Old and new Race/IQ/Etc. papers

Shiao et al., 2012. The Genomic Challenge to the Social Construction of Race

Templer, 2012. Rushton: The great theoretician and his contribution to personality

Figueredo et al., 2012. The measurement of Human Life History strategy

Nyborg, 2012. A conversation with J. Philippe Rushton

Hur, 2012. J.P. Rushton’s contribution to the study of altruism

Nyborg, 2012. Migratory selection for inversely related covariate T- and IQ- nexus traits: Testing the IQ/T-Geo-Climatic-Origin theory by the General Trait Covariance model

Jensen, 2012. Rushton’s contributions to the study of mental ability

Tal, 2009. From heritability to probability

Grove, 2012. Orbital dynamics, environmental heterogeneity, and the evolution of the human brain

McGue et al., 2007. The environments of adopted and non-adopted youth: evidence on range restriction from the Sibling Interaction and Behavior Study (SIBS)

Gottfredson, 1997. Why g matters: The complexity of every day life

Nyborg and Jensen, 1999. Occupation and income related to psychometric g

(Matched for g (not merely IQ), Whites are disadvantaged with respect to income and occupation.)

Rowe et al., 1994. No More Than Skin Deep: Ethnic and Racial Similarity
in Developmental Process

(Analysis of six large data sets. Racial/ethnic groups share the same development processes; no X-factors.)

Rowe and Cleveland, 1996. Academic Achievement in Blacks and Whites: Are the Developmental Processes Similar?

(Best fit model indicates that differences are due to environmental and genetic factors.)

Rowe, 1997. Group Differences in Developmental Processes The Exception; Rowe. No More Than Skin Deep

(Commentary. Racial/ethnic groups share the same development processes.)

Gullickson, 2001. Amalgamations, New and Old: The Strati cation of America’s Mixed Black/White Population

(Bi-racism is the new colorism, author argues.)

Scarr and Weinberg. IQ Test Performance of Black Children Adopted by White Families; The Minnesota Adoption Studies: Genetic Differences and Malleability; Racial-Group Differences in IQ in the Minnesota Transracial Adoption Study: A Reply to Levin and Lynn

(MTRAS results and discussions.)

Nisbett, 2011. The Achievement Gap: Past, Present & Future

(Summary of environmental case.)

Acs, 2011. Downward Mobility from the Middle Class

(Nothing new here — regression to the mean.)

Weiss. Racial Differences and the Probability of C2orf16 rs191912 to be the Major Gene Locus of General Cognitive Ability

(A possible gene of large effect.)

Tanser, 1941. Intelligence of Negroes of Mixed Blood in Canada

Vijver, 2008. On the meaning of cross-cultural differences in simple cognitive measures

(African/European reaction time differences. Comment: interpreted culturally; sociologist fallacy.)

Kanaya and Ceci, 2010. The Flynn Effect in the WISC Subtests Among School Children Tested for Special Education Services

(Score differences found not comparable, again.)

Sabbagh, 2010. THE RISE OF INDIRECT AFFIRMATIVE ACTION: Converging Strategies for Promoting “Diversity” in Selective Institutions of Higher Education in the United States and France

Eppig et al., Parasite prevalence and the distribution of intelligence among the states of the USA

(A partial explanation to the regional variation in the US?)

Fryer, 2010. Racial Inequality in the 21st Century: The Declining Signi cance of Discrimination

(Compare with Jensen and Nyborg’s results above.)

Goldsmith et al. From Dark to Light: Skin Color and Wages Among African-Americans.

(More colorism research. Suggestion: Cross-assortative mating for color and human capital characteristics.)

Fagan and Holland, 2009. Culture-fair prediction of academic achievement

(Waiting for a replication.)

Hacking. Why race still matters

(What passes for the philosophy of biology these days: “About the same time that The Bell Curve was published, ogre naturalists, such as Philippe Rushton in Race, Evolution, and Behavior, made more sweeping claims to biologically grounded racial differences. They claimed that the races are distinguished by many properties rightly prized or feared for different strengths and weaknesses. If that were true, then races would exactly ½t Mill’s de½nition of a real Kind… One deplores both Rushton and The Bell Curve, but there is an absolutely fundamental logical difference between what the two assert. Rushton claimed that the races are real Kinds. One imagines that Herrnstein and Murray thought so too, but what they claimed was that the races are statistically signi½cant classes. And they implied that this is statistically meaningful. Despite the fact that his doctrines have a centuries-old pedigree, we can dismiss the egregious Rushton. We can also refute Murray and Herrnstein.4 Mill’s type of naturalism has contempt for both doctrines. Loathing of these quite recent doctrines and their predecessors has, not surprisingly, produced revulsion against any sort of naturalism about race.”)

Edwards and Oakland, 2006. Factorial Invariance of Woodcock-Johnson III Scores for African Americans and Caucasian Americans

(No psychometric bias in the Woodcock-Johnson III.)

Rippeyoung, 2006. Is it too late baby? pinpointing the emergence of a black-white test score gap in infancy

(Gap grows with age. Comment: rules out prenatal causes.)

INSIDER’S GUIDE TO COLLEGE LIFEADMISSIONS

(The Jewish-Gentile social outcome gap that no one cares about. And it’s bee cause of culture.)

Thalheimer and Cook, 2002. How to calculate effect sizes from published research: A simplified methodology

(Comment: simple formulas that come in handy when calculating the magnitude of a subpopulation difference.)

Charles, 2011. Say it out loud: I’m Black and I’m proud?

(Blacks are proud to be Blacks. Education not a moderator.)

Duncan and Magnuson, 2005. Can Family Socioeconomic Resources Account for Racial and Ethnic Test Score Gaps?

(SES can statistically explain 50% of the difference at young ages. Comment: But (not mentioned by the authors) at older ages, parental SES statistically explains less, which should not be surprising, given the behavioral genetic findings on the within-population relation between IQ and SES. Authors: “Accounting studies find that differences in socioeconomic status explain about half a standard deviation of the initial achievement gaps. But because none of the accounting studies is able to adjust for a full set of genetic and other confounding causes of achievement, we regard them as providing upper-bound estimates of the role of family socioeconomic status.”)

