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. Author manuscript; available in PMC: 2016 Jun 1.
Published in final edited form as: J Fam Psychol. 2015 Jun;29(3):469–478. doi: 10.1037/fam0000090

What Makes Siblings Different? The Development of Sibling Differences in Academic Achievement and Interests

Alexander C Jensen 1, Susan M McHale 2
PMCID: PMC4460605  NIHMSID: NIHMS677858  PMID: 26053351

Abstract

To illuminate processes that contribute to the development of sibling differences, this study examined cross lagged links between parents’ beliefs about sibling differences in academic ability and differences between siblings’ grade point averages (GPAs), and cross lagged links between differences in siblings’ GPAs and sibling differences in academic interests. Data were collected from mothers, fathers, firstborn (M age at Time 1 = 15.71, SD = 1.07) and secondborn (M age at Time 1 = 13.18, SD = 1.29) youth from 388 European American Families on three annual occasions. Findings revealed that, after controlling for siblings’ average grades and prior differences in performance, parents’ beliefs about sibling differences in academic ability predicted differences in performance such that youth rated by parents as relatively more competent than their sibling earned relatively higher grades the following year. Siblings’ relative school performance, however, did not predict parents’ beliefs about differences between siblings’ competencies. Further, after controlling for average interests and grades, sibling differences in GPA predicted differences in siblings’ interests such that youth who had better grades than their siblings reported relatively stronger academic interests the following year. Differences in interest, however, did not predict sibling differences in GPA. Findings are discussed in terms the role of sibling dynamics in family socialization.

Keywords: academic achievement, adolescence, nonshared family environment, parental beliefs, sibling influences, sibling differences

Almost three decades ago Plomin and Daniels (1987) posed the question: “Why are children in the same family so different from one another?” (p. 1). Their question arose from the observation that, although they are 50% genetically similar, on average, and usually grow up in the same home, full biological siblings are typically no more similar to one another than they are to strangers. Indeed, accumulating evidence reveals that siblings often differ in domains ranging from substance use (Whiteman, Jensen, & Maggs, 2013) to anti-social behavior, depression, and general self-worth (Feinberg & Hetherington, 2000), and to academic interests and achievement (Conley, Pfeiffer, & Velez, 2007). Based on their analyses, Plomin and colleagues (Dunn & Plomin, 1991; Plomin, Manke, & Pike, 1996; Reiss et al., 1994) concluded that the major source of variation between siblings was not genetic differences but rather, their nonshared environments.

In the intervening years investigators have made some progress in understanding environmental sources of sibling differences, particularly the role of parents’ differential treatment of siblings (Burt, McGue, Iacono, & Krueger, 2006; Lam, Solmeyer, & McHale, 2012). Much remains to be learned, however, about family socialization processes that may give rise to sibling differences. Accordingly, in this study, we focused on sibling-related family dynamics as one potential source of sibling differences. Given findings of the relatively high levels of heritability for intelligence and cognitive skills (Plomin & Spinath, 2004), and because of the centrality of schooling in youths’ lives, we targeted sibling differences in academic achievement and interests. Grounded in ideas of sibling social comparison and differentiation (Ansbacher & Ansbacher, 1956; Feinberg & Hetherington, 2000; Schachter & Stone, 1986) and drawing on expectancy value theory (Wigfield, & Eccles, 2000), we examined: (a) the role of parents’ beliefs about sibling differences in academic ability in the development of sibling differences in academic achievement; and (b) the role of sibling differences in academic achievement in the development of sibling differences in academic interests. Using three years of longitudinal data, we controlled for siblings’ prior average grades and interests in an effort to isolate the effects of parents’ beliefs about sibling differences and measured sibling differences on youth’s academic development.

Sources of Sibling Differences

Past work highlights that structural and status characteristics such as dyad gender composition, (Iervolino, Hines, Golombok, Rust, & Plomin, 2005; Leavell, Tamis-LeMonda, Ruble, Zosuls, & Cabrera, 2012), birth order (Black, Devereaux, & Salvanes, 2005; Rodgers, Cleveland, van den Oord, & Rowe, 2000; Whiteman, McHale, & Crouter, 2003), and age spacing (Elder & Caspi, 1988) explain some of the differences between siblings. Furthermore, according to Adler’s theory of individual psychology and research on sibling differentiation (Ansbacher & Ansbacher, 1956; Feinberg & Hetherington, 2000; Schachter, Gilutz, Shore, & Adler, 1978) siblings differentiate from one another in order to fill unique niches within the family and thereby reduce competition and rivalry for family resources. Over time this process is thought to make siblings increasingly different from one another.

