Genetic and Shared Environmental Contributions to the Relationship between the Home Environment and Child and Adolescent Achievement

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1 Genetic and Shared Environmental Contributions to the Relationship between the Home Environment and Child and Adolescent Achievement Hobart H. Cleveland The University of North Carolina, Chapel Hill, NC, USA Kristen C. Jacobson The Pennsylvania State University, University Park, PA, USA John J. Lipinski David C. Rowe The University of Arizona, Tucson, AZ, USA The present study used prospective data to examine the relationship between the family environment (as measured by the Home Observation for Measurement of the Environment-Short Form [HOME-SF]) and child and adolescent achievement, and to determine the genetic and environmental contributions to this relationship. Data are from 2108 full- and half-sibling pairs from the National Longitudinal Study of Youth Child data set (NLSY-Child). The average age of participants was 11.9 for older siblings (SD = 3.0) and 8.2 for younger siblings (SD = 2.8). The structural equation modeling program, Mx, was used to obtain the most precise estimates of genetic and environmental contributions to variation in the HOME-SF, variation in achievement, and to the covariation between the HOME-SF and achievement. According to the best-fitting, most parsimonious model, common genetic factors explained approximately one-quarter of the correlation between the HOME-SF and achievement, whereas common shared environmental factors explained the majority (75%) of this relationship. Genetic influences also accounted for over one-third of the variation in both the HOME-SF and achievement. Shared environmental influences explained 35% and 50% of the variation in achievement and the HOME-SF, respectively. The discussion mentions possible mechanisms by which genetic and environmental factors exert their influence on the relationship between the HOME-SF and achievement. Direct all correspondence to: Dr. David C. Rowe, The University of Arizona, Campus Box , Tucson, AZ , USA. dcr019@ag.arizona.edu INTELLIGENCE 28(1): 69± 86 ISSN: Copyright D 2000 by Elsevier Science Inc. All rights of reproduction in any form reserved. 69

2 70 CLEVELAND ET AL. The association of environmental measures with cognitive development and achievement is well established. For example, measures such as socioeconomic status (SES), family structure, and parental involvement have been consistently shown to be related to measures of achievement and cognitive development across populations of children (Chen, Lee, & Stevenson, 1996; Church & Katibak, 1991; Felner et al., 1995; Kurdek & Sinclair, 1988). Research using direct measures of intellectual stimulation and emotional responsiveness, such as the Home Observation for Measurement of the Environment (HOME) Inventory (Caldwell & Bradley, 1984), provides additional supporting evidence for an association between environment and cognitive outcomes (Bradley, Caldwell, & Rock, 1988; Yeates, MacPhee, Campbell, & Ramey, 1983). The degree of environmental causality, however, remains unclear. Behavioral genetic research, in particular, has raised questions about the extent of genetic influence on environmental measures (Plomin & Bergeman, 1991; Plomin, Loehlin, & DeFries, 1985) and about the ultimate role of familial influences (Rowe, 1994). The purpose of this paper is to examine the role of genetic mediation in the relationship between environmental influences, as measured by the HOME-Short Form inventory (HOME-SF), and cognitive outcomes, as assessed by a composite measure of three Peabody Individual Achievement Test subscales (PIATS) and the Peabody Picture Vocabulary Test-Revised (PPVT-R). An additional advantage of the present study is that it uses measures of child achievement obtained approximately 4 years after the HOME-SF inventory was obtained. Thus, the present study is able to investigate how the HOME-SF inventory predicts cognitive development among a sample of school-aged children and adolescents. Several recent studies using the HOME and HOME-SF inventories provide data relevant to this discussion. In his review, Bradley (1993) noted correlations of 0.2 to 0.6 for HOME scores measured in infancy with later academic achievement and intelligence. HOME scores have been found to be significantly correlated with both the Stanford±Binet Intelligence Scale (SBIV; 0.52) and the PPVT-R (0.45; Johnson et al., 1993). Johnson et al. (1993) also found that the HOME accounted for moderate amounts of variance in the SBIV and the PPVT-R for children at age 3 even after controlling for parental SES. These studies demonstrate that there is a moderate-to-strong association between family environment and IQ in children. The implicit assumption in these studies is that the provision of a more intellectually stimulating family environment would cause corresponding increases in IQ. However, there may be alternative explanations for this association. For example, the association between home environment and child IQ may be confounded by level of maternal intelligence. In an attempt to limit the effect of this particular confound, a few studies have examined the relationship between family environment and child IQ while controlling for maternal IQ. For example, using 46 families, Yeates et al. (1983) investigated the direct and indirect influences of maternal IQ, maternal employment, and the HOME inventory on child IQ from 24 to 48 months. Results from their path analyses indicated that at 24 months, virtually all of the variation in child IQ explained by the model (approximately 11%) could be explained by the direct influence of maternal IQ. At 36 and 48 months, the amount of variance in child IQ explained by the model increased to 18% and 24%, respectively. Furthermore, the direct effect of the HOME measure accounted for a significant proportion of the variation at these older ages. By 48 months, for example, the direct effect of the HOME environment accounted for over one-half of the variance in

