Cargando…

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

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 ap...

Descripción completa

Detalles Bibliográficos
Autor principal: Bürkner, Paul-Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151098/
https://www.ncbi.nlm.nih.gov/pubmed/32033073
http://dx.doi.org/10.3390/jintelligence8010005
_version_ 1783521171705167872
author Bürkner, Paul-Christian
author_facet Bürkner, Paul-Christian
author_sort Bürkner, Paul-Christian
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7151098
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-71510982020-04-20 Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models Bürkner, Paul-Christian J Intell Article 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. MDPI 2020-02-04 /pmc/articles/PMC7151098/ /pubmed/32033073 http://dx.doi.org/10.3390/jintelligence8010005 Text en © 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bürkner, Paul-Christian
Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models
title Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models
title_full Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models
title_fullStr Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models
title_full_unstemmed Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models
title_short Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models
title_sort analysing standard progressive matrices (spm-ls) with bayesian item response models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151098/
https://www.ncbi.nlm.nih.gov/pubmed/32033073
http://dx.doi.org/10.3390/jintelligence8010005
work_keys_str_mv AT burknerpaulchristian analysingstandardprogressivematricesspmlswithbayesianitemresponsemodels