French Studies of Children of Low Ses Parents Adopted Into High Ses Families Find:

Intelligence. 2015 Jan-February; 48: 30–36.

Socioeconomic status and the growth of intelligence from infancy through adolescence

Sophie von Stumm

^aSection of Psychology, Goldsmiths Academy of London, SE14 6NW London, UK

Robert Plomin

^bMRC Social, Genetic and Developmental Psychiatry Center, King's College London, Plant of Psychiatry PO80, De Crespigny Park, SE5 8AF London, U.k.

Received 2014 Jul 5; Revised 2014 October 7; Accustomed 2014 Oct vii.

Abstract

Low socioeconomic status (SES) children perform on boilerplate worse on intelligence tests than children from higher SES backgrounds, but the developmental relationship betwixt intelligence and SES has not been adequately investigated. Here, we use latent growth curve (LGC) models to assess associations between SES and individual differences in the intelligence starting bespeak (intercept) and in the charge per unit and direction of alter in scores (slope and quadratic term) from infancy through adolescence in 14,853 children from the Twins Early Evolution Study (TEDS), assessed 9 times on IQ between the ages of two and 16 years. SES was significantly associated with intelligence growth factors: higher SES was related both to a higher starting point in infancy and to greater gains in intelligence over fourth dimension. Specifically, children from low SES families scored on average 6 IQ points lower at age 2 than children from high SES backgrounds; by age xvi, this difference had almost tripled. Although these central results did non vary across girls and boys, we observed gender differences in the development of intelligence in early childhood. Overall, SES was shown to be associated with individual differences in intercepts besides as slopes of intelligence. However, this finding does not warrant causal interpretations of the relationship between SES and the development of intelligence.

Keywords: Intelligence, IQ, Socioeconomic status, Latent growth, Gender

one. Introduction

Private differences in intelligence influence developmental trajectories beyond the lifespan, affecting socioeconomic, psychological, and health outcomes (Deary, 2012). Differences in intelligence have been shown to exist highly stable from early boyhood to late adulthood (Deary, Pattie, & Starr, 2013), merely are more than variable in infancy and childhood, with some children showing substantial gains in intelligence and others considerable losses between infancy and adolescence (Bayley, 1955, Feinstein, 2003, Tucker-Drob and Briley, 2014). These variations in the development of intelligence are likely to be associated with children's family socioeconomic condition (SES; due east.g. Dyume et al., 1999, Heckman, 2006, Tucker-Drob et al., 2011). Children from disadvantaged family backgrounds score on average lower on intelligence tests than their high SES peers (Bradley and Corwyn, 2002, Schoon et al., 2012, Strenze, 2007), and their performance has been suggested to worsen over time, even if they did relatively well in early assessments (Feinstein, 2003). Conversely, loftier SES children are thought to gain in intelligence over time, even if they initially had a lower test score (Feinstein, 2003). Even so, research to engagement on the impact of SES on developmental change in intelligence is inconclusive for two reasons.

First, it has been suggested that the previously reported association between SES and children'southward IQ evolution results from regression to the hateful, considering children with either extremely high or low scores in early IQ tests are less likely to score as extremely in afterwards tests, contained of their family background (Jerrim and Vignoles, 2011, Saunders, 2012). Regression to the mean occurs when children are grouped according to their scores at one measurement occasion and then the groups' evolution is analyzed across subsequent assessments. This statistical artifact tin be avoided by applying latent growth curve (LGC) models to not-selected samples, because LGC analyzes longitudinal information at the level of individuals rather than groups.

2d, most previous studies included intelligence assessments at relatively few ages and at brusk age intervals in early life (Feinstein, 2003, Spinath et al., 2003, von Stumm, 2012; come across Tucker-Drob & Briley, 2014, for a review). No report to date has modeled change in intelligence in a representative sample from infancy through belatedly adolescence, using multiple assessments of intelligence over time that allow for identifying private differences in developmental trajectories. Overcoming these limitations, in the nowadays study we fitted LGC models to intelligence information from the Twins Early Development Study (TEDS), whose participants were assessed 9 times on intelligence from age 2 to 16 years. We so tested the extent to which SES, equally a time-invariant covariate, accounted for private differences in slopes of alter in intelligence from age 2 to 16 years, as well as private differences in the starting points (intercepts) at the age of ii years.