3 THE HOME AND CHILD AND ADOLESCENT ACHIEVEMENT 71 child IQ. Nevertheless, maternal IQ also continued to contribute to variation in child IQ at 36 and 48 months, both directly and indirectly through its effect on the HOME inventory. On the basis of these results, the authors concluded that although maternal IQ continues to predict child IQ at all ages, as children develop, the relative influence of the family environment on child IQ increases. However, Luster and Dubow (1992) reached somewhat different conclusions on the basis of their results for 6- to 8-year-old children. Analysis of their data, drawn from the National Longitudinal Survey of Youth (NLSY), found that HOME-SF measures, when entered after maternal IQ (as measured by the Armed Forces Qualification Test [AFQT]) in a hierarchical multiple regression, accounted for an additional 7% of the variance in PPVT-R scores for 3- to 5-year-olds but only an additional 2% for 6- to 8-year-olds. Their study also found that the correlation between maternal intelligence and child IQ and the correlation between the home environment and child IQ were of similar magnitude for children aged 3 to 5. In contrast, for children aged 6± 8, the association between maternal IQ and child IQ was markedly higher than the association between home environment and child IQ. Although several possible explanations for these findings were posited, the results are consistent with the hypothesis that the heritability of intelligence increases as children develop and mature (Plomin, 1990). Likewise, the direct effect of the home environment on IQ appeared to diminish as children grew older. Finally, a similar study undertaken with an African ± American subsample of 6- to 9-year-olds from the NLSY yielded results consistent with those just mentioned (Luster & McAdoo, 1994). Although HOME scores were significantly correlated with PPVT-R, PIAT-Math, PIAT-RR (Reading Recognition), and PIAT-RC (Reading Comprehension) scales, hierarchical multiple regression analyses revealed that HOME scores predicted only an additional 2% of the variance for each of the four outcome measures when entered after maternal IQ. Maternal IQ, on the other hand, explained an additional 3% to 14% of the variance when entered after the HOME measures. These latter two studies suggest that the measured effects of the home environment on cognitive outcomes among older children may be substantially reduced when genetic factors that are represented by maternal IQ are given causal priority. This is seemingly inconsistent with the Yeates et al. (1983) study of infants, which found that the direct effect of the home environment actually increased from 24 to 48 months (although maternal IQ still remained a significant predictor at all ages). However, given the differences in age between the Yeates et al. (1983) and NLSY samples, it is possible that all three studies are correct. Perhaps the importance of the home environment increases among the early stages of cognitive development, but then decreases as children age. Nevertheless, all three of the above studies used a sample of single children in families to examine the relationship between the home environment and child IQ while controlling for the effects of maternal IQ. Such a strategy does not directly assess the genetic and environmental contributions to individual differences in child IQ, nor does it investigate whether genetic variation influences the relationship between home environment and IQ. A better approach to address these questions is to use a genetically informative research design, i.e., one that includes multiple individuals from the same family and that includes siblings of varying degrees of genetic relatedness (e.g., identical [MZ] and fraternal [DZ] twins; adoptive and nonadoptive siblings).

4 72 CLEVELAND ET AL. For many decades, researchers have been using family pedigree, adoption, and twin designs to tease apart the proportion of variation in IQ that is due to shared environmental influences, nonshared environmental influences, and genetic influences (see Bouchard & McGue, 1981; Plomin, 1990; and Rowe, 1994, for reviews). What these studies consistently find is that the majority of variation in IQ, typically 40% to 70%, is due to genetic factors. For instance, in the widely-cited meta-analysis by Bouchard and McGue (1981), the average, weighted IQ correlation among identical, or monozygotic (MZ) twins was 0.86, and the average, weighted IQ correlation among fraternal, or dizygotic (DZ) twins was Because MZ twins share 100% of their genes, and DZ twins share, on average, only 50% of their genes, the heritability of IQ (i.e., the proportion of variation in IQ that is due to genetic factors) can be estimated by doubling the difference between the correlations for MZ and DZ twins. Using this formula, the heritability of IQ is estimated at Similarly, the proportion of variation in IQ that is due to shared environmental factors can be obtained by subtracting the heritability estimate from the MZ twin correlation. Using the above example, the estimate of the proportion of variation in IQ that is due to shared environmental factors is This implies that similarity in IQ among siblings in the same family is due more to shared genes than to shared environments. Adoption studies further strengthen this conclusion. For example, the average, weighted IQ correlations among unrelated adoptive siblings range from 0.29 to 0.34 (Bouchard & McGue, 1981), indicating, again, that shared rearing environments explain only one-third of the variation in IQ among siblings. More recently, Segal (1999) reported that the IQ correlation for same age, unrelated siblings who had been reared together was only Although her sample was only about 50 pairs, the 0.17 estimate is significantly lower than the 0.50 correlation typically reported for full-siblings reared together (Bouchard & McGue, 1981), indicating that genetic factors explain a larger proportion of variation in IQ than shared family environment factors. Moreover, although estimates of 0.17 to 0.34 for shared environmental influences on variation in IQ imply that family rearing environments do play some role in explaining individual differences in IQ, there is also evidence that shared environmental influences decrease and heritability estimates increase from infancy and childhood to adolescence and adulthood (Plomin, 1990; Wilson, 1983). For example, using data from a variety of different sources, Plomin (1990) reported that whereas the average heritability estimate and the average shared environmental estimate were both approximately 0.30 in childhood, the heritability of IQ rose to 0.50, and the shared environmental effect decreased to 0.10 during adolescence. Similarly, in a recent review of twin and adoption studies of adult IQ, Bouchard (1998) reported that heritability ranged from 0.60 to 0.80, and that shared environmental effects were near zero. If shared environmental influences decrease during adolescence and adulthood, then it is less likely that differences in family rearing environments cause differences in IQ during late childhood and adolescence. On the other hand, although a number of studies have demonstrated that virtually 100% of the correlation between general intelligence and achievement and ability is mediated genetically, the correlation between intelligence and achievement is considerably less than 1.0 (Brooks, Fulker, & DeFries, 1990; Petrill & Thompson, 1993). Thus, clearly

