Graphs and a Note for Odenstad et al. (2008)
AAO$AgeAA <- factor(AAO$AgeAA, levels = c("06m", "712m", "1318m", "1924m", "23y", "45y", "79y"), ordered = T)
levels(AAO$AgeAA) <- c("0-6 Months", "7-12 Months", "13-18 Months", "19-24 Months", "2-3 Years", "4-5 Years", "7-9 Years")
YOE$LoE <- factor(YOE$LoE, levels = c("012y", "1315y", "16y"), ordered = T)
levels(YOE$LoE) <- c("0-12 Years", "13-15 Years", "16+ Years")
IJZAA$ï..AgeAA <- factor(IJZAA$ï..AgeAA, levels = c("012m", "1224m", "24mp", "nr"), ordered = T)
levels(IJZAA$ï..AgeAA) <- c("0-12 Months", "12-24 Months", ">24 Months", "Not Reported")
IJZAASS$ï..AgeAA <- factor(IJZAASS$ï..AgeAA, levels = c("04y", "412y", "1218y", "18yp"), ordered = T)
levels(IJZAASS$ï..AgeAA) <- c("0-4 Years", "4-12 Years", "12-18 Years", "18+ Years")
SKC$Variable <- factor(SKC$Variable, levels = c("IQT1", "IQT2", "IQT3", "IQT4", "IQT5"), ordered = T)
levels(SKC$Variable) <- c("Two Years Old", "Four Years Old", "Seven Years Old", "Thirteen Years Old", "Retest")
HD$Variable <- factor(HD$Variable, levels = c("Adoptive Parent Education", "Biological Parent Education", "Adoptive and Biological Sum", "Nonadopted Biological"))
HTE$Phenotype <- factor(HTE$Phenotype, levels = c("Depression", "Panic-phobia", "Somatization", "Fertility"))library(pacman); p_load(ggplot2, psych, DT, GGally)
dCI <- function(d, n1, n2){
n = n1 * n2/(n1 + n2); nu = n1 + n2-2; ve = n^-1 + d^2/2/nu; ds <- seq(d - 4 * ve^0.5, d + 4 * ve^0.5, ve^0.5/10^4)
UP <- min(ds[which(pt(d * sqrt(n), nu, ds * sqrt(n)) < 0.025)]) * sqrt(n); SUP <- UP/sqrt(n)
LP <- max(ds[which(pt(d * sqrt(n), nu, ds * sqrt(n)) > 0.975)]) * sqrt(n); SLP <- LP/sqrt(n)
return(message(paste("CI=", SLP, "to", SUP)))}
rD <- function(d = 0, r = 0, dw = T){
dn = 2*r/sqrt(1-r^2)
rn = d/(d^2+4)^(1/2)
if(dw == T){cat("d = ", dn, "\n")} else{cat("r = ", rn, "\n")}}
BESD <- function(r){
PC = 0.5 * abs(r) + 0.5
return(message(paste("Your probability of correct classification using a predictor with a performance correlation of", r, "is", PC)))}
describe(AAO); describe(YOE)datatable(SS, extensions = c("Buttons"), options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'print'), scrollX = T))Rationale
Odenstad et al. (2008) presented some interesting data on the cognitive outcomes of transnational adoptees to Swedish families. Their data focused on a prominently adopted group, Koreans. Koreans are uniquely informative for the effects of adoption because of the relatively high quality of preadoption care and the near-randomization into families. To note, the quality of care is still below that of adoptive families, as evidenced by benefits from adoption, and it does appear that children from worse circumstances catch up with their demographically comparable peers. The most prominent agency for adoptions, Holt, was quite explicit about its “first come, first serve” policy regarding adoptions and there is little exception to this policy besides heritage and repeat adoptions. Because of this, to the extent other countries do allow for selective adoption, that might confound the association between age of adoption and child outcomes because those kids who are adopted later may be those kids with the greatest rearing difficulty, appearances the parents regard most negatively (see Volk & Quinsey, 2002), attitudes that are the most sour and so on. Korea removing this confounding allows us to see if there is a difference; if there is, age of adoption effect might be, to some degree, a result of conditioning on the characteristics of the child. Age of adoption effects should be additive so if there is a relationship in the absence of selection, support for an environmental effect of the family is indicated. Age of adoption effects in a sample which allows for selection could represent the effect of the preadoption environment or selection. This is related to Jensen’s cumulative deficit hypothesis (1974, 1977) but fadeout (of effects or shared environments; Loehlin, 2000; Protzko, 2015; Gove et al., 2018; Bailey et al., 2020; Brant et al., 2013; Briley & Tucker-Drob, 2013; Tucker-Drob & Briley, 2014) and latent effects (Jensen, 1997; te Nijenhuis, Jongeneel-Grimen & Armstrong, 2015) are also relevant. Note that adoption effects are not incompatible with considerable heritability (Jensen, 1973).
