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Predicting intraindividual self-concept trajectories during adolescence
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Journal of Adolescence Journal of Adolescence 26 (2003) 586–600 www.elsevier.com/locate/jado
Predicting intraindividual self-concept trajectories during adolescence Jami F. Younga,*, Daniel K. Mroczekb a
Department of Child Psychiatry, Columbia University, 1051 Riverside Drive, Box #74, New York, NY 10032, USA b Fordham University, 441 East Fordham Road, Bronx, NY 10458, USA Accepted 22 July 2003
Abstract The level and stability of both global and domain-specific self-concepts was examined in a study of 253 adolescents, aged 11–20. Intraindividual self-concept trajectories were calculated through growth curve modeling. Six of the self-concept domains were stable over time. In the remaining three domains, there was a significant increase in perceived competency. Growth curve modeling highlighted individual differences in both level and change in self-concept for each of the domains. These differences were partially accounted for by the adolescents’ age and gender, suggesting that these variables are important determinants of adolescent self-concept. r 2003 The Association for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.
1. Introduction Adolescence is a time of physical, cognitive, and social change. Theorists and researchers (e.g., Dusek & Flaherty, 1981; McCarthy & Hoge, 1982; Savin-Williams & Demo, 1984; Rosenberg, 1986; Harter,1988a; Alsaker & Olweus, 1992) have questioned whether these social and biological changes alter one’s self-concept. Although a number of theorists (e.g., Rosenberg, 1986; Harter,1988a) believe that adolescence is a time of instability of self-concept, the early research in this area was inconclusive (Bachman, O’Malley, & Johnston, 1978; Wylie, 1979; Dusek & Flaherty, 1981; McCarthy & Hoge, 1982; Savin-Williams & Demo, 1984). One possible reason for the lack of consistent findings is that the majority of these studies focused almost exclusively on global self-concept rather than domain-specific self-concepts. However, the unidimensional *Corresponding author. E-mail address: [email protected] (J.F. Young). 0140-1971/$30.00 r 2003 The Association for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved. doi:10.1016/S0140-1971(03)00058-7
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approach masks important distinctions that people make when describing and evaluating their ability in various domains (Shavelson, Hubner, & Stanton, 1976; Marsh, 1987; Harter, 1990b). Looking only at global self-concept may have masked changes occurring in the various selfconcept domains, leading to the erroneous conclusion that self-concept is stable (Harter, 1990a). More recent studies have begun to examine change in domain-specific self-concepts during the course of adolescence. For instance, Shavelson and Bolus (1982) found that domain-specific self-concepts were less stable than general self-concept. Others (e.g., Abramowitz, Petersen, & Schulenberg, 1984; Marsh, 1989; Eccles et al., 1989; Wigfield, Eccles, Mac Iver, Reuman, & Midgley, 1991; Bolognini, Plancherel, Bettschart, & Halfon, 1996; Cole et al., 2001) have found that the rate of change differs across domains. For instance, Bolignini et al. (1996), using the Harter Self-Perception Profile for Children to study self-concept change in 12- to 14-year olds, found no change in global self-worth, athletic competence, or behavioral conduct, but found a decrease in physical appearance and social acceptance and an increase in scholastic competence ratings over time. These findings suggest that there are different rates and patterns of change across self-concept domains that are masked by a unidimensional approach to selfconcept. 1.1. Predictors of self-concept trajectories The aforementioned studies have documented some degree of difference among individuals with respect to self-concept trajectories. Using these investigations as a beginning, other researchers have begun to examine the variables responsible for individual differences in selfconcept. One such factor is age. Past studies suggest a decline in global self-concept during preadolescence, a reversal of this decline in early or middle adolescence, and an increase in selfconcept during late adolescence (Piers & Harris, 1964; Simmons, Rosenberg, & Rosenberg, 1973; Dusek & Flaherty, 1981; O’Malley & Bachman, 1983; Marsh, Parker, & Barnes, 1985; Marsh, Smith, Marsh, & Owens, 1988). In terms of domain-specific effects, several studies have found that academic and athletic self-concepts decline in early adolescence (Eccles et al., 1989; Marsh, 1989; Wigfield et al., 1991; Cole et al., 2001), as do appearance and social competence ratings (Marsh, 1989; Bolognini et al., 1996). Fewer studies have examined middle adolescence, but those that have report small increases in self-perceptions of athletic ability, physical appearance, and academic competence during this time (Marsh, 1989; Cole et al., 2001). Gender is another factor that has been examined. Studies have found that female adolescents are more likely than males to have low or declining global self-concept (Simmons & Rosenberg, 1975; Simmons, Blyth, Van Cleave, & Bush, 1979; Bailey et al., 1992; Zimmerman, Copeland, Shope, & Dielman, 1996). Three studies have looked at gender differences in domain-specific selfconcepts. Bolignini et al. (1996) found that females scored significantly lower than males on three of the six self-concept domains: global self-worth, physical appearance, and athletic competence. Cairns, McWhirter, Duffy, and Barry (1990) found that females scored significantly lower than males on cognitive competence, athletic competence, and global self-concept. Most recently, Cole et al. (2001) looked at differences in self-concept as a function of gender. In early adolescence, males had higher athletic, academic, and physical appearance self-perceptions, whereas girls had higher behavioral competency ratings. In middle adolescence, males continued to have higher athletic competence and physical appearance ratings than females.
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1.2. Individual growth curve modeling The lack of evidence in support of change in self-concept may also be attributed to the fact that most of the research conducted to date has looked at mean-level or correlational stability. Both means and correlations are anchored in the aggregate and therefore cannot differentiate group-level from person-level stability (Lamiell, 1981). As such, these techniques cannot properly address the question of whether individual persons exhibit change over time (Lamiell, 1981; Ozer & Gjerde, 1989). The recognition that individual change is masked by group findings has led to more sophisticated statistical applications for tracking change in self-concept, including cluster analysis (Hirsch & DuBois, 1991; Zimmerman et al., 1996), structural equation modeling (Cole et al., 2001), and examination of individual differences in magnitude and direction of change in self-concept (Harter, Whitesell, & Kowalski, 1992; Block & Robins, 1993). The development of hierarchical linear models (HLM), of which growth models is a type, offers another powerful technique for research on individual change (Bryk & Raudenbush, 1992). 1.3. Present study To examine level and stability of self-concept, individual growth modeling (Rogosa, Brandt, & Zimowski, 1982; Ware, 1985; Rogosa & Willett, 1985; Bryk & Raudenbush, 1987) was applied to longitudinal data on global and domain-specific self-concept in adolescents, aged 11–20. First, the study estimated change in a Level 1 model to describe level of self-concept and rate of change in adolescence. Next, a Level 2 model was employed to examine whether age and gender differentiate between those adolescents who had positive self-concepts or showed positive change over time, and those who had negative self-concepts or declined over time.
2. Methods 2.1. Sample Participants for the current study consisted of 253 adolescents, aged 11–20 (M=15.68; s.d.=2.19) who agreed to participate in the study. All participants were either students at Fordham University or students at one of three schools in the community that agreed to participate in the study. We sampled students in the 6th–12th grades in the community schools and Freshmen and Sophomores from Fordham University, resulting in a sample of 11–20-year olds. The schools in the community were a boarding/private day school, grades 6–9; an all-male Catholic high school, grades 9–12; and a public school, grades 6–12. Although these schools were located in different communities (i.e., Connecticut, NJ, and the Bronx), many of the students were from middle class families as assessed by parent educational status. The majority of participants identified themselves as White, with the next largest group self-identifying themselves as Hispanic. The demographic characteristics of the adolescents are listed in Table 1.