2. Methods

2.1. Sample

The Twins Early Evolution Report (TEDS) recruited families of twins born in England and Wales in 1994, 1995, and 1996 (Haworth, Davis, & Plomin, 2013). Since then, the sample has remained representative of the UK population (Kovas, Haworth, Dale, & Plomin, 2007). We excluded twins who suffered from severe medical bug currently or at birth (due east.grand., postal service-natal surgery); whose mothers reported severe medical issues during pregnancy; whose first language was non English; and who had been assessed less than twice on intelligence between the ages of 2 and 16 years. The concluding analysis sample comprised of 14,853 twins; that is, 7426 complete pairs, including 2564 monozygotic pairs and 4862 dizygotic twin pairs, of which 2375 were of opposite sex. Overall, the sample included 7768 girls and 7085 boys.

two.2. Measures

2.two.1. Socioeconomic status (SES)

Parental education and occupation (mother's and father's highest educational qualification and chore status) were recorded at the kickoff contact with the families, when twins were xviii months quondam, and once more when they were 7 years one-time. Family income was assessed when the twins were 9 years old. A composite of parental education and occupation at twins' historic period of 18 months correlated at .77 with a composite of parental education and occupation at twins' age of vii years, which in turn correlated at .57 with family income at twins' age of 9 years, suggesting that SES was relatively stable over time in TEDS (Hanscombe et al., 2012). Data from the assessment at 18 months were used in cases where information at age vii was missing; for all others, records of parental education and occupation at age 7 were employed. Summary SES composites were created as a unit of measurement-weighted sum of the education, occupation, and income after mapping to a standard normal distribution with the rank-based van der Waerden transformation (Hanscombe et al., 2012).

2.ii.2. Intelligence

The twins were assessed at 2, three, iv, 7, 9, 10, 12, fourteen and 16 years on intelligence, using parent-administered tests and ratings of power at ages 2, 3, and 4, and a mixture of web-based, phone-based, and parent-administered tests at later ages. At each testing age, twins completed at to the lowest degree two state-of-the-art power tests. The tests have been described in item elsewhere (Hanscombe et al., 2012) and are only briefly reviewed here.

ii.two.2.1. Measures at ages 2, 3, and four

Parent-administered tests and parent-reported observations were used to assess verbal and nonverbal cognitive abilities. These measures have been validated against standard tests administered by trained testers (Oliver et al., 2002, Saudino et al., 1998). Specifically, nonverbal cerebral performance was assessed using age-appropriate versions of the Parent Report of Children's Abilities (PARCA; Oliver et al., 2002, Saudino et al., 1998), while verbal ability measures included vocabulary and grammer every bit assessed by parent reports for the CDI–III, an extension of the short form of the MacArthur Communicative Development Inventories: Words and Sentences (Fenson et al., 2000).

2.two.2.two. Measures at historic period 7

Children were tested on exact and nonverbal abilities by phone (Petrill, Rempell, Oliver, & Plomin, 2002). Prior to the telephone call, parents were sent a booklet of test items along with testing instructions. The booklet independent two tests of exact tests: the Similarities subtest and the Vocabulary subtest from the Wechsler Intelligence Scale for Children (WISC-III-United kingdom; Wechsler, 1992), and the Picture Completion subtest from the WISC-Iii-Britain and Conceptual Grouping from the McCarthy Scales of Children's Abilities (McCarthy, 1972).

2.2.2.iii. Measures at historic period nine

Participants were mailed a examination booklet with two verbal and ii nonverbal tests to be administered under the supervision of the parent, who had received a corresponding instruction booklet. The exact tests comprised vocabulary and general knowledge tests adapted from the multiple-pick version of the WISC-III-UK (Wechsler, 1992). The nonverbal tests included a Puzzle exam adapted from the Effigy Nomenclature subtest of the Cerebral Abilities Test 3 (CAT3; Smith, Fernandes, & Strand, 2001) and a Shapes examination as well adapted from the CAT3 Figure Analogies subtest (Davis, Arden, & Plomin, 2008).

