Uncertainty in Test Score Data and Classically Defined Reliability of Tests and Test Batteries, using a New Method for Test Dichotomisation
Satyendra Nath Chakrabartty, Kangrui Wang, Dalia Chakrabarty

TL;DR
This paper introduces a new fast method for estimating test reliability based on classical test theory by splitting tests into parallel halves, enabling accurate reliability measurement from a single test administration.
Contribution
It presents a novel, efficient method for splitting tests into parallel halves based on difficulty, allowing reliable estimation from one test administration, applicable to large datasets and test batteries.
Findings
The method achieves near-coincident empirical distributions of test halves.
It enables fast computation of test reliability for large datasets.
The approach extends to reliability estimation of test batteries.
Abstract
As with all measurements, the measurement of examinee ability, in terms of scores that the examinee obtains in a test, is also error-ridden. The quantification of such error or uncertainty in the test score data--or rather the complementary test reliability--is pursued within the paradigm of Classical Test Theory in a variety of ways, with no existing method of finding reliability, isomorphic to the theoretical definition that parametrises reliability as the ratio of the true score variance and observed (i.e. error-ridden) score variance. Thus, multiple reliability coefficients for the same test have been advanced. This paper describes a much needed method of obtaining reliability of a test as per its theoretical definition, via a single administration of the test, by using a new fast method of splitting of a given test into parallel halves, achieving near-coincident empirical…
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Taxonomy
TopicsFault Detection and Control Systems · Bayesian Modeling and Causal Inference · Software System Performance and Reliability