Alder’s ideas about sibling differentiation are consistent with principles from social comparison theory (SCT). SCT (Festinger, 1954; Suls, Martin, & Wheeler, 2002) holds that comparisons with others are integral to the human experience and serve a number of purposes, including developing a self-identity (“I am an athlete”) and maintaining a positive self-concept, such as by selecting a target to whom one compares favorably (“I am a better athlete than my brother”). A tenet of SCT is that individuals are most likely to compare themselves to those they see as similar, making siblings–who share a family home and heritage, who are often close in age, and who often resemble one another physically– a potent target for social comparisons. Although SCT focuses largely on within-individual, psychological processes, as Adler notes, others, particularly parents, also engage in sibling comparisons. In an earlier paper based on the sample for the present study, we reported that almost two thirds of parents described their two children as different across a number of domains and that, according to parents, differences between children were the primary reason why they treated their two children differently (author citation).

Parents’ Beliefs and Sibling Differences in Academic Achievement

Applying principles from expectancy value theory (Wigfield, & Eccles, 2000) may advance understanding of the implications of parents’ comparisons of siblings. Specifically, this theory suggests that when parents have higher expectations for achievement, their children perform better in school (Bandura, Barbaranelli, Caprara, & Pastorelli, 1996; Parsons, Adler, & Kaczala, 1982; Zhang, Haddad, Torres, & Chen, 2011). Integrating ideas about sibling comparisons with expectancy value theory tenets, we predicted that that parents’ expectations or beliefs about their two children’s relative abilities in the academic domain would lead to greater differences between siblings over time.

In contrast to perspectives that target sibling social comparisons as a primary driver of individual child development, expectancy value theory (Wigfield, & Eccles, 2000) suggests a reciprocal link between parents’ beliefs and youth’s achievement, a dynamic that is consistent with research on child-driven effects on parenting (Bell, 1968; Crouter & Booth, 2003). From this perspective, parenting is both cause and consequence of youth behavior and development. Accordingly we anticipated that, in addition to parents’ beliefs about siblings’ relative abilities leading to later differences in achievement, differences between siblings’ achievement would predict parents’ beliefs about differences between siblings’ abilities. Past work has suggested that parents may have greater expectations for the achievement of daughters compared to sons (Bhanot & Jovanovic, 2005; Spera, 2006), and possibly for firstborns compared to later-borns (Hao, Hotz, & Jin, 2008). Thus we tested whether the patterns linking parents’ beliefs and siblings’ relative achievement would differ based on the gender and birth order composition of a sibling dyad.

Sibling Differences in Academic Achievement and Interests

According to expectancy value theory, youth process and internalize their achievement-related experiences in ways that have implications in areas such as youth’s self-concepts of their abilities, the value they place on particular tasks and activities, and ultimately, their achievements. Expectancy value theory highlights the role of youth’s prior achievement-related experiences as a primary influence on the value they place on a given domain of achievement. From this perspective, success in an achievement domain such as academics should foster a positive academic self-concept and thereby, greater “subjective task value” for academic activities. Consistent with this tenet, several studies have shown that youth who perform better in school exhibit higher academic self-concepts and academic interests than youth who perform more poorly (Chen, Yeh, Hwang, & Lin, 2013; Dotterer, McHale & Crouter, 2009; Guay, Marsh, & Boivin, 2003; Marsh, Trautwein, Lüdtke, Köller, & Baumert, 2005).

In this study, we drew on Adlerian theory to incorporate the potential role of sibling comparisons, extending expectancy value theory to test whether, controlling for average levels of GPA and interests, sibling differences in GPA predicted sibling differences in the subjective task value of academic activities, operationalized in terms of youth’s self-rated interest in academics. To the tenet from expectancy value theory that prior success should enhance, and prior failure should detract from subjective task value, Adlerian theory adds that siblings will differentiate because differences between them minimize reasons for social comparison and competition. Thus, we tested the hypothesis that, controlling for average GPA, differences between siblings’ GPAs would predict differences in their academic interests over time, such that siblings who performed better would be relatively more interested, and siblings who performed more poorly than their sister or brother would be relatively less interested in academics. Because siblings may compare themselves based on their academic interests as part of the differentiation process and because declines in academic interests are linked to declines in school achievement (Dotterer, et al., 2009), we also tested the reciprocal direction of effect–that differences between siblings’ academic interests would predict differences in their GPAs.