2.2.ii.4. Measures at historic period 10

Testing was spider web-based, and children completed two exact and 2 not-verbal tests using their home computers (Haworth et al., 2007). Tests were drawn from the WISC-III-PI, including Multiple Choice Information (Full general Cognition), Vocabulary Multiple Choice, and Picture Completion (Wechsler, 1992), and from Raven's Standard Progressive Matrices (Raven, Court, & Raven, 1996).

two.2.2.5. Measures at historic period 12

Testing was web-based and conducted at home computers, using age-matched versions of the tests previously used at age 10, including once more 2 exact and two non-verbal ability tests (Kaplan et al., 1998, Raven et al., 1996, Wechsler, 1992).

two.ii.2.6. Measures at age 14

Twins completed ii spider web-based tests at their home computers, including the WISC-III-PI Vocabulary Multiple Choice for fourteen-year olds (Kaplan et al., 1998), and Raven'southward Progressive Matrices (Raven et al., 1996).

2.2.2.seven. Measures at historic period xvi

Twins completed spider web-based adaptations of Raven's Standard and Advanced Progressive and the Mill-Hill Vocabulary Calibration using their abode computers (Raven et al., 1998, Raven et al., 1996).

3. Analysis

3.i. Latent growth curve models

In a commencement step, beginning principal factors were extracted at each age from the administered intelligence tests. Regression factor scores were transformed into standardized IQ scores with a hateful of 100 and a standard departure of xv (Hanscombe et al., 2012). The comparability of IQ scores is here theoretically inferred, because intelligence was assessed at each measurement occasion with multiple, well validated tests that should take assessed identical constructs, yielding invariant common variance factors of intelligence, even if different tests were administered at different times and ages. Previously, multiple first factors extracted from cognitive exam batteries were shown to exist invariant in adults (Johnson et al., 2004, Johnson et al., 2008), although the invariance of such factors in children or over the grade of time has not been established.

In a second pace, latent growth curve (LGC) models were fitted to two subsamples that each consisted of i twin randomly selected from a pair (sample 1 with N = 7440, and sample 2 with N = 7413). This method enabled a replication of the LGC model across the 2 samples, and it also ensured that model fit statistics were not erroneously inflated considering of the dependence of observations (i.east. relatedness of twins). LGC factors are extracted from repeatedly observed intelligence gene scores and describe a sample's average starting point, typically referred to as intercept, and systematic changes that occur over fourth dimension, which are typically known as slope (McArdle, 2009). LGC factors were modeled to freely correlate. To determine the number of LGC factors that best represented the data, the fit (i.e. χ^two(df)) of a LGC model with 2 growth parameters (intercept and slope) was compared to the fit of a model with 3 growth parameters (intercept, slope and quadratic term). That is, the gradient represents linear changes, while the quadratic growth parameter assesses systematic not-linear accelerations or decelerations of the growth trend (i.due east. systematic curvilinear change not accounted for by the slope). At each age, loadings on the intercept were stock-still to 1, and those on the slope were defined as 0, 1, 2, 5, 7, eight, x, 12, and 14, representing time periods in years betwixt each assessment point, ranging in real fourth dimension from 2 to 16 years. With that, the intercept was defined where the slope had a zippo loading (i.east. at age two). Loadings on the quadratic term were the square of the slope loadings. Both ii- and 3-gene LGC models were specified every bit multi-group models to test for measurement invariance beyond the two random twin subsamples. Here, the fit of an 'unrestricted' baseline multi-group LGC model was compared to a restricted model that held means, intercepts and residuals equal across groups.^ane

Next, multi-grouping LGC models were fitted separately to samples of boys and girls investigate if LGC factors differed beyond gender. Accordingly, the fit of a baseline model, which was 'unrestricted' besides pre-defined factor loadings¹, was compared to the fit of a 'restricted' model that held intercepts, means and residuals equal across boys and girls. Finally, SES was added to the LGC model and specified as a fourth dimension-invariant covariate of the growth parameters in order to investigate the extent to which LGC factors differed as a function of SES.