5 THE HOME AND CHILD AND ADOLESCENT ACHIEVEMENT 73 there are factors other than those related to intelligence that explain individual differences in achievement. Some of these factors are unique genetic influences (i.e., those independent of IQ; e.g., Pedersen, Plomin, & McClearn, 1994), but other factors are likely to be environmental in origin (Petrill, 1997; Thompson, Detterman, & Plomin, 1991). Moreover, empirical research has demonstrated that the heritability of achievement is lower than the heritability of IQ (e.g., Brooks et al., 1990; Petrill & Thompson, 1993). For example, in a multivariate analysis of IQ and reading achievement (assessed using the PIAT-RR, PIAT-RC, and PIAT-Spelling subscales) among 7±20-year-old twins, Brooks et al. (1990) reported that the heritabilities of the three achievement measures ranged from 0.21 to 0.45, compared to the heritability of 0.57 found for IQ. Likewise, in a multivariate analysis of temperament, IQ, and achievement among 6± 15-year-old twins from the Western Reserve Twin Project, Petrill and Thompson (1993) discovered that the heritability of achievement was substantially lower than the heritability of IQ (0.28 vs. 0.46). Petrill and Thompson (1993) also reported that shared environmental influences explained nearly twice as much variation in achievement than they did variation in IQ (66% vs. 38%). Finally, another study using the Western Twin Reserve sample reported that the heritabilities of achievement (reading, math, and language) ranged from 0.19 to 0.29, and that shared environmental influences explained 62% ± 71% of the variation in achievement (Thompson et al., 1991). In contrast, the heritabilities for specific cognitive abilities (verbal, spatial, speed, and memory) ranged from 0.45 to 0.76, and shared environmental influences on cognitive ability were estimated at zero (Thompson et al., 1991). Nevertheless, it is clear from these studies that genetic factors also account for a significant proportion of variation in achievement, although shared family influences may be equally, if not more, important. Behavior genetic research has also determined that variation in measures of the environment is due, at least in part, to genetic influences (Plomin, 1995; Plomin & Bergeman, 1991; Rowe, 1994). For example, the heritability of many dimensions of parenting (with the notable exception of parental control) ranges from approximately 0.20 to 0.56 (e.g., Neale et al., 1994; Plomin, McClearn, Pedersen, Nesselroade, & Bergeman, 1988, 1989; Plomin, Reiss, Hetherington, & Howe, 1994). Furthermore, even studies of objective measures, such as observer ratings of parenting behavior, report significant heritabilities (Lytton, 1977; O'Connor, Hetherington, Reiss, & Plomin, 1995). Because the provision of an intellectually stimulating family environment is determined in large part by the behavior of parents, it should not be surprising that even some of the most objective measures of family environment, such as the HOME, also show substantial heritabilities (e.g., Braungart, Fulker, & Plomin, 1992; Coon, Fulker, DeFries, & Plomin, 1990). Thus, because parents provide genes as well as environments, and because some of the variation in both cognitive outcomes and measures of the environment is due to genetic factors, it is possible that the relationship between family environment and cognition may be mediated genetically (Plomin, 1995; Plomin et al., 1985). To date, only a few studies have examined this question, and their results suggest that at least part of the correlation between family environment and IQ is due to common genetic influences. Specifically, Plomin et al. (1985) compared correlation matrices of two measures of family environment (the FES and HOME scales) with multiple indicators of adjustment (including behavior problems, temperament, and intelligence) for adoptive and nonadop-

