The present analysis, using the NLSY97, attempts to model the structural relationship between the latent second-order g factor extracted from the 12 ASVAB subtests, the parental SES latent factor from 3 indicators of parental SES, and the GPA latent factor from 5 domains of grade point averages. A structural equation modeling (SEM) bootstrapping approach combined with a Predictive Mean Matching (PMM) multiple imputation has been employed. The structural path from parental SES to GPA, independently of g, appears to be trivial in the black, hispanic, and white population. The analysis is repeated for the 3 ACT subtests, yielding an ACT-g latent factor. The same conclusion is observed. Most of the effect of SES on GPA appears to be mediated by g. Adding grade variable substantially increases the contribution of parental SES on the achievement factor, which was partially mediated by g. Missing data is handled with PMM multiple imputation. Univariate and multivariate normality tests are carried out in SPSS and AMOS, and through bootstrapping. Full result provided in EXCEL at the end of the article.
Category Archives: Psychometrics
Specific Aptitude Theory Revisited: Is There Incremental Validity for Training Performance?
Specific Aptitude Theory Revisited: Is There Incremental Validity for Training Performance?
Kenneth G. Brown, Huy Le and Frank L. Schmidt
University of Iowa
INTERNATIONAL JOURNAL OF SELECTION AND ASSESSMENT VOLUME 14 NUMBER 2 JUNE 2006
There has been controversy over the years about whether specific mental abilities increment validity for predicting performance above and beyond the validity for general mental ability (GMA). Despite its appeal, specific aptitude theory has received only sporadic empirical support. Using more exact statistical and measurement methods and a larger data set than previous studies, this study provides further evidence that specific aptitude theory is not tenable with regard to training performance. Across 10 jobs, differential weighting of specific aptitudes and specific aptitude tests were found not to improve the prediction of training performance over the validity of GMA. Implications of this finding for training research and practice are discussed.
Flynn contra Rushton on principal component analysis : A failed replication
While Rushton (1999) demonstrates, using PCA, that g and black-white differences were related, with Flynn Effect (FE) gains over time showing no relationship with the aforementioned variables, Flynn (2000) has challenged Rushton in arguing that Wechsler’s subtest loadings on the Raven test, an universally recognized measure of fluid g, showed positive correlations with both black-white differences and FE gains. Up to now, Flynn’s estimates of g fluid (Gf) has not been scrutinized. I will show presently that the Flynn’s g-fluid (call it, fluid reasoning) and Rushton’s g-crystallized (call it, consolidated knowledge) anomaly was solely due to a single statistical artifact, namely, g_Fluid vector unreliability. By adding additional samples, I created a new, updated Wechsler’s subtest Gf loadings. The present analysis comes to the conclusion that g_Fluid was not in fact correlated with FE gains. Furthermore, this Gf variable has been correlated with other variables as well, such as, heritability (h2), shared environment (c2), nonshared environment (e2), adoption IQ gains, inbreeding depression (ID), and mental retardation (MR). I will also discuss these findings in light of Kan’s (2011) thesis against the hereditarian hypothesis.
IQ Testing : From Correlation to Causation
It is well known and acknowledged that IQ correlates with social outcomes (Herrnstein & Murray, 1994; Jensen, 1998; Gottfredson, 1997), measures of health (Gottfredson, 2003, & Deary, 2004; Reeve & Basalik, 2010), wages (Jones & Schneider, 2008), savings (Jones, 2012), job performance (Ree & Earles, 1994; Hunter & Schmidt, 2004), training success (Ree & Earles, 1991), general knowledge (Reeve, 2004), general economic performance (Jones & Schneider, 2006; Jones, 2011, 2012; Hafer & Jones, 2012; Meisenberg, 2012; Kalonda-Kanyama & Kodila-Tedika, 2012), and this, without mentioning other many correlates of importance (Lynn & Vanhanen, 2012). While IQ critics usually claim that correlational studies tell us nothing about the causal link, there are in fact indirect evidence of a causal link. The present article will introduce some research on this topic.
Les tests QI, de corrélation à causalité
S’il est aujourd’hui bien admis et reconnu que le QI corrèle avec les résultats socio-économiques (Herrnstein & Murray, 1994; Jensen, 1998; Gottfredson, 1997), les indices et mesures de santé (Gottfredson, 2003, & Deary, 2004; Reeve & Basalik, 2010), le salaire (Jones & Schneider, 2008), l’épargne (Jones, 2012), la performance au travail (Ree & Earles, 1994), la performance dans les cours de formation (Ree & Earles, 1991), les connaissances générales (Reeve, 2004), la performance économique générale (Jones & Schneider, 2006; Jones, 2011, 2012; Hafer & Jones, 2012; Meisenberg, 2012; Kalonda-Kanyama & Kodila-Tedika, 2012), et ceci, sans mentionner d’autres corrélats bien évidents (Lynn & Vanhanen, 2012), les critiques continuent à affirmer que la causalité n’est pas prouvée pour autant. Le présent article s’attèle à présenter l’état des recherches actuelles sur le sujet.