Current Study

In sum, grounding our work in Adler’s theory of individual psychology and research on social comparison and sibling deidentification (Ansbacher & Ansbacher, 1956; Festinger, 1954; Schachter & Stone, 1986) and building on expectancy value theory (Wigfield, & Eccles, 2000) we tested the role of sibling-related family dynamics in youth development in the academic domain. Given prior work on the role of gender and birth order in differences in parents’ academic expectations and youth’s achievement (Bhanot & Jovanovic, 2005; Hao et al., 2008; Spera, 2006), we first describe dyad gender constellation and birth order differences in parents’ beliefs about sibling differences in academic abilities as well as sibling differences in GPA and academic interests. Next, we tested that hypotheses that, controlling for average GPA: (a) parents’ beliefs about sibling differences in academic abilities would predict differences between siblings’ GPAs over time; and (b) differences between siblings’ GPAs would predict parents’ beliefs about differences in their children’s academic abilities. Next, we assessed the longitudinal linkages between sibling differences in GPA and siblings’ academic interests, testing the hypotheses that, controlling for average GPA: (a) differences between siblings’ GPAs would predict differences in their academic interests, and (b) differences in siblings’ academic interests would predict differences in their GPAs.

Method

Participants

Participants were mothers and fathers and first- and second-born siblings from 388 families who participated in a larger longitudinal study of family relationships and youth development and adjustment. We used three annual waves of data when siblings were in early and middle adolescence (Times 1, 2 and 3 hereafter). Youth came from two-parent, always-married, primarily European American, working and middle class families with at least two children. They were recruited via letters sent home from school in 17 school districts in a northeastern state. Over 90% of families that responded and met selection criteria (i.e., parents were always-married and the two oldest siblings were no more than 4 years apart in age) agreed to participate. Parents were in their early 40s (mothers M = 41.08, SD = 4.14; fathers M = 43.17, SD = 4.89) and had completed some college or post high school training, on average (mothers M = 14.58, SD = 2.16; fathers M = 14.55, SD = 2.39, on a scale where 12 = high school graduate and 16 = college graduate). For these primarily dual earner families, annual family income averaged $73,861 (SD = $38,109). At Time 1 firstborns averaged 15.71 (SD = 1.07) and second-borns averaged 13.18 (SD = 1.29) years of age. Sibling dyads were nearly evenly distributed among the four possible gender constellations, older sister-younger sister (24%), older sister-younger brother (24%), older brother-younger sister (26%), and older brother-younger brother (26%) Reflecting the population from which this sample was drawn, the sample was almost exclusively European American (U.S. Census Bureau, 2000).

Procedure

Annual in-home interviews were conducted by trained interviewers. Informed consent from parents and assent from youth were first obtained, and families received an honorarium ranging from $100 – $200 depending on the year of the study. Separate and private interviews lasted between 1 (youth) and 3 (parent) hours, and focused on parents’ and adolescents’ family relationships and personal characteristics.

Measures

Parent reports of differences between siblings’ academic abilities

Using a measure adapted from the Sibling Inventory of Differential Experience (Daniels & Plomin, 1985), mothers and fathers were asked: “To what extent are (younger and older sibling’s names) different when it comes to school and the academic arena, such as getting good grades? Would you say that (younger sibling) is a lot better at school work, that (older sibling) is a lot better at school work, or are they somewhere in between?” Parents answered this question using a 5-point scale that captured the direction of sibling differences: 1 (younger sibling a lot more); 2 (younger sibling a little more); 3 (both the same); 4 (older sibling a little more); 5 (older sibling a lot more). The correlations between mothers’ and fathers’ beliefs ranged from .78 to .83 across the three time points and were not significantly different. Thus, we used the average of the two parents’ ratings in the analyses. Across the three time points, 17–19% of parents reported that their children were equal in ability, 33–36% rated the younger child as a little or a lot more capable, and 47–48% rated the older child as more capable.

Differences in GPA

Data on siblings’ school grades came from youth’s most recent report cards, which parents were asked to make available during the home interviews. Letter grades in four subjects, language arts, math, social studies and science, were recoded into numerical values (A = 4; B = 3; C = 2; D = 1; E = 0). On average across all time points, both older (range in mean GPA: 3.18–3.28, SD: .66–.79) and younger (range in mean GPA: 3.18–3.32, SD: .68–.77) siblings’ grades fell in the B to B+ range. To create directional differences scores, GPAs of younger siblings were subtracted from those of older siblings, such that positive values reflected older siblings’ superior performance, negative values reflected the younger siblings’ superior performance, and a score of zero indicated that the siblings’ received the same GPA (range for mean difference in GPA: −.05 − .06, SD: .80 − .91).

