The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index; 13 of the 26 sources its claims cite were checked for support.
Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence
Open accessAbstract
A core hypothesis in developmental theory predicts that genetic influences on intelligence and academic achievement are suppressed under conditions of socioeconomic privation and more fully realized under conditions of socioeconomic advantage: a Gene × Childhood Socioeconomic Status (SES) interaction. Tests of this hypothesis have produced apparently inconsistent results. We performed a meta-analysis of tests of Gene × SES interaction on intelligence and academic-achievement test scores, allowing for stratification by nation (United States vs. non–United States), and we conducted rigorous tests for publication bias and between-studies heterogeneity. In U.S. studies, we found clear support for moderately sized Gene × SES effects. In studies from Western Europe and Australia, where social policies ensure more uniform access to high-quality education and health care, Gene × SES effects were zero or reversed.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This meta-analysis finds a positive gene × socioeconomic status (G × SES) interaction on intelligence in US studies but no such effect—or a reversed effect—in Western Europe and Australia. The cross-national difference is statistically significant and not explained by publication bias.
Key findings
- In US studies, the G × SES interaction was significant and positive (a′ = .074, SE = .020, p < .0005); in non-US studies from Western Europe and Australia, it was trivial and non-significant (a′ = −.027, SE = .022, p = .223). The difference between US and non-US effects was significant (b = .101, SE = .032, p = .001).
- The unconditional meta-analytic mean G × SES effect was non-significant (a′ = .029, SE = .019, p = .136), with significant heterogeneity (random-effect SD = .048, SE = .019, p = .011). Excluding Netherlands studies made non-US effects essentially zero (a′ = −.001, SE = .033, p = .979).
- In the US model, genetic variance in intelligence increased from .24 at 2 SD below mean SES to .61 at 2 SD above mean SES, corresponding to heritability estimates of 26% and 61%, respectively. Shared and non-shared environmental variance decreased with SES, but these interactions were not statistically significant.
- Publication-bias checks found no evidence of bias: funnel asymmetry tests were non-significant, and p-curve analysis indicated evidential value for US studies (z = −3.427, p < .001) but not for non-US studies (z = −1.264, p = .103). A power simulation estimated that 3,300 twin pairs are needed for 80% power based on the meta-analytic US estimate, versus 500 based on Turkheimer et al. (2003).
Methods
- The authors meta-analyzed published and unpublished G × SES studies on objective intelligence/achievement measures, requiring sibling/twin genetic relatedness, known zygosity, childhood family SES as moderator, and no selection on diagnoses or test scores. The final dataset included k = 43 effect sizes from K = 14 independent studies and N = 24,926 twin/sibling pairs (approximately 50,000 individuals); US samples contributed k = 18, K = 8, N = 10,831, and non-US samples k = 25, K = 6, N = 14,095.
- To ensure comparable estimates, the authors reanalyzed raw data, obtained reanalyses from original authors, or reanalyzed published subgroup covariance matrices. They fitted Purcell’s (2002) biometric model of latent Gene × Measured Shared Environment interaction, with Z-transformed phenotypes and SES, extracting a, c, e, s, a′, c′, and e′ parameters.
- Effect sizes were meta-analyzed using random-effects meta-regression in Mplus, allowing multiple effect sizes per sample, weighting by the reciprocal number of effect sizes and inverse sampling variance, and using a sandwich estimator to correct standard errors for non-independence. Moderator analyses and publication-bias/robustness checks were also conducted.
Limitations
- The authors state they could not fully test whether the US G × SES effects were driven by greater racial and ethnic diversity in US samples, though they cite individual studies suggesting this is not the case. They also note that candidate mechanisms for national differences—such as educational quality, medical/educational access, social mobility, and income support—were not directly tested.
- The non-US evidence is limited to Western Europe and Australia, so the “non-US” estimate may not generalize to other nations. The authors also excluded several notable studies for methodological reasons (e.g., Scarr-Salapatek, 1971; NLSY kinship data; Nagoshi & Johnson, 2005; Friend et al.; Prescott et al.).
- Evident from the text: the meta-analysis cannot establish causal environmental mechanisms, and residual non-US heterogeneity was driven by Netherlands studies, meaning the non-US summary may mask country-level variation. Publication-bias tests reduce but cannot entirely rule out bias.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
13 of 26 cited sources checked · 7 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Some subsequent US research has replicated the interaction (Bates, Lewis, & Weiss, 2013; Harden, Turkheimer, & Loehlin, 2007; Tucker-Drob, Rhemtulla, Harden, Turkheimer, & Fask, 2011; Turkheimer, Haley, Waldron, D’Onofrio, & Gottesman, 2003).”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Abstract
What the sources say
- Partly supportedcites Bates, Lewis, & Weiss, 2013 sourceExperimental — accuracy not yet measured
The indexed record’s title (metadata; no section or page) identifies a study of childhood socioeconomic status amplifying genetic effects on adult intelligence, which supports the interaction component. The available passages do not establish that this was US research or that it replicated an earlier finding; searches for the study setting and replication returned only metadata. (LLM, conf=0.72)
- Not matchedcites Harden, Turkheimer, & Loehlin, 2007
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Tucker-Drob, Rhemtulla, Harden, Turkheimer, & Fask, 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“We also replicated the well-established phenomenon of increasing genetic influences, and decreasing shared environmental influences, on intelligence with childhood age (e.g. Briley & Tucker-Drob, 2013; Haworth et al., 2009) in the current meta-analytic dataset.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Discussion
What the sources say
- Supportedcites Briley & Tucker-Drob, 2013 source
The Results section (‘Changes in genetic and environmental factors’) reports that longer intervals between measurements were associated with larger increases in heritability and larger decreases in shared environmentality. The citing authors’ statement about replication in their current dataset describes their own finding, not a finding attributed to Briley and Tucker-Drob. (LLM, conf=0.93)
- Supportedcites Haworth et al., 2009 source
The Abstract reports that, in the full twin sample, genetic influence on intelligence increased from childhood through young adulthood, while shared environmental influence declined significantly from childhood to adolescence. The citing authors’ replication in their own meta-analytic dataset is not attributed to this source. The cited paper’s separate analysis of high cognitive ability did not detect significant age-group differences, but that does not negate the background finding it reports. (LLM, conf=0.89)
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It also indicated that the p-curve is not flatter than one would expect if studies were powered at 33% (z = .122, p = .548)”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Probing for Publication Bias