All models were fitted using total information maximum likelihood interpretation (FIML) assuming data missing at random and no biases of the results (Arbuckle, 1996). Several fit indices evaluated the LGC models' fit, including the model χ² test, the Comparative Fit Alphabetize (CFI), the Tucker–Lewis Alphabetize (TLI), and the RMSEA with Confidence Intervals of 95% (Hu & Bentler, 1999).

4. Results

four.one. Correlations

Across ages, intelligence scores were positively inter-correlated in a simplex pattern, with stronger associations betwixt more proximate assessments, in line with previous studies of unlike samples (e.g., Bartels, Rietveld, van Baal, & Boomsma, 2002; Table 1). SES correlated positively with intelligence at all ages, and increasingly and then, as the children grew older, which is also in line with previous research (eastward.g. Tucker-Drob et al., 2011).

Table 1

Sample sizes and correlations for the IQ and SES data in TEDS from age 2 to sixteen years for a subsample of one randomly selected twin per pair.

		N	1	2	iii	4	five	half dozen	7	eight	ix
1	IQ at 2	4730	–
two	IQ at 3	4522	.66	–
3	IQ at 4	5725	.57	.70	–
4	IQ at 7	4620	.23	.31	.31	–
5	IQ at nine	3059	.26	.35	.33	.41	–
6	IQ at 10	2475	.23	.31	.27	.40	.57	–
seven	IQ at 12	3981	.eighteen	.27	.29	.44	.56	.63	–
viii	IQ at xiv	2599	.21	.26	.24	.40	.46	.51	.63	–
nine	IQ at 16	2224	.21	.26	.22	.42	.45	.50	.58	.64	–
10	SES	6884	.10	.17	.17	.32	.30	.26	.thirty	.36	.35

The correlations of intelligence scores over time in TEDS ranged from .21 to .70 with an boilerplate value of .40, which may announced low compared to other research (e.m. Deary et al., 2013). However, intelligence is more than variable in childhood than in later life (due east.chiliad. Bayley, 1955); besides, intelligence assessment methods in TEDS varied considerably across fourth dimension (see Word for details).

4.two. Latent growth bend models

A two-factor latent growth curve model fitted the information worse (sample 1 with N = 7440: χⁱⁱ (forty) = 1090.35; sample 2 with Due north = 7413: χ^two (twoscore) = 1141.62) than the three-factor model in both twin subsamples (sample 1: χ² (36) = 553.67; sample ii: χ² (36) = 602.31). In multi-group models beyond two samples of ane twin randomly selected from a pair, LGC model χ² values did non differ significantly. Thus, individual differences in intelligence from age ii to sixteen years were here best explained by an intercept (average starting betoken), and factors of linear (slope) and not-linear (quadratic term) alter. All three growth factors accounted for significant variance, suggesting individual differences in intercept and in linear and non-linear change.

In the multi-group models for gender, the fit of unrestricted models differed significantly from the fit of models that held intercepts, means and residuals equal (p < .001; Table 2). In other words, boys and girls differed in their developmental trajectories of intelligence: girls started with an advantage of well-nigh 5 IQ points at the age of 2 years compared to boys. Nonetheless due to unlike values for slope and quadratic term in boys and girls (Tabular array 2), the gender difference in IQ development had mostly disappeared past the age of sixteen years (Fig. 1). Specifically, the slope was negative in girls, while the quadratic term was positive. Every bit a result, girls had on average a college IQ starting point but showed decline thereafter, which was somewhat captivated by the quadratic term. Past comparison, boys' slope was positive and the quadratic negative: thus, they improved from their low average IQ starting signal but the negative quadratic term dampened and even reversed the IQ growth tendency over time.

Latent IQ growth curves for boys and girls from historic period ii to 16 years in two subsamples of ane randomly selected twin per pair from TEDS.

Note. Gender differences in latent growth curves were significant. Models did not differ significantly betwixt subsamples 1 and 2, confirming the measurement invariance of the LGC model beyond samples of twin siblings.

Table two

Sample sizes, model χ², and latent growth factor parameters in boys and girls across two subsamples, each of ane twin randomly selected from a pair, in TEDS.