6 74 CLEVELAND ET AL. tive families when children were aged 12 and 24 months. Results from these multivariate analyses indicated that the average environment ±outcome correlation in adoptive families was only 0.09; for nonadoptive families it was 0.24, indicating genetic influences. However, they also reported that the differences in correlations between adoptive and nonadoptive families were smallest for the correlations between environment and mental development, suggesting that genetic factors may explain less of the correlation between environment and infant cognition than between environment and infant behavior problems or temperament. Yet, in a follow-up analysis of the same data, Coon et al. (1990) compared correlations of family environment at 12 and 24 months with child IQ at age 7 among adoptive and nonadoptive families and concluded that, overall, common genetic factors accounted for substantial part of the environment ±IQ correlation, with more than one-half of the HOME-IQ correlation mediated genetically. Finally, Braungart et al. (1992) compared the cross-correlations between the HOME and IQ in infancy for adoptive and nonadoptive siblings and determined that by age 2, common genetic factors accounted for one-half of the correlation between the HOME and child IQ. Although these early results speak to genetic influences on the relationship between home environment and IQ during infancy and early childhood, less is known about the genetic and environmental influences on the relationship between the home environment and achievement. Moreover, studies have not examined the relationship between home environment and cognition during late childhood and adolescence. On the one hand, if the home environment has a cumulative effect on intelligence, it is possible that shared environmental factors will explain more of the relationship between home environment and cognition during adolescence. Likewise, given that the present study uses a measure of achievement, not IQ, shared environmental influences may explain a larger proportion of the correlation between the home environment and achievement than studies of the correlation between home environment and IQ. On the other hand, given that the heritability of cognition typically increases, and shared environmental effects typically decrease during adolescence, it is also plausible that genetic influences on the relationship between home environment and cognition will be even stronger during adolescence than those found in the two studies of infancy and early childhood. Therefore, the goal of the present study is to further investigate the relationship between the HOME environment scale and achievement using a genetically informative sample of older children and adolescent full- and half-siblings. Because these two sibling groups differ in terms of their genetic relatedness, it is possible to estimate both the proportion of variation in achievement that is due to genetic and environmental factors, and the proportion of the correlation between the HOME and achievement that is caused by common genetic and/or common environmental factors. In line with other research, we expect that achievement will be heritable, and that genetic factors will explain between 20% and 45% of the variation. We also anticipate that shared environmental influences will explain a significant proportion of variation in achievement. Regarding the relationship between family environment and achievement, we expect that common genetic factors will account for a substantial proportion of the correlation between the HOME and adolescent IQ, and that shared environmental factors may or may not contribute to this correlation.

7 THE HOME AND CHILD AND ADOLESCENT ACHIEVEMENT 75 Methods Sample The NLSY Child Data Set The National Longitudinal Survey of Youth (NLSY), sponsored by the Bureau of Labor Statistics, Department of Labor, began in 1979 as a U.S. household probability sample of 11,406 respondents 14 to 21 years old, with some over-sampling of families from lower socioeconomic backgrounds (Center for Human Resource Research, 1994). Respondents have been resurveyed from 1980 through Beginning in 1986, extensive data on children born to female NLSY participants were also collected; as of the 1992 interview, 9360 children had been included. Their data comprise the NLSY Child Data sample (Baker, Keck, Mott, & Quinlan, 1993). Data for the present study are from the 1986, 1988, 1990, and 1992 waves of the NLSY Child data set. Although the NLSY Child Data set represents children born to a largely nationally representative probability sample, its participants (i.e., the children themselves) are not themselves nationally representative. First, both the NLSY original cohort and the NLSY Child Data sets have been subject to some attrition and nonresponse, although retention rates have remained between 90% and 95% during the 15 years of interviews. Second, the children in the Child Data Set were born to those mothers who initiated child bearing at younger ages. Only after the NLSY original cohort females complete their childbearing years will the NLSY children be born to a nationally representative sample of mothers. Lastly, it is the mothers, not the children, who were drawn by the probability sampling. Despite these limitations, this data set has been recognized as extraordinary in terms of the representation of the sampled families and its low rate of sample attrition (Chase-Lansdale, Mott, Brooks-Gunn, & Phillips, 1991). Construction of the Sibling Pairs Data Set Because this study used a behavioral genetics approach to examine genetic and environmental contributions to the relationship between family environment and achievement, it required information on the genetic relatedness of siblings. Although the NLSY does not explicitly contain this information, certain variables permit classification of a large percentage of NLSY-children into kinship pair groups that include both full-siblings and half-siblings (see Rodgers, Rowe, & Li, 1994; Van den Oord & Rowe, 1998, for details). Van den Oord's classification algorithm (Van den Oord & Rowe, 1998) used items about living arrangements from all four waves of data to distinguish full- and half-siblings. His algorithm assumes that mothers do not live simultaneously with different fathers. If mothers reported that both siblings lived with their biological mother and father, then siblings were considered to be full-siblings. If mothers indicated that the father of one sibling lived in the household but that the other sibling's father did not, the children were classified as half-siblings. If neither father lived at home, a second variable that specified how far away the fathers lived (i.e., ``within 10 miles'' to ``more than 200 miles'') was used. If maternal responses on this item differed for the fathers of each child, the siblings were classified as half-siblings. If maternal reports for this item were the same for each child, no definite classification could be made. This procedure classified 2925 full-sibling