Figure 2 from McGue et al. (2007) can also be replicated with Odenstad et al.’s data. Critically, though their graph clearly indicated the effect of adoption was not exceptionally strong, it did not indicate it was absent, it just was not large enough to overwhelm other influences or even create a noticeable positive correlation, as the non-adoptive comparison group showed is found with relatedness. The Swedish data show the same result: a linear, positive relationship between parental socioeconomic status and offspring IQs, but no such relationship among adoptees. Similarly, the effect of age at adoption does not seem large and appears unevidenced in the Korean group and, driven by an outlier category (that could be the result of selection) in the non-Korean group.
Plots
ggplot(AAO, aes(ï..Group, GP, y = GP, fill = AgeAA)) +
geom_bar(aes(fill = AgeAA), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = GPLCIA, ymax = GPUCIA)) + theme_bw() +
scale_x_discrete(labels = c("Korean Adoptees", "Non-Korean Adoptees")) + theme(axis.title.x = element_blank()) + ylab("General Performance") + labs(fill = "Age at Adoption")
ggplot(AAO, aes(ï..Group, VP, y = VP, fill = AgeAA)) +
geom_bar(aes(fill = AgeAA), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = VPLCIA, ymax = VPUCIA)) + theme_bw() +
scale_x_discrete(labels = c("Korean Adoptees", "Non-Korean Adoptees")) + theme(axis.title.x = element_blank()) + ylab("Verbal Performance") + labs(fill = "Age at Adoption")
ggplot(YOE, aes(ï..Group, GP, y = GP, fill = LoE)) +
geom_bar(aes(fill = LoE), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = GPLCIA, ymax = GPUCIA)) + theme_bw() +
scale_x_discrete(labels = c("Korean Adoptees", "Non-Korean Adoptees", "Biological Children")) + theme(axis.title.x = element_blank(), legend.position = "bottom", legend.box = "horizontal") + ylab("General Performance") + labs(fill = "Level of Parent Education")
ggplot(YOE, aes(ï..Group, VP, y = VP, fill = LoE)) +
geom_bar(aes(fill = LoE), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = VPLCIA, ymax = VPUCIA)) + theme_bw() +
scale_x_discrete(labels = c("Korean Adoptees", "Non-Korean Adoptees", "Biological Children")) + theme(axis.title.x = element_blank(), legend.position = "bottom", legend.box = "horizontal") + ylab("Verbal Performance") + labs(fill = "Level of Parent Education")
References
Odenstad, A., Hjern, A., Lindblad, F., Rasmussen, F., Vinnerljung, B., & Dalen, M. (2008). Does age at adoption and geographic origin matter? A national cohort study of cognitive test performance in adult inter-country adoptees. Psychological Medicine, 38(12), 1803–1814. https://doi.org/10.1017/S0033291708002766
Jensen, A. R. (1974). Cumulative deficit: A testable hypothesis? Developmental Psychology, 10(6), 996–1019. https://doi.org/10.1037/h0037246
Jensen, A. R. (1977). Cumulative deficit in IQ of Blacks in the rural South. Developmental Psychology, 13(3), 184–191. https://doi.org/10.1037/0012-1649.13.3.184
Loehlin, J. C. (2000). Group differences in intelligence. In Handbook of intelligence (pp. 176–193). Cambridge University Press. https://doi.org/10.1017/CBO9780511807947.010
Protzko, J. (2015). The environment in raising early intelligence: A meta-analysis of the fadeout effect. Intelligence, 53, 202–210. https://doi.org/10.1016/j.intell.2015.10.006
Gove, A., Dombrowski, E., King, S. J., Pressley, J., & Weatherholt, T. (2018). Persistence and Fadeout of Preschool Participation Effects on Early Reading Skills in Low- and Middle-Income Countries. Global Education Review, 5(2), 85–109.
