individual and group differences in adoption studies of...

Psychological Bulletin1991, Vol. 110, No. 3,392-405

Copyright 1991 by the American Psychological Association, Inc.0033-2909/91/SXOO

Individual and Group Differences in Adoption Studies of IQ

Eric TurkheimerUniversity of Virginia

In the last 5 years, the Colorado and Texas adoption projects have replicated and elaborated thefindings of the classical adoption studies of this century: IQs of adoptees are more strongly relatedto the IQs of their biological parents than to measurable characteristics of their adoptive environ-ment. At the same time, several studies from France have demonstrated substantial IQ gains inchildren adopted from impoverished biological parents to middle-class adoptive homes. The appar-ent contradiction between these two findings is emblematic of the nature-nurture controversy. Acommon model for resolving the paradox, the two-realms hypothesis, is conceptually inadequateand encourages separate analysis of individual and group differences. Subsuming the two kinds offindings in a single model shows that they are less divergent than they seem and highlights the needfor further research into why some contradictions remain.

Despite periodic declarations of peace (Clarke, 1984b; Plo-min, 1983; Scarr & Weinberg, 1980; Wachs, 1983) or victory(Feldman & Lewontin, 1975; Urbach, 1974), the nature-nur-ture debate has not been resolved. There are many reasons forits continuation, some scientific and some ideological. Amongthe most important are the persistently opposite conclusions oftwo varieties of the adoption design. In one (the individual-dif-ference design), IQs of adopted children are related to the IQs oftheir biological parents and some measure of the adoptive envi-ronment. Positive statistical relationships between IQsofbiolog-ical parents and adopted-away children are indicative of a ge-netic effect; positive relationships between rearing environmentand adopted children's IQs demonstrate environmental influ-ences. The other (group-difference) design focuses analysis ondifferences between mean IQs of groups. Children adoptedfrom unfortunate circumstances into more environmentally fa-vorable homes are evaluated to see if the environmental im-provement is accompanied by an increase in IQ.

Beginning with the classic studies of Burks (1928) and Leahy(1935), individual-difference analyses of adopted children's IQshave usually shown stronger relationships with biological par-ents' IQs than with measures of adoptive environment and havethus supported genetically weighted conclusions. In contrast,children adopted from deprived environments into stable adop-tive homes have often shown significant increments in IQ, con-sistent with an environmentally weighted explanation. Indeed,these two findings have sometimes been reported in the samestudy.

The divergence of the individual- and group-difference tradi-tions is now practically complete. Recent reviews of genetic

I am grateful to 1.1. Gottesman, J. C. Loehlin, and L. Willerman forsuggestions on a draft of this article. Robert McCall and several anony-mous reviewers provided indispensible suggestions during the reviewprocess. C. A. Prescott assisted with several aspects of the article. Anyremaining errors are my own.

Correspondence concerning this article should be addressed to EricTurkheimer, Department of Psychology, University of Virginia, Char-lottesville, Virginia 22903-2477. Electronic mail may be sent [email protected].

effects on IQ are essentially tables of familial correlations (Bou-chard & McGue, 1981); reviews of environmental effects em-phasize increases in IQ resulting from special educational orfamily programs (Clarke, 1984a; Rutter, 1985). A recent report(Loehlin, Horn, & Willerman, 1989) from the Texas AdoptionProject does not contain a single mean; a recent adoption studyfrom France (Capron & Duyme, 1989) does not include a singlecorrelation. In genetic circles, there is a growing consensus thatenvironmental differences between families have little or noeffect on IQ (Plomin & Daniels, 1987). At the same time, Schiffand his colleagues (Schiff et al, 1978; Schiff, Duyme, Dumaret& Tomkiewicz, 1982) have published reports of substantial IQgains resulting from changes in environment, with heritabilitiespurported to be close to zero.

The thesis of this article is that the apparently divergent out-comes of individual- and group-difference studies are primar-ily methodological, not substantive in origin. Although vari-ance may be assigned to statistically independent sources, he-redity and environment operate in a single model of causalinfluence regardless of whether scientists study it with individ-ual- or group-difference analyses. AH adoption studies produceboth individual- and group-difference results: Every sample ofadoptees has variance in IQ that can be related to other vari-ables and a mean IQ that can be compared with other groups.Simultaneous analysis of these two types of variation isstraightforward statistically and may lead to a more unifiedmodel of the development of cognitive ability.

Methodological Arguments

Historically, the most common response to the disagreementbetween correlational and mean-difference studies has been todiscount one of them as undependable, the choice betweenthem depending on one's position on a hereditarian-environ-mentalist axis. In a field as rife with methodological complexi-ties and entrenched philosophies as this one, shortcomings arerarely difficult to find. Munsinger (1975a) listed 15 methodologi-cal biases in adoption studies and described the threat of eachto the validity of mean-difference and correlational designs(Munsinger, 1975b). Munsinger (1975a), favoring genetic hy-

392

INDIVIDUAL AND GROUP DIFFERENCES 393

potheses, concluded that mean-difference studies are inher-

ently unreliable, or at least that none to date had sufficiently

overcome the methodological obstacles he noted.

The apparent contradictions in the results of group- and indi-

vidual-difference analyses are unlikely to be reserved by meth-

odological arguments favoring one over the other, because the

two methods are more alike than different. Group- and individ-

ual-difference analyses are not two kinds of adoption study,

they are two ways of analyzing a single adoption design. In fact,

one kind of analysis can readily be converted into the other, as

follows.

Suppose a sample of children are adopted into either

enriched or impoverished environments. A researcher com-

pares the mean IQ of the two groups and finds (using a t test)

that the group in the enriched environment fared better. Alter-

natively, the researcher could have represented group member-

ship as a dichotomous (1, -1) variable. IQ could then be re-

gressed on this variable, which would simply be a dichotomous

measure of environmental quality. The resulting regression coef-

ficient would equal the mean difference between the groups

(Cohen & Cohen, 1983), and the significance of the coefficient

would be precisely the same as the t test of the difference be-

tween the means.

In this light, the contention that there are different method-

ological threats to the validity of individual- and group-differ-

ence designs is seen to be overstated. Consider Munsinger^

(1975b) assertion that selective placement is a threat to correla-

tional studies but that group differences predating adoption

threaten mean-difference studies. If a mean-difference result is

represented as a regression of IQ on a dichotomous environ-

ment, selection for preexisting differences implies that children

with the greatest IQ potential are placed in the better environ-

ment: This is selective placement. Just as statistical techniques

such as path analysis can control for selective placement (ije,

imperfectly), they can also control for preexisting differences in

group means.

Two-Realms Hypothesis

The opposition between the individual- and group-differ-

ence methodologies has led to a compromise view according to

which both genetic and environmental influences are impor-

tant but have different kinds of effects: Environment is said to

account for group differences; genotype accounts for individual

differences. This solution follows from a widely misinterpreted

article by Robert McCall (1981), who proposed that two realms

of development correspond to Cronbach's (1957) two disci-

plines of scientific psychology. Developmental Junctions trace

"the measured value of a given attribute plotted across age";

(McCall, 1981, p. 2) individual differences represent "the rela-

tive consistency of individual differences over age" (McCall,

1981, p. 3). McCall's point was that when a trait is measured

over time, variation between and within measurement occa-

sions are potentially independent, in that the causes of stability

across time are not necessarily the same as the causes of change

or growth. Moreover, McCall argued persuasively that both

kinds of variation needed to be considered in an comprehen-

sive model of development.