	Boys 1	Girls 1	Boys 2	Girls 2
N	3549	3891	3536	3877
χ2(36)	361.27	298.94	402.45	285.94
Intercept	97.17	103.thirteen	97.33	102.93
SEI	0.27	0.26	0.27	0.25
VarianceI	163.52	162.21	164.25	159.42
CI (95%)I	96.64 to 97.lxx	102.63 to 103.63	96.fourscore to 97.86	102.43 to 103.42
Gradient	0.83	− 0.79	0.lxxx	− 0.eighty
SES	0.09	0.08	0.09	0.08
VarianceSouth	seven.44	7.55	viii.06	vii.25
CI (95%)S	0.66 to 0.99	− 0.94 to − 0.64	0.64 to 0.97	− 0.95 to − 0.64
Quadratic	− 0.06	0.03	− 0.05	0.03
SEQ	0.01	0.01	0.01	0.01
VarianceQ	0.02	0.02	0.03	0.02
CI (95%)Q	− 0.07 to − 0.04	0.02 to 0.05	− 0.06 to − 0.04	0.02 to 0.04

four.3. Associations between SES and latent growth curve factors

SES was a significant predictor of all three latent growth factors in boys and girls across 2 subsamples of twins (p < .001 in all cases, Table three). The χ^two of a model that held means, intercepts, and residuals equal across groups did not differ significantly from the χⁱⁱ associated with a model that also constrained the SES regression parameters to exist equal beyond groups (p > .05 in all cases). Thus, the association between SES and intelligence latent growth factors did non vary every bit a function of gender or twin subsample.

Table 3

Regression parameters for the association between SES and IQ latent growth factors in boys and girls across two subsamples of twins from TEDS.

	Bi	SEi	βi	Bs	SEs	βs	Bq	SEq	βq
Boys one	2.19	.39	.12	0.98	.12	.26	− 0.05	.01	− .21
Girls 1	2.73	.37	.15	0.81	.11	.21	− 0.03	.01	− .sixteen
Boys 2	2.05	.39	.11	0.95	.12	.24	− 0.04	.01	− .19
Girls 2	2.70	.37	.xv	0.75	.xi	.twenty	− 0.03	.01	− .15

SES was positively associated with the intercept for intelligence with coefficients ranging from .11 to .15, suggesting that children from more advantaged SES backgrounds had higher intelligence scores in infancy. SES was besides positively related to the slope with coefficients ranging from .xx to .26, which indicates that children from more advantaged SES backgrounds also experienced greater linear gains from age ii to 16 years. Associations between SES and the quadratic term were negative, with coefficients ranging from − .21 to − .xv, which in combination with the observed gender differences in latent growth factors resulted in differently shaped growth curves. In boys, the negative association of SES with the negative quadratic term implied a greater steepness of the growth curves as SES increased. Conversely in girls, the negative association between SES and a positive quadratic term resulted in flatter growth curves with higher SES. Fig. 2 illustrates the human relationship between SES and latent growth in intelligence for boys and girls from low (<− 1 SD), medium (±1 SD) and high (> 1 SD) SES families. Both boys and girls from low SES backgrounds scored on average about vi IQ points lower at age 2 than children from high SES family unit backgrounds. By age sixteen, this discrepancy had multiplied: low SES boys scored on average 15 IQ points less than high SES boys, and in girls this difference amounted to approximately 17 IQ points.

IQ growth curves according to SES background for boys and girls in TEDS.

Note. Lines refer to latent growth curve trajectories. Dots represent the IQ raw means. High SES (triangles) refers to children, whose family SES was at least 1 SD above the SES mean; low SES (squares) refers to children from families who scored 1 SD beneath the SES mean. Medium SES (dots) includes all children, whose families were betwixt − 1 and + one SD of SES.

