Jingchen Liu, Columbia University, Department of Statistics: Statistical Inference for Diagnostic Classification Models
Abstract: Diagnostic classification models (DCM) are an important recent
development in psychological/educational testing. Instead of an overall
test score, a diagnostic test provides each subject with a profile
detailing the concepts and skills (often called “attributes”) that
he/she has mastered. Central to many DCMs is the so-called Q-matrix, an
incidence matrix specifying the item-attribute relationship. It is the
common practice that the Q-matrix is specified by experts when items are
written rather than through data-driven calibration. Such a
non-empirical approach may lead to mis-specification of the Q-matrix and
substantial lack of model fitting, resulting in erroneous interpretation
of testing results. This talk is concerned with data-driven construction
(estimation) of the Q-matrix and related statistical issues of DCMs. I
will first give an introduction to DCMs and an overview of recent
developments, followed by discussions on key issues and challenges. I
will then present some fundamental results on the learnability of the
Q-matrix, including sufficient and necessary conditions for it to be
identifiable from data. I will also present a data-driven construction
of the Q-matrix and estimation of other model parameters, and show that
they are consistent under identifiability conditions.
Tillbaka till huvudsidan.