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A model that excluded these effect sizes (reported in Harden et al., 2007; Tucker-Drob et al., 2011; Turkheimer et al., 2003), yielded a null G × SES effect in non-US samples (a′ = −.027, SE = .022, p = .220)”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Robustness Checks
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our meta-analytic finding of a G × SES interaction in the US, was based on a total sample size of over 10,000 pairs of US twins and siblings.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Implications of Meta-Analytic Effect Size Estimates for Reproducibility
Strength:plain· not yet calibratedn = 10,000Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a significant G × SES effect in the US (a′ = .058, SE = .020, p = .003)”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Robustness Checks
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As instantaneous proportions of variance, these correspond to heritability estimates of 26% at 2 SD’s below the mean SES, and 61% at 2 SD’s above the mean SES.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > G × SES Effects in US and Non-US Samples
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Post-hoc analysis revealed that, exclusive of effect sizes from the Netherlands, residual heterogeneity was nil in the non-US group (τ = .005, SE = .517, p = .993)”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > G × SES Effects in US and Non-US Samples
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“In contrast, when the same procedure was implemented using parameter estimates for FSIQ from Turkheimer et al. (2003), an early and highly publicized study that reported a much larger G × SES than was indicated by the current meta-analysis, results indicated that a minimum sample size of only 500 twin pairs is needed to achieve ≥ 80% power.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Implications of Meta-Analytic Effect Size Estimates for Reproducibility
What the sources say
- Not checked — no full text of the sourcecites Turkheimer et al. (2003) source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, when only non-US studies were entered into p-curve analysis, p-checker did not indicate significant right skew (z = −1.264, p = .103), indicating that there was no evidence for a non-null a’ effect outside of the US.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Probing for Publication Bias
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- Probably not a substantial claim. 6
- It may state two separate claims as one. 20
- The quote only partly states this claim. 2
Every source the paper cites for this claim was checked and supports it.
In the paper
“In order to ensure that effect sizes were placed on a metric that was comparable across studies, we ensured that phenotypes and continuous SES measures were Z-transformed (based on the M and SD from data pooled across all twins and siblings of all relatedness types) prior to fitting the Purcell (2002) model.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Method > Obtaining Effect Size Estimates
What the sources say
- Supportedcites Purcell (2002) source
The citation identifies the model the citing authors fitted, not the source of their Z-transformation procedure. The paper’s “Continuous Moderator Variables” section describes a gene–environment interaction model with continuous moderators, including socioeconomic status. The sentence presents standardization for cross-study comparability as the citing authors’ own choice. (LLM, conf=0.91)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“findings generally supported by heritability estimates from genome-wide data on unrelated individuals (Davies et al., 2011; Plomin et al., 2013).”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Abstract
What the sources say
- Partly supportedcites Davies et al., 2011 sourceExperimental — accuracy not yet measured
The Abstract and Discussion report genome-wide estimates from unrelated adults that support substantial heritability of intelligence. The paper does not show that heritability increases; its estimates concern fluid and crystallized intelligence in middle to older adulthood, not a change over time. (LLM, conf=0.84)
- Partly supportedcites Plomin et al., 2013 sourceExperimental — accuracy not yet measured
The Discussion reports a substantial GCTA estimate for general cognitive ability, and the Genomewide Complex-Trait Analysis section reports estimates from unrelated adults and children. The paper describes increasing heritability from childhood to adulthood as a finding from developmental research, but does not show that genome-wide estimates establish that increase. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“and increase with age (Briley & Tucker-Drob, 2013)”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Abstract
What the sources say
- Supportedcites Briley & Tucker-Drob, 2013 source
The Results section reports that this longitudinal meta-analysis largely confirmed developmental increases in heritability, with larger increases associated with older ages. The Abstract identifies the underlying data as twin and sibling pairs measured repeatedly from infancy through adolescence. (LLM, conf=0.92)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“there was significant support for age differences in the a and c parameters, indicating that genetic variance increases and shared environmental variance decreases with age”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Additional Moderator Analyses
What the sources say
- Partly supportedcites Briley & Tucker-Drob, 2013 sourceExperimental — accuracy not yet measured
The Results section (Changes in genetic and environmental factors; page not provided) reports significant longitudinal increases in heritability and decreases in shared environmentality. But these are changes in proportions of variance: the Discussion (Strengths and limitations; page not provided) says the study could not adequately assess changes in absolute genetic or environmental variance. The claim's unqualified statement about variance therefore exceeds the findings. (LLM, conf=0.87)
- Partly supportedcites Haworth et al., 2009 sourceExperimental — accuracy not yet measured
The Abstract reports significant age-related increases in heritability and a decline in shared environmental influence for the broader 11,000-pair sample, citing an earlier analysis. But this paper’s Results (Heterogeneity model-fitting analyses; page unavailable) found no significant age-group differences in A, C, or E for its high-cognitive-ability sample. The claim does not distinguish the earlier finding from this paper’s own result. (LLM, conf=0.84)
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Other studies, however, have failed to replicate the finding (Bartels, van Beijsterveldt, & Boomsma, 2009; Kremen et al., 2005; Soden-Hensler, 2012; van der Sluis, Willemsen, de Geus, Boomsma, & Posthuma, 2008), including the largest sample to date (Hanscombe et al., 2012).”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Abstract
What the sources say
- Supportedcites Bartels, van Beijsterveldt, & Boomsma, 2009 source
The Results report no obvious differences in twin correlations across maternal-education levels. The Discussion reports only a small genetic-influence difference between the low- and middle-education groups, with the estimate higher in the low-education group; this supports citing Bartels et al. as a failure to replicate the G × SES pattern. The separate claim about the largest sample is attributed to Hanscombe et al., not Bartels et al. (LLM, conf=0.87)
- Supportedcites Kremen et al., 2005 source
