Intelligence in Peru: Students' results in Raven and its relationship to SES

Manrique‐Millones, Denisse; Flores-Mendoza, Carmen; Rivalles, Rosa Millones

doi:10.1016/j.intell.2015.05.004

Cited by 12 publications

(4 citation statements)

References 14 publications

(22 reference statements)

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“…From the comparisons made between the groups, it is possible to understand that the variables representing the SES influenced the assessment of intelligence. Similar results have been pointed out by the literature in several countries (Hanscombe et al, 2012;Jacobsen et al, 2013;Millones, Flores-Mendoza, & Rivalles, 2015; Schoon et al, 2011). with fluid measures.…”

Section: Discussionsupporting

confidence: 88%

“…However, the results found here showed that there is a possibility that an impoverished environment can also affect fluid intelligence, which refers to the individual's ability to solve new and abstract problems without using prior knowledge. Other studies have found similar data when demonstrating relationships between socioeconomic variables and IQ scores using fluid intelligence tests, such as matrix tasks (Colom & Flores-Mendoza, 2007;Flores-Mendoza et al, 2015;Jacobsen et al, 2013;Millones et al, 2015). As explanatory hypotheses for such relationships, one can think that other variables (for example, the executive component of cognitive flexibility), susceptible to environmental stimulation, may be associated vast majority of subtest pairs did not obtain a significant difference in means for the two groups, except for the Block Design and Matrix Reasoning pair, which differed both for public and private school children, but with a small effect size.…”

Section: Stage 2 -Different School Typesmentioning

confidence: 58%

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Intelligence and Socioeconomic Context on Childhood: Comparisons by Place of Residence and School Type

Kroeff

Yates

Filho

et al. 2020

Paidéia (Ribeirão Preto)

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Different aspects of the socioeconomic context can affect intelligence. This study investigated the intellectual performance of children from different places of residence and school types. The analyses compared children from the outskirts (n = 169) and the central region (n = 110) from Porto Alegre; and children from private (n = 49) and public (n = 61) schools. Data collection included the Wechsler Abbreviated Scale of Intelligence (WASI) and sociodemographic record. Analysis of covariance (between groups) and Analysis of Variance for repeated measures (within-group) were used. Children from the outskirts region showed poorer performance on all WASI scores and, in the within-group comparison, lower results on verbal tasks - which did not occur in the central region group. Children from public and private schools differed in all WASI scores but had a similar within-group performance.

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Section: Discussionsupporting

confidence: 88%

Section: Stage 2 -Different School Typesmentioning

confidence: 58%

Intelligence and Socioeconomic Context on Childhood: Comparisons by Place of Residence and School Type

Kroeff

Yates

Filho

et al. 2020

Paidéia (Ribeirão Preto)

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“…Unfortunately, given the small sample size and the multitude of plausible evolutionary environment candidates, it seems very difficult to determine which are the relevant ones, if any. One possible solution is to use subnational datasets which offer much larger sample sizes (Kirkegaard, 2016b;Lynn & Yadav, 2015;Manrique Millones, Flores-Mendoza, & Millones Rivalles, 2015), but also likely more strongly violate the assumption of no recent population migration.…”

Section: Post-out Of Africa Human Divergence In Cognitive Abilitymentioning

confidence: 99%

Genomic ancestry, cognitive ability, and socioeconomic outcomes

Kirkegaard¹,

Fuerst²,

Meisenberg³

2017

Preprint

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Self-identified race/ethnicity (SIRE) groups are known to differ in average cognitive ability, socioeconomic status and genomic ancestry, but the causal paths are disputed. We used data from the Pediatric Imaging, Neurocognition, and Genetics Study (PING; n = 1,369) to examine the relationship between cognitive ability, socioeconomic outcomes and genomic ancestry in the United States.In regression models using 4 different codings of SIRE as a covariate, we found non-trivial relationships between genomic ancestry and outcomes. The relationship between genomic ancestry and cognitive ability was reduced but was still substantial when parental socioeconomic status was controlled for. Interpretation of this is unclear due to possible genetic confounding. Relationships also held when Hispanics and African Americans were analyzed alone.In this sample, the mean IQ by ancestry was approximately 100 for European, 78 for African, and 86 for Amerindian. The sample sizes for the remaining ancestries were too small to produce reliable estimates. The results are consistent with genetic models of cognitive and social inequality, but also with certain environmental models. More sophisticated studies are possible and necessary to resolve the question.

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“…El instrumento es por excelencia una medida de inteligencia fluida (Gf), por tanto la solución no depende de conocimientos previos adquiridos en la educación, más bien se apoya en conocimiento universales simples (arriba / abajo, derecha / izquierda, poco / mucho). Además, Gf es una habilidad que tiene una fuerte relación con el aprendizaje en los primeros años de vida, pues el niño con altas puntuaciones en tareas Gf tiene posibilidades de obtener altas puntuaciones en habilidades académicas (Manrique Millones, Flores-Mendoza, & Millones Rivalles, 2015;Ritchie, Bates, & Plomin, 2015;Tong & Fu, 2013). Asimismo, muestra una relación significativa con la maduración del sistema nervioso central, esencialmente con la producción de la dopamina y el desarrollo de las regiones frontoparietales (Schlagenhauf et al, 2013;Yuan et al, 2012).…”

Section: Introductionunclassified

Matrices progresivas de Raven: punto de corte para preescolares 4 - 6 años

Benítez

Jiménez-Morales

Bringas

2015

Evaluar

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Se realizó un estudio descriptivo - correlacional en varios centros educativos de las provincias de Cienfuegos y Matanzas para determinar un punto de corte del niño preescolar 4 – 6 años. Se utilizó dos muestras: muestra de normalización (N1) y muestra de validación (N2). N1 quedó conformada por 406 niños: 108 con 4 años, 102 con 5 años y 196 con 6 años. N2 quedó conformada por 130 niños: 30 con 4 años, 50 con 5 años y 50 con 6 años. Se utilizó el método Curva Receptor- Operador para seleccionar el punto de corte. La validez de la prueba se obtuvo a través de la correlación con dos tareas de razonamiento (Semejanzas – Diferencias y Operaciones numéricas). El Alfa de Cronbach indicó la confiabilidad de la prueba. La prueba presenta propiedades psicométricas a decuadas para aplicarse en la edad preescolar. Los puntos de corte seleccionados mostraron valores apropiados para detectar el riesgo intelectual en la etapa preescolar.

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Intelligence in Peru: Students' results in Raven and its relationship to SES

Cited by 12 publications

References 14 publications

Intelligence and Socioeconomic Context on Childhood: Comparisons by Place of Residence and School Type

Intelligence and Socioeconomic Context on Childhood: Comparisons by Place of Residence and School Type

Genomic ancestry, cognitive ability, and socioeconomic outcomes

Matrices progresivas de Raven: punto de corte para preescolares 4 - 6 años

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