Unfortunately, McCall's (1981) distinction has been widely

taken to justify the notion that genes and environment can have

separate causal effects between and within samples in cross-

sectional studies, a notion that provides an escape from the

dilemma presented by individual- and group-difference results

of adoption studies. For strict hereditarians, the hypothesis ex-

plains apparent environmental effects after the heritability of

IQ has been declared to be essentially 1.0:

The conservative empirical conclusion to be drawn from theseconfusing data is that mainly genetic factors influence the rankorders of children's IQ scores while environmental differencesmay shift the average group IQ without significantly affecting theindividual children's rankings. This empirical conclusion is diffi-cult to understand, because one might reasonably expect that anadopted child placed in a very superior adopting home would gainmore in IQ than an adopted child placed in an average or poorenvironment. (Munsinger, 1975b, p. 242).

For behavior geneticists more open to the possibility of environ-

mental influence, the two-realms hypothesis justifies contin-

ued attention to the environment despite strong genetic effects:

Even if differences in several demographic measures of familyenvironments do not contribute much to differences in offspring'sIQ scores, however, one must not conclude that the levels of envi-ronments in general make no difference for the development ofintelligence. Obviously, the average performance level of the adop-tive children depends on the average level of their environments(Scarr& Weinberg, 1978, p. 687).

Similar conclusions can be reached by environmentalists will-

ing to consider genetic effects:

Developmental functions and individual differences are statisti-cally independent, though, because the means of two sets of scoresare unrelated to the correlation between the two sets. Thus, experi-mentalists and correlationalists study separate aspects of mentaldevelopment, and their positions regarding intellectual plasticityare distinct because they actually concern different kinds of plas-ticity. (Ramey, Yeates, & Short, 1984, p. 1914)

Finally, the hypothesis can be used to declare genetic effects

completely irrelevant to the problem:

Whatever the "true" heritability coefficient for intelligence is (if,indeed there is such a thing), whether it is high or low, and whatfactors effect its calculation, the essential point is that in the con-text of group differences and what these differences connote, itsnumerical value is irrelevant. What is relevant is whether thesegroup differences can be changed, with what means, and withwhat effect. (Angoff, 1988, p. 716)

Although the two-realms hypothesis is now the received view

of nature and nurture (e.g., Weinberg, 1989), it is implausible to

suggest that the forces shaping the IQs of groups are different

from those shaping the IQs of individuals; environmental and

genetic factors can affect only individuals, one at a time

(Werner, Lane, & Mohanty, 1981). In a given population, there

is one and only one function describing the relationship among

any set of variables. The goal of adoption studies is to elucidate

the unique relationship among IQ, genotype, and environment

(or some aspect of it) in a population. Although this relationship

may be manifested as correlations between continuous vari-

ables or as mean differences between groups and although such

manifestations are themselves subject to variation resulting

from sample-specific characteristics, they all describe the same

394 ERIC TURKHE1MER

relationship among IQ, genotype, and environment. There aretwo realms of variance, between and within groups; there isonly one realm of development.

Environmental and Genetic Effects Between

and Within Groups

The false impression that there are separate causal influenceson group and individual differences in IQ can be traced toseveral misconceptions. The first is a hypothetical scenario evi-dent in the quotations above and sometimes made explicit(Weizman, 1971): Suppose offspring IQ is perfectly correlated(genetically) with maternal IQ but the effect of improved envi-ronment adds a constant 5 points to the IQ of each child. Thiswould result in an increase in the mean IQ (is., the developmen-tal function), but because the individual differences among thechildren would be maintained, the perfect correlation with ma-ternal IQ would be unaffected. The scenario seems to permitperfect heritability of individual differences in the presence ofenvironmental effects on the group mean.

But consider the relationship between IQ and environmentillustrated in Figure 1: For the 5-point increase in mean IQ tooccur, the regression of IQ on environment must have a positiveslope between the environments of the biological and adoptivehomes. In the scenario, the perfect correlation between adopteeand biological mother's IQ is not affected because all adopteesare presumed to receive identical 5-point increases. If, however,the adoptive environments varied along the dotted line, as inFigure 2, different adoptees would receive different increasesaccording to their degree of environmental improvement, thatis, there would be a correlation between environment andadoptee IQ. By the same token, the individual differencesamong the adoptees would now depend in part on their environ-ments, which would necessarily reduce the correlation betweenbiological mother and adoptee IQ.

IQ

IQ1

2

a-'"''4

5

Biological Homes Adoptive HomesEnvironment

Figure 1. Five adoptees receiving identical IQ increments after being

placed in adoptive environments. (Note positive slope of regression.)

Biological Homes Adoptive HomesEnvironment

Figure 2. Five adoptees receiving varying IQ increments after beingplaced in varying adoptive environments. (Note same regression as inFigure 1.)

In the scenario, therefore, the independence of the relation-ships between genes and individual differences on the one handand environment and mean differences on the other is a conse-quence of the unreasonable assumption that all adoptees areplaced in functionally equivalent environments. If the environ-ment is perfectly constant across subjects, it cannot account forany variance in IQ, but this does not imply that there is nocausal relationship between environment and IQ The confu-sion is between the shape of the unstandardized relationshipbetween environment and IQ and the proportion of IQ vari-ance explained by environment, which is a function of theshape of the regression and the variance of environment (Le-wontin, 1974).

A related scenario has often been proposed in the context ofdiscussions of group differences in IQ. Suppose variation in thesize of plants grown in enriched soil is entirely genetic. Thesame is true of plants grown in poor soil, but the mean height ofplants grown in poor soil is substantially less than those grownin enriched soil. Therefore, variation within groups is entirelygenetic, but variation between groups is entirely environ-mental.

This example is more plausible than the adoption scenariobecause it is more reasonable to imagine a sample of clonedplants raised in equally enriched soil than a sample of childrenraised in identical adoptive homes. Nevertheless, it is beset withthe same confusion between partitioning of variance and expla-nation of causal relations. The reason all of the within-groupvariance is explained by heredity is not that there is no causaleffect of environment—the between-groups analysis demon-strates that there is—but because there is no within-group vari-ance in environment. Although it is correct to say that environ-ment accounts for the difference between the groups whereasgenes account for differences within them, a single model of therelations among soil quality, genes, and plant height ultimatelymust account for both partitionings of variance.


Genetic Constraints on Environmental Effects

AngoiFs (1988) argument about the irrelevance of heritabili-ties has a long history (Scarr & Weinberg, 1981) but is true onlyin a very limited technical sense. First of all, it should be clearthat when the regression of IQ on a measure of environment iszero, no amount of change in the mean of that measure (at leastin the range in which the regression was estimated) will haveany effect on the mean of IQ. An increase in the mean amountof time children spend playing volleyball would presumably notresult in an increase in IQ, and the reason is precisely that theregression of IQ on volleyball behavior equals zero. When therelationship between IQ and an environmental variable is non-zero and positive but very small, there is always some degree ofchange in the environmental variable that will produce a de-sired degree of change in IQ, but to conclude that the within-group relationship is therefore irrelevant is farfetched. Amongother difficulties, such a conclusion obscures the important factthat the relative effectiveness of environmental manipulationsis substantially determined by the degree to which potentialenvironmental variables covary with IQ. If one environmentalvariable produces a change of 1 IQ point for every unit changein the environment and another variable produces a change of10, this is relevant to the choice between them.