Differences in academic interests

Adolescents rated their interest in four domains (language arts, math, social studies, and science) at each time point using a measure adapted from Huston, McHale, and Crouter (1985). Each academic subject was rated on a 4-point scale ranging from 1 (not at all) to 4 (very interested). On average, both older (range for mean interest: 2.44 – 2.48, SD: .67– .69) and younger (range for mean interest: 2.33 – 2.48, SD: .69 – .72) siblings reported moderate interest in academics. To create a directional difference score, younger siblings’ interest ratings were subtracted from older siblings’ ratings so that positive values reflected older siblings’ greater interest, negative values reflected younger siblings’ greater interest, and a score of zero indicated equal interest (range for mean difference in interests: .01 – .11, SD: .90 – .92).

Results

Descriptive Analyses

Analytic strategy

Because parents’ often have differing expectations for sons and daughters (Bhanot & Jovanovic, 2005; Spera, 2006) and may also have different expectations for first- versus later-born offspring (Hao et al., 2008) we began by testing whether sibling differences varied as a function of birth order and dyad gender constellation. We used a series of 2 (younger sibling gender) × 2 (dyad gender constellation: same versus mixed) × 3 (time) mixed model analyses of covariance, with younger sibling gender and gender constellation as between groups factors, time as a within-groups factor, and directional sibling differences in parent beliefs, GPA and academic interests as the dependent variables in three separate analyses. Covariates were sibling age spacing, maternal education, paternal education, family size, the sibling dyad’s average GPA (included in the parental belief and interest models), and the dyad’s average academic interest (included in the interest models).

Findings

These analyses (Table 1) revealed a significant, between-family, younger sibling gender X gender constellation interaction for parents’ beliefs about sibling differences in academic abilities, F(1, 155) = 4.62, p = .03, and differences in GPA, F(1, 283) = 7.02, p = .01, but not for academic interests. There were no significant effects for time, suggesting that, on average, parents’ beliefs and sibling differences in GPA did not change in level over time. Significant interactions were followed up by testing mean differences across groups for each dependent variable to identify gender constellation effects, and we assessed birth order effects by testing whether each mean sibling difference score was significantly different from zero. Because patterns did not vary over time, cross time averages are reported.

Table 1.

Cross Time Means (and SDs) for Parents’ Beliefs about Siblings’ Relative Academic Abilities, Sibling Differences in GPA, and Sibling Differences in Academic Interests as a Function of Birth Order and Dyad Gender Constellation.

Birth Order Dyad Gender Constellation
Total Sister-Sister Older Brother-Younger Sister Brother-Brother Older Sister-Younger Brother
Parents’ Beliefs     .17 (1.16)     .25a (1.00)   −.27b (1.18)   .22a (1.18)   .52a (1.13)
Differences in GPA −.02 (.74) −.08a (.63) −.24a (.78) .03a (.74) .21b (.71)
Differences in Interests   .06 (.74)   .10a (.69)   .00a (.84) .11a (.68) .02a (.74)
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Note: Means by gender constellation in the same row that do not share subscripts differ at p < .05. Positively signed scores signify that older siblings are perceived as more competent, obtain higher GPAs and have greater interest in academics; bolded means indicate that difference scores are significantly different from zero (zero = perceived equal ability, equal GPA, or equal interests) at p < .05.

Results revealed that parents rated daughters and older siblings as more academically competent than sons and younger siblings. In terms of GPA, however, older siblings did not outperform younger siblings overall. Rather, older sisters outperformed their younger brothers, and younger sisters outperformed their older brothers. Academic interests did not differ by birth order or gender.