Reardon and Galindo, 2006. Patterns of Hispanic Students’ Math and English Literacy Test Scores in the Early Elementary Grades A Report to the National Task Force on Early Childhood Education for Hispanics

(Not all ethnoracial differences behave the same in relation to age and SES. See table B11, etc.)

Tal, 2010. The Impact of Gene–Environment Interaction
and Correlation on the Interpretation of Heritability

(A really fascinating paper if you follow the larger debate.)

Meisenberg, 2010. Secularization and Desecularization in Our Time

Newman and Lyon, 2009. Recruitment Efforts to Reduce Adverse Impact: Targeted Recruiting for Personality, Cognitive Ability, and Diversity

(Institutional racism — but it’s positive!)

Little. RACIAL MIXTURE IN GREAT BRITAIN- SOME ANTHROPOLOGICAL CHARACTERISTICS OF THE ANGLO-NEGROID CROSS

(I love those old studies.)

Kaufman et al., 2012. Are cognitive g and academic achievement g one and the same g? An exploration on the Woodcock–Johnson and Kaufman tests

(Not identical. More g mysteries.)

JBHE, 1996. African-Born U.S. Residents are the Most Highly Educated Group in American Society

(This much cited article was actually rather nuanced. Whatever the case the results haven’t held.)

Walker and Bridgeman, 2008. Stereotype Threat Spillover and SAT® Scores

(“Whatever the reason for the generally null results in this real-life test administration, it was not statistical power, as this particular study could detect effect sizes of less than 0.05 standard deviations. Thus, like other studies using data from high-stakes operational settings (e.g., Cullen et al., 2004, 2006; Stricker and Ward, 2004), this study showed little evidence consistent with a stereotype threat hypothesis.”)

Masters1 and Bragg, 1999. Morphological Correlates of Speciation in Bush Babies

(But the 75% rule doesn’t apply when it comes to humans — because that would be racist. Quote: “Mayr (1969, p. 190) coined the 75% rule: for subspecies to be valid … Individuals from 4 subspecies of Otolemur crassicaudatus could be distinguished with a 72% strike rate using all 8 characters.”)

Groves, 2002. The What, Why and How of Primate Taxonomy

(But, Hacking et al. tells us, hypothetical human subspecies need to be “natural kinds.” Quote: The keys here are (1) subspecies are populations, geographic segments of a species, not morphs co-occurring with other variants, and (2) they differ from each other on average, not absolutely. The so-called 75% rule, which I have used above, is only a rule-of-thumb, but it becomes rather meaningless to single out populations in which much less than this proportion is distinctive. Unlike species, subspecies have no whatness. They share genes with other subspecies of the same species, so their interrelationships are genetically reticulate. In some taxonomic schools of thought they have no place at all, though it seems to me that it is useful to focus on populations that differ as whole but not absolutely. Subspecies should not be reified: they are simply the point along the continuum of population differentiation, from identity to species, at which it becomes worthwhile to give them a scientific name.)

Masters, 2008. MODERN VARIATION AND EVOLUTIONARY CHANGE IN
THE HOMININ EYE ORBIT

(Human racial, er population, developmental differences in cranial capacity and eye orbit size.)

Lieberman and Jackson, 1995. Race and Three Models of Human Origin

(Contains a specificity critique of Brues’ and Boyd’s race definition. Comment: This is equivalent to arguing that “genetic population” is too indefinite to have “scientific meaning.” Taxonomic meaning versus scientific meaning. More word games.)

Oftendal, 2004. Heritability and Genetic Causation

(Comment: Oftendal maintains that the disagreement is due to conceptual confusion. Agreed. Except that the confusion has been purposefully sowed.)

HRDY. Quantitative Hair Form Variation in Seven Populations.

Smith, 2011. Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in population health research and practice

(Chance and genes.)

Franbourg, et al., 2009. Current research on ethnic hair

(Some would use the term “racial” instead of “ethnic.” Quote: Human hair is categorized into 3 major distinct groups according to ethnic origin: Asian, Caucasian, and African.”)

Irish, 1997. Ancestral dental traits in recent Sub-Saharan Africans and the origins of modern humans

(Comment: One could make the case that there are just two human subspecies. Out-of-Africans and Sub-Saharan Africans. Quote: “Assuming that phenetic expression approximates genetic variation, previous dental morphological analyses of Sub-Saharan Africans by the author show they are unique among the world’s modern populations.”)

Hardison et al., 2010. The Air Force Officer Qualifying Test: Validity, Fairness, and Bias

(SATs show less adverse impact than the AFOQT for Asians, but that’s
not the type of adversity in need of reduction.)

Yaacob et al., 1996. Racial characteristics of human teeth with special emphasis on the Mongoloid dentition

(Quote: “The major racial groups of the world are broadly classified as Caucasoids, Mongoloids, Negroids and Australoids (Australian aborigines). In Peninsular Malaya …)

Hanihara, et al., 2003. Characterization of Biological Diversity Through Analysis of Discrete Cranial Traits

Hanihara, 2008. Morphological Variation of Major Human Populations Based on Nonmetric Dental Traits

Beaver, K. M., Wright, J. P., Boutwell, B. B., Barnes, J. C., DeLisi, M., & Vaughn, M. G. (2012). Exploring the association between the 2-repeat allele of the MAOA gene promoter polymorphism and psychopathic personality traits, arrests, incarceration, and lifetime antisocial behavior. Personality and Individual Differences.

Demetriou, A., Spanoudis, G., Shayer, M., Mouyi, A., Kazi, S., & Platsidou, M. (2013). Cycles in speed-working memory-G relations: Towards a developmental–differential theory of the mind. Intelligence, 41(1), 34-50.

(“The concerting power of G comes from the dynamic inter-relations that it enforces between the various players involved (attention, WM, inference, etc.) rather than from the players sharing common components (van der Maas et al., 2006).”)

Kazi, Smaragda, et al. “Mind–culture interactions: How writing molds mental fluidity in early development.” Intelligence (2012).