Heritability Between and Within Groups

An entirely correct but potentially misleading algebraic analy-sis of between- and within-group heritabilities has been pro-vided by DeFries (1972). For a population composed of twodistinct groups, DeFries showed that the heritability withineach group (/!„,) can be expressed as a function of the totalheritability and two intraclass correlations: r, between the geno-types of groups, and t, between the phenotypes:

12 SWP 13 STP""• ~3— = " ~3~ '

Swo STO(4)

But this analysis only applies to standardized relationshipsamong the variables. It does not imply that there are causallydistinct relationships between and within groups. Introducingsome additional notation, let 4o and &G equal the total geneticvariance and the genetic variance within groups, respectively,and STP and s^P equal the total and within-group phenotypicvariance. Then,

Substituting in Equation 1,

fTlLf =

ft

(2)

(3)

A heritability is the square of the standardized regression ofphenotype on genotype; the corresponding unstandardized co-efficient (denoted as H2) is equal to the heritability multipliedby the ratio of phenotypic to genotypic variance. Multiplyingboth sides of Equation 3 by s^r/Swa and canceling terms,

rj2 if2 /c\nw — n , \j)

which shows that the unstandardized relationship between ge-notype and phenotype is independent of between-groups andwithin-group variance. Because the between-groups andwithin-group variances sum to the total variance for both geno-type and phenotype, the same relationship holds for unstan-dardized between-groups coefficients.

Standardized Versus Unstandardized Coefficients

It follows that a third source of the misconception is the per-sistent tendency of investigators to report results of adoptionstudies in terms of standardized coefficients, which confoundinvariant effects with sample-dependent quantities. Supposetwo genetically identical groups have equivalent regressions (H

and E) of IQ on genes and environment (with mean levels H, E,,and £2), that is,

IQ, = HH + EE,; IQ2 = HH + EE2. (6)

The percentage of IQ variance accounted for by the environ-ment in either study is equal to the square of the standardizedregression coefficient of IQ on environment, which equals

(7)

and the expected mean difference in IQ is given by

E(E, - £j). (8)

Comparing Equations 7 and 8, it can be seen that the standard-ized individual- and mean-difference results share one determi-nant—the unstandardized regression of IQ on environment—and that each has a unique determinant that is sample specific.Group-difference results depend on the mean difference in en-vironment between the groups; individual-difference resultsdepend on the variance of the environment.

Robert McCall (personal communication, February 27,1991) provided a succinct example of the difference betweenpartitioning variance and explaining causal relations:

Years ago, tuberculosis (TB) was highly heritable. This was be-cause the TB bacillus was everywhere, so there was very littlevariation in environment but substantial variation in the biologi-cal disposition to succumb to the disease. Now, public health mea-sures (e.g., sewers and soap, two environmental factors that werenot present in the samples of yesterday), have ensured that mostindividuals do not come in contact with the bacillus, so beingsubjected to a poor health environment accounts for most cases ofthe disease today, and the heritability is very low. In terms ofdevelopment, however, the disease is caused in the same way todayas it was yesterday.

Summary

The two fundamental adoption designs are methods of study-ing one entity: the trivariate relation among genotype, environ-ment, and IQ. The disparity in their results is unlikely to result

396 ERIC TURKHEIMER

entirely from methodological inadequacies in one or the otherdesign, as exclusively environmentalist and hereditarian theo-rists contend, because the two designs are based on differentexpressions of the same fundamental relationships. Neither arethere two realms of development, one manifest in means andthe other in individual differences. Heritabilities and correla-tions of IQ with environmental variables, because they are stan-dardized, vary with differences among studies in genetic andenvironmental variance, and group differences in IQ vary withthe magnitude of genetic and environmental differences be-tween the groups, but there are not two types of effects on IQ,one influencing correlations and the other influencing means.

Analysis of Individual and Group Differences

Reaction Norms

The invariant trivariate relationship among genotype, envi-ronment, and IQ (or any phenotype) is called a reaction norm.The reaction norm, a concept from classical behavior genetics(Dobzhansky, 1955) that was first applied to IQ by Gottesman(1963, 1968), is a graphical representation of the relationshipamong genotype, environment, and a trait. It is usually drawnas a series of regressions of a trait on environment, one regres-sion for each of several genotypes, which is simply a two-di-mensional contour map of the response surface for predictingone dependent variable from two independent variables (Fig-ure 3).

The most important characteristic of the reaction norm as ameans of representing the outcome of adoption studies is thatthere is only one of them. To the extent genotype, environment,and IQ can be represented as unidimensional variables, there isonly one response surface describing their relationship. Eachpoint on the surface represents the expected value of IQ forpeople with a particular pairing of genotype and environment.The shape of a reaction norm depends on the joint effect ofgenes and environment on IQ. Linear slopes along the environ-

Mean Phenotypic IQ

1 SO-

25 _

Genotype D

Restricted Natural Habitat Enriched

Fayorableness of Environment

Figure 3. Hypothetical reaction norm.

mental axis represent linear effects of environment; linear spac-ings between the lines representing genotypes represent lineargenetic effects; parallel environmental slopes for different geno-types represent additivity (U, absence of statistical interaction)of genotype and environment.

In the remainder of this article, I propose that (a) the purposeof adoption studies is to estimate reaction norms, (b) the tradi-tional results of adoption studies — heritabilities, correlationswith environmental measures, and mean differences betweengroups — confound the reaction norm with local characteristicsof individual studies, and (c) there are well-known statisticalmethods to perform simultaneous analysis of individual andmean differences, resulting in more complete knowledge aboutboth. In the analyses that follow, I assume reaction norms to belinear and additive. Both assumptions are discussed in the sub-sequent section. No assumptions are made about correlationbetween genotype and environment.

Biological Mothers and Adopted Children

In the simplest instantiation of the adoption design, the IQsof adopted children (IQA) are regressed on a measure of theirbiological mothers (DM) and their adoptive mothers (AM),which may covary as a result of selective placement. If all mea-sures of the biological and adoptive mothers are expressed asdeviations from a population mean, a regression equation canbe estimated:

(IQA - 100) = bh(BM - I, (9)

where Aj and be are equal to the slopes of the reaction norm forIQA in terms of BM and AM, p. is the population mean of theparental measure, R is a residual variance, and / is an intercept.

To obtain a heritability from the reaction norm, the regres-sion equation must be standardized. In the standardized equa-tion, ft = bh(faMlsA) and ft = b,(sAa/sA) (Loehlin, 1987). Under asimple set of assumptions (equal heritabilities in the parentaland child generations, absence of assortative mating), heritabil-ity can be computed as h2 - 2 ft. Although beta weights can beinterpreted as the percentage of adoptee variance accounted forby biological and adoptive mothers, the interpretation in termsof the reaction norm is lost, confounded with the variances ofbiological and adoptive mothers.

A second consideration concerns the mean IQ in the unstan-dardized analysis and the interpretation of the intercept. Be-cause the intercept in Equation 9 is determined as

the mean IQs do not enter into the determination of bh and b, aslong as the intercept is free to take any value. In addition, onecan see that the intercept consists of the difference between theobserved deviation of the adoptees (IQA) and that predictablefrom the means of the biological and adoptive mothers. A testof the null hypothesis that 7=0, therefore, tests whether theindividual-difference portion of the analysis (bh and be) is suffi-cient to explain the group-difference portion of the analysis (themean differences among the adoptees and the two groups ofmothers).