Bailey, D. H., Duncan, G. J., Cunha, F., Foorman, B. R., & Yeager, D. S. (2020). Persistence and Fade-Out of Educational-Intervention Effects: Mechanisms and Potential Solutions. Psychological Science in the Public Interest.: A Journal of the American Psychological Society, 21(2), 55–97. https://doi.org/10.1177/1529100620915848
Brant, A. M., Munakata, Y., Boomsma, D. I., DeFries, J. C., Haworth, C. M. A., Keller, M. C., Martin, N. G., McGue, M., Petrill, S. A., Plomin, R., Wadsworth, S. J., Wright, M. J., & Hewitt, J. K. (2013). The Nature and Nurture of High IQ: An Extended Sensitive Period for Intellectual Development. Psychological Science, 24(8), 1487–1495. https://doi.org/10.1177/0956797612473119
Briley, D. A., & Tucker-Drob, E. M. (2013). Explaining the Increasing Heritability of Cognitive Ability Across Development: A Meta-Analysis of Longitudinal Twin and Adoption Studies. Psychological Science, 24(9), 1704–1713. https://doi.org/10.1177/0956797613478618
Tucker-Drob, E. M., & Briley, D. A. (2014). Continuity of Genetic and Environmental Influences on Cognition across the Life Span: A Meta-Analysis of Longitudinal Twin and Adoption Studies. Psychological Bulletin, 140(4), 949–979. https://doi.org/10.1037/a0035893
Jensen, A. R. (1997). Adoption data and two g-related hypotheses. Intelligence, 25(1), 1–6. https://doi.org/10.1016/S0160-2896(97)90003-9
te Nijenhuis, J., Jongeneel-Grimen, B., & Armstrong, E. L. (2015). Are adoption gains on the g factor? A meta-analysis. Personality and Individual Differences, 73, 56–60. https://doi.org/10.1016/j.paid.2014.09.022
Jensen, A. R. (1973). Let’s understand Skodak and Skeels, finally. Educational Psychologist, 10(1), 30–35. https://doi.org/10.1080/00461527309529086
McGue, M., Keyes, M., Sharma, A., Elkins, I., Legrand, L., Johnson, W., & Iacono, W. G. (2007). The environments of adopted and non-adopted youth: Evidence on range restriction from the Sibling Interaction and Behavior Study (SIBS). Behavior Genetics, 37(3), 449–462. https://doi.org/10.1007/s10519-007-9142-7
Postscript
I have found additional adoption data. The first data come from Leahy (1935). Leahy compared the correlations of various variables like Otis IQ scores or socioeconomic status measures with child’s IQs between adopted and biological families. In each case, correlations were greater for biological families. Subsequently, the adoptive correlations were compared to those from two others studies, by Burks and Freeman, Holzinger & Mitchell. Burks’ study produced similar results to Leahy’s, but Freeman’s showed much greater adoptive parent correlations. They were all similarly large (n’s of around 180), but Freeman’s was notably younger than the rest and they were all studies of children rather than adults. A potential issue to consider with respect to the topic of correlations with IQs is the issue of invariance, where, for many early studies, parents frequently showed considerably lower scores because of their functional illiteracy by comparison to their children (Burt often mentioned this); as such, parents were frequently underestimated due to test bias. As far as I know, there is no way to correct for this, but it seems quite important for, e.g., Skodak & Skeels (1949). Some of the decline in correlations between children and adoptees and their biological parents in later studies may be due, to some extent, to range restriction in IQs, but perhaps not much (see, e.g., Loehlin, 1980). Leahy humored the possibility that, through selection, adoptee genotypes could become correlated with those of their adoptive parents (i.e., selection confounding).
Regarding the effects of age, the Wilson effect is of considerable interest. Part of the verification of it outside of twin studies comes from other designs like virtual twin studies (e.g., Segal, 1997; Segal et al., 2007) or looking at adoptive versus biological parent correlations to their children’s traits across ages (e.g., Loehlin, 2016). There are many other methods, but they generally agree: the shared environment represented in typical ACE models is there, it is largely not dominance, and it does fade, and this leads to a reduction in adoptive parent-child correlations with limited effects on biological parent-child correlations.
