Open AccessMARGINAL MAXIMUM LIKELIHOOD ESTIMATION FOR A PSYCHOMETRIC MODEL OF DISCONTINUOUS DEVELOPMENT
Author(s) -
Mislevy Robert J.,
Wilson Mark
Publication year - 1992
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/j.2333-8504.1992.tb01505.x
Subject(s) - item response theory , marginal likelihood , metric (unit) , bayes factor , bayes' theorem , latent variable , econometrics , computer science , statistics , artificial intelligence , mathematics , maximum likelihood , psychometrics , bayesian probability , operations management , economics
Standard item response theory (IRT) models posit latent variables to account for regularities in students' performances on test items. They can accommodate learning only if the expected changes in performance are smooth and, in an appropriate metric, uniform over items. Wilson's “Saltus” model extends the ideas of IRT to development that occurs in stages, where expected changes can be discontinuous, show different patterns for different types of items, and even exhibit reversals in probabilities of success on certain tasks. Examples include Piagetian stages of psychological development and Siegler's rule‐based learning. This paper derives marginal maximum likelihood (MML) estimation equations for the structural parameters of the Saltus model and suggests a computing approximation based on the EM algorithm. For individual examinees, Empirical Bayes probabilities of learning‐stage are given, along with proficiency parameter estimates conditional on stage membership. The MML solution is illustrated with simulated data and an example from the domain of mixed number subtraction.