Segal, N. L. (2012). Personality similarity in unrelated look-alike pairs: Addressing a twin study challenge. Personality and Individual Differences.

Salter, F. & Harpending, H. Rushton’s theory of ethnic nepotism. Personality and Individual Differences xxx (2012) xxx–xxx

Nyborg, H. J. Philippe Rushton: Eminent scientist, pioneer, and gentleman, died 2 October 2012. Personality and Individual Differences xxx (2012) xxx–xxx

O’Boyle E. H. & McDaniel M. A.Criticisms of employment testing: A commentary. O’Boyle Jr., Ernest H.; McDaniel, Michael A. Phelps, Richard P. (Ed), (2009). Correcting fallacies about educational and psychological testing. (pp. 181-197).

Detterman, D. Thank you, Arthur Jensen (August 24, 1923–October 22, 2012). Intelligence xxx (2012) xxx–xxx

Lynn, R. Obituary Arthur Robert Jensen, 1924–2012. Intelligence xxx (2012) xxx–xxx

Rowe, D. C. (2002). IQ, birth weight, and number of sexual partners in White, African American, and mixed race adolescents. Population & Environment, 23(6), 513-524.

Cleveland, H. H., Jacobson, K. C., Lipinski, J. J., & Rowe, D. C. (2000). Genetic and shared environmental contributions to the relationship between the home environment and child and adolescent achievement. Intelligence, 28(1), 69-86.

Garcia, J., & Quintana-Domeque, C. (2007). The evolution of adult height in Europe: a brief note. Economics & Human Biology, 5(2), 340-349.

Madera, J. M., & Abbott, J. (2012). The Diversity-Validity Dilemma. Cornell Hospitality Quarterly, 53(1), 31-39.

Mill, R. & Stein L. Disentangling skin-color discrimination from family background differences among African-Americans. Work in progress. (This is marked: Do not cite or circulate.)

Cordero-Guzman, H. R. (2001). Cognitive skills, test scores, and social stratification: The role of family and school-level resources on racial/ethnic differences in scores on standardized tests (AFQT). The Review of Black Political Economy, 28(4), 31-71.

Woodley, M. A., & Meisenberg, G. (2013). A Jensen effect on dysgenic fertility: An analysis involving the National Longitudinal Survey of Youth. Personality and Individual Differences.

Duncan, B., & Trejo, S. J. (2011). Who Remains Mexican? Selective Ethnic Attrition and the Intergenerational Progress of Mexican Americans. Latinos and the Economy, 285-320.

Furtado, D. (2012). Human capital and interethnic marriage decisions. Economic inquiry, 50(1), 82-93.

Model, S., & Fisher, G. (2002). Unions between blacks and whites: England and the US compared. Ethnic and Racial Studies, 25(5), 728-754.

Chiswick, B. R., & Houseworth, C. (2011). Ethnic intermarriage among immigrants: Human capital and assortative mating. Review of Economics of the Household, 9(2), 149-180.

Duncan, B., & Trejo, S. (2012). The complexity of immigrant generations: Implications for assessing the socioeconomic integration of Hispanics and Asians.

Duncan, B., & Trejo, S. J. (2009). Ancestry versus ethnicity: the complexity and selectivity of Mexican identification in the United States.

Pan, Y., & Wang, K. S. (2011). Spousal concordance in academic achievements and IQ: a principal component analysis. Open Journal of Psychiatry, 1(2), 15-19.

Borjas, G. J., Bronars, S. G., & Trejo, S. J. (1992). Self-selection and internal migration in the United States. Journal of Urban Economics, 32(2), 159-185.

Hanushek, E. A., & Woessmann, L. (2009). Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation (No. w14633). National Bureau of Economic Research.

Hanushek, E. A., & Woessmann, L. (2010). The economics of international differences in educational achievement (No. w15949). National Bureau of Economic Research.

Koenig, K. A., Frey, M. C., & Detterman, D. K. (2008). ACT and general cognitive ability. Intelligence, 36(2), 153-160.

Frey, M. C., & Detterman, D. K. (2004). Scholastic assessment or g? The relationship between the Scholastic Assessment Test and general cognitive ability. Psychological science, 15(6), 373-378.

Bodenhorn, H. (2006). Colorism, complexion homogamy, and household wealth: Some historical evidence. The American economic review, 256-260.

Lynn, R. (2002). Skin color and intelligence in African Americans. Population & Environment, 23(4), 365-375.

Hill, M. E. (2002). Skin color and intelligence in African Americans: A reanalysis of Lynn’s data. Population & Environment, 24(2), 209-214.

Lynn, R. (2002). Skin Color and Intelligence in African Americans: A Reply to Hill. Population & Environment, 24(2), 215-218.

Bonilla, C., Boxill, L. A., Donald, S. A. M., Williams, T., Sylvester, N., Parra, E. J., … & Kittles, R. A. (2005). The 8818G allele of the agouti signaling protein (ASIP) gene is ancestral and is associated with darker skin color in African Americans. Human genetics, 116(5), 402-406.

Harrison, M. S. (2005). Colorism in the job selection process: are there preferential differences within the Black race? (Doctoral dissertation, University of Georgia).

Keith, V. (2009). A colorstruck world: Skin tone, achievement and self-esteem among african-american women. Nakano, G. Shades of Difference: Why Skin Color Matters.

Reuter, E. B. (1917). The superiority of the mulatto. The American Journal of Sociology, 23(1), 83-106.

Magnuson, K. A., & Duncan, G. J. (2006). The role of family socioeconomic resources in the black–white test score gap among young children. Developmental Review, 26(4), 365-399.

Heckman, J. J., & Masterov, D. V. (2007). The productivity argument for investing in young children. Applied Economic Perspectives and Policy, 29(3), 446-493. Web Appendix. (See figures A8b to A8d for a great illustration of the Sociologist Fallacy).

Fry, R. (2007). Are Immigrant Youth Faring Better in US Schools? 1. International Migration Review, 41(3), 579-601.

