of multiple choice questions (MCQs) in seeking large-scale, high-stakes, educational assessment is widely established. Students' responses to test items with a multiple-choice question format enable assess the extent of students' understanding and also help make valuable decisions about the quality of questions that make robust assessments possible. The use of discrimination index (DI) to analyse MCQs is also widely prevalent in literature Kelly(1939). This paper makes a case for using a novel approach to analyzing data using the DI. The case for novelty is argued through an empirical, comparative analysis on three sets of data: conjecture data, data from an exam for screening talented students for a competitive event (two examples), and data from an international competitive academic event. The scheme is developed to handle the data gathered from different question formats such as MCQs, Long answer questions (LAQs) and a combination of these two question formats. A code has been developed for carrying out computational analysis on large data sets. A comparison with the conventional approach to data analysis establishes the worthiness of ideas proposed for making meaningful inferences and simultaneously renders it possible to attend to nuances that are greatly compromised while analyzing huge data-sets. The paper brings a critical value-addition to the body of analytical knowledge building.