Sibling Differences and Academic Functioning

Analytic strategy

Using Mplus 6 (Muthén & Muthén, 2010) we tested a series of structural equation models (SEM) to address our hypotheses. The first set of analyses (Figure 1; hereafter referred to as the beliefs models), examined whether parents’ beliefs about sibling differences in academic abilities predicted sibling differences in GPA and whether sibling differences in GPA predicted parents’ beliefs about sibling differences in academic abilities—controlling for prior average levels of GPA. The second set (Figure 2; hereafter referred to as the interests models) tested whether sibling differences in GPA predicted differences in academic interests and whether differences in academic interests predicted differences in GPA. Following recommendations for cross-lagged analyses (Burt et al., 2006; Rovine & Liu, 2012) we conducted these analysis in a series of steps to determine the best fitting model. Model 1 in each analysis examined the stability of each measure across time (beliefs model, paths g1g2, g2g3, b1b2, and b2b3; interests model, paths gi1gi2, gi2gi3, i1i2, and i2i3), as well as within-time correlations (beliefs model, paths r1, r2, and r3; interests model, paths ri1, ri2, and ri3). Model 2 included all paths from the stability model as well as paths from GPA differences to beliefs (beliefs model, paths g1b2 and g2b3) or differential interests (interests model, paths gi1i2 and gi2i3). Model 3 included the paths from the stability model as well as lagged paths from parents’ beliefs to differences in GPA for the beliefs model (paths b1g2 and b2g3), and from differences in interests to differences in GPA for the interests model (paths i1gi2 and i2gi3). Model 4 included all paths. We used the chi-squared statistic (Χ2), root-mean-square-error of approximation (RMSEA), and comparative fit index (CFI) to ascertain the best fitting model.

Fig. 1.

Fig. 1

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Conceptual model of links between sibling differences in GPA and parents’ beliefs of differences in siblings’ academic abilities. Stability model indicated by paths g1g2, g2g3, b1b2, and b2b3. GPA-driven model indicated by all stability paths as well as g1b2 and g2b3. Beliefs-driven model marked by all stability paths and b1g2 and b2g3. Reciprocal model indicated by all paths. Double headed arrows indicate correlations. Endogenous variables at Times 2 and 3 controlled for gender constellation, younger sibling gender, age difference, younger sibling age at Time 1, maternal education, paternal education, family size, and siblings’ average GPA across time points, but control variables were omitted from the figure for parsimony. Coefficients for the final model, the beliefs driven model, are reported.

Fig. 2.

Fig. 2

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Conceptual model of links between sibling differences in GPA and sibling differences in academic interest. Stability model indicated by paths gi1gi2, gi2gi3, i1i2, and i2i3. GPA driven model indicated by all stability paths as well as gi1i2 and gi2i3. Interests driven model marked by all stability paths and i1gi2 and i2gi3. Reciprocal model indicated by all paths. Double headed arrows indicate correlations. Endogenous variables at Times 2 and 3 controlled for gender constellation, younger sibling gender, age difference, younger sibling age at Time 1, maternal education, paternal education, family size, siblings’ average GPA across time points, and siblings’ average academic interests across time points, but control variables were omitted from the figure for parsimony. Coefficients for the final model, the GPA driven model, are reported.

Additionally we conducted a multi-group analysis for both the beliefs and the interests models based on four gender constellation groups (older sister, younger sister; older brother, younger sister; older brother, younger brother; older sister, younger brother). Multi-group analyses were conducted for Model 2, Model 3, and Model 4 described above.

Findings

Results for the beliefs model are presented in Table 2. The Χ2 difference between the stability model (Model 1; Figure 2) and the GPA-driven model (Model 2) was not statistically significant, indicating that, at least by adolescence, there was no support for the hypothesis that differences between siblings’ GPAs predicted parents’ beliefs about siblings’ relative abilities. The Χ2 difference between the beliefs-driven model (Model 3) and the stability model was significant, however, indicating that parents’ beliefs about siblings’ relative academic abilities predicted later differences in their GPAs, beyond the stability coefficients. The Χ2 difference between the stability model and the reciprocal model (Model 4) was statistically significant, but the reciprocal model did not fit significantly better than the beliefs-driven model. Although the beliefs-driven model and the reciprocal model had the same values for the RMSEA and CFI indices, the beliefs-driven model was selected as the best fitting model because it was more parsimonious. These results support the notion that parents’ beliefs about siblings’ relative abilities are linked to later differences in siblings’ academic performance. Furthermore, the additional multi-group analyses on the GPA-driven model, beliefs-driven model and the reciprocal model suggested that these patterns are similar across sibling dyads, regardless of the siblings’ genders.

Table 2.

Standardized Path Coefficients and Fit Statistics for Models Linking Sibling Differences in GPA and Parents’ Perceptions of Sibling Differences in Academic Ability (N = 388).