ggplot(Leahy, aes(ï..Variable, rIQ, y = rIQ, fill = Group)) +
geom_bar(aes(fill = Group), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = LCI, ymax = UCI)) + theme_bw() +
scale_x_discrete(labels = c("Father's Otis", "Mother's Otis", "Midparent Otis", "Father's Vocabulary", "Mother's Vocabulary", "Midparent Vocabulary", "Environmental Status", "Cultural Index", "Father's Education", "Mother's Education", "Midparent Education", "Father's Occupational Status")) + theme(axis.title.x = element_blank(), legend.position = c(0.045, 0.94), legend.box = "horizontal", axis.text.x = element_text(angle = 33, vjust = 1, hjust = 1)) + ylab("IQ Correlation") + labs(fill = "Adoption Status")
ggplot(FBL, aes(ï..Variable, rIQ, y = rIQ, fill = Group)) +
geom_bar(aes(fill = Group), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = LCI, ymax = UCI)) + theme_bw() +
scale_x_discrete(labels = c("Father's IQ", "Mother's IQ", "Midparent IQ", "Father's Vocabulary", "Mother's Vocabulary", "Midparent Vocabulary", "Home Rating", "Cultural Index", "Father's Education", "Mother's Education", "Midparent Education", "Father's Occupational Status")) + theme(axis.title.x = element_blank(), legend.position = c(0.32, 0.9), legend.box = "horizontal", axis.text.x = element_text(angle = 33, vjust = 1, hjust = 1)) + ylab("IQ Correlation") + labs(fill = "Adoption Status")## Warning: Removed 6 rows containing missing values (geom_bar).
van Ijzendoorn, Juffer & Poelhuis (2005) meta-analyzed 62 adoption studies (N = 17,767) to assess the effect of adoption and many related variables on IQ. They included, for example, the effects of age at adoption and the age the IQ test was conducted. I have plotted the values they reported below. Age at adoption effects are inconsistent and age at assessment effects are consistent with older people, to an extent, being smarter than younger people.
ggplot(IJZAA, aes(ï..AgeAA, dIQ, y = dIQ, fill = ï..AgeAA)) +
geom_bar(aes(fill = ï..AgeAA), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = LCI, ymax = UCI)) + theme_bw() +
scale_x_discrete(labels = c("0-12 Months", "12-24 Months", ">24 Months", "Not Reported")) + theme(axis.title.x = element_blank()) + ylab("IQ Difference (d)") + labs(fill = "Age at Adoption")
ggplot(IJZAASS, aes(ï..AgeAA, dIQ, y = dIQ, fill = ï..AgeAA)) +
geom_bar(aes(fill = ï..AgeAA), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = LCI, ymax = UCI)) + theme_bw() +
scale_x_discrete(labels = c("0-4 Years", "4-12 Years", "12-18 Years", ">18 Years")) + theme(axis.title.x = element_blank()) + ylab("IQ Difference (d)") + labs(fill = "Age at Assessment")
Skodak & Skeels (1949) is a commonly cited example of the strength of environmental effects from adoption. I am not sure exactly how this follows, but the general argument is that the adoptees had IQs roughly twenty points above their mothers and this indicated the prominence of the environment and the meaninglessness of heritability. There are many reasons why this is wrong, chief among them, that a heritability is about variances rather than mean effects. Perhaps more importantly, (1) the results fall in line with other adoption studies, (2) the mean effect is exaggerated and from the era where bias with respect to parent-child comparisons was a common concern and, (3) the sample was still quite young at the last testing. The low correlations between the scores from the first testing and other variables were probably due to the low reliability of tests administered at the sample’s age (around two). I think it is safe to assume that age effects would have considerably reduced the discordance between biological mother scores and those of their children. Jensen (1973) attempted to correct some of the misconceptions about heritability and gains in and Flynn tried to explain how the mother-child IQ gap was inflate due to norm obsolescence (Flynn, 1993). Jensen (1997) also later looked at another hypothesis from adoption studies: that of whether the effects were general, and, as it turned out, they did not seem to be in the data he analyzed (this may limit their utility). Another result (Jensen & Reynolds, 1982) is conceptually similar. Adoption results - for correspondence, specificity of effects, etc. - should be (meta-)analyzed. It is a shame that Capron & Duyme were not as forthcoming with their data and that the Cahier de Psychologie Cognitive is defunct.