8 76 CLEVELAND ET AL. pairs and 877 half-sibling pairs. Pairs where genetic relatedness could not be determined were excluded from analysis. The pairs formed included all possible sibling sets within each family. For example, in the case of a three child family with siblings a, b, and c, the program could create three sibling pairs: a and b, a and c, and b and c. It should be noted that this pairing procedure creates a mild degree of statistical dependency among pairs. Of the 2925 full-sibling and 877 half-siblings identified by the classification algorithm, 1614 full-sibling and 494 half-sibling pairs had valid data for both the HOME-SF and the achievement measure for both siblings. Of these, 652 pairs were Black, 550 Hispanic, and 906 Anglo. For the analyses, all sibling pairs were first sorted by age, so that sibling 1 (i.e., the first sibling) was always the older sibling. Siblings were split approximately equally across sexes, with 49.4% of first siblings and 51.7% of second siblings being male. The mean ages were 11.9 years (SD = 3.0 years) and 8.2 years (SD = 2.8 years) for older and younger siblings, respectively. Measures Achievement This study used four separate indices from the 1990 and 1992 assessments to form a composite measure of achievement: one IQ test (the Peabody Picture Vocabulary Test-Revised; [PPVT-R]) and three Peabody Individual Achievement Test (PIATS) subscales. The Peabody Vocabulary Picture Test-Revised (PPVT-R) provides an estimate of verbal aptitude by measuring an individual's receptive (i.e., hearing) vocabulary for standard English (Baker et al., 1993). The PPVT-R was given to children aged 3 and older and consists of 175 items of generally increasing difficulty. During the interview a word is read aloud, and the child points to one of four pictures that best describes that particular word's meaning. A score is assigned to each child according to where on the test the child misidentifies six out of eight consecutive words. The PPVT-R score was averaged with scores from three of the PIAT sub-scales: Mathematics, Reading Recognition, and Reading Comprehension, which measure quantitative reasoning, reading achievement, and ability to derive meaning from written sentences, respectively. The NLSY Handbook states that the PIAT is ``among the most widely used brief assessments of academic achievement having demonstrably high test ± retest reliability and concordant validity'' (Baker et al., 1993, p. 133). The PIAT was administered to all NLSY Children aged 5 and greater. All four scales were standardized to a mean of 100 and a standard deviation of 15 based on available age norms (Baker et al., 1993). Because of the high intercorrelations among scales (bivariate correlations ranged from 0.58 to 0.83), the four indicators of achievement (i.e., the standardized versions of the 3 PIAT subscales and the PPVT-R) were then averaged together. To increase reliability, the 1990 and 1992 scores for this achievement composite were then averaged into a single achievement measure. HOME-SF Home environments that should foster emotional and cognitive development were assessed using the HOME-SF (Baker et al., 1993). 2 The HOME-SF is a modification of

9 THE HOME AND CHILD AND ADOLESCENT ACHIEVEMENT 77 Table 1. Correlation Matrix of HOME Scores and Achievement for Full- and Half-Siblings HOME_S1 Achievement_S1 HOME_S2 Achievement_S2 HOME_S Achievement_ HOME_S Achievement_ Notes: S1 = Sibling 1; S2 = Sibling 2. Full-sibling correlations are above the diagonal; Half-sibling correlations are below the diagonal. the HOME inventory (Caldwell & Bradley, 1984), and uses both interviewer observation (e.g., yes/no reports of items such as ``[mother/guardian] conversed with child excluding scolding or suspicious comments'') and maternal self-report items (e.g., ``How many children's books does your child have of his or her own?'' and ``About how many hours is the TV on in your home each day?''). The original HOME scale, from which the NLSY HOME-SF was derived, has reliabilities in the high 0.80s to the low 0.90s (cited in Baker et al., 1993). Cronbach's Alpha for the HOME-SF scale for children over 3 ranges from 0.69 to 0.71 over the 1986 and 1988 assessments (Baker et al., 1993). Correlations between the HOME-SF scale in 1986 and the PIAT subscales in 1990 range from 0.28 to 0.30 (Baker et al., 1993). Moreover, previous studies using the data from the NLSY have found the HOME-SF to be significantly related to the Peabody Picture Vocabulary Test-Revised (Ferron, N'Gandu, & Garrett, 1995). The present study used the average of the total HOME-SF reports from the 1986 and 1988 assessments (r = 0.54; Baker et al., 1993) to create a single indicator of home environment. This composite score was then standardized to a mean of 100 and a standard deviation of 15 to make it more comparable to the achievement composite. Results Correlational Analyses The within-sibling HOME scores were found to be significantly related to achievement (within-sibling, cross-trait correlations equal 0.47 and 0.38 for sibling 1 and sibling 2, respectively, N = 2108, p < 0.001), indicating that the HOME-SF inventory is a fairly good predictor of child achievement 4 years later. Table 1 presents the HOME-SF and achievement correlations for full- and half-siblings, separately. Full-sibling correlations are presented above the diagonal; those of the half-siblings are below. Cross-trait, cross-sibling correlations are indicated in italics. Examination of these correlations reveals several patterns. First, genetic influences on achievement are apparent as the correlation between full-siblings exceeds that of half-siblings (0.59 vs. 0.35). However, shared environmental influences on achievement are also indicated, as the half-sibling correlation is more than one-half the full-sibling correlation. In contrast, shared environment appears to contribute more strongly to HOME scores, as the full-sibling correlation is only slightly greater than the half-sibling correlation (0.67 vs. 0.61). Lastly, the cross-trait, cross-sibling correlations among full-siblings (r = 0.40 and r = 0.36) are greater than those of the half-siblings (r = 0.19 and r = 0.30).