Model 1
Stability		 Model 2
GPA-Driven		 Model 3
Beliefs-Driven		 Model 4
Reciprocal
Path coefficients
GPA T1 → GPA T2 (g1g2) .55*** .56*** .37*** .39***
GPA T2 → GPA T3 (g2g3) .49*** .50*** .23*** .25***
Beliefs T1 → Beliefs T2 (b1b2) .89*** .80*** .91*** .86***
Beliefs T2 → Beliefs T3 (b2b3) .97*** .85*** .96*** .89***
GPA T1 → Beliefs T2 (g1b2) – .11   – .06  
GPA T2 → Beliefs T3 (g2b3) – .10   – .06  
Beliefs T1 → GPA T2 (b1g2) – – .24*** .23**
Beliefs T2 → GPA T3 (b2g3) – – .37*** .26***
Fit statistics
Χ2 (df) 110.36*** (61) 106.18*** (59) 75.20 (59) 73.85 (57)
ΔΧ2 compared to Model 1 – 4.18   35.16***   36.51***  
ΔΧ2 compared to Model 2 – – 30.98***   32.33***  
ΔΧ2 compared to Model 3 – – – 1.35   
RMSEA .05   .05   .03   .03  
CFI .98   .98   .99   .99  
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Note. RMSEA = root-mean-square error of approximation; CFI = comparative fit index.

*

p < .05

**

p < .01

***

p < .001

Results for the analyses linking siblings’ differential GPAs with differences in their academic interests are presented in Table 3. The Χ2 difference between the stability model (Model 1; Figure 2) and the GPA-driven model (Model 2) was statistically significant, indicating that differences in GPA predicted differences between siblings’ interests beyond the stability coefficients. The Χ2 difference between the stability model and the interests-driven model (Model 3) was not statistically significant. The Χ2 difference between the stability and the reciprocal model (Model 4) was statistically significant, but the reciprocal model did not fit significantly better than the GPA-driven model. Although the RMSEA and CFI indices indicated good fit for both the GPA-driven model and the reciprocal model, the GPA-driven model was selected as the best fitting model for reasons of parsimony. Overall, these results suggest that siblings’ relative academic performance is linked with future sibling differences in academic interest. Additionally, the results of the multi-group analysis on the GPA-driven model, interests-driven model and the reciprocal model suggest that these patterns are similar for sibling dyads regardless of their genders.

Table 3.

Standardized Path Coefficients and Fit Statistics for Models Linking Sibling Differences in GPA and Siblings’ Difference in Academic Interest (N = 387).

Model 1
Stability		 Model 2
GPA-Driven		 Model 3
Interests-Driven		 Model 4
Reciprocal
Path coefficients
GPA T1 → GPA T2 (gi1gi2) .56*** .57*** .37*** .56***
GPA T2 → GPA T3 (gi2gi3) .38*** .40*** .55*** .38***
Interests T1 → Interests T2 (i1i2) .53*** .50*** .53*** .50***
Interests T2 → Interests T3 (i2i3) .44*** .42*** .45*** .43***
GPA T1 → Interests T2 (gi1i2) – .15*** – .15***
GPA T2 → Interests T3 (gi2i3) – .12** – .11**
Interests T1 → GPA T2 (i1gi2) – – .04 .04
Interests T2 → GPA T3 (i2gi3) – – .06 .06
Fit statistics
Χ2 (df) 51.56*** (24) 33.54 (22) 49.41*** (22) 31.58* (20)
ΔΧ2 compared to Model 1 – 18.02*** 2.15 19.97***
ΔΧ2 compared to Model 2 – – 15.87*** 1.95
ΔΧ2 compared to Model 3 – – – 17.81***
RMSEA .05 .04 .06 .04
CFI .95 .98 .95 .98
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Note. RMSEA = root-mean-square error of approximation; CFI = comparative fit index.

*

p < .05

**

p < .01

***

p < .001

Discussion

Almost three decades have passed since Plomin and Daniels (1987) raised their question about sibling differences, and we still know little about family processes that explain sibling differentiation. This study drew on early writings by Adler (Ansbacher & Ansbacher, 1956), Festinger (1954), and Schachter (Schachter & Stone, 1986) which highlight the potential significance of sibling social comparisons in individual development and adjustment, as well as expectancy value theory (Wigfield, & Eccles, 2000), which directs attention to socializers’ beliefs about youth abilities and the task value of activities in youth achievement, to examine the development of sibling differences in academic functioning. Descriptive analyses revealed that parental beliefs about differences between their children varied as a function of sibling dyad gender constellation and birth order, but that actual differences in GPA were evident only in mixed sex sibling dyads—wherein sisters performed better than brothers. Further, consistent with our hypotheses, parents’ beliefs about differences between their children’s academic abilities predicted differences between siblings’ GPAs, and differences between siblings’ GPAs predicted differences in their academic interest. In contrast, we found no evidence that differences in siblings’ GPAs predicted parental beliefs about siblings’ relative competence or that differences in siblings’ interests predicted differences in siblings’ GPAs during adolescence. Below we discuss the implications of these findings and their contributions to our understanding of the role of sibling-related family dynamics in youth’s academic achievement.