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Table 1 Demographic characteristics of adolescents n=253 N
%
Adolescent sex Male Female
138 115
54.5 45.5
Ethnicity White Hispanic Asian African-American Other
204 25 13 7 4
80.6 9.9 5.1 2.8 1.6
52 60 76 65
20.6 23.7 30.0 25.7
61 124 68
24.1 49.0 26.9
Maternal education Some high school High school graduate Some college College graduate Graduate school Missing
7 41 54 67 79 5
2.8 16.2 21.3 26.5 31.2 2.0
Paternal education Some high school High school graduate Some college College graduate Graduate school Missing
7 48 41 59 91 7
2.8 19.0 16.2 23.3 36.0 2.8
School Fordham University School A School B School C Age 11–13 14–16 17–20
2.2. Procedure The study utilized a multiwave assessment to collect data from the adolescents. All adolescents were to be evaluated at four timepoints over a 1-year period. Data collection began in the winter of Year 1 and ended the following winter so that subjects were seen across two different school
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years. Of the 253 adolescents, 56 completed only two of the four timepoints, 95 completed only three timepoints, and 102 completed all four timepoints. 2.3. Measures Self-concept. Self-concept was assessed by the Self-Perception Profile for Adolescents (SPPA; Harter, 1988b), a widely used 45-item questionnaire that measures global self-worth and eight specific domains of self-concept. The SPPA has adequate reliability and validity, with Cronbach alphas for the nine scales ranging from 0.78 to 0.95 depending on the sample used (Harter, 1988b). In the current study, the average alphas across timepoints ranged from 0.76 to 0.92. We conducted a factor analysis of the SPPA at the first timepoint using principal components extraction with varimax rotation. We extracted nine factors, which corresponded well to global self-concept and the eight specific self-concept domains. There was some fusion of subscales, for example the global and appearance items clustered together. However, the overall picture indicated that the nine selfconcept subscales were separate enough to be treated as independent, although related, scales. Predictors of intercept and change. To explain the individual differences in trajectories, we considered two sociodemographic variables that may be associated with level of self-concept or self-concept change: age at baseline and gender. Demographic variables. All participants were asked to complete a brief demographic form at Time 1. On this form, adolescents were asked their ethnicity (using categories of: White, Asian, African-American, Hispanic, Native American, and other) and their parents’ educational status. This information was used to describe the sample. 2.4. Data analysis To examine intraindividual change in self-concept, we used individual growth modeling techniques (Rogosa et al., 1982; Ware, 1985; Rogosa & Willett, 1985; Willett, Ayoub, & Robinson, 1991; Rogosa & Saner, 1995). In the within-person (Level 1) model, self-concept was regressed on time for each person. This yielded fixed effect and random effect estimates of the intercept and slope parameters for each of the self-concept domains. The fixed effects define the overall trajectory for the sample and the random effects define the individual deviations from the overall trajectory. Significant variances within the random effects indicate interindividual differences in level of self-concept or self-concept change. We estimated linear rather than non-linear models because Siegel (1975) and Hui and Berger (1983) showed that over short to moderate periods of time, linear models render a good approximation of non-linear effects. A Level 2 model used the slopes and intercepts as outcomes to identify possible predictors of level of self-concept and self-concept change. To test whether a variable was a significant predictor of individual differences in trajectories, we used the method recommended by Singer (1998) in which the predictor is entered along with a slope-by-predictor interaction. The coefficient for the predictor variable indicates whether the relationship between the predictor and initial status is significant. The interaction between the predictor variable and the slope indicates whether the rate of change in self-concept varies with differences in level of the predictor variable.
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3. Results In order to relate our findings to previous studies, preliminary analyses were conducted to examine mean-level change. These analyses were preliminary because repeated measures ANOVAs require complete data at all timepoints and, as such, do not maximally utilize the available data. Table 2 shows means and standard deviations for all of the self-concept domains at each of the four timepoints. The top half of Table 2 shows means based on all cases available from each timepoint. The bottom half of Table 2 displays means on only those participants with complete data at all four timepoints. To determine whether there was absolute-level change over time, repeated measures ANOVAs were performed on each of the nine self-concept domains for adolescents who had complete data at all four timepoints (102 of the 253 participants). There was a significant overall effect of time on two of the nine domains: romantic appeal, which showed an increase over time, Wilks L=0.88, F(3, 98)=4.38, p o0.001, and global self-worth, which showed a decrease over time, Wilks L=0.90, F(3, 98)=3.50, po0.05. Differential stability, as assessed by correlations across the four
Table 2 Means and standard deviations of self-concept domains All cases Self-concept domain
Global self-worth Scholastic competence Social acceptance Athletic competence Job competence Romantic appeal Behavioral conduct Close friendships Physical appearance
T1 (n=261)
T2 (n=196)
T3 (n=177)
T4 (n=203)
M
s.d.
M
s.d.
M
s.d.
M
s.d.
3.02 3.03 3.12 2.61 3.15 2.67 2.87 3.23 2.57
0.712 0.693 0.669 0.833 0.601 0.682 0.626 0.752 0.786
3.02 3.08 3.08 2.57 3.24 2.72 2.94 3.26 2.58
0.708 0.647 0.662 0.865 0.626 0.721 0.637 0.757 0.775
3.02 3.07 3.07 2.65 3.22 2.73 2.99 3.27 2.73
0.685 0.689 0.681 0.829 0.649 0.732 0.680 0.745 0.804
3.03 3.09 3.12 2.71 3.22 2.85 2.95 3.21 2.69
0.673 0.648 0.639 0.808 0.613 0.713 0.650 0.759 0.790
Complete cases Self-concept domain
Global self-worth Scholastic competence Social acceptance Athletic competence Job competence Romantic appeal Behavioral conduct Close friendships Physical appearance
T1 (n=102)
T2 (n=102)
T3 (n=102)
T4 (n=102)
M
s.d.
M
s.d.
M
s.d.
M
s.d.
3.13 3.12 3.09 2.51 3.13 2.67 2.94 3.22 2.56
0.703 0.724 0.690 0.897 0.653 0.691 0.623 0.777 0.782
3.06 3.15 3.07 2.48 3.29 2.69 2.92 3.23 2.58
0.752 0.676 0.697 0.892 0.608 0.726 0.646 0.788 0.791
3.04 3.11 3.06 2.50 3.30 2.78 3.03 3.25 2.70
0.731 0.690 0.703 0.855 0.676 0.706 0.650 0.747 0.827
3.06 3.18 3.14 2.56 3.30 2.88 3.05 3.25 2.73
0.721 0.659 0.675 0.882 0.611 0.726 0.641 0.750 0.841
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timepoints, also was examined. Listwise correlations for all domains were quite high, in the 0.61– 0.89 range (p=0.001), indicating that many participants retained their relative rank (ordinal position) over time. 3.1. Intraindividual growth models Level 1 analyses. Next, self-concept was modeled over time using individual growth curve techniques, utilizing all the available data for subjects with 2 or more timepoints. Individual growth curve modeling yielded fixed effect and random effect estimates of the intercept and slope parameters that define the average trajectories for each of the self-concept domains. The first column of Table 3 lists the fixed effect estimates. Three of the self-concept domains had average slopes that were significantly different from zero (job competence, p=0.032; romantic appeal, p=0.0001; physical appearance, p=0.013), indicating a significant increase in perceived competency in these domains over the course of the study. The fixed effect estimates of the intercepts were all significantly different from zero. The scales range from 1 to 4, so the fact that the intercept was different from zero is uninteresting. What is interesting, though, is the differences among the intercepts of the various domains. The mean intercepts (level of selfconcept at the initial timepoint) for athletic competence, romantic appeal, behavioral conduct, and physical appearance were all below 3.0, suggesting less adequate self-evaluations in these domains. The second column in Table 3 lists the estimated variance of the intercepts and slopes for each of the domains and the corresponding significance tests. Random effect estimates defined the variability around the slopes among the adolescents. The variance estimates of the slopes were significantly different from zero for six of the nine self-concept domains, meaning that there were individual differences in rate of change in these domains: social acceptance (p=0.0001), athletic competence (p=0.034), job competence (p=0.031), romantic appeal (p=0.42), close friendships (p=0.0004), and physical appearance (p=0.0004). Two of the domains, global self-worth and behavioral conduct, had variance estimates of the slopes that approached significance (global selfworth, p=0.058; behavioral conduct, p=0.051). Scholastic competence had a slope variance that did not significantly differ from zero (p=0.124), indicating that there was not much variation around the average trajectory for this domain. The random effect estimates of the intercepts also were significantly different from zero for all self-concept domains, meaning that there were significant individual differences in initial level of self-concept in all domains (p=0.0001). Level 2 analyses. The second level of the model attempted to explain individual differences in trajectories when differences were observed. With regard to the slopes of the trajectories, there were individual differences in slope trajectories for social acceptance, athletic competence, job competence, romantic appeal, close friendships, and physical appearance that could potentially be explained by Level 2 predictors. Only these domains were included in the slopes as outcomes analyses. The fact that the variance estimates of the intercepts were significantly different from zero in all domains means that there were individual differences in initial level of self-concept that could potentially be explained by Level 2 predictors. Thus, all self-concept domains were included in the intercepts-as-outcomes analyses of Level 2. First, significant relationships among the predictors and change in the various self-concept domains were examined. The significant findings are listed in Table 4. Gender significantly
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Table 3 Fixed and random effects estimates of individual growth models of self-concept Self-concept domain
Fixed effect estimate (se)
Random effect estimate (se)
Global self-worth Slope Intercept
0.004(0.004) 3.020(0.046)
0.001(0.000) 0.397(0.048)
Scholastic competence Slope Intercept
0.006(0.004) 3.003(0.045)
0.000(0.000) 0.405(0.045)
Social acceptance Slope Intercept
0.001(0.004) 3.104(0.043)
0.002(0.000) 0.359(0.041)
Athletic competence Slope Intercept
0.007(0.004) 2.599(0.54)
0.001(0.000) 0.628(0.065)
Job competence Slope Intercept
0.009(0.004) 3.153(0.038)
0.001(0.000) 0.256(0.033)
Romantic appeal Slope Intercept
0.019(0.005) 2.643(0.045)
0.001(0.001) 0.360(0.046)
Behavioral conduct Slope Intercept
0.008(0.004) 2.883(0.040)
0.001(0.000) 0.295(0.036)
Close friendships Slope Intercept
0.003(0.005) 3.244(0.050)
0.002(0.001) 0.462(0.055)
Physical appearance Slope Intercept
0.012(0.005) 2.281(052)
0.002(0.000) 0.551(0.060)
Po0.05. Po0.01.
predicted change in athletic competence, t(4 3 6)=2.28, p=0.023. The athletic competence scores of females increased, on average, 0.018 points more than males over the course of the study, controlling for where they started, with gender accounting for 9.8% of the variation in athletic competence slopes. Gender also significantly predicted change in job competence ratings, t(4 3 5)=2.16, p=0.031. Females in the study had, on average, growth rates that were 0.018 points higher than the males in the study. The inclusion of gender in the model accounted for 6.7% of the variation in job competence change.