The Discussion (p. 12) reports no increase in genetic variance with parental education and concludes that the study found a parental-education × shared-environment interaction only, not a parental-education × gene interaction. The 'largest sample to date' clause is attributed to Hanscombe et al. (2012), not to Kremen et al. (LLM, conf=0.90)
- Partly supportedcites Hanscombe et al., 2012 sourceExperimental — accuracy not yet measured
The Discussion reports that this large UK-representative study did not replicate the predicted genetic interaction between SES and children's IQ. The Methods (Participants) report 8,716 twin pairs, but the passages in our copy do not establish that it was the largest sample to date. (LLM, conf=0.82)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Indeed, both phenomena may reflect a process of increased and accumulated effects of gene-environment transactions with the increased opportunity that comes with both social class and age (Tucker-Drob et al., 2013; Turkheimer & Horn, 2014).”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Discussion
What the sources say
- Supportedcites Tucker-Drob et al., 2013 source
The Abstract says transactional processes can produce increasing genetic influence with both age and environmental opportunity. The Socioeconomic Differences in Heritability section links opportunity to SES and notes that positive Gene × SES findings have mostly come from the United States. The claim appropriately presents this shared mechanism as a possibility, not an established cause. (LLM, conf=0.93)
- Not matchedcites Turkheimer & Horn, 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There is also some indication that the shared and non-shared environmental variance components decrease with SES, although these interactions were not statistically significant.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > G × SES Effects in US and Non-US Samples
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but no G × SES effect for non-US studies (a′ = −.001, SE = .033, p = .979).”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > G × SES Effects in US and Non-US Samples
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“None of these moderators achieved statistical significance.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Additional Moderator Analyses
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In conclusion, all robustness checks indicated the presence of a significantly positive G × SES interaction in the US but not in Western Europe/Australia, with the difference between US and non-US studies being statistically significant.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Robustness Checks
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“our meta-analytic dataset consisted of k = 43 effect sizes from a total of N = 24,926 pairs of twins and siblings (approximately 50,000 individuals) participating in K = 14 independent studies.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Summary of Meta-Analytic Dataset
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data were relatively evenly split across US (k = 18, K = 8, N = 10,831) and Non-US (Western Europe and Australia) samples (k = 25, K = 6, N = 14,095).”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Summary of Meta-Analytic Dataset
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and a significant difference between US and non-US studies (difference = .135, SE = .042, p = .001).”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Robustness Checks
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When only US studies were entered into p-curve analysis, p-checker indicated that the p-curve is, in fact, right skewed, indicating that these studies contain evidential value (z = −3.427, p < .001).”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Probing for Publication Bias
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We did not find evidence of publication or reporting bias in our meta-analytic dataset. Both visual and formal tests of funnel plot asymmetry were not significant, both when applied to the overall meta-analytic dataset, and when separately applied to effects sizes from US and non-US samples.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Discussion
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Genes account for considerably more variation in intelligence at both higher ages and in higher US socioeconomic contexts.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Consistent with the cross national difference identified in our primary meta-analysis, p-curve analysis further indicated evidential value for a non-zero G × SES effect in US studies, but did not indicate evidential value for a nonzero G × SES effect in the non-US studies.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Discussion
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, in studies conducted outside the US (in Western Europe and Australia), the best estimate for G × SES magnitude was very slightly negative, and not significantly different from zero.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We included multiple effect sizes per sample (e.g. for different intelligence measures, different waves, or different measures of SES), weighted meta-regression models by the reciprocal number of effect sizes included for the corresponding sample and by the inverse of each parameter’s sampling variance, and implemented a sandwich estimator that corrects standard errors for non-independence of data”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Method > Meta-Analyzing Effect Sizes
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“Additionally, Harden et al. (2007) reported evidence for G × SES interaction in a positively selected sample of US adolescents that contained very few racial minorities.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Results > Cross-National Differences in Racial Diversity
What the sources say
- Not matchedcites Harden et al. (2007)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We used sophisticated methods for estimating random effects meta-regressions in nested data (i.e., in a meta-analytic dataset that contained multiple effect sizes per study).”
- Also stated in: § Meta-Analyzing Effect Sizes · plain
“Random effects models do not require the traditional assumption that all effect sizes were derived from the same population with a single true effect size. Rather, they allow for population variation in the true effect size above and beyond variation attributable to sampling error.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Method
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § Meta-Analyzing Effect Sizes · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Because this methodology required that all effect sizes and standard errors be calculated using a consistent modeling approach, we typically (a) obtained the raw data and re-analyzed it ourselves, (b) were provided with parameter estimates from re-analyses by the original study authors (all authors contacted complied with our request), or (c) re-analyzed the published sub-group covariance matrices”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Method
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we meta-analyzed tests of G × SES interaction on intelligence and academic achievement test scores, allowing for stratification by nation (US vs non-US), and conducted rigorous tests for publication bias and between-study heterogeneity.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results indicate that G × SES effects are not uniform, but can rather take positive, zero, and even negative values depending on factors that differ at the national-level.”
§ Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence > Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The authors state this as an assumption.
A continuous normal distribution underlies ordered categorical SES distributions.