Schwartz, B. S., Glass, T. A., Bolla, K. I., Stewart, W. F., Glass, G., Rasmussen, M., … & Bandeen-Roche, K. (2004). Disparities in cognitive functioning by race/ethnicity in the Baltimore Memory Study. Environmental health perspectives, 112(3), 314.

Reynolds, C. R., & Jensen, A. R. (1983). WISC-R subscale patterns of abilities of Blacks and Whites matched on Full Scale IQ. Journal of Educational Psychology, 75(2), 207.

Jensen, A. R., & Weng, L. J. (1994). What is a good g?. Intelligence, 18(3), 231-258.

Jensen, A. R. (1974). The strange case of Dr. Jensen and Mr. Hyde?.

Jensen, A. R. (1993). Spearman’s hypothesis tested with chronometric information-processing tasks. Intelligence, 17(1), 47-77.

Jensen, A. R. (1978). Sir Cyril Burt in perspective.

Jensen, A. R. (1981). Raising the IQ: The Ramey and Haskins study. Intelligence, 5(1), 29-40.

Jensen, A. R., & Reynolds, C. R. (1982). Race, social class and ability patterns on the WISC-R. Personality and Individual Differences, 3(4), 423-438.

Jensen, A. R. (1985). Race differences and type II errors: A comment on Borkowski and Krause. Intelligence, 9(1), 33-39.

Jensen, A. R., & Johnson, F. W. (1994). Race and sex differences in head size and IQ. Intelligence, 18(3), 309-333.

Jensen, A. R. (1995). Psychological research on race differences.

Jensen, A. R. (1982). Level I/Level II: Factors or categories?.

Jensen, A. R. (1974). Interaction of Level I and Level II abilities with race and socioeconomic status. Journal of Educational Psychology, 66(1), 99.

Jensen, A. R., & Inouye, A. R. (1980). Level I and Level II abilities in Asian, white, and black children. Intelligence, 4(1), 41-49.

Jensen, A. R. (1969). Jensen’s theory of intelligence: A reply. Journal of Educational Psychology, 60(6p1), 427.

Jensen, A. R. (1998). Jensen on “Jensenism”. Intelligence, 26(3), 181-208.

Jensen, A. R. (1972). Interpretation of heritability.

Vernon, P. A., & Jensen, A. R. (1984). Individual and group differences in intelligence and speed of information processing. Personality and Individual Differences, 5(4), 411-423.

Jensen, A. R. (1985). Humphreys’s attenuated test of Spearman’s hypothesis. Intelligence, 9(3), 285-289.

Jensen, A. R. (2003). Do age-group differences on mental tests imitate racial differences?. Intelligence, 31(2), 107-121.

Jensen, A. R. (1977). Cumulative deficit in IQ of Blacks in the rural South. Developmental Psychology; Developmental Psychology, 13(3), 184.

Jensen, A. R. (1974). Cumulative deficit: A testable hypothesis?. Developmental Psychology, 10(6), 996.

Jensen, A. R. (1966). Cumulative deficit in compensatory education. Journal of School Psychology, 4(3), 37-47.

Jensen, A. R. (1969). Criticism or propaganda?.

Edward Reed, T., & Jensen, A. R. (1993). Cranial capacity: new Caucasian data and comments on Rushton’s claimed Mongoloid-Caucasoid brain-size differences. Intelligence, 17(3), 423-431.

Naglieri, J. A., & Jensen, A. R. (1987). Comparison of black-white differences on the WISC-R and the K-ABC: Spearman’s hypothesis. Intelligence, 11(1), 21-43.

Vincent, K. R. (1991). Black/White IQ differences: Does age make the difference?. Journal of Clinical Psychology, 47(2), 266-270.

Nyborg, H., & Jensen, A. R. (2000). Black–white differences on various psychometric tests: Spearman’s hypothesis tested on American armed services veterans. Personality and Individual Differences, 28(3), 593-599.

Jensen, A. R., & McGurk, F. C. (1987). Black-white bias in ‘cultural’and ‘noncultural’test items. Personality and individual differences, 8(3), 295-301.

Jensen, A. R. (1997). Adoption data and two g-related hypotheses. Intelligence, 25(1), 1-6.

Jensen, A. R. (1959). A statistical note on racial differences in the Progressive Matrices. Journal of Consulting Psychology, 23(3), 272.

Jensen, A. R. (1977). An examination of culture bias in the Wonderlic Personnel Test. Intelligence, 1(1), 51-64.

Levin, M., & Hocutt, M. (2001). Reply to Keita. Philosophy of the Social Sciences, 31(3), 395-403.

Shayer, M., Ginsburg, D., & Coe, R. (2007). Thirty years on–a large anti‐Flynn effect? The Piagetian test Volume & Heaviness norms 1975–2003. British Journal of Educational Psychology, 77(1), 25-41.

Hunt, E. (2012). What Makes Nations Intelligent?. Perspectives on Psychological Science, 7(3), 284-306.

Sackett, P. R., & Ellingson, J. E. (2006). THE EFFECTS OF FORMING MULTI‐PREDICTOR COMPOSITES ON GROUP DIFFERENCES AND ADVERSE IMPACT. Personnel Psychology, 50(3), 707-721.

Montie, J. E., & Fagan, J. F. (1988). Racial differences in IQ: Item analysis of the Stanford-Binet at 3 years. Intelligence, 12(3), 315-332.

Sternberg, R. J., & Kaufman, J. C. (1998). Human abilities. Annual review of psychology, 49(1), 479-502.

Saccuzzo, D. P., & Johnson, N. E. (1995). Traditional psychometric tests and proportionate representation: An intervention and program evaluation study. Psychological assessment, 7(2), 183.

Pae, H. K., Wise, J. C., Cirino, P. T., Sevcik, R. A., Lovett, M. W., Wolf, M., & Morris, R. D. (2005). The woodcock reading mastery test impact of normative changes. Assessment, 12(3), 347-357.

Saccuzzo, D. P., Johnson, N. E., & Russell, G. (1992). Verbal versus performance IQs for gifted African-American, Caucasian, Filipino, and Hispanic children. Psychological Assessment, 4(2), 239.

