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Myszkowski N, Storme M. Modeling Sequential Dependencies in Progressive Matrices: An Auto-Regressive Item Response Theory (AR-IRT) Approach. J Intell 2024; 12:7. [PMID: 38248905 PMCID: PMC10817306 DOI: 10.3390/jintelligence12010007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/30/2023] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
Measurement models traditionally make the assumption that item responses are independent from one another, conditional upon the common factor. They typically explore for violations of this assumption using various methods, but rarely do they account for the possibility that an item predicts the next. Extending the development of auto-regressive models in the context of personality and judgment tests, we propose to extend binary item response models-using, as an example, the 2-parameter logistic (2PL) model-to include auto-regressive sequential dependencies. We motivate such models and illustrate them in the context of a publicly available progressive matrices dataset. We find an auto-regressive lag-1 2PL model to outperform a traditional 2PL model in fit as well as to provide more conservative discrimination parameters and standard errors. We conclude that sequential effects are likely overlooked in the context of cognitive ability testing in general and progressive matrices tests in particular. We discuss extensions, notably models with multiple lag effects and variable lag effects.
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Affiliation(s)
- Nils Myszkowski
- Department of Psychology, Pace University, New York, NY 10004, USA
| | - Martin Storme
- IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Économie Management, 59000 Lille, France
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Ivanovic D, Zamorano F, Soto-Icaza P, Rojas T, Larraín C, Silva C, Almagià A, Bustamante C, Arancibia V, Villagrán F, Valenzuela R, Barrera C, Billeke P. Brain structural parameters correlate with University Selection Test outcomes in Chilean high school graduates. Sci Rep 2022; 12:20562. [PMID: 36446926 PMCID: PMC9709063 DOI: 10.1038/s41598-022-24958-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 11/22/2022] [Indexed: 11/30/2022] Open
Abstract
How well students learn and perform in academic contexts is a focus of interest for the students, their families, and the entire educational system. Although evidence has shown that several neurobiological factors are involved in scholastic achievement (SA), specific brain measures associated with academic outcomes and whether such associations are independent of other factors remain unclear. This study attempts to identify the relationship between brain structural parameters, and the Chilean national University Selection Test (PSU) results in high school graduates within a multidimensional approach that considers socio-economic, intellectual, nutritional, and demographic variables. To this end, the brain morphology of a sample of 102 students who took the PSU test was estimated using Magnetic Resonance Imaging. Anthropometric parameters, intellectual ability (IA), and socioeconomic status (SES) were also measured. The results revealed that, independently of sex, IA, gray matter volume, right inferior frontal gyrus thickness, and SES were significantly associated with SA. These findings highlight the role of nutrition, health, and socioeconomic variables in academic success.
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Affiliation(s)
- Daniza Ivanovic
- Laboratory of Nutrition and Neurological Sciences, Human Nutrition Area, Institute of Nutrition and Food Technology Dr. Fernando Monckeberg Barros (INTA), University of Chile, Santiago, Chile.
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile.
| | - Francisco Zamorano
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Clínica Alemana, Universidad del Desarrollo, Santiago, Chile
| | - Patricia Soto-Icaza
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile
| | - Tatiana Rojas
- Laboratory of Nutrition and Neurological Sciences, Human Nutrition Area, Institute of Nutrition and Food Technology Dr. Fernando Monckeberg Barros (INTA), University of Chile, Santiago, Chile
| | - Cristián Larraín
- Radiology Department, Facultad de Medicina-Clínica Alemana, Universidad del Desarrollo, Santiago, Chile
| | - Claudio Silva
- Radiology Department, Facultad de Medicina-Clínica Alemana, Universidad del Desarrollo, Santiago, Chile
| | - Atilio Almagià
- Laboratory of Physical Anthropology and Human Anatomy, Institute of Biology, Faculty of Sciences, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Claudia Bustamante
- Laboratory of Nutrition and Neurological Sciences, Human Nutrition Area, Institute of Nutrition and Food Technology Dr. Fernando Monckeberg Barros (INTA), University of Chile, Santiago, Chile
| | - Violeta Arancibia
- Department of Global Partnership for Education (GPE) World Bank, Washington, USA
| | - Francisca Villagrán
- Laboratory of Nutrition and Neurological Sciences, Human Nutrition Area, Institute of Nutrition and Food Technology Dr. Fernando Monckeberg Barros (INTA), University of Chile, Santiago, Chile
| | - Rodrigo Valenzuela
- Department of Nutrition, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Cynthia Barrera
- Department of Nutrition, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Pablo Billeke
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile.