“The assumption that a continuous normal distribution underlies ordered categorical SES distributions was considered reasonable, because the studies that did employ continuous measures of SES typically transformed their SES measure to normality if it was not already approximately normally distributed.”
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Not confirmedz = 1.253reported p = .895 · recomputed p = .210
“(z = 1.253, p = .895”
- Not confirmedz = 3.427reported p = 1 · recomputed p < .001
“(z = 3.427, p = 1.000”
- Not confirmedz = -1.264reported p = .103 · recomputed p = .206
“(z = −1.264, p = .103”
- Not confirmedz = 0.122reported p = .548 · recomputed p = .903
“(z = .122, p = .548”
- Not confirmedz = 1.264reported p = .897 · recomputed p = .206
“(z = 1.264, p = .897”
- Consistentz = -3.427reported p < .001 · recomputed p < .001
“(z = −3.427, p < .001”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Self-check results appear here once an independent check has confirmed them.
Citations
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Items 101–200 of 362
Uban, K. A., Kan, E., Wozniak, J. R., Mattson, S. N., Coles, C. D., & Sowell, E. R. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00085 · Cited by 28
“Further, heritability of IQ is modified by SES, with higher SES contexts amplifying genetic influences (Turkheimer et al., 2003), particularly in nations where quality of education and health care vary as a function of SES like the United States (Tucker-Drob and Bates, 2016).”
BackgroundInfluentialPredictors of cognitive, behavioural and academic difficulties in NF1
Geoffray, M., Robinson, L., Ramamurthy, K., Manderson, L., O’Flaherty, J., Lehtonen, A., Tordjman, S., Green, J., Vassallo, G., & Garg, S. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.06.002 · Cited by 21
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“For one, the U.K., where our sample is from, and USA, where the sample in the study by Damian and colleagues was recruited, differ in their socioeconomic spectrum, with the USA exhibiting greater social inequality than the U.K. (Blundell et al., 2017; Tucker-Drob & Bates, 2016).”
BackgroundArrhenius, B., Gyllenberg, D., Chudal, R., Lehti, V., Sucksdorff, M., Sourander, O., Virtanen, J.-P., Torsti, J., & Sourander, A. (2018) · BMC Public Health · doi:10.1186/s12889-018-5650-z · Cited by 23
“The reasons for the socioeconomically unequal distribution of LCDs and overall poorer academic achievement among children have been proposed to be both genetic and environmental [1, 8, 17].”
BackgroundXie, Q., Zheng, M., Ho, C. S.-H., McBride, C., Fong, F. L. W., Wong, S. W. L., & Chow, B. W.-Y. (2022) · Behavior Genetics · doi:10.1007/s10519-021-10096-2 · Cited by 24
Judd, N., Sauce, B., & Klingberg, T. (2022) · npj Science of Learning · doi:10.1038/s41539-022-00148-5 · Cited by 27
“In line with this, a meta-analysis on twin research found the heritability of intelligence to increase with higher SES in the United States 63 .”
InfluentialGhirardi, G., & Bernardi, F. (2025) · Social Science Research · doi:10.1016/j.ssresearch.2025.103174 · Cited by 28
Ahun, M. N., Brendgen, M. R., Côté, S. M., Girard, A., Vitaro, F., Tremblay, R. E., Boivin, M., & Dionne, G. (2022) · Archives of Women s Mental Health · doi:10.1007/s00737-022-01207-x · Cited by 21
Beam, C. R., Turkheimer, E. N., Finkel, D., Levine, M. E., Zandi, E., Guterbock, T. M., Giangrande, E. J., Ryan, L., Pasquenza, N., & Davis, D. W. (2019) · Behavior Genetics · doi:10.1007/s10519-019-09983-6 · Cited by 23
The Meaning of “Cause” in GeneticsPreprint
Lynch, K. E. (2021) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a040519 · Cited by 23
“However, this fi nding has failed to replicate in other large, quantitative genetic studies (Figlio et al. 2017), and a meta-analysis of this effect showed moderate effects in U.S. populations and no effect in European and Australian populations (Tucker-Drob and Bates 2016).”
ResultTurkheimer, E. N., Beam, C. R., Sundet, J. M., & Tambs, K. (2017) · Behavior Genetics · doi:10.1007/s10519-017-9857-z · Cited by 20
“A narrative review (Turkheimer and Horn 2012) and a recent meta-analysis (Tucker-drob and Bates 2015) have reached a consistent Abstract We examine a sample of Norwegian twin conscripts for evidence of an interaction between parental education and the heritability of general cognitive ability (GA).”
BackgroundCognitive Ability in Everyday Life: The Utility of Open-Source Measures
Revelle, W., Dworak, E. M., & Condon, D. M. (2020) · Current Directions in Psychological Science · doi:10.1177/0963721420922178 · Cited by 22
Genotype-Environment Interactions
Bell, E. (2020) · Springer eBooks · doi:10.1007/978-3-319-24612-3_755 · Cited by 20
Giangrande, E. J., & Turkheimer, E. N. (2021) · Perspectives on Psychological Science · doi:10.1177/17456916211017498 · Cited by 21
“For example, Tucker-Drob and Bates (2016) conducted the most comprehensive examination of the Scarr-Rowe interaction to date, meta-analyzing data from 14 studies including approximately 50,000 participants.”
BackgroundInfluentialUnderstanding “What Could Be”: A Call for ‘Experimental Behavioral Genetics’
Burt, S. A., Plaisance, K. S., & Hambrick, D. Z. (2018) · Behavior Genetics · doi:10.1007/s10519-018-9918-y · Cited by 18
Altschul, D., Wraw, C., Der, G., Galé, C. R., & Deary, I. J. (2019) · Hypertension · doi:10.1161/hypertensionaha.118.12164 · Cited by 18
The comparative analysis of intelligence.