Flanagan, D. P., & McGrew, K. S. (1998). Interpreting Intelligence Tests from Contemporary Gf-Gc Theory: Joint Confirmatory Factor Analysis of the WJ-R and KAIT in a Non-White Sample. Journal of School Psychology, 36(2), 151-182.

McBee, M. (2010). Examining the probability of identification for gifted programs for students in Georgia elementary schools: A multilevel path analysis study. Gifted Child Quarterly, 54(4), 283-297.

Lohman, D. F., & Lakin, J. (2007). Nonverbal test scores as one component of an identification system: Integrating ability, achievement, and teacher ratings. Alternative assessments for identifying gifted and talented students, 41-66.

Lohman, D. F. (2006). Beliefs about differences between ability and accomplishment: From folk theories to cognitive science. Roeper Review, 29(1), 32-40.

Murray, C. (2007). The magnitude and components of change in the black–white IQ difference from 1920 to 1991: A birth cohort analysis of the Woodcock–Johnson standardizations. Intelligence, 35(4), 305-318.

Burchinal, M., McCartney, K., Steinberg, L., Crosnoe, R., Friedman, S. L., McLoyd, V., & Pianta, R. (2011). Examining the Black–White Achievement Gap Among Low‐Income Children Using the NICHD Study of Early Child Care and Youth Development. Child development, 82(5), 1404-1420.

Anderson, L., Hoffman III, R. R., Tate, B., Jenkins, J., Parish, C., Stachowski, A., & Dressel, J. D. (2011). Assessment of Assembling Objects (AO) for Improving Predictive Performance of the Armed Forces Qualification Test (No. 029625.0. 002.00). ICF INTERNATIONAL INC FAIRFAX VA.

Tucker-Drob, E. M. (2012). Preschools Reduce Early Academic-Achievement Gaps A Longitudinal Twin Approach. Psychological science, 23(3), 310-319.

Aud, S., Fox, M. A., & KewalRamani, A. (2010). Status and Trends in the Education of Racial and Ethnic Groups. NCES 2010-015. National Center for Education Statistics.

Neal, D. (2006). Why has black–white skill convergence stopped?. Handbook of the Economics of Education, 1, 511-576.

Mayfield, J. W., & Reynolds, C. R. (1997). Black-white differences in memory test performance among children and adolescents. Archives of Clinical Neuropsychology, 12(2), 111-122.

Watkins, M. W., Lei, P. W., & Canivez, G. L. (2007). Psychometric intelligence and achievement: A cross-lagged panel analysis. Intelligence, 35(1), 59-68.

Spitz, H. H. (1986). Preventing and curing mental retardation by behavioral intervention: An evaluation of some claims. Intelligence, 10(3), 197-207.

Naglieri, J. A., Rojahn, J., Matto, H. C., & Aquilino, S. A. (2005). Black-white differences in cognitive processing: A study of the planning, attention, simultaneous, and successive theory of intelligence. Journal of Psychoeducational Assessment, 23(2), 146-160.

Sparrow, S. S., & Davis, S. M. (2000). Recent advances in the assessment of intelligence and cognition. Journal of Child Psychology and Psychiatry, 41(1), 117-131.

Bobko, P., & Roth, P. L. (2012). REVIEWING, CATEGORIZING, AND ANALYZING THE LITERATURE ON BLACK‐WHITE MEAN DIFFERENCES FOR PREDICTORS OF JOB PERFORMANCE: VERIFYING SOME PERCEPTIONS AND UPDATING/CORRECTING OTHERS. Personnel Psychology.

Rowe, D. C., & Rodgers, J. L. (1997). Poverty and behavior: Are environmental measures nature and nurture?. Developmental Review, 17(3), 358-375.

Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the environment: a systematic review. Psychological medicine, 37(5), 615-626.

Vinkhuyzen, A. A. E., Van Der Sluis, S., De Geus, E. J. C., Boomsma, D. I., & Posthuma, D. (2010). Genetic influences on ‘environmental’factors. Genes, Brain and Behavior, 9(3), 276-287.

Tizard, B., & Hodges, J. (1978). The effect of early institutional rearing on the development of eight year old children. Journal of child psychology and psychiatry, 19(2), 99-118.

Johnson, J. L. (2001). Racial and gender differences in the five factors of personality within military samples (No. DEOMI-RSP-00-7). DEFENSE EQUAL OPPORTUNITY MANAGEMENT INST PATRICK AFB FL.

Sacerdote, B. (2002). Slavery and the intergenerational transmission of human capital (No. w9227). National Bureau of Economic Research.

Goldammer, C. (2012). Racial Gaps in Cognitive and Noncognitive Skills: The Asian Exception

Woodley, M. A., & Meisenberg, G. (2012). Ability differentials between nations are unlikely to disappear.

Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., & Turkheimer, E. (2012). Group differences in IQ are best understood as environmental in origin.

Brown, C. (2003). Relatively Equal Opportunity in the Armed Forces: Impacts on Children of Military Families. Working Paper. University of Michigan.

Crosby, D. A., & Dunbar, A. S. (2012). PATTERNS AND PREDICTORS OF SCHOOL READINESS AND EARLY CHILDHOOD SUCCESS AMONG YOUNG CHILDREN IN BLACK IMMIGRANT FAMILIES. Migration Policy Institute.

Jackson, M. (2012). Parenting Behavior, Health, and Cognitive Development among Children in Black Immigrant Families: Comparing the United States and the United Kingdom. Migration Policy Institute.

(refer to: http://www.migrationinformation.org/integration/cbi_home.cfm)

Nordin, M. (2007). Studies in Human Capital, Ability and Migration (Doctoral dissertation, Lund University).

Reskin, B. (2012). The Race Discrimination System. Annual Review of Sociology, 38, 17-35.

Conley, D., Pfeiffer, K. M., & Velez, M. (2007). Explaining sibling differences in achievement and behavioral outcomes: The importance of within-and between-family factors. Social Science Research, 36(3), 1087-1104.