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Golino H, Shi D, Christensen AP, Garrido LE, Nieto MD, Sadana R, Thiyagarajan JA, Martinez-Molina A. Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychol Methods 2020; 25:292-320. [PMID: 32191105 PMCID: PMC7244378 DOI: 10.1037/met0000255] [Citation(s) in RCA: 146] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide-network plot-that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies have found EGA to be superior to traditional methods, they are limited in the conditions considered. These issues are addressed through an extensive simulation study that incorporates a wide range of plausible structures that may be found in practice, including continuous and dichotomous data, and unidimensional and multidimensional structures. Additionally, two new EGA techniques are presented: one that extends EGA to also deal with unidimensional structures, and the other based on the triangulated maximally filtered graph approach (EGAtmfg). Both EGA techniques are compared with 5 widely used factor analytic techniques. Overall, EGA and EGAtmfg are found to perform as well as the most accurate traditional method, parallel analysis, and to produce the best large-sample properties of all the methods evaluated. To facilitate the use and application of EGA, we present a straightforward R tutorial on how to apply and interpret EGA, using scores from a well-known psychological instrument: the Marlowe-Crowne Social Desirability Scale. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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A Mokken Scale Analysis of the Last Series of the Standard Progressive Matrices (SPM-LS). J Intell 2020; 8:jintelligence8020022. [PMID: 32384666 PMCID: PMC7712996 DOI: 10.3390/jintelligence8020022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 04/22/2020] [Accepted: 04/24/2020] [Indexed: 11/17/2022] Open
Abstract
Raven's Standard Progressive Matrices Raven (1941) is a widely used 60-item long measure of general mental ability. It was recently suggested that, for situations where taking this test is too time consuming, a shorter version, comprised of only the last series of the Standard Progressive Matrices (the SPM-LS; Myszkowski and Storme (2018)) could be used, while preserving satisfactory psychometric properties Garcia-Garzon et al. (2019); Myszkowski and Storme (2018). In this study, I argue, however, that some psychometric properties have been left aside by previous investigations. As part of this special issue on the reinvestigation of Myszkowski and Storme's dataset, I propose to use the non-parametric Item Response Theory framework of Mokken Scale Analysis Mokken (1971, 1997) and its current developments Sijtsma and van der Ark (2017) to shed new light on the SPM-LS. Extending previous findings, this investigation indicated that the SPM-LS had satisfactory scalability ( H = 0 . 469 ), local independence and reliability ( M S = 0 . 841 , L C R C = 0 . 874 ). Further, all item response functions were monotonically increasing, and there was overall evidence for invariant item ordering ( H T = 0 . 475 ), supporting the Double Monotonicity Model Mokken (1997). Item 1, however, appeared problematic in most analyses. I discuss the implications of these results, notably regarding whether to discard item 1, whether the SPM-LS sum scores can confidently be used to order persons, and whether the invariant item ordering of the SPM-LS allows to use a stopping rule to further shorten test administration.
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Partchev I. Diagnosing a 12-Item Dataset of Raven Matrices: With Dexter. J Intell 2020; 8:jintelligence8020021. [PMID: 32384646 PMCID: PMC7712999 DOI: 10.3390/jintelligence8020021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/21/2020] [Accepted: 04/27/2020] [Indexed: 11/16/2022] Open
Abstract
We analyze a 12-item version of Raven’s Standard Progressive Matrices test, traditionally scored with the sum score. We discuss some important differences between assessment in practice and psychometric modelling. We demonstrate some advanced diagnostic tools in the freely available R package, dexter. We find that the first item in the test functions badly—at a guess, because the subjects were not given exercise items before the live test.
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