 Multidimensional and longitudinal item response models for non-ignorable dataVera Lúcia F. Santos, Fernando A.S. Moura, Dalton F. Andrade and Kelly C.M. Gonçalves Computational Statistics & Data Analysis, 2016, vol. 103, issue C, 91-110 Abstract: A multidimensional item response approach is proposed to model non-ignorable responses in multiple-choice educational data. The model considers latent traits related to individual proficiency as well as the propensity to answer items. Thus, in addition to modeling the probability of scoring on an item, the probability of answering it is also modeled. Simulation studies are presented to evaluate the efficiency of the estimation procedure in recovering the true values of the model parameters considering several particular cases of the dimensions of proficiency and propensity. The simulation study also compares the proposed approach with others commonly applied in practice. A further extension to cope with longitudinal data with non-ignorable missing item responses is also proposed, together with an application to a Brazilian longitudinal educational evaluation study. Keywords: Bayesian inference; Education evaluation; Non-ignorable missing data; MCMC (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:103:y:2016:i:c:p:91-110 DOI: 10.1016/j.csda.2016.05.002 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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