Leak, J., Duncan, G. J., Li, W., Magnuson, K., Schindler, H., & Yoshikawa, H. (2010, October). Is timing everything? How early childhood education program impacts vary by starting age, program duration, and time since the end of the program. In UC-Irvine working paper, presented at the fall 2010 meetings of the Association for Public Policy Analysis and Management, Boston, MA.

Nielsen, F., & Roos, J. M. (2011). Genetics of Educational Attainment and the Persistence of Privilege at the Turn of the 21st Century.

Lang, K., & Lehmann, J. Y. K. (2011). Racial discrimination in the labor market: Theory and empirics (No. w17450). National Bureau of Economic Research.

Inbar, Y., & Lammers, J. (2012). Political diversity in social and personality psychology. Perspectives on Psychological Science, 7(5), 496-503.

Tetlock, P. E. (2012). Rational Versus Irrational Prejudices How Problematic Is the Ideological Lopsidedness of Social Psychology?. Perspectives on Psychological Science, 7(5), 519-521.

Prentice, D. A. (2012). Liberal Norms and Their Discontents. Perspectives on Psychological Science, 7(5), 516-518.

Jussim, L. (2012). Liberal Privilege in Academic Psychology and the Social Sciences Commentary on Inbar & Lammers (2012). Perspectives on Psychological Science, 7(5), 504-507.

Redding, R. E. (2012). Likes Attract The Sociopolitical Groupthink of (Social) Psychologists. Perspectives on Psychological Science, 7(5), 512-515.

Randall, V. R. (2012). The Misuse of the LSAT: Discrimination Against Blacks and Other Minorities in Law School Admissions. St. John’s Law Review, 80(1), 4.

(The author incredibly — or not — contends that disparate impact is ipso facto institutional discrimination)

Garry, P. (2008). How Strictly Scrutinized?: Examining the Educational Benefits the Court Relied Upon in Grutter. Pepperdine Law Review, 35(3).

Cole, E. F., Morand-Ferron, J., Hinks, A. E., & Quinn, J. L. (2012). Cognitive Ability Influences Reproductive Life History Variation in the Wild. Current Biology.

Healy, S. D., Bacon, I. E., Haggis, O., Harris, A. P., & Kelley, L. A. (2009). Explanations for variation in cognitive ability: behavioural ecology meets comparative cognition. Behavioural processes, 80(3), 288-294.

Miller, G. F., & Penke, L. (2007). The evolution of human intelligence and the coefficient of additive genetic variance in human brain size. Intelligence, 35(2), 97-114.
.

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14 Responses to Old and new Race/IQ/Etc. papers

  1. Kiwiguy says:

    Seeing these makes me think what a loss Rowe was for HBD type research.

  2. Kiwiguy says:

    Have you seen this one? Looks relatively interesting.

    http://onlinelibrary.wiley.com/doi/10.1111/j.1467-6435.2012.00532.x/abstract

  3. nikcrit says:

    “Cognitive tests and subtests vary in their level of cognitive complexity or in the amount of mental manipulation involved. Some tests are more simple, such as forward digit span, which involves repeating a list of digits, and some tests are more complex, such as backwards digit span, which involves repeating a list a digits backwards. They vary in their level of cognitive specificity. Some tap into abilities specific to a test (or task) and some tap into abilities general across tasks. For example, Peabody Picture Vocabulary, which involves pointing at pictures that match terms (e.g. Yacht), largely taps into verbal ability, while Raven’s Matrices largely taps into a general mental ability. The later is more g-loaded and the former is more s-loaded. As a result, the later is more predicative of performance across diverse tasks. As it happens, cognitive complexity and cognitive generality correlate, a phenomenon which is prima fascia evidence of general intelligence. This correlation between cognitive complexity and generality, of course, is not logically necessary. Were there multiple intelligences (e.g. Gardner), for example, cognitive complexity would, instead, correlate with cognitive specificity, as complex cognitive tasks would draw upon the specific abilities particular to a task rather than an ability general across tasks.”

    I can’t quite manage a confident grip on this passage, but I recall about a year or so ago highlighting it and commenting upon it in another post of yours here.
    The point was that these separate cognitive tests —- the forwards-digit recall and the backwards-digit recall —– were examples in which cultural or qualitative elements between the races could come into play and skew the results of any quantitative measure in the service or quest of verifying hereditarian-intelligence measures.
    Anyhow, it’s not clear to me the specific point you’re making in referencing it here, but I’m wondering if I was on to something when I raised the issue sometime back; if there is some cultural factor that makes for a somewhat ineffable ‘control’ element in setting up such quantitative measures?

    • Chuck says:

      “results of any quantitative measure in the service or quest of verifying hereditarian-intelligence measures”

      I’m not sure what you’re saying either, I’ve tried to be as clear as possible about this. Here are the levels of claims: (1) There is a score difference between Blacks and Whites in the US (established) (2) This represents a latent ability difference (i.e., there’s no psychometric bias) (established) Refer to “Factorial Invariance of Woodcock-Johnson III Scores for African Americans and Caucasian Americans (2006)” for example. (3) This represents, in part, a difference in statistical general intelligence (more or less established) (4) This represents a difference in robustly biologically conditioned general intelligence (not established, but supported) (5) This robustly biologically conditioned general intelligence difference is genetically conditioned (all evidence is equivocal).

      Stupid hereditarians and stupid anti-hereditarians will make the following errors: (1) Confuse a score difference with a latent ability difference — or worse with a genetically conditioned difference. (For example, anti’s will point to the Flynn effect — yet see: Kanaya and Ceci, 2010. The Flynn Effect in the WISC Subtests Among School Children Tested for Special Education Services. Alternatively, hereditarians will assume that a international score difference represents a true ability difference. (2) Confuse a latent ability difference with a statistical g difference. This is similar to (1) but subtly, and importantly, different. In the generally accepted model of cognitive abilities there are three levels (specific factors, broad factors, and the general factor) and a between group ability difference that is established to be unbiased, in principle, could be a lower factor difference. These factor differences, within groups, are mostly environmental — so one would assume the same between. (3) Confuse a statistical g difference with a robustly biological difference. Hypothetically, a statistical g difference could be culturally conditioned (e,g,, the Dynamic model of g).– and the issue is not settled. (4) Confuse a biologically conditioned g difference with a genetic difference.