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Table 4 Predictors of change in self-concept Self-concept domain
Variable
Fixed effect estimate (se)
Random effect estimate (se)
Athletic competence Job competence
Gender Gender
0.018(0.008) 0.018(0.008)
0.007(0.000) 0.001(0.000)
Fixed effect estimate (se)
Random effect estimate (se)
Po0.05.
Table 5 Predictors of initial level of self-concept Self-concept domain Global self-worth Scholastic competence Athletic competence Job competence Romantic appeal Close friendships Physical appearance
Variable Gender Gender Age Gender Age Age Gender Age Gender
0.189(0.091) 0.207(0.088) 0.111(0.024) 0.537(0.102) 0.061(0.017) 0.047(0.020) 0.322(0.098) 0.055(0.023) 0.231(0.102)
0.392(0.047) 0.395(0.044) 0.571(0.061) 0.557(0.058) 0.239(0.032) 0.352(0.045) 0.440(0.053) 0.537(0.059) 0.539(0.059)
Po0.05. Po0.01.
Next, significant relationships among the predictors and initial self-concept were examined. Table 5 lists the significant findings. Age significantly predicted initial ratings of athletic competence, t(2 4 7)= 4.65, p=0.0001. For every year baseline age increased, initial levels of athletic competence decreased by 0.111 points, resulting in a 0.999 point difference between the youngest and the oldest adolescents. The inclusion of age in the model decreased the variance estimate of initial status from 0.628 to 0.571, accounting for 9.1% of the variation in level of athletic competence ratings. Age was also a significant predictor of job competence, t(2 4 7)=3.54, p=0.0005 and romantic appeal ratings, t(2 4 7)=2.32, p=0.021. Older adolescents had higher job competency and romantic appeal ratings than younger adolescents, with age accounting for 6.6% of the variation in job competence, and 2.2% of the variation in romantic appeal ratings. Lastly, age was a significant predictor of physical appearance ratings, t(2 4 7)= 2.36, p=0.019. The oldest adolescents had ratings that were, on average, 0.495 points lower than the youngest adolescents. Age accounted for 2.5% of the variation in physical appearance intercepts. Gender was a significant predictor of the intercept for several of the domains, including global self-worth, t(4 3 7)= 2.08, p=0.038. Because gender was coded as 0 for male and 1 for female, the negative coefficient for the fixed effect estimate of global self-worth indicates that, on average, female adolescents had lower global self-worth scores than males. Gender accounted for 1.3% of the variance. Gender was also a significant predictor of initial levels of scholastic competence, t(4 3 6)= 2.35, p=0.019, and athletic competence, t(4 3 6)= 5.28, p=0.0001). On average,
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female adolescents had lower levels of scholastic competence and athletic competence than males, with gender accounting for 2.5% of the variation in initial school competence ratings and 11.3% of the variation in athletic competence slopes. Gender also predicted initial levels of close friendships, t(4 3 7)=3.29, p=0.001. Females, on average, had close friendship ratings that were higher than males, with gender accounting for 4.8% of the variation. Lastly, gender significantly predicted initial ratings of physical appearance, t(4 3 6)= 2.27, p=0.024, with females having lower ratings of physical appearance than males. Gender accounted for 2.2% of the variance in appearance ratings.
4. Discussion The existing self-concept literature has been phrased largely as a change versus stability question. Using individual growth modeling, we found different rates and patterns of change across the various domains suggesting that there is a distribution of change in self-concept. Three domains had average slopes that changed over the course of the study: job competence, romantic appeal, and physical appearance. Some of these changes likely represent social changes that occur during puberty. Jobs and romance are two domains that become increasingly more important as adolescents mature. In fact, job competence and romantic appeal are two of the three additional domains measured on the SPPA as compared to Harter’s Self-Perception Profile for Children (1985), reflecting Harter’s (1988a) belief that these domains begin to emerge and become important in adolescence. The significant positive changes suggest that, as another year passed, the adolescents became more comfortable in these areas. Global self-worth did not change significantly over the course of the study. This lends support to the proposal of Shavelson et al. (1976) and others that global self-worth is more stable than domain-specific self-concept. Individual growth modeling also highlighted differences among individuals, both in terms of level of self-concept and self-concept change over time. These individual differences were explained, in part, by age and gender. Age at baseline was a significant predictor of level of selfconcept for several of the domains. Older adolescents had higher job competence and romantic appeal ratings than younger adolescents. This finding likely reflects normal developmental processes where adolescents become increasingly more involved in jobs and romantic relationships as they get older. Since these two domains are relatively new competencies in adolescence (Harter, 1988b), it is not surprising that it takes a while for adolescents to have positive selfconcepts in these areas. Older adolescents in this study had lower athletic competence and physical appearance ratings than younger adolescents. This contradicts Cole et al. (2001) finding of small increases in these domains in middle to late adolescence. The discrepancy between these findings may be due to methodological differences in the two studies. In particular, Cole et al. (2001) studied two cohorts longitudinally over the course of several years, whereas the current study examined adolescents across several age ranges over the course of 1 year. The discrepancy may also be due to the different age ranges studied. Cole’s (2001) group studied adolescents as they transitioned into high school, whereas, in the current study, we included college freshmen and sophomores. We hypothesize that the athletic competence ratings of college students may be lower than younger adolescents who are involved in structured athletic activities through school, such as team sports
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or gym. It may be that adolescents in college are not as involved in athletics as they used to be, and as a result are not feeling as good about their abilities. In addition, in college, it is much more difficult to make athletic teams than it is in middle school or high school. It is possible that, as adolescents move to increasingly larger schools with more competitive sports teams, they become aware of their own athletic limitations. The decrease in physical appearance ratings may be linked to the decrease in athletic competence. As adolescents enter college and become less involved in athletics, they may become more critical about their physical appearance. Further research examining self-concept during the transition to college is warranted. Gender was also a significant predictor of level of self-concept for several of the domains. Females had lower global self-worth, scholastic competence, athletic competence, and physical appearance ratings than males. These findings are consistent with the work of several other research groups (e.g., Marsh, 1989; Wigfield et al., 1991; Bolognini et al., 1996; Cole et al., 2001), suggesting that these gender differences are consistent across different ages and different samples. The biological and social changes that occur during adolescence, particularly the intensification of gender roles, may be responsible for these gender differences (Hill & Lynch, 1983). This is only the second study to look at gender differences in global ratings of scholastic competence, rather than breaking academics into specific subject areas. Previous research (e.g., Eccles et al., 1989; Wigfield et al., 1991), examining subject-specific self-concepts found that males report higher perceptions of math ability, whereas females report higher perceptions of verbal ability. However, the findings from the current study and the Cole et al. (2001) study suggest that when academic competence is assessed more globally, females rate themselves more negatively than males. The sample we studied was a largely middle class, white sample. For this distinct group, there were individual differences in self-concept that were partially explained by age and gender. We postulate that a more diverse sample would mean greater individual differences in self-concept trajectories, both in regard to level of self-concept and rate of change. Whether social class and ethnicity predict these individual differences in self-concept is still up for debate, as the research to date has been largely inconclusive (Simmons & Rosenberg, 1975; Kanouse et al., 1980; Hirsch & DuBois, 1991; Bailey et al., 1992; Zimmerman et al., 1996; Brown et al., 1998). More studies are needed. 4.1. Future research Although level of self-concept, and to a lesser extent change in self-concept, was significantly predicted by several of the variables we examined, much of the variation of the intercepts and slopes was not explained. It is possible that the inclusion of other variables would have improved the prediction. To explore this possibility, we conducted supplementary analyses to determine if there were differences among the adolescents of the four schools. We found significant differences across schools in several of the self-concept domains: scholastic competence, athletic competence, job competence, and close friendships. Although many of the differences among the schools may be attributable to differences in the age or gender of the students, several of the findings cannot be explained by demographic variables. For instance, there were two schools that had younger adolescents. The adolescents in one of these schools had significantly higher athletic competence ratings than adolescents in other schools. The fact that this occurred in just one of these schools suggests that age is not the sole