ggparcoord(SS,
columns = 23:27,
showPoints = T,
title = "Parallel Coordinates for IQs by Age",
alphaLines = 0.6, missing = "exclude") +
theme_bw() +
theme(plot.title = element_text(size = 10, hjust = 0.5)) + ylab("IQ") + xlab("Measurement Time") + scale_x_discrete(labels = c("Two Years Old", "Four Years Old", "Seven Years Old", "Thirteen Years Old", "Retest"))
print(cor(SS[c(4:10,23:27)], use ="pairwise.complete.obs"), digits = 3)## FosterFatherEdu FosterMotherEdu MidfosterEdu TrueFatherEdu
## FosterFatherEdu 1.0000 0.5480 0.9064 0.0796
## FosterMotherEdu 0.5480 1.0000 0.8501 0.1141
## MidfosterEdu 0.9064 0.8501 1.0000 0.1062
## TrueFatherEdu 0.0796 0.1141 0.1062 1.0000
## TrueMotherEdu 0.2547 0.2077 0.2657 0.6171
## MidtrueEdu 0.1461 0.1624 0.1710 0.9243
## TrueMotherIQ 0.1099 0.0641 0.1017 0.1474
## IQA1 0.0561 -0.0120 0.0292 0.0526
## IQA2 0.0344 0.0303 0.0369 0.3677
## IQA3 0.0122 0.0859 0.0510 0.2609
## IQA4 0.0695 0.0463 0.0672 0.3726
## IQA4L 0.0082 0.0344 0.0225 0.4390
## TrueMotherEdu MidtrueEdu TrueMotherIQ IQA1 IQA2 IQA3
## FosterFatherEdu 0.255 0.1461 0.1099 0.0561 0.0344 0.0122
## FosterMotherEdu 0.208 0.1624 0.0641 -0.0120 0.0303 0.0859
## MidfosterEdu 0.266 0.1710 0.1017 0.0292 0.0369 0.0510
## TrueFatherEdu 0.617 0.9243 0.1474 0.0526 0.3677 0.2609
## TrueMotherEdu 1.000 0.8708 0.6000 0.0800 0.2929 0.3540
## MidtrueEdu 0.871 1.0000 0.2867 0.0326 0.3781 0.3414
## TrueMotherIQ 0.600 0.2867 1.0000 0.0370 0.2746 0.3473
## IQA1 0.080 0.0326 0.0370 1.0000 0.5337 0.4450
## IQA2 0.293 0.3781 0.2746 0.5337 1.0000 0.6936
## IQA3 0.354 0.3414 0.3473 0.4450 0.6936 1.0000
## IQA4 0.362 0.4170 0.3805 0.3572 0.5772 0.7127
## IQA4L 0.314 0.4320 0.4148 0.3438 0.5946 0.7473
## IQA4 IQA4L
## FosterFatherEdu 0.0695 0.0082
## FosterMotherEdu 0.0463 0.0344
## MidfosterEdu 0.0672 0.0225
## TrueFatherEdu 0.3726 0.4390
## TrueMotherEdu 0.3620 0.3137
## MidtrueEdu 0.4170 0.4320
## TrueMotherIQ 0.3805 0.4148
## IQA1 0.3572 0.3438
## IQA2 0.5772 0.5946
## IQA3 0.7127 0.7473
## IQA4 1.0000 0.9241
## IQA4L 0.9241 1.0000summary(lm(scale(IQA4) ~ scale(MidfosterEdu), data = SS))##
## Call:
## lm(formula = scale(IQA4) ~ scale(MidfosterEdu), data = SS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.79745 -0.64415 0.07509 0.74381 2.38138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.920e-16 1.003e-01 0.000 1.000
## scale(MidfosterEdu) 6.715e-02 1.008e-01 0.666 0.507
##
## Residual standard error: 1.003 on 98 degrees of freedom
## Multiple R-squared: 0.00451, Adjusted R-squared: -0.005648
## F-statistic: 0.444 on 1 and 98 DF, p-value: 0.5068summary(lm(scale(IQA4) ~ scale(MidtrueEdu), data = SS))##
## Call:
## lm(formula = scale(IQA4) ~ scale(MidtrueEdu), data = SS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9324 -0.5213 0.1068 0.5796 2.0522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0262 0.1186 -0.221 0.82590
## scale(MidtrueEdu) 0.4141 0.1196 3.463 0.00102 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9106 on 57 degrees of freedom
## (41 observations deleted due to missingness)
## Multiple R-squared: 0.1738, Adjusted R-squared: 0.1594
## F-statistic: 11.99 on 1 and 57 DF, p-value: 0.001019summary(lm(scale(IQA4) ~ scale(MidfosterEdu) + scale(MidtrueEdu), data = SS))##
## Call:
## lm(formula = scale(IQA4) ~ scale(MidfosterEdu) + scale(MidtrueEdu),
## data = SS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.86844 -0.46849 0.04811 0.51829 2.08270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02733 0.11913 -0.229 0.819351
## scale(MidfosterEdu) -0.08059 0.11872 -0.679 0.500026
## scale(MidtrueEdu) 0.42826 0.12194 3.512 0.000886 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.915 on 56 degrees of freedom
## (41 observations deleted due to missingness)
## Multiple R-squared: 0.1806, Adjusted R-squared: 0.1513
## F-statistic: 6.171 on 2 and 56 DF, p-value: 0.003784cor.test(SS$MidfosterEdu, SS$MidtrueEdu, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidfosterEdu and SS$MidtrueEdu
## t = 1.3102, df = 57, p-value = 0.1954
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08899513 0.40915109
## sample estimates:
## cor
## 0.1709839cor.test(SS$MidfosterEdu, SS$IQA1, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidfosterEdu and SS$IQA1
## t = 0.28965, df = 98, p-value = 0.7727
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1681372 0.2243760
## sample estimates:
## cor
## 0.02924684cor.test(SS$MidfosterEdu, SS$IQA2, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidfosterEdu and SS$IQA2
## t = 0.36579, df = 98, p-value = 0.7153
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1606579 0.2316634
## sample estimates:
## cor
## 0.03692545cor.test(SS$MidfosterEdu, SS$IQA3, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidfosterEdu and SS$IQA3
## t = 0.50592, df = 98, p-value = 0.614
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1468511 0.2450011