      My point has been that (1) the US B-W difference is not due to psychometric bias. Differences between have the same psychometric meaning as within. (2) It’s a statistical g difference — so it could be a genetic g difference and it has the predictive validity — and social importance — of a g difference (3) hypothetically “cultural or qualitative elements between the races could come into play” if certain models of general intelligence are correct.
      But they would be working though general intelligence. (4) There are various lines of support against (3) — one of which I was pointing to above. But yes, this hasn’t been ruled out as a possibility like psychometric bias has. (5) (3) could be ruled out by using neuroimaging to compare the IQ related structural differences between Black and Whites and see if they are similar to the differences within. This is not going to be done for PC reasons though. The only study in which it sort of was done (Karama, 2009, 2011) supports a biological similarity — which implies that the difference between Bs and Ws is no more “culture” than the difference within either group. (6) Given (1) to (5) a partial genetic hypothesis is still plausible at least for the US difference. I have mixed feeling on how much the various lines of evidence actually supports a genetic over a environmental hypothesis.

      What I find disturbing is the lack of interest in these important distinctions — and in new data — shown by so called HBDers. But maybe the correctly realize that investing time in this issue is a waste.

  4. szopeno says:

    Thanks a lot for your blog. Because I was affraid of “confirmation bias”, I specifically tried to find informations criticising hereditarian positions, and I must say that it seems to me (an amateur I admit) as it is a very honest presentation and critique of all arguments. Keep on good work, mate 🙂

  5. JL says:

    Fagan and Holland, 2009. Culture-fair prediction of academic achievement

    (Waiting for a replication.)

    I don’t doubt that the lack of B-W gap could be replicated. Fagan has rediscovered the Level I-Level II ability distinction, which Jensen came up with in the 1960s. Fagan is essentially saying that intelligence is all about rote memory, because his “culture-fair” test of intelligence is a test of memorization of word meanings. As Jensen showed long ago, there are few racial differences in rote learning ability even when there are large g differences. Fagan’s theory is silly, and it’s weird that he has completely ignored Jensen’s research on this.

    Hacking. Why race still matters

    (What passes for the philosophy of biology these days

    A year later, Hacking adopted a perhaps more reasonable stance on race in this essay, writing:

    We must erase erase one worthy item from the former dogma of liberal attitudes: that all race science is biased balderdash, in particular, that the genetic variation between two randomly chosen members of one racial group is just as great as that between two randomly chosen members of different races. — We owe the scientic argument to Richard Lewontin, who put it in place over thirty years ago. — Edwards’s 2003 theoretical refutation of Lewontin, attending to correlations among traits and genetic markers, is now widely judged to be correct. — The upshot is that stereotypical features of race are associated both with ancestral geographical origin and, to some extent, with genetic markers.

    —-

    Kaufman et al., 2012. Are cognitive g and academic achievement g one and the same g? An exploration on the Woodcock–Johnson and Kaufman tests

    (Not identical. More g mysteries.)

    Isn’t it reasonable to expect that cog-g and academic-g are not exactly the same?

    • Chuck says:

      (1) “Fagan’s theory is silly, and it’s weird that he has completely ignored Jensen’s research on this.”

      Fagan addresses the issue of g-loading:

      “A final theoretical question that may be raised is whether the newlearning tasks used in the present study are devoid of the general factor (g) and, thus, show no racial-ethnic differences in performance. Such is not the case. In accordance with the manner in which Jensen (1998) derives g, estimates of g in the present study were based on a principal factor analysis (un-rotated)…” (Though, note that the Black sample size = only 92).

      Isn’t a level I ability just a g-loaded ability? I guess I don’t understand how Jensen’s level II and II relate to Carrol’s three stratums. Can you have a g-loaded level I difference? Here is the abstract from one paper:

      Level I and Level II Abilities in Three Ethnic Groups

      “A large battery of various tests of intelligence, scholastic achievement, and short-term memory was administered to some 2,000 white, Negro and Mexican-American pupils in grades, 4,5, and 6 in a largely agricultural school district in the central valley of California. The three grades were used as separate replications of the study. Factor analysis (i.e., principal components) with oblique rotation yielded three main factors, identified as fluid (gf) and crystalized (gc) intelligence (both are aspects of Level II ability in Jensen’s theory) and a memory factor (a Level I ability).”

      By your explanation, how would you explain the g-loading of the Fagan test?

      (2) “A year later, Hacking adopted a perhaps more reasonable stance on race in this essay, writing”

      On the subject of race, our contemporaneous “philosophers” tend to have views that are even more demented than anthropologists and social scientists. Notice Hacking’s comment: “The upshot is that stereotypical features of race are associated.” He and a large swath of philosophers of science took Lewontin’s fallacy as a proof against phenotypic concordance. This, incidentally, led him to accuse Rushton of being an “Ogre Naturalist” for pointing out the phenotypic patterns.

      (3) “Isn’t it reasonable to expect that cog-g and academic-g are not exactly the same?”

      I wouldn’t have predicted it. If you posit that statistical cog g is a manifestation of some underlying biological g, which produces the positive manifold across diverse cognitive tests and batteries of them, why would you suppose that this statistical g would be different from a statistical achievement g, which purportedly has the same origin?

    • Chuck says:

      You know, C. Brand gave me a similar level I, Level II explanation for the apparent lack of a UK achievement gap. And I replied likewise.

    • JL says:

      By your explanation, how would you explain the g-loading of the Fagan test?

      It seems that they computed a common factor for Fagan’s new learning test, and claimed that it’s the g factor. But to get a proper g factor, you must have a diverse battery of tests. The general factor of the new learning test is not g but rather some memory factor which is probably not highly correlated with g. If you factor analyzed Fagan’s test together with, say, the WAIS battery, it would probably have a low loading on g.