Example: Skodak and Skeels. The prototypical analysis ofgroup- and individual-difference results follows from the series


of studies by Skodak and Skeels (Skeels, 1936,1938; Skodak,1938; Skodak&Skeels, 1945,1949). Skodak and Skeels reporteda longitudinal study of 139 children placed for adoption beforethe age of 6 months. Subjects were tested with the Kuhlman-Binet Intelligence Scale or Stanford-Binet Intelligence Scale at4, 7, and 13 years, by which time attrition had reduced thesample to 100.

According to Skodak and Skeels, IQs of the biological parentsof these children were below average. The mean IQ of 63 testedmothers from the final sample of 100 was 85.7; the averageeducational level of 92 biological mothers was 9.8 grades, andfor fathers it was 10 grades; the average occupational level of 73of the biological fathers was in the range of moderately skilledlaborers. The adoptive parents were not tested for IQ but weresubstantially better educated than the biological parents.

Two apparently contradictory results were reported. Theadoptees' mean IQ was 116.8 at 2 years, then slowly decreasedto about 108 by age 13. The correlation between adoptees' IQand that of their biological parents increased from .04 at thefirst testing to .31 at the final testing, and correlations of childIQ with adoptive parent education remained near zerothroughout. Although the fundamental methodology of thestudy has been cogently criticized (McNemar, 1940; Munsinger,1975a; Spitz, 1986), it is more instructive for present purposesto take the results at face value and consider their implicationsfor the analysis of genetic and environmental effects on IQ.

Jensen's Analysis. This most famous discrepancy betweengroup and individual differences also produced the most con-certed attempt to resolve them. Jensen (1973a) provides an ex-ception to the prevalent confusion and rancor surroundinggroup-difference analyses of adoption studies. Despite someminor points that I raise about Jenseris analysis and though hisresolution of group and individual results may be unduly opti-mistic, the analysis is important because it demonstrates thatgroup- and individual-difference results belong in the samemodel. A modified presentation of Jensen's analysis follows, interms of Equation 9.

Following Jensen (1973a), the standardized regression of theIQ of the adopted child on the IQ of the biological mother (ft)equals Viff IQgu. Jensen assumes h2 = .7, ft = .35, and (in oneversion of his analysis) random mating between biologicalmothers and the unmeasured biological fathers, who are there-fore estimated to have a mean IQ at the population mean of 100.Substituting these values and the group means into Equation 9,we obtain

(106- 100) = .35(85.7 - 100) + bA(AM - 100)+ /. (11)

Jensen then takes 0.2 as an estimate of the proportion of thevariance of adoptee IQ accounted for by AM. The standardizedregression coefficient ft equals the square root of this quantity,or 0.45. Finally, Jensen asks what value the mean adoptive envi-ronment must take for the intercept in Equation 9 to be equal tozero, that is, for the difference between the biological mothersand the adoptees to be completely accounted for by the within-group relationships. Measuring the environment in terms of IQpoints, he sets 7=0 and solves for AM, obtaining a value of 12 5.Accepting Jensen's assumptions for the moment, his analysisdemonstrates that for the individual-difference analysis to ac-

count for the mean difference between the adopted childrenand their biological mothers, the mean level of the environmentwould have to be 1% standard deviations above the populationmean.

Several aspects of Jensen^ analysis require further comment.First and most important, it was not based on Skodak andSkeels's data. In particular, the estimate of .2 for the proportionof variance accounted for by the adoptive families is difficult tojustify in terms of the actual data, as is demonstrated below.Second, it is not a good idea to base this kind of analysis onstandardized coefficients. Jensen's analysis uses an h2 value ofabout .7 (again, not computed from Skodak and Skeels's data).Suppose, however, that a sample consisted of adopted childrenof biological mothers who all had identical IQs of 80. The valueof h2 resulting from such data would necessarily be 0, but thegenetic expectation for the IQ of the adoptees would be still beaffected by their mother's low IQ, in an amount determined bythe unstandardized regression of adoptee IQ on biologicalmother's IQ.

Reanafysis. Skodak and Skeels (1949) provided an appendixwith IQs for the 100 adoptees remaining at the end of the study,IQs for some of their biological mothers, and years of educationfor most biological and adoptive parents. These data were fit toEquation 9 by regressing adoptee IQ (IQ Examination 4 in then-Table 17) on biological mother's IQ and adoptive midparenteducation (from their Table 16). If education was available foronly one of the adoptive parents, it was used as an estimate ofthe midparent value.

Conducting such an analysis highlights some of the method-ological weaknesses of the parent-adoptee design for analysis ofindividual and group differences. The biological parents andthe adoptees were not administered the same IQ test, so it isdifficult to know how scaling differences between the IQs mayhave influenced the results. In addition, the mean deviation ofthe adoptees is a function of the deviation of the biological andadoptive parents from their respective population means, so theestimates of the population means are crucial to the analysis.For the biological parents' IQs, a population mean of 100 is anobvious choice; for years of education, however, the choice isnot so obvious. I used 12 years for the reanalysis.

Biological mother's IQ and adoptive midparent educationwere both available for 63 adoptees. Fitting the data to Equation9 resulted in the estimated regression equation

(IQA ~ 100) = .37(7(2™, - 100)

- 12)+ 11.67, (12)

which accounted for 1 5% of the variance in adoptee IQ. Thecoefficient for biological mother IQ and the intercept were bothsignificantly different from 0 (p = .0001); the coefficient foradoptive midparent education was not. Standardizing the analy-sis resulted in a standardized regression coefficient for biologi-cal parent IQ equal to .39 (tf « .78), accounting for all 15% ofthe variance explained by the model.

What do we learn from this analysis? Biological mother's IQis positively associated with adoptee IQ, but adoptive midpar-ent education is not. The slope of the reaction norm for adopteeIQ along the genetic axis is equal to about one third of an adop-tee IQ point per IQ point of the biological mother. In addition,

398 ERIC TURKHE1MER

the rejection of the null hypothesis of / = 0 demonstrates that

the mean IQ of adoptees cannot be accounted for by the terms

included in the model. Adoptive parent IQ is not helping, be-

cause the regression coefficient is in the wrong direction. The

design does not tell us anything about what else might account

for the mean adoptee IQ, except that it is uncorrelated with

either biological parent IQ or adoptive parent education.

Separated Siblings

A more powerful analysis can be achieved when the IQs of

two groups of children are compared, for example, when a sam-

ple of adoptees is compared with their nonadopted siblings. In

adoptees, a characteristic of the biological parents (BP) is used

as a measure of genotype and a characteristic of the adoptive

parents (AP) is used a measure of rearing environment. For the

nonadopted siblings, the measure of the biological parent is an

index of both genotype and rearing environment.

For the adoptees (subscript A), as before,

(1QA - 100) = blA(BP - A, (13)

whereas for the nonadopted (home, subscript H) siblings, for

whom the biological parent provides both genotype and rear-

ing environment,

(IQH - 100) = bla(BP - A + bj^BP -n) + R + IH. (14)

Obviously, b,H and bw cannot be estimated in the nonadopted

sample, but they can be determined by requiring them to be

equal in adoptees and nonadoptees (Coon, Carey, & Fulker,

1990; cf. Wright, 1931, p. 161). Under this assumption, the dif-

ference between the mean IQs of the adopted and nonadopted

siblings equals

~ 1Q.H (15)

The difference in means therefore consists of one portion that

can be attributed to the difference in rearing environment and a

residual portion that cannot. A test of the null hypothesis that

(IA — /„) = 0 tests whether the difference in rearing environ-

ments is sufficient to account for the mean difference between

the groups.