## sample estimates:
## cor
## 0.05103916cor.test(SS$MidfosterEdu, SS$IQA4, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidfosterEdu and SS$IQA4
## t = 0.6663, df = 98, p-value = 0.5068
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1309913 0.2601414
## sample estimates:
## cor
## 0.06715465cor.test(SS$MidfosterEdu, SS$IQA4L, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidfosterEdu and SS$IQA4L
## t = 0.22298, df = 98, p-value = 0.824
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1746717 0.2179730
## sample estimates:
## cor
## 0.02251897cor.test(SS$MidtrueEdu, SS$IQA1, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidtrueEdu and SS$IQA1
## t = 0.24663, df = 57, p-value = 0.8061
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2253172 0.2863372
## sample estimates:
## cor
## 0.03264893cor.test(SS$MidtrueEdu, SS$IQA2, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidtrueEdu and SS$IQA2
## t = 3.0838, df = 57, p-value = 0.003149
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1351303 0.5782199
## sample estimates:
## cor
## 0.3781277cor.test(SS$MidtrueEdu, SS$IQA3, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidtrueEdu and SS$IQA3
## t = 2.7421, df = 57, p-value = 0.008142
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09347122 0.54943196
## sample estimates:
## cor
## 0.3413821cor.test(SS$MidtrueEdu, SS$IQA4, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidtrueEdu and SS$IQA4
## t = 3.4633, df = 57, p-value = 0.001019
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1800992 0.6081042
## sample estimates:
## cor
## 0.4169516cor.test(SS$MidtrueEdu, SS$IQA4L, method = "pearson", conf.level = 0.95)##
## Pearson's product-moment correlation
##
## data: SS$MidtrueEdu and SS$IQA4L
## t = 3.6166, df = 57, p-value = 0.0006337
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1978230 0.6195585
## sample estimates:
## cor
## 0.4320196ggplot(SKC, aes(Group, rIQ, y = rIQ, fill = Variable)) +
geom_bar(aes(fill = Variable), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = LCI, ymax = UCI)) + theme_bw() +
scale_x_discrete(labels = c("Adoptive", "Biological")) + theme(axis.title.x = element_blank(), legend.position = "bottom") + ylab("IQ - Midparent Education Correlation") + labs(fill = "Measurement Time")
Halpern-Manners et al. (2020) conducted an excellent study, assessing the sources of transmission of inequality in achievement test scores, relating them to adoptive and biological parent education levels. They accounted for range restriction in adoptive environments - in two ways, though they only mentioned one -, potential selection biases, pre- and neonatal effects and they largely obviated any issue with age at adoption effects with the use of a sample adopted at extremely early ages. They also tested for gene-environment interactions and found no such evidence. It is worth noting the potency of parental education as a socioeconomic status marker over alternatives (see, e.g., Rindermann & Ceci, 2018).
ggplot(HM, aes(Test, Beta, y = Beta, fill = Variable)) +
geom_bar(aes(fill = Variable), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = LCI, ymax = UCI)) + theme_bw() +
scale_x_discrete(labels = c("Letter-Word Identification", "Word Attack", "Mathematical Fluency")) + theme(legend.position = "bottom") + xlab("Woodcock-Johnson Test") + ylab("Unstandardized Beta") + labs(fill = "Independent Variable")
ggplot(HD, aes(Test, Beta, y = Beta, fill = Variable)) +
geom_bar(aes(fill = Variable), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = LCI, ymax = UCI)) + theme_bw() +
scale_x_discrete(labels = c("Letter-Word Identification", "Word Attack", "Mathematical Fluency")) + theme(legend.position = "bottom") + xlab("Woodcock-Johnson Test") + ylab("Unstandardized Beta") + labs(fill = "Independent Variable")
The biological part of the sum (averaged across the three tests) was 64% and the adoptive part, 36%. It is reasonable to say the adoptive parent coefficient is not attributable to selection because of the correlations between adoptive and biological parents for some of the variables mentioned in the study (e.g., r = -0.03 for years of education, 0.01 for age, 0.04 for household income, 0.05 for mother’s anxiety and -0.05 for mother’s depression). The mean age children were placed into their adoptive homes was 6.2 days old (SD = 12.5 days). I don’t know the children’s ages at testing time, but they were “age appropriate”, which sounds like adolescence and, perhaps, not yet at the point where heritability reaches its asymptote. Interestingly, when the authors looked at the norming sample (i.e., nonadopted biological in the graph above), restricting the sample to families with more than 12 years of education - to mimic the range of parental educational attainment observed in the adoptive families -, betas for parental education were both statistically and practically unaffected, indicating that range restriction was unlikely to be concerning.