5. Word

Our results propose that family socioeconomic condition (SES) impacts children's development of intelligence from infancy through adolescence. Children of the highest and everyman SES backgrounds were on average separated by vi IQ points at the age of ii years. By the age of xvi, the IQ gap had almost tripled (Fig. ii). Thus, children from more disadvantaged families non only did worse than their peers in early intelligence tests but their intelligence handicap amplified over time, suggesting a long-term agglomeration of SES influences on cognitive development. Nosotros desire to emphasize here that these SES influences comprise not only ecology variance simply likewise their association with children'southward cognitive growth is likely to be partially mediated by genetic factors (Trzaskowski et al., 2014). However, the electric current study pattern only allows for speculating almost the mechanisms that potentially underlie the association between SES and IQ growth. It is plausible that children from higher SES families experience greater opportunities for and support in cerebral engagement and learning than children from more disadvantaged homes (Bradley & Corwyn, 2002). Differences in the availability of learning opportunities, support and resources are thought to accentuate individual differences in cognitive power (Hayes, 1962, von Stumm, 2012). That said, the precise mechanisms underlying the association between SES and intelligence growth curves are yet to exist identified.

Nosotros also observed meaning gender differences in the IQ starting point and in the growth curves for cognitive development, with girls outperforming boys at the ages of 2, three, and 4 years. However in later childhood and boyhood, gender differences in cognitive growth diminished and by the age of sixteen years, the differences had disappeared. Our findings concur with reports about gender differences in cerebral abilities and in encephalon anatomy (e.grand. Ganjavi et al., 2011, Haier et al., 2005). However to our knowledge, no comparable longitudinal data are available that would allow replicating the pattern of gender differences in cognitive development that nosotros observed here. Furthermore, nosotros establish no differences in the association between SES and cerebral development beyond boys and girls.

5.1. Strengths and limitations

Our written report has many notable strengths, including a large sample of twins representative of the Uk population (Kovas et al., 2007) with intelligence assessed ix times between the ages of 2 and 16 years. The main weakness for longitudinal comparisons is that unlike measures of and assessment methods for intelligence were used as different ages, thus misreckoning historic period and methodological differences. For the initial iii assessment waves at the twins' ages of 2, 3, and 4 years, tests were parent-administered, merely at later ages, children completed phone-based and spider web-based IQ tests without much parental involvement. These measurement differences may have resulted in our lower than expected correlations between intelligence scores across twins' ages. That said, the correlations observed here are just marginally lower compared to estimates from other samples at comparable ages (Bayley, 1955, Bartels et al., 2002, Lobo and Galloway, 2013). A second limitation is our treatment of family SES as a time-invariant covariate in the analyses, although SES indicators did vary over fourth dimension in our sample (Hanscombe et al., 2012). Notwithstanding, the stability of SES was here greater than its degree of change with correlations of 3 SES measurements exceeding .5 over a period of seven years, suggesting that treating SES equally time-invariant covariate was appropriate. A third limitation is that, although our sample consisted of twins, we did not conduct genetic analyses, primarily because SES is a betwixt-family variable. As such, information technology is not acquiescent to genetic analysis using the twin method, which relies on within family differences.

6. Conclusions

This study showed that children from lower SES backgrounds tend to perform on average worse on intelligence tests than children from more privileged homes as early as at the historic period of 2 years. Furthermore, SES accentuated these differences throughout childhood and adolescence: the 6-point IQ divergence in infancy betwixt children from low and high SES homes almost tripled past the time the children were 16 years quondam. Our findings ostend changes in intelligence throughout early life and advise a meaningful human relationship between IQ growth and socioeconomic factors.

Acknowledgments

We thank the TEDS twins and their parents who have contributed to the study since the twins were infants. TEDS is supported by the Great britain Medical Inquiry Quango [G0901245; and previously G0500079], with additional support from the U.s.a. National Institutes of Health [HD044454; HD059215]. RP is supported past a Medical Research Quango Research Professorship award [G19/two] and a European Research Quango Advanced Investigator accolade [295366].

Footnotes

¹Configural measurement invariance is given in LGC multi-grouping models, considering cistron loadings are pre-divers to describe intercept (loadings of 1), slope (loadings from 0 to 14) and quadratic term (loadings from 0 to 196).

