Ant Colony Optimization for Parallel Test Assembly

Abstract

Ant Colony Optimization (ACO) algorithms have previously been used to compile single short scales of psychological constructs. In the present article, we showcase the versatility of the ACO to simultaneously construct multiple parallel short scales that adhere to several competing and interacting criteria. Based on an initial pool of 120 knowledge items, we assembled three 12-item tests that (a) adequately cover the construct at a domain level, (b) follow a unidimensional measurement model, (c) allow reliable and (d) precise measurement of fact knowledge, and (e) are gender fair. Moreover, we aligned the test characteristic and test information functions of the three tests to establish the equivalence of the tests. We cross-validated the assembled short scales and investigated their association with the full scale and covariates that were not included in the optimization procedure. Finally, we discuss potential extensions in metaheuristic test assembly and the equivalence of parallel knowledge tests in general.