Flaim, M. E., & Blaisdell, A. P. (2020) · Psychological Bulletin · doi:10.1037/bul0000306 · Cited by 24
“…the environment is and are unique to populations at the time of estimate, meaning the same characteristic can have different estimates depending on who is sampled and when (Nisbett, 2009; Sauce & Matzel, 2018; Tucker-Drob & Bates, 2016; Turkheimer, Haley, Waldron, D’Onofrio, & Gottesman, 2003).”
BackgroundSkvarc, D., Penny, A., Harries, T., Wilson, C. J. L., Joshua, N., & Byrne, L. K. (2021) · Journal of Children and Media · doi:10.1080/17482798.2021.1878045 · Cited by 20
“More recent meta-analyses suggest that SESs impact further diminishes when additional environmental factors are taken into consideration, such as school access equity (Tucker-Drob & Bates, 2016) or parental involvement in child education (Barger, Kim, Kuncel, & Pomerantz, 2019).”
BackgroundMerz, E. C., Strack, J., Hurtado, H., Vainik, U., Thomas, M. L., Evans, A. C., & Khundrakpam, B. S. (2022) · Human Brain Mapping · doi:10.1002/hbm.26034 · Cited by 21
“Some research has suggested gene-by-SES interactions may predict cognitive ability such that the heritability of intelligence is lower in low-SES family environments and higher in high-SES family environments (Tucker-Drob & Bates, 2016).”
BackgroundAnum, A. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.880005 · Cited by 18
Spengler, M., Gottschling, J., Hahn, E., Tucker–Drob, E. M., Harzer, C., & Spinath, F. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196597 · Cited by 16
Self-citationEvidence for the Scarr–Rowe Effect on Genetic Expressivity in a Large U.S. Sample
Menie, M. A. W. O., Pallesen, J., & Sarraf, M. A. (2018) · Twin Research and Human Genetics · doi:10.1017/thg.2018.63 · Cited by 16
“Perhaps one reason that Figlio et al. (2017) were unable to detect the effect in their young sample is that environmental quality in the United States among those with low SES has improved in the decades since theWLS cohort was born (the Figlio et al., 2017 cohorts were born between 1994 and 2002, approximately six de…”
MethodResultInfluentialGene-environment interplay in early life cognitive development
von Stumm, S., Kandaswamy, R., & Maxwell, J. M. (2023) · Intelligence · doi:10.1016/j.intell.2023.101748 · Cited by 20
Genotype-Environment Interactions
Bell, E. (2016) · Springer eBooks · doi:10.1007/978-3-319-28099-8_755-1 · Cited by 17
Matthews, L. J. (2021) · Studies in History and Philosophy of Science Part A · doi:10.1016/j.shpsa.2021.10.021 · Cited by 16
Ghirardi, G., Gil-Hernández, C. J., Bernardi, F., van Bergen, E., & Demange, P. (2024) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2024.100960 · Cited by 16
Genomic basis of delayed reward discounting
Gray, J. C., Sanchez‐Roige, S., de Wit, H., MacKillop, J., & Palmer, A. A. (2019) · Behavioural Processes · doi:10.1016/j.beproc.2019.03.006 · Cited by 17
Kandler, C., Zapko–Willmes, A., Richter, J., & Riemann, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-813995-0.00007-8 · Cited by 17
Skvarc, D., Talbot, M., Harries, T., Wilson, C. J. L., Joshua, N., & Byrne, L. K. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.638319 · Cited by 22
“Household resources moderate several crucial predictors of academic performance, such as greater parental involvement, household educational attainment, and child access to classrooms with fewer students (Tucker-Drob and Bates, 2016; Barger et al., 2019).”
BackgroundConsistency of noncognitive skills and their relation to educational outcomes in a UK cohort
Morris, T., Smith, G. D., van den Berg, G. J., & Davies, N. M. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01661-8 · Cited by 22
Multivariate analysis of the Scarr-Rowe interaction across middle childhood and early adolescence
Giangrande, E. J., Beam, C. R., Carroll, S. L., Matthews, L. J., Davis, D. W., Finkel, D., & Turkheimer, E. N. (2019) · Intelligence · doi:10.1016/j.intell.2019.101400 · Cited by 16
Investigating the mechanisms of G × SES interactions for education
Ruks, M. (2022) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2022.100730 · Cited by 16
Country-by-genotype-by-environment interaction in childhood academic achievement
de Zeeuw, E. L., & Boomsma, D. I. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718938115 · Cited by 17
Schwartz, J. A. (2015) · Intelligence · doi:10.1016/j.intell.2015.07.004 · Cited by 14
Hendriks, A. M., Finkenauer, C., Nivard, M. G., Beijsterveldt, C. E. M. V., Plomin, R. J., Boomsma, D. I., & Bartels, M. (2019) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-019-01357-x · Cited by 14
“A similar explanation is proposed for the different findings of the moderating effect of SES on the contribution of genetic and environmental factors to intelligence [35, 36].”
BackgroundGenetic Endowments and Wealth Inequality
Barth, D. J., Papageorge, N., & Thom, K. (2018) · National Bureau of Economic Research · doi:10.3386/w24642 · Cited by 16
Rea‐Sandin, G., Clifford, S., Doane, L. D., Davis, M. C., Grimm, K. J., Russell, M. T., & Lemery‐Chalfant, K. (2022) · Journal of Experimental Psychology General · doi:10.1037/xge0001298 · Cited by 14
Shi, R., Chang, X., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Desrivières, S., Flor, H., Grigis, A., Garavan, H., Gowland, P. A., et al. (2024) · Science Advances · doi:10.1126/sciadv.adp3751 · Cited by 14
“The decomposition of the interaction effects between gene and ME into different environment pathways could be much more complex as shown in previous studies ( 51 , 85 , 86 ).”