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contributor to this finding. We know that this school encourages students to be involved in athletic teams regardless of their athletic ability. This approach may be beneficial for adolescents’ athletic self-concept, at least at this age. That same school had lower scholastic competence ratings than other schools. These findings suggest that there is either something about the school context that affects adolescents’ self-concepts and/or that certain schools attract adolescents with different patterns of academic and athletic success. The former hypothesis is supported by earlier studies that have highlighted the importance of the school context on students’ self-concepts (Eccles, Lord, & Midgley, 1991). Further research comparing various school types is warranted. If future research can determine what self-concept domains need to be attended to in particular school types, then school-based programmes can be designed to specifically address these areas. Another possible predictor of change in self-concept that should be examined in future research is initial level of self-concept. Previous research (Harter, 1993; Hirsch & DuBois, 1991; Zimmerman et al., 1996) suggests that initial level of self-concept may predict whether an individual is likely to show self-concept change. The random effect estimates of the covariances of the intercepts and slopes shed light on this question. There were significant negative relationships between the intercept and the slope for several of the domains: scholastic competence, social acceptance, close friendships, and physical appearance. These significant relationships indicate that initial level of self-concept was significantly associated with change in self-concept in these domains. The negative covariances can be interpreted in one of two ways. The first interpretation is that adolescents who started with higher self-concept scores declined over the course of the study. The second, and we believe more plausible interpretation, is that adolescents who started with higher self-concept scores did not show as much change as other adolescents who started with more moderate self-concepts. The latter interpretation is supported by prior research. Hirsch and DuBois (1991) and Zimmerman et al. (1996) found that adolescents with the highest and lowest levels of self-concept were less likely to show change over time. These findings, taken in combination, suggest that change in self-concept is dependent on initial level of self-concept. If level of self-concept can predict the magnitude and direction of change in self-concept, measuring self-concept at one timepoint might be a useful way of identifying adolescents who are at risk for declining self-concepts. 4.2. Limitations and conclusions Although this study had a strong methodological design and utilized important analytic techniques for examining change within individuals, several limitations in the study need to be addressed. First, the study examined self-concept over the course of a year, limiting the conclusions that can be made about long-term change in adolescence. It is possible that the time studied was too short and that, as Alsaker and Olweus (1992) suggested, longer intervals of time are needed to document change. In addition, due to the shorter time period, only linear effects were examined. This prevented the examination of non-linear time effects. Second, although the students were enrolled in four different schools, many of the students studied were from middle-class families. Although this limits the generalizability of these results, it is worthwhile to have studied middle-class adolescents since many studies have focused exclusively on low-income students. In addition, as we discussed earlier, the self-concept trajectories that we found may have been affected by our choice of schools, thereby limiting their generalizability to
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other school types. Third, although we asked students of all ages to complete the questionnaires, some of the ages were underrepresented in the sample, making conclusions about the adolescents in these age groups more tenuous. Future research should include students of all ages, in multiple types of schools (e.g., public schools, private schools, coeducational, and single-sex schools), and from a diverse range of communities to determine if there are significant differences by social class, community-type, and/or school-type. Lastly, we only administered one measure of self-concept. This limited our analyses to the domains covered in the Self-Perception Profile for Adolescents. The inclusion of other measures that assess additional self-concept domains would have provided a more comprehensive view of self-concept at this age and perhaps would have led to different results concerning which selfconcept domains changed over time. Despite these limitations, important conclusions can be made. The analytic model utilized proved to be an important improvement in the analysis of longitudinal self-concept data. By examining individual trajectories of self-concept over time, we were able to establish that although adolescents as a group have primarily stable self-concepts, many adolescents showed change over time. These individual differences in level and change in self-concept were explained partly by age and gender. Future research on self-concept in adolescence needs to move beyond assessing global level of self-concept at one or two timepoints to studying changes in domain-specific self-concepts over the course of adolescence and the determinants of such change.
References Abramowitz, R. H., Petersen, A. C., & Schulenberg, J. E. (1984). Changes in self-image during early adolescence. New Directions for Mental Health Services, 22, 19–28. Alsaker, F. D., & Olweus, D. (1992). Stability of global self-evaluations in early adolescence: A cohort longitudinal study. Journal of Research on Adolescence, 2, 123–145. Bachman, J. G., O’Malley, P. M., & Johnston, J. J. (1978). Youth in transition, Vol. 6. Adolescence to adulthood: A study of change and stability in the lives of young men. Ann Arbor, MI: Institute for Social Research. Bailey, S. M., Burbridge, L., Campbell, P., Jackson, B., Marx, F., & McIntosh, P. (1992). The AAUW report: How schools shortchange girls. Washington DC: AAUW Educational Foundation and National Education Association. Block, J., & Robins, R. W. (1993). A longitudinal study of consistency and change in self-esteem from early adolescence to early adulthood. Child Development, 64, 909–923. Bolognini, M., Plancherel, B., Bettschart, W., & Halfon, O. (1996). Self-esteem and mental health in early adolescence: Development and gender differences. Journal of Adolescence, 19, 233–245. Brown, K. M., McMahon, R. P., Biro, F. M., Crawford, P., Schreiber, G. B., Similo, S. L., Waclawiw, M., & StriegelMoore, R. (1998). Changes in self-esteem in Black and White girls between the ages of 9 and 14 years. Journal of Adolescent Health, 23, 7–19. Bryk, A. S, & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101, 147–158. Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models in social and behavioral research: Applications and data analysis methods. Newbvuy Park, CA: Sage. Cairns, E., McWhirter, L., Duffy, U., & Barry, R. (1990). The stability of self-concept in late adolescence: Gender and situational effects. Personality and Individual Differences, 11, 937–944. Cole, D. A., Maxwell, S. E., Martin, J. M., Peeke, L. G., Seroczynski, A. D., & Tram, J. M., et al. (2001). The development of multiple domains of child and adolescent self-concept: A cohort sequential longitudinal design. Child Development, 72(6), 1723–1746.
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Dusek, J. B., & Flaherty, J. F. (1981). The development of the self-concept during the adolescent years. Monographs of the Society for Research in Child Development, 46(4, Serial No. 191), 1–49. Eccles, J. S., Lord, S., & Midgley, C. (1991). What are we doing to early adolescents? The impact of educational contexts on early adolescents. American Journal of Education, 99, 521–542. Eccles, J. S., Wigfield, A., Flanagan, C. A., Miller, C., Reuman, D. A., & Yee, D. (1989). Self-concepts, domain values, and self-esteem: Relations and changes at early adolescence. Journal of Personality, 57, 283–310. Harter, S. (1985). Manual for the self-perception profile for children. Denver, CO: University of Denver. Harter, S. (1988a). Developmental and dynamic changes in the nature of the self-concept: Implications for child psychotherapy. In S. Shirk (Ed.), Cognitive development and child psychotherapy (pp. 119–160). New York: Plenum Press. Harter, S. (1988b). Self-perception profile for adolescents. Denver, CO: University of Denver Press. Harter, S. (1990a). Adolescent self and identity development. In S. S. Feldman & G. R. Elliot (Eds.), At the threshold: The developing adolescent (pp. 352–387). Cambridge, MA: Harvard University Press. Harter, S. (1990b). Processes underlying adolescent self-concept formation. In R. Montemayor, G. R. Adams, & T. P. Guillota (Eds.), From childhood to adolescence: A transitional period? (pp. 205–139). Newbury Park, CA: Sage Publications. Harter, S. (1993). Causes and consequences of low self-esteem in children and adolescents. In R. F. Baumeister (Ed.), Self-esteem: The puzzle of low self-regard (pp. 87–116). New York: Plenum Press. Harter, S., Whitesell, N. R., & Kowalski, P. (1992). Individual differences in the effects of educational transitions on young adolescent’s perceptions of competence and motivational orientation. American Educational Research Journal, 29, 777–807. Hill, J. P., & Lynch, M. E. (1983). The intensification of gender-related role expectations during early adolescence. In J. Brooks-Gunn & A. C. Petersen (Eds.), Girls at puberty (pp. 201–228). New York: Plenum Press. Hirsch, B. J., & DuBois, D. L. (1991). Self-esteem in early adolescence: The identification and prediction of contrasting longitudinal trajectories. Journal of Youth and Adolescence, 20, 53–72. Hui, S. L., & Berger, J. O. (1983). Empirical Bayed estimation in rates of longitudinal studies. Journal of the American Statistical Association, 78, 753–760. Kanouse, D. E., Haggstrom, G. W., Blaschke, T. J., Kahan, J. P., Liowski, W., & Morrison, P. A. (1980). Effects of postsecondary experiences on aspirations, attitudes, and self-conceptions. Santa Monica, CA: Rand. Lamiell, J. T. (1981). Toward an idiothethic psychology of personality. American Psychologist, 36, 276–289. Marsh, H. W. (1987). The hierarchical structure of self-concept: An application of hierarchical confirmatory factor analysis. Journal of Educational Measurement, 24, 17–39. Marsh, H. W. (1989). Age and sex effects in multiple dimensions of self-concept: Preadolescence to early adulthood. Journal of Educational Psychology, 81, 417–430. Marsh, H. W, Parker, J., & Barnes, J. (1985). Multidimensional adolescent self-concept: Their relationship to age, sex, and academic measures. American Educational Research Journal, 22, 422–444. Marsh, H. W., Smith, I. D., Marsh, M. R., & Owens, L. (1988). The transition from single-sex to coeducational high schools: Effects on multiple dimensions of self-concept and on academic achievement. American Educational Research Journal, 25, 237–269. McCarthy, J. D., & Hoge, D. R. (1982). Analysis of age effects in longitudinal studies of adolescent self-esteem. Developmental Psychology, 18, 372–379. O’Malley, P., & Bachman, J. (1983). Change and stability between ages 13 and 23. Developmental Psychology, 19, 257–268. Ozer, D. J., & Gjerde, P. F. (1989). Patterns of personality consistency and change from childhood through adolescence. Journal of Personality, 57, 483–507. Piers, E., & Harris, D. (1964). Age and other correlates of self-concept in children. Journal of Educational Psychology, 55, 91–95. Rogosa, D. R., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92, 726–748. Rogosa, D. R., & Saner, H. (1995). Longitudinal data analysis examples with random coefficient models. Journal of Educational and Behavioral Statistics, 20, 149–169.
