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Article

Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models

Department of Computer Science, Aalto University, Konemiehentie 2, 02150 Espoo, Finland
Received: 21 November 2019 / Revised: 27 January 2020 / Accepted: 29 January 2020 / Published: 4 February 2020
(This article belongs to the Special Issue Analysis of an Intelligence Dataset)
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. View Full-Text
Keywords: Standard Progressive Matrices; Item Response Theory; Bayesian statistics; brms; Stan; R Standard Progressive Matrices; Item Response Theory; Bayesian statistics; brms; Stan; R
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MDPI and ACS Style

Bürkner, P.-C. Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models. J. Intell. 2020, 8, 5. https://doi.org/10.3390/jintelligence8010005

AMA Style

Bürkner P-C. Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models. Journal of Intelligence. 2020; 8(1):5. https://doi.org/10.3390/jintelligence8010005

Chicago/Turabian Style

Bürkner, Paul-Christian. 2020. "Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models" Journal of Intelligence 8, no. 1: 5. https://doi.org/10.3390/jintelligence8010005

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