Viewing Education Policy through a Genetic Lens
Asbury, K., & Wai, J. (2019) · Journal of School Choice · doi:10.1080/15582159.2019.1705008 · Cited by 16
“This is a highly cited finding but perhaps the most interesting element is that the pattern does not replicate elsewhere in the world (Tucker-Drob & Bates, 2016).”
BackgroundGene-environment interactions and school tracking during secondary education: Evidence from the U.S.
Uchikoshi, F., & Conley, D. (2021) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2021.100628 · Cited by 12
Liu, J., Yang, Y., Kong, T., Liu, R., & Luo, L. (2025) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2025.102625 · Cited by 13
Stienstra, K., & Karlson, K. B. (2023) · Social Science Research · doi:10.1016/j.ssresearch.2023.102870 · Cited by 13
Faßbender, K., Wiebe, A. W., & Bates, T. C. (2019) · Behavior Genetics · doi:10.1007/s10519-018-9943-x · Cited by 15
“…research on factors supporting increased agency, as manifested in outcomes such as self-control, cognitive development and educational attainment has focused on family-level factors, often with socioeconomic status (SES) as an environmental moderator (Tucker-Drob and Bates 2016; Turkheimer et al.”
BackgroundSelf-citationHolden, L. R., Haughbrook, R., & Hart, S. A. (2022) · New Directions for Child and Adolescent Development · doi:10.1002/cad.20485 · Cited by 14
Educational Tracking and the Polygenic Prediction of Education
Lahtinen, H., Martikainen, P., Korhonen, K., Morris, T., & Myrskylä, M. (2024) · Sociological Science · doi:10.15195/v11.a8 · Cited by 16
The Academic Development Study of Australian Twins (ADSAT): Research Aims and Design
Larsen, S. A., Little, C. W., Grasby, K. L., Byrne, B., Olson, R. K., & Coventry, W. L. (2020) · Twin Research and Human Genetics · doi:10.1017/thg.2020.49 · Cited by 12
Raffington, L., Prindle, J., & Shing, Y. L. (2018) · Developmental Psychology · doi:10.1037/dev0000529 · Cited by 12
Verbitskaya, L. A., Zinchenko, Y. P., Malykh, S. B., Gaidamashko, I. V., Калмык, О. А., & Tikhomirova, T. N. (2020) · Psychology in Russia State of Art · doi:10.11621/pir.2020.0201 · Cited by 12
“In particular, it has been shown that cognitive resources play a greater role in the success of school education in a less heterogeneous and more effective educational environment (Tikhomirova & Malykh, 2017; Tucker-Drob & Bates, 2016).”
BackgroundCommentary: Mendelian randomization and education–Challenges remain
McMartin, A., & Conley, D. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa160 · Cited by 13
Heritability of cortical morphology reflects a sensory-fugal plasticity gradientPreprint
Vainik, U., Paquola, C., Wang, X., Zheng, Y., Bernhardt, B. C., Mišić, B., & Dagher, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.03.366419 · Cited by 12
Strauß, H., Venables, P. H., & Zentner, M. R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0278618 · Cited by 13
“Genetic effects on IQ have generally been found to be modest in low-income samples [60, 61], and age-to-age changes in cognitive outcomes appear to be mainly environmentally induced (e.”
BackgroundConte, F. P., Costantini, G., Rinaldi, L., Gerosa, T., & Girelli, L. (2020) · Intelligence · doi:10.1016/j.intell.2020.101466 · Cited by 12
Genetic and Environmental Influences on Cognitive Abilities in Extreme Poverty
Hur, Y., & Bates, T. C. (2019) · Twin Research and Human Genetics · doi:10.1017/thg.2019.92 · Cited by 11
Self-citationLi, X. J., Gao, M., Chen, X. X., Rong, Y. M., Huang, L. L., & Huang, J. (2023) · World Journal of Oncology · doi:10.14740/wjon1737 · Cited by 10
Morphometric traits predict educational attainment independently of socioeconomic background
Valge, M., Meitern, R., & Hõrak, P. (2019) · BMC Public Health · doi:10.1186/s12889-019-8072-7 · Cited by 13
“For instance, genetic influences on educational attainment may vary across the context of childhood SEP [14, 20, 21].”
BackgroundHalpern-Manners, A., Marahrens, H., Neiderhiser, J. M., Natsuaki, M. N., Shaw, D. S., Reiss, D., & Leve, L. D. (2020) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2020.100486 · Cited by 9
Social adversity reduces polygenic score expressivity for general cognitive ability, but not height
Peñaherrera-Aguirre, M., Woodley, M. A., Sarraf, M. A., & Beaver, K. M. (2022) · Twin Research and Human Genetics · doi:10.1017/thg.2022.3 · Cited by 10
“…than crudely proxied via the use of SIRE, as in Guo & Stearns, 2002) uniquely impacts, through adverse gene-by-environment interactions, GCA (contrast this with the extensive body of research into the adverse effects of low SES on the additive heritability of GCA; e.g., Tucker-Drob & Bates, 2015).”
BackgroundSocioeconomic Status and Adolescent Alcohol Involvement: Evidence for a Gene–Environment Interaction
Davis, C. N., & Slutske, W. S. (2018) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.2018.79.725 · Cited by 10
“In addition, researchers should explore this phenomenon in contexts other than the United States, which may vary in the extent to which access to education, health care, and social welfare differs across the socioeconomic spectrum (Tucker-Drob & Bates, 2016).”