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Rogosa, D., & Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50, 203–228. Rosenberg, M. (1986). Self-concept from middle childhood through adolescence. In J. Suls & A. G. Greenwald (Eds.), Psychological perspectives on the self, Vol. 3 (pp. 107–135). Hillsdale, NJ: Lawrence Erlbaum Associates. Savin-Williams, R. C., & Demo, D. H. (1984). Developmental change and stability in adolescent self-concept. Developmental Psychology, 20, 1100–1110. Shavelson, R. J., & Bolus, R. (1982). Self-concept: The interplay of theory and methods. Journal of Educational Psychology, 74, 3–17. Shavelson, R. J., Hubner, J. J., & Stanton, G. C. (1976). Self-concept: Validation of construct interpretation. Review of Educational Psychology, 46, 407–441. Siegel, D. G. (1975). Several approaches for measuring average rates of change for a second degree polynomial. American Statistician, 39, 95–101. Simmons, R. G., Blyth, D. A., Van Cleave, E. F., & Bush, D. M. (1979). Entry into early adolescence: The impact of school structure, puberty, and early dating on self-esteem. American Sociological Review, 44, 948–967. Simmons, R. G., & Rosenberg, F. (1975). Sex, sex roles, and self-image. Journal of Youth and Adolescence, 4, 229–258. Simmons, R. G., Rosenberg, F., & Rosenberg, M. (1973). Disturbances in the self-image at adolescence. American Sociological Review, 38, 553–568. Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 24, 323–355. Ware, J. H. (1985). Linear models for the analysis of longitudinal studies. American Statistician, 39, 95–101. Wigfield, A., Eccles, J. S., Mac Iver, D., Reuman, D. A., & Midgley, C. (1991). Transitions during early adolescence: Changes in children’s domain-specific self-perceptions and general self-esteem across the transition to junior high school. Developmental Psychology, 27, 552–565. Willett, J. B., Ayoub, C. C., & Robinson, D. (1991). Using growth curve modeling to examine systematic differences in growth: An example of change in the functioning of families at risk of maladaptive parenting, child abuse, or neglect. Journal of Consulting and Clinical Psychology, 59, 38–47. Wylie, R. C. (1979). The self-concept, Vol. 2. Theory and research on selected topics. Lincoln, NE: University of Nebraska Press. Zimmerman, M. A., Copeland, L. A., Shope, J. T., & Dielman, T. E. (1996). A longitudinal study of self-esteem: Implications for adolescent development. Journal of Youth and Adolescence, 26, 117–141.
Journal of Adolescence Journal of Adolescence 26 (2003) 586–600 www.elsevier.com/locate/jado
Predicting intraindividual self-concept trajectories during adolescence Jami F. Younga,*, Daniel K. Mroczekb a
Department of Child Psychiatry, Columbia University, 1051 Riverside Drive, Box #74, New York, NY 10032, USA b Fordham University, 441 East Fordham Road, Bronx, NY 10458, USA Accepted 22 July 2003
Abstract The level and stability of both global and domain-specific self-concepts was examined in a study of 253 adolescents, aged 11–20. Intraindividual self-concept trajectories were calculated through growth curve modeling. Six of the self-concept domains were stable over time. In the remaining three domains, there was a significant increase in perceived competency. Growth curve modeling highlighted individual differences in both level and change in self-concept for each of the domains. These differences were partially accounted for by the adolescents’ age and gender, suggesting that these variables are important determinants of adolescent self-concept. r 2003 The Association for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.
1. Introduction Adolescence is a time of physical, cognitive, and social change. Theorists and researchers (e.g., Dusek & Flaherty, 1981; McCarthy & Hoge, 1982; Savin-Williams & Demo, 1984; Rosenberg, 1986; Harter,1988a; Alsaker & Olweus, 1992) have questioned whether these social and biological changes alter one’s self-concept. Although a number of theorists (e.g., Rosenberg, 1986; Harter,1988a) believe that adolescence is a time of instability of self-concept, the early research in this area was inconclusive (Bachman, O’Malley, & Johnston, 1978; Wylie, 1979; Dusek & Flaherty, 1981; McCarthy & Hoge, 1982; Savin-Williams & Demo, 1984). One possible reason for the lack of consistent findings is that the majority of these studies focused almost exclusively on global self-concept rather than domain-specific self-concepts. However, the unidimensional *Corresponding author. E-mail address: [email protected] (J.F. Young). 0140-1971/$30.00 r 2003 The Association for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved. doi:10.1016/S0140-1971(03)00058-7
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approach masks important distinctions that people make when describing and evaluating their ability in various domains (Shavelson, Hubner, & Stanton, 1976; Marsh, 1987; Harter, 1990b). Looking only at global self-concept may have masked changes occurring in the various selfconcept domains, leading to the erroneous conclusion that self-concept is stable (Harter, 1990a). More recent studies have begun to examine change in domain-specific self-concepts during the course of adolescence. For instance, Shavelson and Bolus (1982) found that domain-specific self-concepts were less stable than general self-concept. Others (e.g., Abramowitz, Petersen, & Schulenberg, 1984; Marsh, 1989; Eccles et al., 1989; Wigfield, Eccles, Mac Iver, Reuman, & Midgley, 1991; Bolognini, Plancherel, Bettschart, & Halfon, 1996; Cole et al., 2001) have found that the rate of change differs across domains. For instance, Bolignini et al. (1996), using the Harter Self-Perception Profile for Children to study self-concept change in 12- to 14-year olds, found no change in global self-worth, athletic competence, or behavioral conduct, but found a decrease in physical appearance and social acceptance and an increase in scholastic competence ratings over time. These findings suggest that there are different rates and patterns of change across self-concept domains that are masked by a unidimensional approach to selfconcept. 1.1. Predictors of self-concept trajectories The aforementioned studies have documented some degree of difference among individuals with respect to self-concept trajectories. Using these investigations as a beginning, other researchers have begun to examine the variables responsible for individual differences in selfconcept. One such factor is age. Past studies suggest a decline in global self-concept during preadolescence, a reversal of this decline in early or middle adolescence, and an increase in selfconcept during late adolescence (Piers & Harris, 1964; Simmons, Rosenberg, & Rosenberg, 1973; Dusek & Flaherty, 1981; O’Malley & Bachman, 1983; Marsh, Parker, & Barnes, 1985; Marsh, Smith, Marsh, & Owens, 1988). In terms of domain-specific effects, several studies have found that academic and athletic self-concepts decline in early adolescence (Eccles et al., 1989; Marsh, 1989; Wigfield et al., 1991; Cole et al., 2001), as do appearance and social competence ratings (Marsh, 1989; Bolognini et al., 1996). Fewer studies have examined middle adolescence, but those that have report small increases in self-perceptions of athletic ability, physical appearance, and academic competence during this time (Marsh, 1989; Cole et al., 2001). Gender is another factor that has been examined. Studies have found that female adolescents are more likely than males to have low or declining global self-concept (Simmons & Rosenberg, 1975; Simmons, Blyth, Van Cleave, & Bush, 1979; Bailey et al., 1992; Zimmerman, Copeland, Shope, & Dielman, 1996). Three studies have looked at gender differences in domain-specific selfconcepts. Bolignini et al. (1996) found that females scored significantly lower than males on three of the six self-concept domains: global self-worth, physical appearance, and athletic competence. Cairns, McWhirter, Duffy, and Barry (1990) found that females scored significantly lower than males on cognitive competence, athletic competence, and global self-concept. Most recently, Cole et al. (2001) looked at differences in self-concept as a function of gender. In early adolescence, males had higher athletic, academic, and physical appearance self-perceptions, whereas girls had higher behavioral competency ratings. In middle adolescence, males continued to have higher athletic competence and physical appearance ratings than females.