References

Arbuckle J.50. Total information estimation in the presence of incomplete data. In: Marcoulides M.A., Schumacker R.E., editors. Advanced structural equation modeling: Issues and techniques. Erlbaum; Hillsdale, NJ: 1996. pp. 243–277. [Google Scholar]

Bartels M., Rietveld M.J., van Baal G.C., Boomsma D.I. Genetic and environmental influences on the evolution of intelligence. Beliefs Genetics. 2002;32:237–249. [PubMed] [Google Scholar]

Bayley Northward. On the growth of intelligence. American Psychologist. 1955;10:805–818. [Google Scholar]

Bradley R.H., Corwyn R.F. Socioeconomic status & kid development. Almanac Review of Psychology. 2002;53:371–399. [PubMed] [Google Scholar]

Davis O.S.P., Arden R., Plomin R. g in middle childhood: Moderate genetic and shared environmental influence using various measures of full general cognitive ability at 7, 9 and x years in a large population sample of twins. Intelligence. 2008;36(1):68–80. [Google Scholar]

Deary I.J. Intelligence. Annual Review of Psychology. 2012;63:453–482. [PubMed] [Google Scholar]

Deary I.J., Pattie A., Starr J.M. The stability of intelligence from age 11 to age 90 years: The Lothian birth cohort of 1921. Psychological Science. 2013;12:2361–2368. [PubMed] [Google Scholar]

Dyume Chiliad., Dumaret A.G., Tomkiewicz South. How can we heave IQs of 'tedious children'? A late adoption written report. Proceedings of the National University of Science. 1999;96:8790–88794. [PMC free article] [PubMed] [Google Scholar]

Feinstein L. London Schoolhouse of Economics, Middle for Economic Operation; 2003. Very early cognitive evidence. Centre piece (24–30) [Google Scholar]

Fenson Fifty., Pethick S., Renda C., Cox J.Fifty., Dale P.S., Reznick S. Brusque-class version of the MacArthur Communicative Development Inventories. Practical Psycholinguistics. 2000;21:95–116. [Google Scholar]

Ganjavi H., Lewis J.D., Bellec P., Macdonald P.A., Waber D.P., Evans A.C. Negative associations betwixt corpus callosum midsagittal area and IQ in a representative sample of healthy children and adolescents. PLoS ONE. 2011;6:e19698. [PMC free article] [PubMed] [Google Scholar]

Haier R.J., Jung R.E., Yeo R.A., Caput K., Alkire One thousand.T. The neuroanatomy of general intelligence: Sex matters. NeuroImage. 2005;25:320–327. [PubMed] [Google Scholar]

Hanscombe K.B., Trzaskowski M., Haworth C.Yard.A., Davis O.South.P., Dale P.S., Plomin R. Socioeconomic condition (SES) and children's intelligence (IQ): In a Britain-representative sample SES moderates the environmental, not genetic, result on IQ. PLoS ONE. 2012;seven(2) [PMC free commodity] [PubMed] [Google Scholar]

Haworth C.M.A., Davis O.S.P., Plomin R. Twins Early on Development Report (TEDS): A genetically sensitive investigation of cognitive and behavioural development from childhood to young machismo. Twin Research and Human being Genetics. 2013;16(01):117–125. [PMC free commodity] [PubMed] [Google Scholar]

Haworth C.M.A., Harlaar Due north., Kovas Y., Davis O.Due south.P., Oliver B.R., Hayiou-Tomas M.E. Internet cerebral testing of big samples needed in genetic research. Twin Research and Human Genetics. 2007;10:554–563. [PubMed] [Google Scholar]

Hayes K.J. Genes, drives, and intellect. Psychological Reports. 1962;10:299–342. [Google Scholar]

Heckman J.J. Skill germination and the economics of investing in disadvantaged children. Science. 2006;312:1900–1902. [PubMed] [Google Scholar]

Hu L., Bentler P.One thousand. Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. 1999;6:1–55. [Google Scholar]

Jerrim J., Vignoles A. DoQSS working paper, London, UK. 2011. Social mobility: Regression to the hateful and the cerebral development of high ability children from disadvantaged homes. [Google Scholar]

Johnson Westward., Bouchard T.J., Jr., Krueger R.F., McGue Thousand., Gottesman I.I. Merely one 1000: Consistent results from three test batteries. Intelligence. 2004;34:95–107. [Google Scholar]

Johnson West., te Nijenhuis J., Bouchard T.J., Jr. Still only 1 g: Consequent results from 5 test batteries. Intelligence. 2008;36:81–95. [Google Scholar]