10 78 CLEVELAND ET AL. Figure 1. The full path model. Note: HOME = HOME-SF measure. ACH = achievement measure. Subscripts denote siblings 1 and 2. A K = common genetic influences; C K = common shared environmental influences; E K = common nonshared environmental influences; A ua = unique genetic influences on achievement; A uh = unique genetic influences on the HOME-SF; C ua = unique shared environmental influences on achievement; C uh = unique shared environmental influences on the HOME-SF; E ua = unique nonshared environmental influences on achievement; E uh = unique nonshared environmental influences on the HOME-SF; R = the coefficient of genetic relatedness. R = 0.5 for full-siblings and R = 0.25 for half-siblings. This suggests that some of the association between the HOME scores and achievement is genetically mediated. Model Fitting Analyses The structural equation modeling program, Mx (Neale, 1997), was used to estimate the extent of genetic mediation on the association between HOME-SF scores and achievement, and the genetic and environmental influences on the variation in the HOME-SF and in achievement. The full model is shown in Fig. 1. This model provided for common genetic (A K ), shared environmental (C K ), and nonshared environmental influences (E K ), as well as unique genetic, shared environmental and nonshared environmental influences for achievement and the HOME (A ua and A uh, C ua and C uh, and E ua and E uh, respectively). Genetic influences are correlated 0.5 for full-siblings, and 0.25 for half-siblings. In contrast, shared environmental influences are perfectly correlated among siblings, regard-

11 THE HOME AND CHILD AND ADOLESCENT ACHIEVEMENT 79 Table 2. Results from Hierarchical Model-Fitting Analyses Parameter a Goodness-of-Fit Indices Model A K C K E K A ua C ua A uh C uh x 2 df RMSEA NFI x 2 df 1. Full *** ± ± Dropping Common Factors *** *** *** *** *** 1 Dropping Unique Factors *** ** *** *** *** *** *** Notes: Model 2 is compared to the full model. All other models are compared to Model 2. + : Parameter is freely estimated. : Parameter set to zero. AK = common genetic influence. CK = common shared environmental influence. EK = common nonshared environmental influence. Aua = specific genetic influence on achievement. Cua = specific shared environmental influence on achievement. A uh = specific genetic influence on the HOME. C uh = specific shared environmental influence on the HOME. RMSEA = root mean square error approximation. NFI = normed fit index. a All models also include specific nonshared environmental influences on achievement and the HOME (not shown). ***p <

12 80 CLEVELAND ET AL. Figure 2. Standardized parameter estimates from the best-fitting model. Note: HOME = HOME-SF measure. ACH = achievement measure. A K = common genetic influences; C K = common shared environmental influences; A uh = unique genetic influences on the HOME-SF; C uh = unique shared environmental influences on the HOME-SF; E uh = unique nonshared environmental influences on the HOME-SF; A ua = unique genetic influences on achievement; E ua = unique nonshared environmental influences on achievement. Because parameters are identical for sibling 1 and sibling 2, only one set of parameters is displayed. Although the common parameters (A K and C K ) were constrained to be equal for the two measures (i.e., the HOME and ACH measures), slight variance differences in the two measures resulted in slightly different standardized values. less of their degree of genetic relatedness. Finally, by definition, nonshared environmental influences are uncorrelated across siblings. Table 2 shows the fit statistics for the full model (Model 1). Because the chi-square test is sensitive to sample size (Neale & Cardon, 1992), the Root Mean Square Error Approximation (RMSEA) was used to evaluate model fit. By this estimate (0.08) the full model fit the data well, as RMSEA estimates between 0.10 and 0.05 represent a good fit between the model and the data (Neale, 1997). Likewise, the value of the Normed Fit Index (NFI) was greater than 0.90, also indicating an overall good fit. Nested submodels were then tested on the data to determine the best fitting combination of parameters. The change in chi-square test can be used to evaluate comparative model fit. If the chi-square from the smaller model is not significantly different than the full model (i.e., the comparison model), then both models are assumed to fit the model equally well. Thus, the smaller, more parsimonious model is retained. Because the E K parameter from the full model was estimated at 0.0, the first of the nested submodels dropped the common nonshared environmental influence (Model 2).