BackgroundThe Nigerian Twin and Sibling Registry: An Update
Hur, Y., Jeong, H., Kang, M. C., et al. (2019) · Twin Research and Human Genetics · doi:10.1017/thg.2019.110 · Cited by 10
“…has been very little genetically informative research using twins living below the low end of the socioeconomic continuum in many Western countries (Tucker-Drob & Bates, 2016), the impact of extreme poverty on gene × environmental transaction in psychological and health Author for correspondence:…”
BackgroundSelf-citationHur, Y.-M. (2020) · Intelligence · doi:10.1016/j.intell.2020.101483 · Cited by 9
von Stumm, S., & Nancarrow, A. F. (2024) · Intelligence · doi:10.1016/j.intell.2024.101834 · Cited by 8
How neighbourhood effects vary by achievement level
Borgen, N. T., & Zachrisson, H. D. (2024) · European Sociological Review · doi:10.1093/esr/jcae034 · Cited by 12
Intelligenz(tests) verstehen und missverstehen
Sparfeldt, J. R., Becker, N., Greiff, S., Kersting, M., König, C. J., Lang, J. W. B., & Beauducel, A. (2022) · Psychologische Rundschau · doi:10.1026/0033-3042/a000597 · Cited by 9
Tosto, M. G., Garon‐Carrier, G., Gross, S. I., Petrill, S. A., Malykh, S. B., Malki, K., Hart, S. A., Thompson, L. A., Karadaghi, R. L., Yakovlev, N., et al. (2018) · British Journal of Educational Psychology · doi:10.1111/bjep.12259 · Cited by 10
Joint Consideration of Means and Variances Might Change the Understanding of Etiology
Burt, S. A., & Johnson, W. (2022) · Perspectives on Psychological Science · doi:10.1177/17456916221096122 · Cited by 9
Socioeconomic status and parenting-related differences in preschoolers' working memory
Gao, X., Lee, K. L., & Permpoonputtana, K. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102406 · Cited by 12
Zapko–Willmes, A., & Theocharis, Y. (2024) · Political Psychology · doi:10.1111/pops.12987 · Cited by 14
Heritability × SES Interaction for IQ: Is it Present in US Adoption Studies?
Loehlin, J. C., Corley, R. P., Reynolds, C. A., & Wadsworth, S. J. (2021) · Behavior Genetics · doi:10.1007/s10519-021-10080-w · Cited by 8
Hajovsky, D. B., & Chesnut, S. R. (2022) · Journal of School Psychology · doi:10.1016/j.jsp.2022.05.001 · Cited by 8
“A meta-analysis of gene x SES interactions on intelligence and academic achievement by Tucker-Drob and Bates (2016) also supports findings of environmental influences on intellectual development.”
BackgroundAusterberry, C., Fearon, P. R. M., Ronald, A., Leve, L. D., Ganiban, J. M., Natsuaki, M. N., Shaw, D. S., Neiderhiser, J. M., & Reiss, D. (2021) · Child Development · doi:10.1111/cdev.13706 · Cited by 10
Genetic contributions of noncognitive skills to academic developmentPreprint
Malanchini, M., Allegrini, A. G., Nivard, M. G., Biroli, P., Rimfeld, K., Cheesman, R., von Stumm, S., Demange, P., van Bergen, E., Grotzinger, A. D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.03.535380 · Cited by 8
“33,34 Studies that examined this possibility have focused on the role of socioeconomic disadvantage across a broad range of contexts, including family socioeconomic status 35,36 and the school environment 37,38 .”
Self-citationPetersen, D. B., Konishi-Therkildsen, A., Clark, K. D., DeRobles, A. K., Frahm, A. E., Jones, K., Lettich, C., & Spencer, T. D. (2024) · Journal of Speech Language and Hearing Research · doi:10.1044/2024_jslhr-23-00594 · Cited by 7
Sum, P. E., & Bădescu, G. (2023) · International Journal of Educational Development · doi:10.1016/j.ijedudev.2023.102843 · Cited by 11
Gene-environment interaction analysis of school quality and educational inequality
Stienstra, K., Knigge, A., & Maas, I. (2024) · npj Science of Learning · doi:10.1038/s41539-024-00225-x · Cited by 9
“Forboth outcomes, support for the Scarr-Rowe interaction has been mixed 40,42 – 44 .”
Intelligence Can Be Used to Make a More Equitable Society but Only When Properly Defined and Applied
Holden, L. R., & Hart, S. A. (2021) · Journal of Intelligence · doi:10.3390/jintelligence9040057 · Cited by 7
Mills, M. C. (2022) · Edward Elgar Publishing eBooks · doi:10.4337/9781789909432.00022 · Cited by 8
Norasakkunkit, V., Uchida, Y., & Takemura, K. N. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.02085 · Cited by 7
“Inequality of opportunities is becoming an ever-increasing reality such that no matter how much genetic predisposition to intelligence one has, the socioeconomic circumstances into which one is born much more likely determines actual academic success (Tucker-Drob and Bates, 2016).”
BackgroundThe interplay between cognitive ability, alcohol consumption, and health characteristics
Degerud, E., Ystrøm, E., Tambs, K., Ariansen, I., Mørland, J. G., Magnus, P. M., Smith, G. D., & Næss, Ø. (2017) · Psychological Medicine · doi:10.1017/s0033291717003543 · Cited by 8
Dolan, C. V., Huijskens, R. C. A., Minică, C. C., Neale, M. C., & Boomsma, D. I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/702738 · Cited by 7
Quinn, P. D., & D'Onofrio, B. M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-809324-5.21826-9 · Cited by 9
The effects of spanking on psychosocial outcomes: revisiting genetic and environmental covariation
Barbaro, N., Connolly, E. J., Sogge, M., Shackelford, T. K., & Boutwell, B. B. (2022) · Journal of Experimental Criminology · doi:10.1007/s11292-021-09496-5 · Cited by 6
“As some final points of consideration, it is important to recall that parameter estimates in sibling studies can and do fluctuate depending on historical context and other cultural factors—they are neither fixed nor static values (Barnes et al., 2014b; Tucker-Drob & Bates, 2016; Turkheimer, 2000).”