A straightforward method of testing this null hypothesis is as

follows. Under the assumption that the within-group regres-

sions are equivalent in the adoptees and nonadoptees, Equa-

tions 1 3 and 14 can be combined to express the regression of

adopted and home children (lQAJf) on the characteristics of

biological and rearing parents (BP and RP).

IQA.H = - IH)(GROUP) + /„, (16)

where GROUP is a dummy variable that takes the value 1 for an

adopted child and 0 for a nonadopted child. Its regression coeffi-

cient is equal to the difference between IA and IH in Equations 1 3

and 1 4. Note that when GROUP equals 1 , Equation 1 6 is equiva-

lent to Equation 13, and when GROW equals 0, Equation 16 is

equivalent to Equation 14.

This analysis demonstrates why the separated sibling design

is more powerful than the mother-adoptee design for analysis

of group and individual differences. In addition to the method-

ological advantage of permitting the siblings to be tested with

the same IQ test at roughly the same time, under the assump-

tion that the siblings have identical biological parents (i.e., the

same father) the biological parents are not involved in the group

difference. Moreover, the key null hypothesis involves the dif-

ference between two intercepts, so as long as the rearing environ-

ments of the groups are measured on the same scale, one does

not have to be concerned with the population means.

Example: Schiff and colleagues. The importance of sepa-

rated siblings for the study of group differences in IQ has been

highlighted by the work of Schiff and his colleagues (Schiff et al,

1978,1982; Schiff & Lewontin, 1986; a similar analysis, using

many of the same subjects, was reported by Dumaret & Stew-

art, 1985). Schiff and colleagues studied the IQ and academic

achievement of 20 pairs of half-siblings, of which one member

of each pair was adopted, and the other was raised by the biolog-

ical mother. The sibling pairs were selected for large differences

in environment. The adopted siblings (A siblings) were raised in

upper-middle-class homes, whereas those remaining in their

original environment (B siblings) were raised in homes that

were working-class at best. The study also included 12 addi-

tional adopted children who did not have siblings in the study

and 19 additional B siblings. The latter were additional siblings

of the 20 A children; the 20 B children included in the study

were selected for proximity in age to the A siblings.

The A children performed substantially better than their B

siblings on IQ tests and in school. The mean IQ of the A chil-

dren (over several IQ tests, two per child, administered at a

variety of ages from 6 to 13 years) was 108.9, and for the B

children it was 94.6; many more B than A children repeated a

grade or were placed in a special class. As with Skodak and

Skeels. it is most useful for present purposes to accept the stud-

ies' empirical findings and concentrate on their implications

for the magnitude and mechanism of the effect of environment

on IQ. Nevertheless, as will be seen below, it is hardly a settled

matter that the differences in ability and achievement between

siblings are attributable to the environment.

Granting for the time being that the entire difference be-

tween A children and B children is environmental, Schiff and

Lewontin's analysis of means still leaves many questions unan-

swered. Most important, it does not address the magnitude of

the environmental effect. The environmental effect on the

mean in Equation 15 depends on both the magnitude of the

regression of IQ on environment and the amount of difference

in environment between the two groups of siblings. The latter is

a function of characteristics of the sample, which was selected

to maximize the effect of environment. Not all adopted chil-

dren come from homes as poor as those in the study, nor are all

adopted into homes as well off. Environmentalists have

correctly insisted that strong within-group genetic relationships

do not militate against the possibility of environmental differ-

ences between groups (Angoff, 1988; Feldman & Lewontin,

1975). It follows that group effects that are based on large differ-

ences in environment do not necessarily demonstrate a power-

ful relationship between environment and intelligence: If group

differences in environment are sufficiently large, small within-

group relationships can be greatly magnified.

Reanalysis. I retabulated Schiff and Lewontin's (1986) data

for the 20 pairs of adopted and nonadopted siblings, following


Schiff and Lewontinls procedures as closely as possible (Table

1). In the three cases for which pairs of twins were included in

the A group, the mean for each pair was computed on each

variable and included as a single subject. The mean of the two

IQ scores obtained for each subject is used for analysis.

Although the A and B children show the substantial group

difference in IQ reported by Schiff and Lewontin (1986), the

correlational data suggest that explanation of the difference is

complex. Most important, the IQs of the adopted children cor-

relate more highly with the occupational status of their biologi-

cal fathers (f = -.34, ns; correlations are negative because high

scores correspond to low-status occupations on Schiff and Le-

wontin's scale) than with that of their adoptive father (r = -.25,

ns). Although neither correlation nor the difference between

them are statistically significant, correlations will be very diffi-

cult to detect in these data because of restriction of variance in

the occupational variables. Fifteen out of 20 biological fathers

had occupations rated 32; 14 out of 20 adoptive fathers had

occupations rated 10.

Schiff and Lewontin's (1986) data was fit to Equation 16 using

PROC GLM in SAS (SAS Institute, 1985), resulting in the fol-

lowing regression equation, which accounted for 36% of the IQ

variance in the siblings:

Table 1

IQ of Adopted (A) and Nonadopted (B) Children

and Paternal Occupation

Occupation rating11

A child

114.5106.5105.92.5

112.598.5

110.125.110.5103.123.121.122.598.5

105.597.5

117.99.5

102.5113.5

j

B child

104.591.5

117.593.594.74.98.593.589.77.2599.69.5

109.98.598.588.5

102.594.5

107.591.

Adoptivefather

1210101210101010101010131011101010181310

BiologicalFather

3232323232333232323232323235323333323232

M

108.9 94.6 11.0 32.3

SD

9.6 11.6 2.0 0.7

Note. From Education and Class (pp. 50,64-65) by M. Schiff and R.Lewontin, 1986, Oxford, England: Clarendon Press. Copyright 1986 byOxford University Press, Incorporated. Adapted by permission.

IQA.H = -1.1(BP) - .9«(RP) + 6.50(GROUP) 4- 184.0 (17)

The occupation of the biological and adoptive fathers did not

contribute significantly to the analysis, although the effects of

both were in the expected direction. These unstandardized co-

efficients estimate the slopes of the reaction norm: about 1 IQ

point per each point on the occupation scale for adoptive fa-

thers and about 2 IQ points for each occupation point of biologi-

cal fathers. The coefficient for the GROUP variable, which tests

for the difference between the intercepts in Equations 13 and 14,

is not significantly different from zero, suggesting (within the

limitations of statistical power inherent in such a small study)

that the difference in measured rearing environments is suffi-

cient to account for the observed group difference.

Additional information may be obtained by omitting the

nonsignificant GROUP term from the model, which constitutes

an assumption that the adoptive and nonadoptive intercepts are

equal or, equivalently, that all differences between the groups

can be accounted for by rearing environment. The resulting

regression equation is

IQA.H = -2.30(BP) - M(AP) + 190.8. (18)

The coefficient for occupation of rearing father is now highly

significant (p < .0001), because it has been assumed that rear-

ing environment accounts for all mean differences.