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1.2. Individual growth curve modeling The lack of evidence in support of change in self-concept may also be attributed to the fact that most of the research conducted to date has looked at mean-level or correlational stability. Both means and correlations are anchored in the aggregate and therefore cannot differentiate group-level from person-level stability (Lamiell, 1981). As such, these techniques cannot properly address the question of whether individual persons exhibit change over time (Lamiell, 1981; Ozer & Gjerde, 1989). The recognition that individual change is masked by group findings has led to more sophisticated statistical applications for tracking change in self-concept, including cluster analysis (Hirsch & DuBois, 1991; Zimmerman et al., 1996), structural equation modeling (Cole et al., 2001), and examination of individual differences in magnitude and direction of change in self-concept (Harter, Whitesell, & Kowalski, 1992; Block & Robins, 1993). The development of hierarchical linear models (HLM), of which growth models is a type, offers another powerful technique for research on individual change (Bryk & Raudenbush, 1992). 1.3. Present study To examine level and stability of self-concept, individual growth modeling (Rogosa, Brandt, & Zimowski, 1982; Ware, 1985; Rogosa & Willett, 1985; Bryk & Raudenbush, 1987) was applied to longitudinal data on global and domain-specific self-concept in adolescents, aged 11–20. First, the study estimated change in a Level 1 model to describe level of self-concept and rate of change in adolescence. Next, a Level 2 model was employed to examine whether age and gender differentiate between those adolescents who had positive self-concepts or showed positive change over time, and those who had negative self-concepts or declined over time.
2. Methods 2.1. Sample Participants for the current study consisted of 253 adolescents, aged 11–20 (M=15.68; s.d.=2.19) who agreed to participate in the study. All participants were either students at Fordham University or students at one of three schools in the community that agreed to participate in the study. We sampled students in the 6th–12th grades in the community schools and Freshmen and Sophomores from Fordham University, resulting in a sample of 11–20-year olds. The schools in the community were a boarding/private day school, grades 6–9; an all-male Catholic high school, grades 9–12; and a public school, grades 6–12. Although these schools were located in different communities (i.e., Connecticut, NJ, and the Bronx), many of the students were from middle class families as assessed by parent educational status. The majority of participants identified themselves as White, with the next largest group self-identifying themselves as Hispanic. The demographic characteristics of the adolescents are listed in Table 1.
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Table 1 Demographic characteristics of adolescents n=253 N
%
Adolescent sex Male Female
138 115
54.5 45.5
Ethnicity White Hispanic Asian African-American Other
204 25 13 7 4
80.6 9.9 5.1 2.8 1.6
52 60 76 65
20.6 23.7 30.0 25.7
61 124 68
24.1 49.0 26.9
Maternal education Some high school High school graduate Some college College graduate Graduate school Missing
7 41 54 67 79 5
2.8 16.2 21.3 26.5 31.2 2.0
Paternal education Some high school High school graduate Some college College graduate Graduate school Missing
7 48 41 59 91 7
2.8 19.0 16.2 23.3 36.0 2.8
School Fordham University School A School B School C Age 11–13 14–16 17–20
2.2. Procedure The study utilized a multiwave assessment to collect data from the adolescents. All adolescents were to be evaluated at four timepoints over a 1-year period. Data collection began in the winter of Year 1 and ended the following winter so that subjects were seen across two different school
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years. Of the 253 adolescents, 56 completed only two of the four timepoints, 95 completed only three timepoints, and 102 completed all four timepoints. 2.3. Measures Self-concept. Self-concept was assessed by the Self-Perception Profile for Adolescents (SPPA; Harter, 1988b), a widely used 45-item questionnaire that measures global self-worth and eight specific domains of self-concept. The SPPA has adequate reliability and validity, with Cronbach alphas for the nine scales ranging from 0.78 to 0.95 depending on the sample used (Harter, 1988b). In the current study, the average alphas across timepoints ranged from 0.76 to 0.92. We conducted a factor analysis of the SPPA at the first timepoint using principal components extraction with varimax rotation. We extracted nine factors, which corresponded well to global self-concept and the eight specific self-concept domains. There was some fusion of subscales, for example the global and appearance items clustered together. However, the overall picture indicated that the nine selfconcept subscales were separate enough to be treated as independent, although related, scales. Predictors of intercept and change. To explain the individual differences in trajectories, we considered two sociodemographic variables that may be associated with level of self-concept or self-concept change: age at baseline and gender. Demographic variables. All participants were asked to complete a brief demographic form at Time 1. On this form, adolescents were asked their ethnicity (using categories of: White, Asian, African-American, Hispanic, Native American, and other) and their parents’ educational status. This information was used to describe the sample. 2.4. Data analysis To examine intraindividual change in self-concept, we used individual growth modeling techniques (Rogosa et al., 1982; Ware, 1985; Rogosa & Willett, 1985; Willett, Ayoub, & Robinson, 1991; Rogosa & Saner, 1995). In the within-person (Level 1) model, self-concept was regressed on time for each person. This yielded fixed effect and random effect estimates of the intercept and slope parameters for each of the self-concept domains. The fixed effects define the overall trajectory for the sample and the random effects define the individual deviations from the overall trajectory. Significant variances within the random effects indicate interindividual differences in level of self-concept or self-concept change. We estimated linear rather than non-linear models because Siegel (1975) and Hui and Berger (1983) showed that over short to moderate periods of time, linear models render a good approximation of non-linear effects. A Level 2 model used the slopes and intercepts as outcomes to identify possible predictors of level of self-concept and self-concept change. To test whether a variable was a significant predictor of individual differences in trajectories, we used the method recommended by Singer (1998) in which the predictor is entered along with a slope-by-predictor interaction. The coefficient for the predictor variable indicates whether the relationship between the predictor and initial status is significant. The interaction between the predictor variable and the slope indicates whether the rate of change in self-concept varies with differences in level of the predictor variable.
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3. Results In order to relate our findings to previous studies, preliminary analyses were conducted to examine mean-level change. These analyses were preliminary because repeated measures ANOVAs require complete data at all timepoints and, as such, do not maximally utilize the available data. Table 2 shows means and standard deviations for all of the self-concept domains at each of the four timepoints. The top half of Table 2 shows means based on all cases available from each timepoint. The bottom half of Table 2 displays means on only those participants with complete data at all four timepoints. To determine whether there was absolute-level change over time, repeated measures ANOVAs were performed on each of the nine self-concept domains for adolescents who had complete data at all four timepoints (102 of the 253 participants). There was a significant overall effect of time on two of the nine domains: romantic appeal, which showed an increase over time, Wilks L=0.88, F(3, 98)=4.38, p o0.001, and global self-worth, which showed a decrease over time, Wilks L=0.90, F(3, 98)=3.50, po0.05. Differential stability, as assessed by correlations across the four
Table 2 Means and standard deviations of self-concept domains All cases Self-concept domain
Global self-worth Scholastic competence Social acceptance Athletic competence Job competence Romantic appeal Behavioral conduct Close friendships Physical appearance
T1 (n=261)
T2 (n=196)
T3 (n=177)
T4 (n=203)
M
s.d.
M
s.d.
M
s.d.
M
s.d.
3.02 3.03 3.12 2.61 3.15 2.67 2.87 3.23 2.57
0.712 0.693 0.669 0.833 0.601 0.682 0.626 0.752 0.786
3.02 3.08 3.08 2.57 3.24 2.72 2.94 3.26 2.58
0.708 0.647 0.662 0.865 0.626 0.721 0.637 0.757 0.775
3.02 3.07 3.07 2.65 3.22 2.73 2.99 3.27 2.73
0.685 0.689 0.681 0.829 0.649 0.732 0.680 0.745 0.804
3.03 3.09 3.12 2.71 3.22 2.85 2.95 3.21 2.69
0.673 0.648 0.639 0.808 0.613 0.713 0.650 0.759 0.790
Complete cases Self-concept domain
Global self-worth Scholastic competence Social acceptance Athletic competence Job competence Romantic appeal Behavioral conduct Close friendships Physical appearance
T1 (n=102)
T2 (n=102)
T3 (n=102)
T4 (n=102)
M
s.d.