BackgroundHeritability and Etiology: Heritability estimates can provide causally relevant information
Egeland, J. (2022) · Personality and Individual Differences · doi:10.1016/j.paid.2022.111896 · Cited by 7
Evidence for the Scarr–Rowe Effect on Genetic Expressivity in the Health and Retirement Study
Menie, M. A. W. O., Peñaherrera‐Aguirre, M., Dunkel, C. S., & Sarraf, M. A. (2021) · Twin Research and Human Genetics · doi:10.1017/thg.2021.13 · Cited by 7
“…this method to estimate the magnitude of the Scarr–Rowe effect in the UK BioBank sample; they found that the effect was reversed relative to what is typically found in the USA, which is consistent with the overall direction of the effect outside of the USA that Tucker-Drob and Bates (2015) report.”
BackgroundMethodResultHow Intelligence Research Can Inform Education and Public Policy
Wai, J., & Bailey, D. H. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.028 · Cited by 6
Tikhomirova, T. N., Сергеевич, М. А., Lysenkova, I. A., & Malykh, S. B. (2021) · Psychology in Russia State of Art · doi:10.11621/pir.2021.0407 · Cited by 6
Mönkediek, B., Diewald, M., & Lang, V. (2023) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2023.100824 · Cited by 5
Mothers-Offspring Resemblance in Intelligence and Its Relationship to Socioeconomic Status
Flores-Mendoza, C. E., Santiago, B. M. C., Andrade, D. M., Fontes, W. R. P., Rodrigues, L. A., & Tuma, C. A. M. (2017) · Paidéia (Ribeirão Preto) · doi:10.1590/1982-43272766201712 · Cited by 5
Deciphering the causal association and co-disease mechanisms between psoriasis and breast cancer
Li, X., Huang, L., Yan, Y., Rong, Y., Chen, X., Gao, M., & Huang, J. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1304888 · Cited by 4
The Effects of SES, Grade-Repeating, and IQ in a Game-Based Approximate Math Intervention
Langfus, J., Maiche, A., de León, D., Fitipalde, D., Mailhos, Á., & Halberda, J. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815952-1.00002-5 · Cited by 7
Hasan, A., & Afzal, M. (2019) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2019.05.005 · Cited by 5
Exploring the Fetal Origins Hypothesis Using Genetic Data
Trejo, S. (2024) · Social Forces · doi:10.1093/sf/soae018 · Cited by 8
Breinholt, A., Ware, E. B., Fomby, P., Notterman, D. A., Schneper, L. M., & Mitchell, C. M. (2025) · Longitudinal and Life Course Studies · doi:10.1332/17579597y2025d000000042 · Cited by 5
Making Reading Easier: How Genetic Information Can Help
Luciano, M. (2017) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/2372732217720464 · Cited by 5
“This effect has not been observed in samples from Western Europe and Australia, where social policies support fairly even access to high-quality education and medical care (Tucker-Drob & Bates, 2015).”
BackgroundTesting the moderation of quantitative gene by environment interactions in unrelated individualsPreprint
Tahmasbi, R., Evans, L. M., Turkheimer, E. N., & Keller, M. C. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/191080 · Cited by 5
“Almost all the US twin studies are conducted in 16 adolescent and early childhood, while this study and [20] are on adults (see Table S1).”
BackgroundResultInfluentialAdvantageous early-life environments cushion the genetic risk for ischemic heart disease
Baker, S., Biroli, P., van Kippersluis, H., & von Hinke, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314056121 · Cited by 6
Finkel, D., Finch, B. K., Gatz, M., Christensen, K., Franz, C. E., Karlsson, I., Kremen, W. S., Krueger, R. F., Lupton, M. K., Martin, N. G., et al. (2025) · Twin Research and Human Genetics · doi:10.1017/thg.2025.10036 · Cited by 4
Mind the gap: the interplay between genes and neighbourhood context on educational achievement
Silva, L., Tropf, F. C., & Wolfram, T. (2025) · European Sociological Review · doi:10.1093/esr/jcaf001 · Cited by 8
Fraemke, D., Willems, Y. E., Okbay, A., Lindenberger, U., Zinn, S., Wagner, G. G., Richter, D., Harden, K. P., Tucker–Drob, E. M., Hertwig, R., et al. (2025) · Psychological Science · doi:10.1177/09567976251350965 · Cited by 8
Self-citationDefining and Measuring Intelligence
Coyle, T. R. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.003 · Cited by 4
Metadata
- Authors
- Tucker-Drob, E. M., & Bates, T. C.
- Affiliations
- Department of Psychology, University of Texas at Austin
- University of Edinburgh (GB)
- ORCID
- Bates, Timothy C.
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2015-12-15
- Volume
- 27
- Issue
- 2
- Pages
- 138-149
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- PMC
- PMC4749462
- OpenAlex
- W2197899923
- Cited by
- 359
- References
- 48
- Citations by year
- 2026: 26 · 2025: 28 · 2024: 46 · 2023: 28 · 2022: 45 · 2021: 40 · 2020: 29 · 2019: 39 · 2018: 31 · 2017: 30 · 2016: 14 · 2015: 1
- FWCI
- 38.23
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Psychological Science (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Russell Sage Foundation
- National Institutes of Health (5p2chd042849-22)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (# P2CHD042849, R21 HD081437, R24 HD042849, P01HD031921)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Social Sciences › Sociology and Political Science › Intergenerational and Educational Inequality Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- intelligence, academic performance, genetic influences on intelligence, cross-national differences, publication bias, study heterogeneity, educational inequality, Western Europe, Australia
- MeSH terms
- Educational Measurement, Humans, Intelligence, Models, Genetic, Models, Psychological, Social Class, United States, Gene-Environment Interaction
About these checks
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