  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.