Investigation of the relationship between mental retardation with heritability and environmentality of the Wechsler subtests
The present analysis is an extension of Spitz’s earlier (1988) study on the relationship between mental retardation (MR) lower score and subtest heritability (h2) and g-loadings. These relationships were found to be positive. But Spitz himself haven’t tested the possibility that MR (lower) score could be related with shared (c2) or nonshared (e2) environment. I use the WAIS and WISC data given in my earlier post, and have found that MR is not related with c2 and e2 values. These findings nevertheless must be interpreted very carefully because the small number of subtests (e.g., 10 or 11) is a very critical limitation.
Méta-analyse des corrélations entre le facteur g et les indices d’héritabilité et environnementalité des sous-tests de QI
Il existe aujourd’hui assez peu de tests ayant investi la question de la relation entre le facteur g et l’héritabilité. Herman Spitz (1988) est probablement l’un des premiers connus à avoir tenté un tel test. Rijsdijk (2002) également démontre une telle relation. La seule méta-analyse connue à ce jour vient de van Bloois et al. (2009, p. 61) malgré le fait qu’elle soit encore restée non publiée. Quoi qu’il en soit, aucun n’avait semble-t-il tenté de tester un possible lien entre facteur g et environnement partagé ou non-partagé. Il aurait peut-être été problématique pour la théorie héréditariste si la corrélation entre g et l’héritabilité (h2) est équivalente à celle entre g et l’environnement, partagé (c2) ou non partagé (e2), sauf si ladite théorie fournit l’explication au phénomène. Rushton (2007) a été peut-être le seul à le tenter, bien qu’utilisant un tout autre test QI, les matrices progressive de Raven. La corrélation entre g et h2 était positive mais plus faible qu’entre g et e2, bien que g et c2 corrélait à zéro. L’échantillon de jumeaux utilisé par Rushton était néanmoins assez faible. La présente analyse s’attèle donc à présenter des résultats méta-analytiques.
Gains de QI par l’effet de la pratique : Absence du facteur g
Dans le présent article seront analysées deux études présentant des effets ‘test-retest’ sur le QI, précisément Watkins (2007) et Schellenberg (2004, 2006). Les deux études utilisent le test d’intelligence de Wechsler. Malgré la présence d’un gain de QI, la corrélation entre changement du score et la saturation en g des sous-tests du Wechsler est négative, comme l’indiquaient déjà des études précédentes. Dans la mesure où g, l’ingrédient actif des tests cognitifs (Gottfredson, 1997), est absent des gains cognitifs, on devrait en conclure que l’effet de la pratique n’influence pas l’intelligence manifeste, mais uniquement les scores observables, et non les scores latents.
Psychometric intelligence and achievement: A cross-lagged panel analysis
Psychometric intelligence and achievement: A cross-lagged panel analysis
Marley W. Watkins, Pui-Wa Lei, Gary L. Canivez (2007)
Abstract
There has been considerable debate regarding the causal precedence of intelligence and academic achievement. Some researchers view intelligence and achievement as identical constructs. Others believe that the relationship between intelligence and achievement is reciprocal. Still others assert that intelligence is causally related to achievement. The present study addressed this debate with a cross-lagged panel analysis of WISC-III and achievement test scores of 289 students assessed for special education eligibility with a test–retest interval of 2.8 years. The optimal IQ–achievement model reflected the causal precedence of IQ on achievement. That is, the paths from IQ scores at time 1 to IQ and achievement scores at time 2 were significant whereas the paths from achievement scores at time 1 to IQ scores at time 2 were not significant. Within the limits imposed by the design and sample, it appears that psychometric IQ is a causal influence on future achievement measures whereas achievement measures do not substantially influence future IQ scores.
The causal factor underlying the correlation between psychometric g and scholastic performance
The causal factor underlying the correlation between psychometric g and scholastic performance
Dasen Luo, Lee A. Thompson, Douglas K. Detterman (2003)
Abstract
Structural equation models were fitted to covariances among 9 Cognitive Abilities Test (CAT) variables, 11 Wechsler Intelligence Scale for Children—Revised (WISC-R) subtest scores, and 3 Metropolitan Achievement Test (MAT) scaled scores, administered to a sample of 532 primary school children who participated in the Western Reserve Twin Project. The models were designed to test the hypothesis that factors representing basic cognitive processes, extracted from the nine CAT variables, were the main causal determinants for the observed correlation between psychometric g and scholastic performance, which were represented, respectively, by a general factor extracted from the WISC-R and a factor from the MAT. Structural relations between the CAT factors as the primary independent variables, psychometric g as a secondary independent variable, and scholastic performance as the dependent variables were estimated, and the R² change indicating the higher-order shared variability between g and scholastic performance was evaluated. After the influence of a CAT general factor was controlled, the WISC-R general factor accounted for about 6% of the variability in the MAT scholastic factor, as opposed to as much as 30% of the zero-order variability shared by the two variables. The results were not seriously affected by the exclusion of nonchronometric measures of the cognitive tasks from the model, suggesting that individual differences in mental speed are a main causal factor underlying the observed correlation between general intelligence and scholastic performance in children between the ages of 6 and 13.
Analyse Economique 