M
s.d.
M
s.d.
M
s.d.
3.13 3.12 3.09 2.51 3.13 2.67 2.94 3.22 2.56
0.703 0.724 0.690 0.897 0.653 0.691 0.623 0.777 0.782
3.06 3.15 3.07 2.48 3.29 2.69 2.92 3.23 2.58
0.752 0.676 0.697 0.892 0.608 0.726 0.646 0.788 0.791
3.04 3.11 3.06 2.50 3.30 2.78 3.03 3.25 2.70
0.731 0.690 0.703 0.855 0.676 0.706 0.650 0.747 0.827
3.06 3.18 3.14 2.56 3.30 2.88 3.05 3.25 2.73
0.721 0.659 0.675 0.882 0.611 0.726 0.641 0.750 0.841
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timepoints, also was examined. Listwise correlations for all domains were quite high, in the 0.61– 0.89 range (p=0.001), indicating that many participants retained their relative rank (ordinal position) over time. 3.1. Intraindividual growth models Level 1 analyses. Next, self-concept was modeled over time using individual growth curve techniques, utilizing all the available data for subjects with 2 or more timepoints. Individual growth curve modeling yielded fixed effect and random effect estimates of the intercept and slope parameters that define the average trajectories for each of the self-concept domains. The first column of Table 3 lists the fixed effect estimates. Three of the self-concept domains had average slopes that were significantly different from zero (job competence, p=0.032; romantic appeal, p=0.0001; physical appearance, p=0.013), indicating a significant increase in perceived competency in these domains over the course of the study. The fixed effect estimates of the intercepts were all significantly different from zero. The scales range from 1 to 4, so the fact that the intercept was different from zero is uninteresting. What is interesting, though, is the differences among the intercepts of the various domains. The mean intercepts (level of selfconcept at the initial timepoint) for athletic competence, romantic appeal, behavioral conduct, and physical appearance were all below 3.0, suggesting less adequate self-evaluations in these domains. The second column in Table 3 lists the estimated variance of the intercepts and slopes for each of the domains and the corresponding significance tests. Random effect estimates defined the variability around the slopes among the adolescents. The variance estimates of the slopes were significantly different from zero for six of the nine self-concept domains, meaning that there were individual differences in rate of change in these domains: social acceptance (p=0.0001), athletic competence (p=0.034), job competence (p=0.031), romantic appeal (p=0.42), close friendships (p=0.0004), and physical appearance (p=0.0004). Two of the domains, global self-worth and behavioral conduct, had variance estimates of the slopes that approached significance (global selfworth, p=0.058; behavioral conduct, p=0.051). Scholastic competence had a slope variance that did not significantly differ from zero (p=0.124), indicating that there was not much variation around the average trajectory for this domain. The random effect estimates of the intercepts also were significantly different from zero for all self-concept domains, meaning that there were significant individual differences in initial level of self-concept in all domains (p=0.0001). Level 2 analyses. The second level of the model attempted to explain individual differences in trajectories when differences were observed. With regard to the slopes of the trajectories, there were individual differences in slope trajectories for social acceptance, athletic competence, job competence, romantic appeal, close friendships, and physical appearance that could potentially be explained by Level 2 predictors. Only these domains were included in the slopes as outcomes analyses. The fact that the variance estimates of the intercepts were significantly different from zero in all domains means that there were individual differences in initial level of self-concept that could potentially be explained by Level 2 predictors. Thus, all self-concept domains were included in the intercepts-as-outcomes analyses of Level 2. First, significant relationships among the predictors and change in the various self-concept domains were examined. The significant findings are listed in Table 4. Gender significantly
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Table 3 Fixed and random effects estimates of individual growth models of self-concept Self-concept domain
Fixed effect estimate (se)
Random effect estimate (se)
Global self-worth Slope Intercept
0.004(0.004) 3.020(0.046)
0.001(0.000) 0.397(0.048)
Scholastic competence Slope Intercept
0.006(0.004) 3.003(0.045)
0.000(0.000) 0.405(0.045)
Social acceptance Slope Intercept
0.001(0.004) 3.104(0.043)
0.002(0.000) 0.359(0.041)
Athletic competence Slope Intercept
0.007(0.004) 2.599(0.54)
0.001(0.000) 0.628(0.065)
Job competence Slope Intercept
0.009(0.004) 3.153(0.038)
0.001(0.000) 0.256(0.033)
Romantic appeal Slope Intercept
0.019(0.005) 2.643(0.045)
0.001(0.001) 0.360(0.046)
Behavioral conduct Slope Intercept
0.008(0.004) 2.883(0.040)
0.001(0.000) 0.295(0.036)
Close friendships Slope Intercept
0.003(0.005) 3.244(0.050)
0.002(0.001) 0.462(0.055)
Physical appearance Slope Intercept
0.012(0.005) 2.281(052)
0.002(0.000) 0.551(0.060)
Po0.05. Po0.01.
predicted change in athletic competence, t(4 3 6)=2.28, p=0.023. The athletic competence scores of females increased, on average, 0.018 points more than males over the course of the study, controlling for where they started, with gender accounting for 9.8% of the variation in athletic competence slopes. Gender also significantly predicted change in job competence ratings, t(4 3 5)=2.16, p=0.031. Females in the study had, on average, growth rates that were 0.018 points higher than the males in the study. The inclusion of gender in the model accounted for 6.7% of the variation in job competence change.
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Table 4 Predictors of change in self-concept Self-concept domain
Variable
Fixed effect estimate (se)
Random effect estimate (se)
Athletic competence Job competence
Gender Gender
0.018(0.008) 0.018(0.008)
0.007(0.000) 0.001(0.000)
Fixed effect estimate (se)
Random effect estimate (se)
Po0.05.
Table 5 Predictors of initial level of self-concept Self-concept domain Global self-worth Scholastic competence Athletic competence Job competence Romantic appeal Close friendships Physical appearance
Variable Gender Gender Age Gender Age Age Gender Age Gender
0.189(0.091) 0.207(0.088) 0.111(0.024) 0.537(0.102) 0.061(0.017) 0.047(0.020) 0.322(0.098) 0.055(0.023) 0.231(0.102)
0.392(0.047) 0.395(0.044) 0.571(0.061) 0.557(0.058) 0.239(0.032) 0.352(0.045) 0.440(0.053) 0.537(0.059) 0.539(0.059)
Po0.05. Po0.01.
Next, significant relationships among the predictors and initial self-concept were examined. Table 5 lists the significant findings. Age significantly predicted initial ratings of athletic competence, t(2 4 7)= 4.65, p=0.0001. For every year baseline age increased, initial levels of athletic competence decreased by 0.111 points, resulting in a 0.999 point difference between the youngest and the oldest adolescents. The inclusion of age in the model decreased the variance estimate of initial status from 0.628 to 0.571, accounting for 9.1% of the variation in level of athletic competence ratings. Age was also a significant predictor of job competence, t(2 4 7)=3.54, p=0.0005 and romantic appeal ratings, t(2 4 7)=2.32, p=0.021. Older adolescents had higher job competency and romantic appeal ratings than younger adolescents, with age accounting for 6.6% of the variation in job competence, and 2.2% of the variation in romantic appeal ratings. Lastly, age was a significant predictor of physical appearance ratings, t(2 4 7)= 2.36, p=0.019. The oldest adolescents had ratings that were, on average, 0.495 points lower than the youngest adolescents. Age accounted for 2.5% of the variation in physical appearance intercepts. Gender was a significant predictor of the intercept for several of the domains, including global self-worth, t(4 3 7)= 2.08, p=0.038. Because gender was coded as 0 for male and 1 for female, the negative coefficient for the fixed effect estimate of global self-worth indicates that, on average, female adolescents had lower global self-worth scores than males. Gender accounted for 1.3% of the variance. Gender was also a significant predictor of initial levels of scholastic competence, t(4 3 6)= 2.35, p=0.019, and athletic competence, t(4 3 6)= 5.28, p=0.0001). On average,
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female adolescents had lower levels of scholastic competence and athletic competence than males, with gender accounting for 2.5% of the variation in initial school competence ratings and 11.3% of the variation in athletic competence slopes. Gender also predicted initial levels of close friendships, t(4 3 7)=3.29, p=0.001. Females, on average, had close friendship ratings that were higher than males, with gender accounting for 4.8% of the variation. Lastly, gender significantly predicted initial ratings of physical appearance, t(4 3 6)= 2.27, p=0.024, with females having lower ratings of physical appearance than males. Gender accounted for 2.2% of the variance in appearance ratings.