Whilst not an adoption study, Halpern-Manners, Schnabel & Hernandez (2016) conducted an interesting twin-control study to assess the extent of confounding involved in the relationship between mental health and educational attainment. Notably, they used fertility as a positive control to assess if they were well-powered enough to detect their effects of interest; the relationship between educational attainment and fertility was not affected. Correction for measurement error, allowing nonlinear effects, checking within different birth cohorts, and restraining their sample to reduce nonshared environmental differences also did not affect their results.
ggplot(HTE, aes(Group, B, y = B, fill = Phenotype)) + #betas are not corrected for measurement error
geom_bar(aes(fill = Phenotype), position = position_dodge(0.9), stat = "identity", color = "black") +
geom_errorbar(position = position_dodge(0.9), width = 0.25, aes(ymin = LCI, ymax = UCI)) + theme_bw() +
scale_x_discrete(labels = c("General Population", "Monozygotic Twins")) + theme(legend.position = "bottom", axis.title.x = element_blank()) + ylab("Beta for Effect of Condition on Educational Attainment") + labs(fill = "Independent Variable")
It could be interesting to think of adoption gains in terms of coin flips. If the population \(\sigma\) of 15 points is preserved within sibling pairs, IQ gains of two, three, four, and five points (commonly-cited figures) correspond to correctly picking the higher-IQ sibling by way of picking the sibling from the higher socioeconomic status adoptive family of 53.4%, 55%, 56.6% and 58.2%, respectively. In terms of Cohen’s U3, these would be 53.7%, 55.6%, 57.5% and 59.3%.
BESD(0.067); BESD(0.100); BESD(0.132); BESD(0.164)## Your probability of correct classification using a predictor with a performance correlation of 0.067 is 0.5335## Your probability of correct classification using a predictor with a performance correlation of 0.1 is 0.55## Your probability of correct classification using a predictor with a performance correlation of 0.132 is 0.566## Your probability of correct classification using a predictor with a performance correlation of 0.164 is 0.582However, the variance is reduced to the extent that siblings are correlated, whether it be due to genetics, shared environments or something else. This change is linear under normality, so if you have a heritability of, say, 25% and shared environmentality of 50%, the resulting \(\sigma\) is \((1 - (0.25 + 0.50)) * 15 = 3.75\) points. As such, the classification accuracy increases. For the same gains, these become 62.9%, 68.6%, 73.6% and 77.8%, respectively. It is not perfect, but it’s considerable! In Cohen’s U3 terms these would be 64.7%, 71.4%, 77.5% and 82.7%.
BESD(0.258); BESD(0.371); BESD(0.471); BESD(0.555)## Your probability of correct classification using a predictor with a performance correlation of 0.258 is 0.629## Your probability of correct classification using a predictor with a performance correlation of 0.371 is 0.6855## Your probability of correct classification using a predictor with a performance correlation of 0.471 is 0.7355## Your probability of correct classification using a predictor with a performance correlation of 0.555 is 0.7775If nature of nurture effects indicated by polygenic scores are taken as non-genetic socioeconomic status effects and we take the proportion of the total r explained by nature of nurture effects as accurate, Willoughby et al.’s (2019), Bates et al.’s (2018) and de Zeeuw et al.’s (2019) results would yield
BESD(sqrt(0.004/0.085)); BESD(0.058/0.15); BESD(0.31) #the last estimate is an approximate guess based on the middling of the prior estimates, but it's close enough based on the values - which are unstated - in de Zeeuw's study## Your probability of correct classification using a predictor with a performance correlation of 0.216930457818656 is 0.608465228909328## Your probability of correct classification using a predictor with a performance correlation of 0.386666666666667 is 0.693333333333333## Your probability of correct classification using a predictor with a performance correlation of 0.31 is 0.655It should be remembered that the latter two studies used achievement tests, which are more “crystallized” and susceptible to educational effects, even if they do not represent construct-level effects and gains may be somewhat overstated. It is also worth noting that in all three cases, p values were marginal, at 0.041, 0.005 and 0.026, respectively, with well-sized samples. The Cohen’s U3 for these would be 62.3%, 72.3% and 67.8%.