Sibling Differences in Academic Functioning

Our descriptive analyses implicate dyad gender constellation and birth order in parents’ beliefs about sibling differences in academic competence as well as in sibling differences in GPA. Since the early 1990s when the American Association of University Women concluded that schools were shortchanging girls (AAUW, 1992), the gender gap in school achievement, then thought to favor boys, has been reversed. Girls now outperform boys in every subject, from elementary through high school, and projections are that in 2015, nearly 60% of college freshmen will be women (U.S. Department of Education, 2010). Published data on the gender gap in school achievement generally come from between-family comparisons of boys and girls from different families, and our findings showed that this same pattern was evident in within-family comparisons of brothers and sisters. Findings from quantitative behavior genetics studies documenting the heritability of intelligence (Plomin & Spinath, 2004), however, suggest that sisters and brothers should be relatively similar in their academic skills. Studies that “unpack” what it is about gender that explains sister-brother differences in school achievement and parents’ beliefs about their daughters’ versus sons’ academic competencies may inform understanding of widening gender differences in academic achievement.

Although gender differences in GPA were congruent with parents’ beliefs about gender differences in academic achievement, the pattern for birth order revealed that, with the exception of older brother-younger sister pairs, firstborns were viewed as more academically competent than secondborns, even though differences in their GPAs were not statistically different from zero. A large and hotly debated body of literature has investigated birth order effects on achievement, testing hypotheses from the resource dilution and confluence models (Downey, 1995; Zajonc, 1976), with inconsistent results. Our findings add a new element to this discourse in documenting birth order differences in parents’ beliefs about differences in their children’s academic competencies. Analysis of the bases for parents’ beliefs was beyond the scope of this study and awaits future research. At any given time during childhood and adolescence, older siblings are likely to outperform their younger siblings given their greater maturity, leading parents to generalize about their greater competence. And, due to their trailblazing role, firstborns’ achievements are likely to be highly salient to parents: By the time later born children hit learning milestones, parents may view their achievements as only-to-be-expected. Parents’ beliefs also may reflect cultural stereotypes about firstborns as more achievement and authority-oriented (Sulloway, 1996). In this light, however, the beliefs of parents with firstborn sons and secondborn daughters are striking. As with gender, a direction for future research is to investigate what it is about birth order that may explain parents’ beliefs about their children’s differential competence.

Parents’ Beliefs and Sibling Differences in Academic Achievement

Drawing on about sibling social comparison and differentiation (Ansbacher & Ansbacher, 1956; Festinger, 1954; Schachter & Stone, 1986) and from expectancy value theory (Wigfield, & Eccles, 2000), we hypothesized that parents’ beliefs about their children’s differential academic abilities would lead to differences between siblings’ GPAs that corresponded to parents’ beliefs. Consistent with this hypothesis, analyses revealed that offspring whom parents believed to be relatively more academically competent outperformed their siblings in school, and offspring whom parents believed to be relatively less competent were outperformed by their siblings, after controlling for relative achievement the prior year and average GPA. These findings are consistent with expectancy value theory and with past research that documents positive associations between parents’ beliefs and youth’s academic achievement (Bandura et al., 1996; Parsons et al., 1982; Zhang et al., 2011). Our study extended earlier research, which has focused on the effects of parents’ beliefs about an individual child, to incorporate the potential implications of a sibling-related family dynamic—parents’ beliefs about differences between their children. Most children in the U.S. and around the world grow up in a home with one or more siblings (McHale, Updegraff & Whiteman, 2012), meaning that sibling comparisons are likely a central–though understudied–family dynamic. An important next step in understanding how sibling social comparisons may have their effects is to investigate the individual and family processes that emanate from parents’ beliefs about sibling differences. Parents’ beliefs may be linked to differential investments of time and resources in their children, for example, and siblings may pick up on their parents’ beliefs regarding their relative abilities and use those cues to differentiate from one another in ways that fulfill parental expectations.