The appropriate conclusion to be drawn from this portion of

Schiff and Lewontin's (1986) data can be summarized as fol-

lows: To the extent one assumes that the mean difference be-

tween adopted and nonadopted siblings is caused by rearing

environment, the environment can be shown to make a signifi-

cant contribution; if one does not make this assumption, one

cannot show a significant contribution of the environment. In

addition, the genetic effect in these data is at least as large as the

environmental effect.

Multiple Groups of Adoptees: Capron and Duyme

The most recently reported adoption study, conducted in

France by Capron and Duyme (1989), is an example of an adop-

tion study that was analyzed entirely in terms of means; within-

group relations were completely ignored. Capron and Duyme

analyzed a sample of four groups of adoptees approximating a

"cross-fostering" design, including a very unusual group with

highly educated biological parents and poorly educated adop-

tive parents.

Capron and Duyme (1989) found main effects of both biologi-

cal and adoptive parent education in the group means. Note

that the data necessary to compute within-group relations were

available (they were required for the analysis of variance Ca-

pron and Duyme describe) but not reported. As with Schiff and

Lewontin's (1986) analysis, Capron and Duyme's analysis of

means does not address the magnitude of either the genetic or

environmental effect, because the mean differences among the

groups are the result of the slopes of the reaction norm (adoptee

IQ points per year of parental education) and the amount of

difference in parental education among the groups.

If it is assumed (in the absence of sufficient data to perform a

statistical test) that the within-group regressions are equivalent

in the four groups and that differences in biological and adop-

400 ERIC TURKHEIMER

live parent education completely account for the mean differ-ences (i.e, the intercepts are also equivalent across groups), themean IQ of each group (IQ0) can be expressed in terms of thegroup means for midparent education of the biological andadoptive parents:

IQc = + I. (19)

Substituting the group means reported by Capron and Duyme(1989), I obtained

iH15.3 + 6£16.8 + /= 119.6

/ = 107.5

= 92.4

.8 + i£17.3 + I = 103.6 (20)

These equations can be solved, resulting in b/, = 1.9, be = 1.1, /=72.9. The important result of Capron and Duyme, therefore, isthat the slopes of the reaction norm for adoptee IQ are about 2points per year of biological parent education and 1 point peryear of adoptive parent education.

Although the within-group variance for education in thisstudy is small, it is not zero, and absent the two-realms hypothe-sis, there is no obvious reason why the genetic and environmen-tal differences among the groups of adoptees should not bemanifest within groups as well as between them. To account forthe group differences, one would expect a correlation of .10between adoptive parent education and adoptee IQ withingroups (1.1 multiplied by the ratio of within-group standarddeviations for IQ and education, computed from Capron &Duyme's [1989] Table 1, equal to 13.21 and 1.14, respectively);the corresponding correlation with biological parent educationwould be. 16.

Conclusion

The foregoing analyses demonstrate that the several tradi-tional outcomes of adoption studies—heritabilities, standard-ized relations between adoptee phenotype and rearing environ-ment, and group differences in mean phenotype—share onedeterminant, the reaction norm, as estimated by the unstan-dardized regression of offspring phenotype on parental geno-type and rearing environment. Each outcome also has a uniquedeterminant that can be expected to vary from sample to sam-ple and study to study. Heritabilities and correlations of adopteephenotype with rearing environment depend on the variancesof genotype and rearing environment; mean differences be-tween groups depend on the magnitude of the difference be-tween the groups' rearing environments.

The data-analytic methods that have been proposed are nei-ther novel nor particularly complex. In fact, they are analogousto the most familiar method for simultaneous analysis of be-tween-groups and within-group relations: analysis of covari-ance (ANCOVA). In ANCOVA, a dependent variable is re-gressed on a dummy variable for class membership and a contin-uous variable (the covariate). Under the assumption ofequivalent regressions of the dependent variable on the covar-iate at each level of the class variable (cf. the assumption ofequal b weights in the reanalysis of Schiff and colleagues), one

can test whether the mean of the dependent variable differsamong levels of the class variable after the effect of the covariatehas been removed. Equation 16 is such an ANCOVA model.

The analogy between classical ANCOVA and the models re-quired for adoption studies has been obscured because re-searchers' intentions are somewhat different in the two situa-tions. ANCO\A is usually used to ask whether group meansstill differ after the covariate has been controlled; in adoptionstudies, one is asking whether group differences can be ex-plained by the covariate. These are the same question, fromdifferent points of view: In the former, the researcher is bettingagainst the null hypothesis of no difference; in the latter, thesubstantive theory of environmental influence on the groupdifference is forwarded if the null hypothesis is not rejected.

Complications, Explanations, and Recommendations

To clarify the methodological point that individual andgroup differences can and should be analyzed in the same sta-tistical model, a number of simplifying assumptions have beenmade. In addition, the analyses have not always been successfulin providing a unified explanation of individual and group dif-ferences in terms of their common determinant, the reactionnorm. The final section of the article examines the conse-quences of relaxing some of the assumptions, offers some expla-nations of why individual and group differences remain diffi-cult to resolve, and makes recommendations about how futureadoption studies should be analyzed.

Statistical Issues

Genotype-environment correlation. Genotype and environ-ment are correlated in natural families, and the most funda-mental reason for conducting adoption studies is that one ex-pects a substantially lower correlation in adoptees. Moderatecorrelations between genotype and environment do not presentgreat difficulties from a statistical point of view, because multi-ple regression and related methods are expressly designed toestimate independent effects of predictor variables in the pres-ence of moderate correlations among predictors.

Correlations among predictors have different consequencesfor interpretation of standardized and unstandardized coeffi-cients, however. The expected value of unstandardized coeffi-cients is not a function of the correlations among them. If Y=2Xi + 3X2 + 7, this remains true regardless of changes in thecorrelation between Xt and X2. Increases in the correlation of/fi and Xi magnify the standard errors of the unstandardizedestimates, and at very high levels of correlation, estimation be-comes virtually impossible, but the expected values of the esti-mates still do not change.

On the other hand, interpretation of standardized betaweights of correlated predictors is quite complex. When predic-tors are correlated, the variance of the dependent variable mustbe partitioned into (at least; see below) three parts: a portionaccounted for by Xl, a portion accounted for by X2, and a por-tion accounted for by the correlation between the two. Thedifficulty is that the correlation between genotype and environ-ment is not a fixed property of families but depends to a greatdegree on how families are sampled. By design, the correlation


in adoption studies is smaller than that in natural families, sothe proportion of variance accounted for by the various compo-nents is not the same in natural and adoptive families. One canestimate the heritability of IQ in terms of ideal adoptive fami-lies in which genotype and environment are uncorrelated, buthow does this figure apply to natural families, in which geno-type and environment are, and will remain, highly correlated?

Genotype x Environment interaction. The same argumentapplies to the Genotype X Environment interaction. The pres-ence of an interaction between two predictors complicates in-terpretation of the main effects. If genotype and environmentinteract in the prediction of adoptee IQ, one can no longer stateunambiguously that the slope of the reaction norm for IQ isequal to some number of adoptee IQ points per IQ point of thebiological mother, because the value will depend in part on thelevel of environment. In addition, problems with statisticalpower are particularly acute in detecting interactions (Wahl-sten, 1990). Nonetheless, one can still estimate the reactionnorm in terms of an unstandardized regression equation thatincludes a term for the product of genotype and environment,for example,

I, (21)

(22)

which predicts a mean IQ equal to

b,BM + bAM + bJSM-AM + I.

