文章基本信息

标题：Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models
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作者：Paul-Christian Bürkner
期刊名称：Journal of Intelligence
电子版ISSN：2079-3200
出版年度：2020
卷号：8
期号：1
页码：5-22
DOI：10.3390/jintelligence8010005
出版社：MDPI Publishing
摘要：Raven’s Standard Progressive Matrices (SPM) test and related matrix-based tests are widely applied measures of cognitive ability. Using Bayesian Item Response Theory (IRT) models, I reanalyzed data of an SPM short form proposed by Myszkowski and Storme (2018) and, at the same time, illustrate the application of these models. Results indicate that a three-parameter logistic (3PL) model is sufficient to describe participants dichotomous responses (correct vs. incorrect) while persons’ ability parameters are quite robust across IRT models of varying complexity. These conclusions are in line with the original results of Myszkowski and Storme (2018). Using Bayesian as opposed to frequentist IRT models offered advantages in the estimation of more complex (i.e., 3–4PL) IRT models and provided more sensible and robust uncertainty estimates.
关键词：Standard Progressive Matrices; Item Response Theory; Bayesian statistics; brms; Stan; R Standard Progressive Matrices ; Item Response Theory ; Bayesian statistics ; brms ; Stan ; R