13 THE HOME AND CHILD AND ADOLESCENT ACHIEVEMENT 81 The non-significant change in chi-square revealed this parameter could be dropped without reducing the model fit (x 2 = 0.0, p > 0.99). In contrast, subsequent dropping of either the common shared environment influence (Model 3) or the common genetic influence (Model 4) did disrupt model fit (x 2 = 319.7, p < 0.001; x 2 = 34.6, p < 0.001, respectively). Similarly, dropping the unique genetic influence on achievement (Model 5), the unique genetic influence on HOME-SF (Model 7), or the unique shared environmental influence on the HOME-SF (Model 6) also resulted in significant increases in chi-square (see Table 2). However, dropping the unique shared environmental influence on achievement did not reduce model fit (x 2 = 0.0, p > 0.99; Model 8). Thus, this model was accepted as the final model (see Table 2). Fig. 2 presents the parameter estimates from the best-fitting model. The final model provides evidence of the importance of both genetic and shared environmental contributions to the association between HOME-SF scores and achievement. Using the path coefficients from this model, the genetic contribution to the association between the HOME-SF and achievement can be estimated to be approximately 23% (0.28 * 0.32/[0.28 * * 0.59] = 0.229). Likewise, the influence of common shared environment on the covariation between HOME-SF and achievement can be estimated to be approximately 77% (0.51 * 0.59/[0.28 * * 0.59] = 0.771). Thus, genetic factors explain almost one-quarter of the correlation between the HOME-SF and achievement. Shared environmental factors account for the remaining three-quarters. The parameter estimates presented in Fig. 2 can also be used to partition the total variance of both achievement and HOME-SF into genetic, shared environment, and nonshared environment influences. According to calculations based on these parameters, genetic factors explained 36.3% of the variation in achievement and 38.1% of the variation in the HOME-SF. Shared environment accounted for an additional 34.8% of the variation in achievement and 50.0% of the variation in the HOME-SF. The remainder (29.2% and 13.0% of the variation in achievement and the HOME-SF, respectively) was taken up by nonshared environmental influences. 3 Discussion The present study investigated the extent to which genetic and shared environmental factors contributed to the relationship between a commonly used measure of the home environment (the HOME-SF scale) and achievement in late childhood and adolescence. The HOME-SF correlated 0.47 and 0.38 with child achievement measured approximately 4 years later; thus, family environment is a relatively good predictor of child achievement. As expected, genetic factors accounted for a significant proportion of the covariation between family environment and achievement, accounting for almost one-quarter (23%) of the correlation. However, the majority of the correlation (77%) was explained by shared environmental factors. Genetic factors and shared environmental factors also explained the majority of variance in both the HOME-SF measure and our measure of achievement. Specifically, genetic factors accounted for approximately 36% of the variation in achievement and 38% of the variation in the HOME-SF. Shared environmental factors accounted for an additional 35% and 50% of the variation in achievement and the HOME-SF, respectively.

14 82 CLEVELAND ET AL. In general, results from the present study are largely consistent with previous research. The heritability of 0.36 for achievement is consistent with other studies that have found that genetic influences accounted for between 21± 45% of the variance in achievement (e.g., Brooks et al., 1990; Thompson et al., 1991; Petrill & Thompson, 1993). Likewise, the present study found that genetic factors accounted for 38% of the variance in the HOME-SF, adding to the growing body of research that has demonstrated substantial genetic influence on measures of the home environment, including parenting behaviors (Braungart et al., 1992; Coon et al., 1990; Jacobson & Rowe, in press; Lytton, 1977; Neale et al., 1994; O'Connor et al., 1995; Plomin, 1995; Plomin & Bergeman, 1991; Plomin et al., 1988, 1989; Rowe, 1981, 1983, 1994). However, although the present study found that genetic factors account for 25% of the correlation between the HOME and achievement, a somewhat unexpected result was that shared environmental factors account for the majority of the covariance (i.e., 75%). One reason for the greater shared environmental influence on the covariation between family environment and achievement may be due to the particular family environment measure used in the present study. Specifically, the HOME scale contains items that must be identical for all children within a family (e.g., average number of hours the television is on). This serves to increase the correlations between all siblings, regardless of their level of genetic relatedness. A constant effect added to both types of sibling correlations (i.e., full- and half-siblings) would inflate the shared environment estimate. The fact that nonshared environmental influences explained only 12 ± 13% of the variation in the HOME-SF indicates that there is a strong familial resemblance for HOME scores among siblings. Using patterns of non-adoptive sibling correlations on items from the HOME scale, Chipuer and Plomin (1992) were able to identify two subscales of the HOME when used in infancy and early childhood: a nonshared subscale and a shared subscale. On the basis of comparisons of correlations among adoptive and nonadoptive siblings, the authors concluded that these influences represented primarily environmental, as opposed to genetic influences. Hence, it is possible that some of the items in the HOME-SF scale contributed to the large shared environmental influence on the relationship between the HOME-SF and achievement found in the present study. Although a comparison of the individual items in the HOME-SF scale is not possible with the present data set, future research should examine the genetic and environmental influences on other, more child-specific measures of intellectual stimulation. Nevertheless, two previous studies of the relationship between the HOME and IQ in infancy and early childhood found that genetic factors accounted for a greater proportion (approximately 50%) of the correlation (Braungart et al., 1992; Coon et al., 1990). The present study used a measure of achievement, not IQ, which may explain the somewhat weaker genetic effect on the HOME±cognition relationship in the present study. Likewise, if family environment has a cumulative effect on achievement, this would explain the greater shared environmental effect during late childhood and adolescence, compared to infancy and early childhood. Finally, particulars of the selection of our analysis sample may also explain the relatively low amount of genetic mediation. Specifically, the original NLSY study over-sampled families from lower socioeconomic statuses. Further, the children of the NLSY examined in the present study are children from younger mothers. Thus, the present sample most likely