The presence of Genotype X Environment interactions meansthat the shape of the reaction norm is more complex but doesnot change its basic interpretation.

For standardized coefficients, however, the presence of inter-action exacerbates all of the difficulties in variance partitioningalready discussed in terms of genotype-environment correla-tion. There are now at least four sources of variance (genotype,environment, their correlation, and their interaction), of whichthe latter two cannot be unambiguously attributed to eithergenotype or the environment. At this level of nonindependent,nonadditive complexity, the coefficient of heritability comesperilously close to meaninglessness (Layzer, 1974; Wahlsten,1990).

Unexplained Group Differences

Although it has been demonstrated that the mean IQ ofadoptees is potentially explainable in terms of within-grouprelationships, in several well-known instances, adoptees appearto demonstrate an increment in mean IQ that cannot be ex-plained by the very small within-group environmental relation-ships that are found. The best-known example of this phenome-non, Skodak and Skeels's studies, was discussed in detail above.A very similar phenomenon occurred on a smaller scale in theTexas Adoption Project, leading to a brief and unresolved con-troversy about group and individual differences in adoptees(Horn, 1985; Walker & Emory, 1985). Indeed, the perceptionthat it is somehow easier to change the mean of IQ than it is todemonstrate environmental correlates of IQ has been the pri-mary source of the mistaken enthusiasm for the two-realmshypothesis.

Once the two-realms hypothesis is dismissed as an explana-tion of the results of adoption studies, two classes of possibili-

ties remain. Either the group differences are the result of subtlemethodological features of the research design, or they repre-sent genuine environmental effects that are difficult to measurein terms of individual differences. It has already been suggestedthat group differences are difficult to dismiss on strictly meth-odological grounds, because the group-difference design is notfundamentally different from any other adoption design. If thegroup differences are truly environmental in origin, then itmust be explained why correlational analyses fail to detectthem.

Selection. Adoption was once hailed as a "natural experi-ment," but unfortunately it isnt; adoption is a natural quasi-ex-periment. If adoptees were randomly selected for adoption, onecould be relatively certain that adopted and nonadopted groupswere genetically and biologically equivalent before they wereexposed to contrasting environments. In the real world, thepossibility exists that pre-existing differences in sibships influ-ence the likelihood that one sibling will be adopted.

Schiff and Lewontin (1986) go to some lengths to argue thatthis effect was probably small in their adoption study. Theysuggest that by selecting adopted children according to lowoccupational status of the biological father, they ensured thatpaternal differences between adopted and nonadopted chil-dren would favor the nonadopted, whose fathers were unse-lected; they suggest that the possible bias in the adopted chil-dren resulting from selection for absence of organic pathologywas mirrored in the national norms with which they were com-pared, and that an organically impaired (deaf) nonadoptedchild was also excluded; and they suggest that biases resultingin the selection of "alert" children for adoption can be expectedto be small. Ad hoc arguments of this type date from Freeman,Holzinger, and Mitchell (1928), but a critical examination ofthe history of adoption studies mandates extreme caution inthis regard (see especially Leahy, 1931).

Selection in individual- and group-difference designs are es-sentially the same phenomenon. The difference lies in the rela-tive ease of quantifying the selection to account for it statisti-cally. In individual-difference studies, selective placement canbe estimated by a correlation between the genotype of theadoptees and a measure of the adoptive environment. Mean-difference designs are based on two children from the samebiological mother, whose IQ is therefore not useful as an indexof possible genetic differences between the siblings. Such differ-ences can arise from biological fathers, normal genetic varia-tion, or nongenetic congenital or perinatal effects; in any casethey are difficult to measure.

Nonlinearities and threshold effects. All analyses in this arti-cle have been based on an assumption of linear relations amonggenotype, environment, and IQ. One way of interpreting theresults of individual- and group-difference analyses of adoptionstudies is as evidence of nonlinearity in the regression of IQ onenvironment. Group-difference studies almost all involve chil-dren who are removed from poor environments and placed inadequate ones; individual-difference studies, because they in-volve differences among adoptive environments, are usually re-stricted to differences among environments that are at leastmiddle-class.

Jensen (1973b) suggested that disparities between group- andindividual-difference findings could be explained if the rela-

402 ERIC TURKHEIMER

tion between environment and IQ took the form of a threshold,

as illustrated in Figure 4. If such a phenomenon exists, it should

be evident in correlational analyses as well as in analyses of

differences between groups. That is, if the slope of the regres-

sion of IQ on environment is greater at low levels of environ-

ment, one would find stronger correlations between environ-

ment and IQ at this end of the scale. Others (Scarr & Weinberg,

1976) have pointed out that regardless of whether the relation-

ship between environment and IQ is linear, the variance of

important environmental variables will be restricted because

adoptive environments are selected, so correlations (but not

unstandardized regression coefficients) between environment

and IQ would be attenuated.

Nonlinearity and threshold effects do not offer easy solutions

to the problems posed by adoption studies. To produce a mean

difference between children reared in good and poor environ-

ments, but no correlation between the quality of the environ-

ment and IQ among the adoptees, there would have to be no

adoptive homes below the threshold. If some adoptive environ-

ments were below the environmental threshold, the adoptees

placed in these environments would have lower IQs, and a

correlation between environment and IQ would result.

Microemironment. Jensen (1981) proposed another alterna-

tive: "My hunch is that the nongenetic variance in IQ is the

result of such a myriad of microenvironmental events as to

make it extremely difficult, if not impossible, to bring more

than a small fraction of these influences under experimental

control" (p. 33). Actually, there are two possibilities: Either a

small percentage of the environment can be controlled, as in a

study of a particular environmental variable, or all of it can be

controlled, albeit loosely, in a study of total environmental

change.

Consider the path model in Figure 5, which represents the

environmental effects on the IQs of two separated siblings (IQ,

and IQ2). The unmeasured environment is represented as two

latent variables (E, and £j) that are only modestly correlated

with a measured aspect of the environment (EM, and EM2).

The means of E^ and EM2 are one unit greater than the means

ofE, and EM,, respectively. The correlation between the mea-

sured environment and IQ, within each sibling group, will equal

IQThreshold

Biological

HomesAdoptive

Homes

Figure 4. Hypothetical threshold model describing relationshipbetween environment and IQ.

but the difference between the means of IQ for the two groups

will equal

.5 + .1 =.6,

allowing for a greater effect on the mean than is evident in the

correlations.

The magnitude of the relationship between the unmeasured

environment and IQ is usually based on the correlation be-

tween unrelated siblings raised together, a quantity with a con-

troversial history. Figure 6 is a path model of this correlation,

which can be seen to equal e2, the square of the total effect of the

environment shared by the siblings. The most recent summary

of IQ correlations (Bouchard & McGue, 1981) lists an average

value of . 30, which would result in a value of e of over .5, making

group- and individual-difference results far easier to resolve.

However, Plomin and Daniels (1987) recently called into

question the magnitude of this effect. The highest correlation in

the Bouchard and McGue (1981) tabulation is the figure of .64

reported by Skodak (1950) for a sample of 46 pairs of unrelated

children reared together, which appears to include a pair of

identical twins among the presumably unrelated pairs (Loeh-

lin, personal communication, 1986). More recent studies have

also reported lower figures, some of them near zero (Fulker,

DeFries, & Plomin, 1988; Scarr & Weinberg, 1 978; Teasdale &

Owen, 1984; see also McCartney Harris, & Bernieri, 1990).

