Author: Chuck (Page 5 of 6)

Ethnic/Race Differences in Aptitude by Generation in the United States: An Exploratory Meta-analysis

An early version of this paper was posted on June 25th. The paper has since been extensively edited and corrected and, subsequently, published at Open Differential Psychology on July 25/26th, 2014. The paper and data files can be found here at the Open Differential Psychology site.

PDF.

Abstract

Cognitive ability differences between racial/ethnic groups are of interest to social scientists and policy makers. In many discussions of group differences, racial/ethnic groups are treated as monolithic wholes. However, subpopulations within these broad categories need not perform as the racial/ethnic groups do on average. Such subpopulation differences potentially have theoretical import when it comes to causal explanations of racial/ethnic differentials. As no meta-analysis has previously been conducted on the topic, we investigated the magnitude of racial/ethnic differences by migrant generations (first, second, and third+). We conducted an exploratory meta-analysis using 18 samples for which we were able to decompose scores by sociologically defined race/ethnicity and immigrant generation. For Blacks and Whites of the same generation, the first, second, and third+ generation B/W d-values were 0.79, 0.79, and 1.00. For Hispanics and Whites of the same generation, the first, second, and third+ generation H/W d-values were 0.76, 0.67, and 0.57. For Asians and Whites of the same generation, the first, second, and third+ generation d-values were -0.08, -0.21, and 0.00. Relative to third+ generation Whites, the average d-values were 0.99, 0.84, and 1.00 for first, second, and third+ generation Black individuals, 1.04, 0.71, and 0.57 for first, second, and third+ generation Hispanic individuals, 0.16, -0.18, and -0.01 for first, second, and third+ generation Asian individuals, and 0.24 and 0.04 for first and second generation Whites.

Keywords: Immigrants, group differences, race, ethnicity, aptitude, National IQ

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Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark

Fuerst, J., Kirkegaard, E. O. (2014). Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark.

Abstract

We obtained data from Denmark for the largest 71 immigrant groups by country of origin. We show that three important socialeconomic variables are highly predictable from the Islam rate, IQ, GDP and height of the countries of origin. We further show that there is a general immigrant socioeconomic factor and that country of origin national IQs, Islamic rates, and GDP strongly predict immigrant general socioeconomic scores.

Do National IQs Predict U.S. Immigrant Cognitive Ability and Outcomes? An Analysis of the National Longitudinal Survey of Freshman

Apparently so.

Fuerst, J., Kirkegaard, E. O. (2014). Do National IQs Predict U.S. Immigrant Cognitive Ability and Outcomes? An Analysis of the National Longitudinal Survey of Freshman. Open Differential Psychology.

Abstract

We discuss the global hereditarian hypothesis of race differences in g and test it on data from the NLSF. We find that migrants country of origin’s IQ predicts GPA and SAT/ACT.

ACE Analysis of the NLSY79 AFQT by Race/Ethnicity

Much has been written about social class differences in the heritability of cognitive ability, little about racial and ethnic differences. I will leave a review of the issue, a discussion of our meta-analytic results, and a report of our technically complex CNLSY ACE x race/ethnicity analysis to my more loquacious (and apt) colleagues. Here I present results based on the (effectively) small NLSY79 kinship sample.
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GED scores by Ethnicity and Nation

I took a gander at the 2010, 2011, and 2012 GED total scores by race and nation (from “GED Testing Statistical Reports”). The sample sizes were small. Unfortunately, the earlier reports, which go back to the ’80s didn’t provide scores for Bermuda, the Virgin Islands, and Jamaica+Cayman+St.Martin; as these scores were what I was particularly curious about, I didn’t include scores from earlier years. The scores aren’t representative, etc., etc. but they, nonetheless, provide a tad of info on e.g., (self-identified) ethnic differences in Bermuda. The score were averaged across the three years mentioned. The d-values presented at the bottom are inter-national. Those presented on the right are intra-national. The differences are roughly consistent with Richard Lynn’s Global Bell Curve position.

GEDRACENATION

Excel.

L&V's (2012) National IQs predict 2011-2012 GRE scores for 114 citizenship groups, 2010 + 2012 TOEFL scores for 157 citizenship groups, PISA scores of migrants from 62 nations of origin across 17 destination nations, 19th century (birth cohort 1820) numeracy rates across 54 nations, and early 20th century (birth cohort 1890) numeracy across 129 nations

MH’s (02/11/2014) Excel File Here.