Importantly, parents’ beliefs predicted differences between siblings’ GPAs, but we did not find evidence of the reverse direction of effect: Differences between siblings’ GPAs did not predict parental beliefs. Our data showed that parents’ beliefs were quite stable over time, much more so than the stability of GPA differences. By the time siblings are adolescents, parents may have developed firm ideas about what their children are like and how they compare–beliefs that are less malleable than youth’s GPAs. Because adolescence is a developmental period marked by dramatic change, it may not be surprising that sibling differences are less stable than parents’ beliefs. Although our results did not support a child effects model, it may be that siblings’ relative abilities at earlier ages played a role in the development of parents’ beliefs. The emergence of parents’ beliefs about sibling differences is an important direction for future research. However, an equally important direction is to incorporate information about the potential implications of social comparisons between and among their children into parent education programs. Experimental designs could provide conclusive evidence of the role of parents’ beliefs in their children’s differential achievement, and effective parent education programs could help ensure that youth who may be quite academically competent, albeit just not as competent as a sibling, are not shortchanged by sibling-related family dynamics.

Sibling Differences in Academic Achievement and Interests

Further building upon sibling social comparison and differentiation (Ansbacher & Ansbacher, 1956; Festinger, 1954; Schachter & Stone, 1986) and expectancy value (Wigfield, & Eccles, 2000) concepts, we hypothesized a positive association between sibling differences in GPA and later differences in academic interests. Consistent with this hypothesis, controlling for prior interests, youth who had higher GPAs relative to their siblings became comparatively more interested in academics the following year. Findings from prior research are in keeping with expectancy value theory in documenting that youth who perform more poorly in school are subsequently less interested in academics (Guay et al., 2003; Marsh et al., 2005). We built on this research, which has focused on individual youth, to incorporate a broader family perspective and showed that youth’s achievement relative to a sibling was a unique predictor of differences between siblings’ interests. These findings are consistent with the idea that youth compare themselves to their siblings and differentiate in an effort to find a unique niche within the family and minimize competition and rivalry (Ansbacher & Ansbacher, 1956; Schachter & Stone, 1986; Sulloway, 1996). In this way, sibling social comparisons serve as a major force in youth’s identity development: Within the family system, youth who outperform their sisters or brothers may come to think of themselves and be treated by others as the “smart child” in the family, and their self-concepts, in turn may further motivate interest in academics. Most research on sibling influences to date has tested social learning tenets and highlighted the role of siblings as models to explain their similarities (Whiteman e al., 2013). Siblings also can serve as foils, however—a reference point for differentiation. Importantly, to the extent that these competing processes of modeling and differentiation are in operation, the effects of sibling influences may be underestimated. Thus, to better understand the role of sibling-related family dynamics in youth development, next steps for research are to measure modeling and differentiation processes directly (Whiteman, Jensen, & Maggs, 2014) and to investigate the conditions under which siblings identify with one another’s behavior and characteristics – or try to be different.

Conclusions

In the face of its contributions, this study is limited by its focus on adolescent-aged European-American siblings. As we noted, at younger ages, differences between siblings may shape parents beliefs, and a direction for research is to examine how parents’ ideas about similarities and differences between their children emerge and develop over time. It will also be important to study sibling-related family processes in other socio-cultural groups. Culturally-grounded attitudes and beliefs as well as contextually-based opportunities and constraints help to shape birth order and gender distinctions in siblings’ family roles, and cultural norms may have implications for both parental beliefs and sibling social comparison processes (Weisner, 1989). Additionally, each parent’s beliefs were assessed with one item. A strength of our study was that mothers and fathers provided independent ratings of sibling differences, with high inter-rater agreement. Future studies, however, should consider using a more comprehensive measure that may more fully capture parents’ beliefs about sibling differences.

Despite its limitations, this study advances understanding of sibling dynamics in families’ socialization of youth academic achievement. Our findings suggest that sibling social comparisons may have implications for individual youth: Everyday opportunities for social comparisons within families may mean that small differences between siblings early in life become more pronounced over time, and their developing self-identities and sense of their place in the family may have long term implications for youth’s achievement motivation and aspirations. These sibling-related family processes also may be tied to the larger social ecology. As we noted, current trends show that boys are falling behind girls in their academic achievement; these gender differences are especially apparent in minority families (U.S. Department of Education, 2010). To the extent that stereotypes about gender–for example, that academics is a feminine domain of achievement—follow from these gendered patterns, parental beliefs that daughters are more competent than sons may be reinforced, and in turn, further national trends in academic achievement. At the most general level, our findings document the significance of sibling-related family dynamics—a relatively neglected topic in the family and developmental literatures—and they suggest that the characteristics and experiences of siblings are part of the reason why children from the same family can be so different.

Contributor Information

Alexander C. Jensen, Brigham Young University

Susan M. McHale, The Pennsylvania State University

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