Recommendations

Heritability. Many calls for the abolition of heritabilities

have already been issued, without observable success (Feldman

& Lewontin, 1975; Layzer, 1974). Heritability (and its counter-

part, the proportion of variance accounted for by environment,

sometimes called "environmentality") has usually been criti-

cized in an environmentalist context, in which the inconstancy

and oversimplification of heritability coefficients have been

taken as evidence that behavior is not transmitted genetically.

Behavior is transmitted genetically, but when sources of vari-

ance are random and correlated (as opposed to fixed and inde-

pendent, as in animal breeding), heritability is an inadequate

measure of genetic effects.

What is usually overlooked is that an equally simple, but con-

siderably more stable and interpretable, quantity underlies heri-

tability and other products of variance partitioning: the reac-

tion norm. The reaction norm, as modeled by unstandardized

regressions, does not vary with changes in genotypic or environ-

mental variance or covariance. Interactions or nonlinearities

complicate the shape of the reaction norm but not its interpre-

tation, whereas components of variance become practically un-

interpretable.

Group differences. Environmentalists champion mean dif-

ferences in IQ as an alternative to heritabilities, but no real

advantage is to be gained. It is no more meaningful to ask,

"How much can IQ be raised?" than it is to ask, "What percent-

age of IQ variance is accounted for by genotype?" In a particu-

lar study, the difference in mean IQ between groups raised in


Figure 5. Path model of the effects of the total environment (E, and

(EM, and EM2 on the IQs of two separated siblings (IQ, and IQJ.

and the measured environment

contrasting environments confounds the magnitude of the envi-ronmental effect and the degree of environmental differencebetween the groups. In a study of adoptees raised in enrichedand deprived adoptive environments, simply observing that thefortunate adoptees had a mean IQ 8 points higher than theirdeprived counterparts only tells half the story. It would be moreuseful to conclude as follows: Each grade of adoptive mother'seducation was associated with an increase of 1 IQ point in theadoptees. The two groups of adoptive mothers had a differenceof five grades of education, accounting for 5 IQ points of thedifference between the adoptees. The other 3 points were notaccounted for by the model.

Study design. In his review of the adoption literature, Mun-

singer (1975a) stated that "the resemblance between adoptedchildren's IQs and their adoptive parents' intelligence (givencertain methodological restrictions. . .) is a direct estimate ofthe environmental part of the nature-nurture equation" (p.624). It has since become clear that this statement is false, andtherefore the classically symmetrical adoption design, in whichadoptees' IQ is correlated with those of their adoptive and bio-logical parents, no longer applies. Covariation between unre-lated children reared together is a more appropriate estimate ofthe environmental effect on IQ.

Possible selection of adoptees. Researchers using group-dif-ference analyses must consider the possibility that selection ofadoptees influences their results. Neither ad hoc argument nor

Figure 6. Path model showing that the correlation between two unrelated children reared together (IQ,

and /&) equals e2. (G, and Gz = genetic endowments of children, £ = shared environment of children, h =effect of genotype on IQ, e = effect of environment on IQ.)

404 ERIC TURKHEIMER

aggressive titling ("a direct answer from a French study," "the

irrelevance of IQ genetic studies") is a substitute for empirical

analysis of the problems posed by selection of adoptees. Medi-

cal data such as birth weight are sometimes available (as they

are in Schiff & colleagues; the groups do not differ) and could

potentially rule out some hypotheses about how selection does

or does not occur.

Environmental explanations of group differences in intelli-

gence will become convincing when within-group relationships

between environmental variables and intelligence can be dem-

onstrated to be of sufficient magnitude to account for the group

differences. Would such an analysis prove that the group differ-

ences were caused by the environment? It would not. Proof of

causation is ultimately a matter of research design, not statisti-

cal analysis, and because random assignment of adoptees will

never be possible, the question cannot, strictly speaking, be

resolved. Successful resolution of between- and within-group

environmental analyses would provide an environmental

model that fits the available data. A model that fits is not defini-

tive evidence, but is more than is available now.

McCall revisited. McCalFs (1981) original two-realms article

was expressly about development, and the two-realms model

makes a good deal more sense when applied to longitudinal

data. A developmental function, that is, a plot of a phenotype as

a function of time, has a mean and a shape, and it is perfectly

sensible to propose that the two components may have different

environmental and genetic causes. That is, the factors that

cause a person to have a particular mean IQ throughout the life

span may be different from the factors that cause the person to

change at a given point in time.

Until recently, adoption studies were analyzed cross-section-

ally, without regard to growth or change. Advances in structural

equation modeling (McArdle & Epstein, 1987) have permitted

analysis of genetic and environmental effects on stability and

growth of IQ in adoptees (Loehlin et al, 1989; Phillips & Fulker,

1989). Note that structural equation techniques designed for the

purpose of analyzing growth and stability invariably include

both means and covariances in the models (Dolan, Molenaar &

Boomsma, 1991), which is a significant cause for optimism.

Conclusion

The purpose of adoption studies is to capitalize on the re-

duced correlation between genotype and environment in adop-

tees to permit more precise estimation of the reaction norm. As

modeled by an unstandardized regression equation, the reac-

tion norm is simultaneously and equally responsible for deter-

mining the within- and between-groups results of adoption

studies. The mean IQ of groups is not more malleable than

individual differences among the groups' members, nor are

within-group effects somehow irrelevant to effects on the mean.

Within- and between-group relationships are linked by proper-

ties of regression and analysis of variance that have been under-

stood for at least 50 years (Burks, 1938) but which have been

obscured by the rhetoric of the nature-nurture debate.

Both genotype and environment affect IQ, but the effect of

individual genes or single environmental events on human abili-

ties are often too small to detect. The foundation of behavior

genetics lies in quantitative methods using sums and differences

of genetic effects between people with differing degrees of ge-

netic relationship to estimate a total genetic contribution. Envi-

ronmental effects are also easier to detect when they are

summed, but the current state of research methodology does

not permit very subtle summations.

Most recent adoption studies, whether genetically or environ-

mentally inclined (e.g, the Texas and Colorado Adoption Proj-

ects, the various French studies), contain sufficient data for anal-

ysis of both individual and group differences, as the reanalyses

in this article have demonstrated. The false compromise of-

fered by the two-realms hypothesis has blinded researchers to

the contradictions that remain to be resolved in the results of

the French group-difference analyses and the American indi-

vidual-difference analyses and has justified continued separa-

tion of the statistical analyses that, if integrated, might begin to

resolve them.

Beginning in the 1920s, adoption studies played a key role in

establishing beyond a reasonable doubt that genetic effects

were present in a great many behavioral phenotypes, IQ promi-

nent among them. The recent studies of Schiff and colleagues

have played a similar role in establishing reliable effects of rear-

ing environment. Assertions that genetic or environmental ef-

fects are not significantly different from zero can no longer be

supported; assertions that they are significantly different from

zero are superfluous. Now that this fundamental task has been

accomplished, it is time to lay the tired nature-nurture debate

to rest and turn our attention to Anastasi's (1958) question, still

unanswered more than 30 years later: How?

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Received January 25,1990

Revision received October 23,1990

Accepted May 8,1991 •

individual and group differences in adoption studies of...

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