15 THE HOME AND CHILD AND ADOLESCENT ACHIEVEMENT 83 contains children and adolescents from more impoverished environments than traditional twin and adoptive samples. Rutter (1985) suggested that disadvantaged environments are more likely to have an effect on achievement than more advantaged environments. Moreover, there is some evidence that shared environmental influences on variation in cognition are stronger for adolescents from lower socioeconomic statuses ( Rowe, Jacobson, & Van den Oord, in press). Therefore, the greater shared environmental influence found in the present study may partly reflect the distribution of the NLSY children across a range of social class levels. To the extent that the stronger shared environmental influence on the HOME ± cognition relationship replicates with other samples of older children and adolescents, results from the present study should be encouraging to policy makers, as they suggest that increasing the intellectual stimulation of home environments may be associated with corresponding increases in achievement. Furthermore, the strong shared environmental effect found in this sample of children of the NLSY suggests that such interventions may do the most good in more impoverished environments. This coincides with the group of children and adolescents that are the most likely targets of interventions. However, there may be a limit to the effectiveness of interventions, as genetic factors also account for approximately one-quarter of the correlation between the HOME-SF and achievement in childhood and adolescence. Strengths and Limitations The present study has a number of strengths. First, the use of two kinship groups makes it possible to separate genetic effects on the relationship between the HOME-SF and achievement from environmental effects, a capacity not found in studies of one kinship or single children. Second, the fact that the NLSY contains large numbers of African American and Hispanic children means that results may be more applicable to children and adolescents in more needy environments than findings from more typical twin and adoption studies. Finally, the NLSY-Child sample is large and contains considerable ethnic diversity. Thus, the results from the present study should be generalizable. The present study also has certain limitations. Specifically, although the present study reveals that both genetic and environmental factors contribute to the correlation between the HOME-SF and achievement, the mechanisms by which these factors exert their influence are as yet unidentified. For example, it seems plausible to presume that maternal intelligence may be partly responsible for the genetic mediation of the HOME-achievement relationship. However, a study of cognition in infancy and early childhood found that although maternal IQ correlated with both HOME scores and child IQ, partialling out the effect of maternal IQ did not change the pattern of HOME-child IQ correlations among adoptive and biological families (Plomin & DeFries, 1983), suggesting that maternal IQ may not be responsible for the genetic mediation of the HOME-achievement correlation. However, this hypothesis remains to be tested in samples of older children and adolescents, and with measures of achievement. Likewise, it would also be helpful to uncover the mechanisms responsible for the common environmental influence on the family environment±cognition relationship. To date, there have been some forays in this direction. For example, in an attempt to identify factors that account for the discrepancy between IQ and achievement among twins from the Western Reserve Twin Project, Petrill and Thompson (1993) investigated the genetic

16 84 CLEVELAND ET AL. and environmental influences on the relationship between IQ, achievement, and childhood temperament (e.g., sociability, emotionality, attentiveness and activity). Results indicated that although genetic factors accounted for nearly all of the relationship between IQ and achievement, both genetic factors and environmental factors contributed to the relationship between IQ and temperament. Moreover, nearly 100% of the relationship between achievement and temperament could be explained by shared environmental factors. However, these authors also noted that their specific measure of temperament was not strongly related to either IQ or achievement, and that temperament failed to explain the correlation between IQ and achievement. In more recent work, Petrill (1998) used a composite measure of school-related temperament and behavior (including social maturity, attentiveness, persistence, and activity) to predict the discrepancy between IQ and achievement in the same sample of twins. Similar to the previous results, the relationship between school-related temperament/behavior and achievement could be explained almost entirely by shared environmental factors. In addition, this composite measure of school-related temperament and behavior predicted the discrepancy between IQ and achievement, suggesting that school-related behaviors (and their etiology) may be important avenues to explore. Finally, sibling studies also provide a unique opportunity to explore the nonshared family environmental factors that may be related to cognition. Although accounting for only a small part of the variation, Rodgers, Rowe, and May (1994) found that differences in number of books owned by siblings predicted sibling differences in reading recognition. Likewise, differential exposure to museums was predictive of sibling differences in mathematical ability. Thus, in sum, behavior genetic research offers a variety of ways in which genetic and environmental contributions to individual differences in cognition can be explored. Acknowledgements: We thank Edwin J.G.O. Van den Oord for providing the kinship links used to identify the full- and half-siblings. This research was supported by a grant (HD21973) from the National Institute of Child Health and Development to the last author. Notes 1. It should be noted assortative mating among parents and selective placement among adoptive samples can also inflate estimates of shared environmental influences. 2. The HOME-SF was modified from the original HOME inventory in the following ways: First, the HOME- SF is approximately half as long as the HOME, making it well suited for large scale studies. Second, approximately one-half of the HOME-SF items are rewordings of original dichotomous observer ratings that have been transformed into multiple-response formats and have been completed by the mothers in the study. The remaining items are dichotomous observer ratings of the home environment (see Baker et al., 1993, for complete details). 3. Numbers do not sum to exactly 100% due to rounding errors. References Baker, P. C., Keck, C. K., Mott, F. L., & Quinlan, S. V. (1993). NLSY child handbook-revised edition: A guide to the 1986±1990 National Longitudinal Survey of Youth child data set. Columbus, Ohio: Center for Human Resource Research, The Ohio State University. Bouchard, T. J. (1998). Genetic and environmental influences on adult intelligence and specific mental abilities. Human Biology, 70, 257±279.

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