Postscript References
Leahy, A. M. (1935). A study of adopted children as a method of investigating nature-nurture. Journal of the American Statistical Association, 30, 281–287. https://doi.org/10.2307/2278243
Loehlin, J. C. (1980). Recent adoption studies of IQ. Human Genetics, 55(3), 297–302. https://doi.org/10.1007/BF00290207
Segal, N. L. (1997). Same-age unrelated siblings: A unique test of within-family environmental influences on IQ similarity. Journal of Educational Psychology, 89(2), 381–390. https://doi.org/10.1037/0022-0663.89.2.381
Segal, N. L., McGuire, S. A., Havlena, J., Gill, P., & Hershberger, S. L. (2007). Intellectual similarity of virtual twin pairs: Developmental trends. Personality and Individual Differences, 42(7), 1209–1219. https://doi.org/10.1016/j.paid.2006.09.028
Loehlin, J. C. (2016). What Can an Adoption Study Tell Us About the Effect of Prenatal Environment on a Trait? Behavior Genetics, 46(3), 329–333. https://doi.org/10.1007/s10519-015-9730-x
van Ijzendoorn, M. H., Juffer, F., & Poelhuis, C. W. K. (2005). Adoption and cognitive development: A meta-analytic comparison of adopted and nonadopted children’s IQ and school performance. Psychological Bulletin, 131(2), 301–316. https://doi.org/10.1037/0033-2909.131.2.301
Skodak, M., & Skeels, H. M. (1949). A Final Follow-Up Study of One Hundred Adopted Children. The Pedagogical Seminary and Journal of Genetic Psychology, 75(1), 85–125. https://doi.org/10.1080/08856559.1949.10533511
Flynn, J. R. (1993). Skodak and skeels: The inflated mother-child IQ gap. Intelligence, 17(4), 557–561. https://doi.org/10.1016/0160-2896(93)90019-2
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. https://doi.org/10.1016/0191-8869(82)90007-1
Halpern-Manners, A., Marahrens, H., Neiderhiser, J. M., Natsuaki, M. N., Shaw, D. S., Reiss, D., & Leve, L. D. (2020). The intergenerational transmission of early educational advantages: New results based on an adoption design. Research in Social Stratification and Mobility, 67, 100486. https://doi.org/10.1016/j.rssm.2020.100486
Rindermann, H., & Ceci, S. J. (2018). Parents’ Education Is More Important Than Their Wealth in Shaping Their Children’s Intelligence: Results of 19 Samples in Seven Countries at Different Developmental Levels. Journal for the Education of the Gifted, 41(4), 298–326. https://doi.org/10.1177/0162353218799481
Halpern-Manners, A., Schnabel, L., Hernandez, E. M., Silberg, J. L., & Eaves, L. J. (2016). The Relationship between Education and Mental Health: New Evidence from a Discordant Twin Study. Social Forces, 95(1), 107–131. https://doi.org/10.1093/sf/sow035
Willoughby, E. A., McGue, M., Iacono, W. G., Rustichini, A., & Lee, J. J. (2019). The role of parental genotype in predicting offspring years of education: Evidence for genetic nurture. Molecular Psychiatry. https://doi.org/10.1038/s41380-019-0494-1
Bates, T. C., Maher, B. S., Medland, S. E., McAloney, K., Wright, M. J., Hansell, N. K., Kendler, K. S., Martin, N. G., & Gillespie, N. A. (2018). The Nature of Nurture: Using a Virtual-Parent Design to Test Parenting Effects on Children’s Educational Attainment in Genotyped Families. Twin Research and Human Genetics, 21(2), 73–83. https://doi.org/10.1017/thg.2018.11
de Zeeuw, E. L., Kan, K.-J., van Beijsterveldt, C. E. M., Mbarek, H., Hottenga, J.-J., Davies, G. E., Neale, M. C., Dolan, C. V., & Boomsma, D. I. (2019). The moderating role of SES on genetic differences in educational achievement in the Netherlands. Npj Science of Learning, 4(1), 1–8. https://doi.org/10.1038/s41539-019-0052-2