Previously, we looked at the association between L&V’s (2012) National IQs, GMAT scores, and English Proficiency scores. We extend that analysis here by including 2010-2012 GRE (quantitative, verbal, and total) scores, 2010 + 2012 TOEFL scores, 2003-2009 migrant PISA scores, and national numeracy rates from the 19th and early 20th century.

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Quick Post: L&V's National IQs predict GMAT scores across 173 nations

Introduction

The GMAT is a graduate entrance test used by more than 5,900 business programs offered by more than 2,100 universities worldwide. While the test is given in English, it is designed to be as minimally English dependent as necessary to predict successful completion of Business programs taught in English. Further, the test is carefully scrutinized for item bias. Rudner (2012) explains:

Yes, the GMAT test is administered in English and is designed for programs that teach in English. But the required English skill level is much less than what students will need in the classroom. The exam requires just enough English to allow us to adequately and comprehensively assess Verbal reasoning, Quantitative reasoning and Integrated Reasoning skills….

We carefully review our questions using criteria defining good item construction. We also compute statistics to assess whether our questions are appropriate across culture groups. We constantly update guidelines for our item writers, including a master list of terms and phrases to avoid in order to assure cultural fairness. By using carefully defined and thorough item development and review processes, along with statistical analyses to flag questions with possible cultural bias, we have developed a test that minimizes the impact of culture and language. The GMAT exam is the best objective measure of the likelihood of success in management programs across the globe.

Despite the claimed lack of bias and apparent predictive validity of the test, there is substantial global variance in scores. Rudner (2012) attributes this variance largely to differences in native language spoken and to differences in self-selection.

We decided to explore to what extent global differences could be accounted for differences in National IQ. To do this, we examined the relation between measures of national cognitive ability, English language proficiency, English language usage, and GMAT scores by reported citizenship. We also sought to determine to what extent GMAT scores could be used to index the National IQs for poorly investigated regions such as North Korea, Rwanda, and St. Kitts.

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An Analysis of the NLSY79 and NLSY97 Full Sibling Correlations by Race

In his classic work, Educability and Group Differences, Arthur Jensen presented a number of lines of evidence in defense of his thesis that the Negro-White difference in psychometric intelligence had a congenital component. On the basis of full sibling correlations and relations, Jensen offered the following arguments:

(a1) The full sibling correlations for Blacks and Whites are comparable; (a2) unshared environmental hypotheses, such as nutritional ones, would predict otherwise (pg. 338-339).

(b1) The full sibling correlations for Blacks and Whites are comparable; (b2) a shared environmental hypothesis of group differences would predict otherwise, assuming that the within population heritablities were the same (pg. 108-109).

(c1) The average absolute difference between full siblings is no greater for Blacks than for Whites; (c2) unshared environmental hypotheses, such as nutritional ones, would predict otherwise (pg. 338-339).

(d1) When matching Blacks and Whites on IQ, one sees differential sibling regression, a differential regression which does not decrease with increasing IQ; (d2) an environmental hypothesis of group differences would not predict this (pg. 118-119). Continue reading

A few New Analyses

Hu (2013, September, 5; 2013, July, 5; 2013, August, 18) has raised some interesting points. I will comment on a few of them here and present several new analyses.

Cultural Loading, Heritability, and the BW gap

As Meng Hu noted, Kan et al. (2011) showed that subtest cultural-loadings, as they estimated them, correlated both with the magnitude of the B/W subtest gaps and with subtest heritability estimates. The authors interpreted these associations as support for a GxE hypothesis of individual differences and offered a model similar to that proposed by Flynn and Dickens (2001). Moreover, Kan et al. (2011) saw the associations between cultural-load and heritability and between cultural-load and the magnitude of the BW gap as problematic for what they termed a biological g model. Below, I will show that g-loadings fully mediate the association between cultural loadings and the two other variables noted and therefore that what is in need of explanation is only the association between cultural-loadings and g-loadings. I will then proceed to offer an account for this.

First, I looked to see if g-loadings mediated the association between the BW gap and cultural loadings. They did. Then I looked to see if cultural-loadings mediated the association between the BW gap and g-loadings. They did not fully. The results are shown below. As reliability estimates were not presented for all subtests, I ran the analysis with and without reliability corrections. Continue reading

A Brief Comment on Hu (2013, April, 18): The Meaning of Differential Regression

Number 4 in the social science’s top 10 list of “grand challenge questions that are both foundational and transformative” (Giles, 2010) is: “How do we reduce the ‘skill gap’ between black and white people in America?” Presumably, figuring out the cause of this psychometric intelligence differential would help when it comes to deciding how best to minimize it. If so, we can thank Meng Hu for his recent efforts focused on determining the cause. This includes his recent extensive exploration of differential regression.
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