      If blacks and whites have approximately the same mean and the same SD for Level I abilities, it means that if you match them on Level II (=g), there’s a black advantage on Level I. In this study, they matched whites and blacks for full-scale IQ and then looked at the profile differences between races for performance, verbal, and memory factors. Blacks had a .24 SD advantage on the memory factor compared to whites with the same IQs (whites slightly outscored matched blacks on the other factors).

      If a white advantage on the small g component of Fagan’s test is counterbalanced by a small black advantage on the non-g component (which accounts for most variance), then there’s no b-w gap. But there’s of course no data to calculate if this is indeed the case here.

      I wouldn’t have predicted it. If you posit that statistical cog g is a manifestation of some underlying biological g, which produces the positive manifold across diverse cognitive tests and batteries of them, why would you suppose that this statistical g would be different from a statistical achievement g, which purportedly has the same origin?

      Academic g is computed from tests that disproportionately load on verbal ability, so academic g is “contaminated” with variance associated with verbal ability. Cog-g is computed from tests that represent a balanced mix of different abilities, so the g factor will be less contaminated with any non-g variance. In the CHC taxonomy, academic g closely resembles crystallized intelligence (Gc), and Gc usually has a loading of .8 or so on a higher order g factor.

      You know, C. Brand gave me a similar level I, Level II explanation for the apparent lack of a UK achievement gap. And I replied likewise.

      I don’t think the Level I/II difference is very relevant for the UK situation. School achievement is very g loaded.

      • Chuck says:

        JL, I was wondering if I could ask you about the regression to the mean studies. It seems to me that the only information that can be gleaned from them is that Black families are depressed uniformly relative to White families across the IQ spectrum. That’s semi-interesting. Now — would it be correct to say that were the 1 SD depressing effect which afflicts the Black population relative to the White, normally distributed across the Black population — say with standard deviations of 0.25 (so 32% would be depressed 1.25 and .75 SD and so on)– we wouldn’t see this — as Blacks who were less depressed would have higher IQs and those who were more would have lower IQs. Basically, I’m wondering if the uniform differential regression across the IQ spectrum necessarily implies that Blacks are uniformly affected relative to Whites of the same IQ. To put this another way, how variable (between Blacks) could the effect on IQ be before the sibling regression lines would look different — not sure if that makes sense.

  6. JL says:

    I’m not sure if I understand your question. Do you mean differential “depression” effects within or between families?

    A regression toward the mean study of blacks and whites in the UK could be informative, because representativeness issues would be much less relevant there.

    • Chuck says:

      JL thanks for the reply. Apologies for the lack of clarity. I am trying to determine what the regression to the mean results — specifically the finding that across the IQ spectrum Blacks regress fairly uniformly to a lower mean — imply for environmental explanations of the US B-W gap. That is, what types of environmental explanations are they inconsistent with, if any?I commented on this elsewhere — concluding that the results imply that the factors depressing the Black IQ (relative to Whites) must be pretty uniform. I’m just wondering if this is correct:

      “Variable-between and uniform-between influences differ in terms of the distribution of the environmental effect. In the former case, the effect is not distributed equally — some members of a population are affected more and some less — and in the latter case it is. In both instances there is variance within groups. To illustrate: We could imagine a situation such that the # of books a person has influences intelligence. And that the correlation between the # of books and IQ is 0.2 and that the standard deviation of the # of books is 1. And that for both of the groups in question, Blacks and Whites, the # of books is normally distributed. Now, further, we could imagine an initial condition in which both groups started out with the same average number of books. If 5 books were latter confiscated from every Black individual, in our situation, depressing each individual’s IQ by 1 SD relative to the individual’s IQ at the initial condition, we would have a uniform-between influence — since Black individuals would be uniformly depressed relative to Whites. In this situation, were we to match Blacks and Whites for the same IQ at the latter time, we would find that all Black individuals had 5 less books than White individuals (5 x 0.2 = 1 SD) of the same IQ. Accordingly, a Black individual with an IQ of 115 would be equivalent to White individual who would have had an IQ of 130 where he not deprived 5 books. Alternatively, if the Black population was divided into quarters and 0, 2, 8, and 10 books were latter confiscated from each, depressing the quarters 0 SD, 0.4 SD, 1.6 SD, and 2 SD respectively, we would have a variable-between influence. In this situation, were we to match Blacks and Whites for the same IQ at the latter time, we would find that some individuals would have 0 less books and some would have 10 less books. Some Black individuals would be affected by the various influences depressing the Black population on average, and some would be unaffected. Now many people assume that the mean difference is due to variable-between influences; they typically don’t suppose that Blacks with an IQ of >115 are afflicted to the same degree that Blacks with an IQ <85 are.

      Variable-between influences. Were the influences depressing the Black population variable, then Black individual who had higher IQs would, on average, be less affected, as less affected Black individuals would have higher IQs. Differential sibling regression is an index of depressive influence. If Blacks and Whites are matched for the same IQ, and the siblings of Blacks regress to a lower mean than that to which the siblings of Whites regress, we can reasonably conclude that both the matched Blacks and their siblings are depressed by the magnitude of the differential regression divided by 0.6. (The alternative is to argue that the unmatched Black siblings are depressed but the matched ones are not; the problem with the alternative is readily obvious: Typically, siblings of IQ matched Blacks are found to regress 0.6 SD below siblings of IQ matched Whites, when Blacks and Whites differ on average by 1 SD. Were we simply to posit a uniformly depression of 1 SD, we would get our differential regression of 0.6. However, were we to posit that Black sibs were variable depressed 0.6 SD (by factors which vary within families, of course) we could only explain 0.3 SD of the total depression (0.6 x ½ of our sibs). The other 0.7 SD would have to act either variably or uniformly or through some combination of the two with respect to the Blacks siblings. Whatever way we would get a differential regression of substantially more that 0.6. (e.g., uniform: 0.7 SD x 0.6 + variable: 0.6 SD.). Now, since siblings of IQ matched Blacks do show differential regression (i.e, they regress to a different mean) and since this regression is no less at the upper end of the IQ spectrum than the lower end, we can conclude that the influences depressing the Black population are not substantially of the variable-between sort."

      I'm trying to squeeze out all the analytic leverage I can get from the various pieces of evidence.

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