Kaplan E., Fein D., Kramer J., Delis D., Morris R. The Psychological Corporation; New York: 1998. WISC-Iii as a Process Musical instrument (WISC-III-PI) [Google Scholar]

Kovas Y., Haworth C.M.A., Dale P.S., Plomin R. The genetic and ecology origins of learning abilities and disabilities in the early on school years. Monographs of the Society for Inquiry in Child Development. 2007;72:1–144. [PMC free article] [PubMed] [Google Scholar]

Lobo K.A., Galloway J.C. Assessment and stability of early learning abilities in preterm and full-term infants across the get-go two years of life. Research in Developmental Disabilities. 2013;34(5):1721–1730. [PMC gratuitous article] [PubMed] [Google Scholar]

McArdle J.J. Latent variable modeling of differences and changes with longitudinal information. Annual Review of Psychology. 2009;threescore:577–605. [PubMed] [Google Scholar]

McCarthy D. The Psychological Corporation; New York: 1972. McCarthy scales of children's abilities. [Google Scholar]

Oliver B., Dale P.S., Saudino K., Petrill Due south.A., Pike A., Plomin R. The validity of parent-based assessment of non-exact cognitive abilities of three-year olds. Early on Child Developmental Care. 2002;172:337–348. [Google Scholar]

Petrill Due south.A., Rempell J., Oliver B., Plomin R. Testing cerebral abilities by telephone in a sample of half-dozen- to 8-year-olds. Intelligence. 2002;xxx:353–360. [Google Scholar]

Raven J.C., Court J.H., Raven J. Oxford University Press; Oxford, UK: 1996. Manual for Raven'due south progressive matrices and vocabulary scales. [Google Scholar]

Raven J.C., Courtroom J.H., Raven J. Oxford Psychologists Printing; Oxford, Britain: 1998. Manual for Raven'south avant-garde progressive matrices. [Google Scholar]

Saudino G.J., Dale P.Southward., Oliver B., Petrill S.A., Richardson V., Rutter G. The validity of parent-based assessment of the cognitive abilities of 2-year-olds. British Periodical of Developmental Psychology. 1998;xvi:349–363. [Google Scholar]

Saunders P. Civitas enquiry report. 2012. Social mobility delusions. [Google Scholar]

Schoon I., Jones Eastward., Cheng H., Maughan B. Family hardship, family instability, and cerebral development. Journal of Epidemiology and Community Health. 2012;66:716–722. [PubMed] [Google Scholar]

Smith P., Fernandes C., Strand South. nferNELSON; Windsor, UK: 2001. Cognitive Abilities Test 3 (CAT3) [Google Scholar]

Spinath F.M., Ronald A., Harlaar N., Cost T.S., Plomin R. Phenotypic g early in life: On the etiology of general cerebral ability in a big population sample of twin children anile ii to four. Intelligence. 2003;31:195–210. [Google Scholar]

Strenze T. Intelligence and socioeconomic success: A meta-analytic review of longitudinal enquiry. Intelligence. 2007;35:401–426. [Google Scholar]

Trzaskowski Chiliad., Harlaar N., Arden R., Krapohl E., Rimfeld M., McMillan A. Genetic influence on family socioeconomic status and children'south intelligence. Intelligence. 2014;42:83–88. [PMC costless article] [PubMed] [Google Scholar]

Tucker-Drob E.G., Briley D.A. Continuity of genetic and environmental influences on cognition across the life span: A meta-assay of longitudinal twin and adoption studies. Psychological Message. 2014;140:949–979. [PMC gratuitous article] [PubMed] [Google Scholar]

Tucker-Drob E.One thousand., Rhemtulla M., Harden M.P., Turkheimer Eastward., Fask D. Emergence of a gene-by-socioeconomic status interaction on babe mental power between 10 months to two years. Psychological Science. 2011;22:125–133. [PMC costless article] [PubMed] [Google Scholar]

von Stumm South. You are what you eat? Repast type, socio-economic status and cerebral ability in childhood. Intelligence. 2012;forty:576–583. [Google Scholar]

Wechsler D. The Psychological Corporation; London, UK: 1992. Wechsler intelligence calibration for children — Third edition UK (WISC-IIIUK) manual. [Google Scholar]

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4641149/

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