4. Discussion The existing self-concept literature has been phrased largely as a change versus stability question. Using individual growth modeling, we found different rates and patterns of change across the various domains suggesting that there is a distribution of change in self-concept. Three domains had average slopes that changed over the course of the study: job competence, romantic appeal, and physical appearance. Some of these changes likely represent social changes that occur during puberty. Jobs and romance are two domains that become increasingly more important as adolescents mature. In fact, job competence and romantic appeal are two of the three additional domains measured on the SPPA as compared to Harter’s Self-Perception Profile for Children (1985), reflecting Harter’s (1988a) belief that these domains begin to emerge and become important in adolescence. The significant positive changes suggest that, as another year passed, the adolescents became more comfortable in these areas. Global self-worth did not change significantly over the course of the study. This lends support to the proposal of Shavelson et al. (1976) and others that global self-worth is more stable than domain-specific self-concept. Individual growth modeling also highlighted differences among individuals, both in terms of level of self-concept and self-concept change over time. These individual differences were explained, in part, by age and gender. Age at baseline was a significant predictor of level of selfconcept for several of the domains. Older adolescents had higher job competence and romantic appeal ratings than younger adolescents. This finding likely reflects normal developmental processes where adolescents become increasingly more involved in jobs and romantic relationships as they get older. Since these two domains are relatively new competencies in adolescence (Harter, 1988b), it is not surprising that it takes a while for adolescents to have positive selfconcepts in these areas. Older adolescents in this study had lower athletic competence and physical appearance ratings than younger adolescents. This contradicts Cole et al. (2001) finding of small increases in these domains in middle to late adolescence. The discrepancy between these findings may be due to methodological differences in the two studies. In particular, Cole et al. (2001) studied two cohorts longitudinally over the course of several years, whereas the current study examined adolescents across several age ranges over the course of 1 year. The discrepancy may also be due to the different age ranges studied. Cole’s (2001) group studied adolescents as they transitioned into high school, whereas, in the current study, we included college freshmen and sophomores. We hypothesize that the athletic competence ratings of college students may be lower than younger adolescents who are involved in structured athletic activities through school, such as team sports
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or gym. It may be that adolescents in college are not as involved in athletics as they used to be, and as a result are not feeling as good about their abilities. In addition, in college, it is much more difficult to make athletic teams than it is in middle school or high school. It is possible that, as adolescents move to increasingly larger schools with more competitive sports teams, they become aware of their own athletic limitations. The decrease in physical appearance ratings may be linked to the decrease in athletic competence. As adolescents enter college and become less involved in athletics, they may become more critical about their physical appearance. Further research examining self-concept during the transition to college is warranted. Gender was also a significant predictor of level of self-concept for several of the domains. Females had lower global self-worth, scholastic competence, athletic competence, and physical appearance ratings than males. These findings are consistent with the work of several other research groups (e.g., Marsh, 1989; Wigfield et al., 1991; Bolognini et al., 1996; Cole et al., 2001), suggesting that these gender differences are consistent across different ages and different samples. The biological and social changes that occur during adolescence, particularly the intensification of gender roles, may be responsible for these gender differences (Hill & Lynch, 1983). This is only the second study to look at gender differences in global ratings of scholastic competence, rather than breaking academics into specific subject areas. Previous research (e.g., Eccles et al., 1989; Wigfield et al., 1991), examining subject-specific self-concepts found that males report higher perceptions of math ability, whereas females report higher perceptions of verbal ability. However, the findings from the current study and the Cole et al. (2001) study suggest that when academic competence is assessed more globally, females rate themselves more negatively than males. The sample we studied was a largely middle class, white sample. For this distinct group, there were individual differences in self-concept that were partially explained by age and gender. We postulate that a more diverse sample would mean greater individual differences in self-concept trajectories, both in regard to level of self-concept and rate of change. Whether social class and ethnicity predict these individual differences in self-concept is still up for debate, as the research to date has been largely inconclusive (Simmons & Rosenberg, 1975; Kanouse et al., 1980; Hirsch & DuBois, 1991; Bailey et al., 1992; Zimmerman et al., 1996; Brown et al., 1998). More studies are needed. 4.1. Future research Although level of self-concept, and to a lesser extent change in self-concept, was significantly predicted by several of the variables we examined, much of the variation of the intercepts and slopes was not explained. It is possible that the inclusion of other variables would have improved the prediction. To explore this possibility, we conducted supplementary analyses to determine if there were differences among the adolescents of the four schools. We found significant differences across schools in several of the self-concept domains: scholastic competence, athletic competence, job competence, and close friendships. Although many of the differences among the schools may be attributable to differences in the age or gender of the students, several of the findings cannot be explained by demographic variables. For instance, there were two schools that had younger adolescents. The adolescents in one of these schools had significantly higher athletic competence ratings than adolescents in other schools. The fact that this occurred in just one of these schools suggests that age is not the sole
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contributor to this finding. We know that this school encourages students to be involved in athletic teams regardless of their athletic ability. This approach may be beneficial for adolescents’ athletic self-concept, at least at this age. That same school had lower scholastic competence ratings than other schools. These findings suggest that there is either something about the school context that affects adolescents’ self-concepts and/or that certain schools attract adolescents with different patterns of academic and athletic success. The former hypothesis is supported by earlier studies that have highlighted the importance of the school context on students’ self-concepts (Eccles, Lord, & Midgley, 1991). Further research comparing various school types is warranted. If future research can determine what self-concept domains need to be attended to in particular school types, then school-based programmes can be designed to specifically address these areas. Another possible predictor of change in self-concept that should be examined in future research is initial level of self-concept. Previous research (Harter, 1993; Hirsch & DuBois, 1991; Zimmerman et al., 1996) suggests that initial level of self-concept may predict whether an individual is likely to show self-concept change. The random effect estimates of the covariances of the intercepts and slopes shed light on this question. There were significant negative relationships between the intercept and the slope for several of the domains: scholastic competence, social acceptance, close friendships, and physical appearance. These significant relationships indicate that initial level of self-concept was significantly associated with change in self-concept in these domains. The negative covariances can be interpreted in one of two ways. The first interpretation is that adolescents who started with higher self-concept scores declined over the course of the study. The second, and we believe more plausible interpretation, is that adolescents who started with higher self-concept scores did not show as much change as other adolescents who started with more moderate self-concepts. The latter interpretation is supported by prior research. Hirsch and DuBois (1991) and Zimmerman et al. (1996) found that adolescents with the highest and lowest levels of self-concept were less likely to show change over time. These findings, taken in combination, suggest that change in self-concept is dependent on initial level of self-concept. If level of self-concept can predict the magnitude and direction of change in self-concept, measuring self-concept at one timepoint might be a useful way of identifying adolescents who are at risk for declining self-concepts. 4.2. Limitations and conclusions Although this study had a strong methodological design and utilized important analytic techniques for examining change within individuals, several limitations in the study need to be addressed. First, the study examined self-concept over the course of a year, limiting the conclusions that can be made about long-term change in adolescence. It is possible that the time studied was too short and that, as Alsaker and Olweus (1992) suggested, longer intervals of time are needed to document change. In addition, due to the shorter time period, only linear effects were examined. This prevented the examination of non-linear time effects. Second, although the students were enrolled in four different schools, many of the students studied were from middle-class families. Although this limits the generalizability of these results, it is worthwhile to have studied middle-class adolescents since many studies have focused exclusively on low-income students. In addition, as we discussed earlier, the self-concept trajectories that we found may have been affected by our choice of schools, thereby limiting their generalizability to
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other school types. Third, although we asked students of all ages to complete the questionnaires, some of the ages were underrepresented in the sample, making conclusions about the adolescents in these age groups more tenuous. Future research should include students of all ages, in multiple types of schools (e.g., public schools, private schools, coeducational, and single-sex schools), and from a diverse range of communities to determine if there are significant differences by social class, community-type, and/or school-type. Lastly, we only administered one measure of self-concept. This limited our analyses to the domains covered in the Self-Perception Profile for Adolescents. The inclusion of other measures that assess additional self-concept domains would have provided a more comprehensive view of self-concept at this age and perhaps would have led to different results concerning which selfconcept domains changed over time. Despite these limitations, important conclusions can be made. The analytic model utilized proved to be an important improvement in the analysis of longitudinal self-concept data. By examining individual trajectories of self-concept over time, we were able to establish that although adolescents as a group have primarily stable self-concepts, many adolescents showed change over time. These individual differences in level and change in self-concept were explained partly by age and gender. Future research on self-concept in adolescence needs to move beyond assessing global level of self-concept at one or two timepoints to studying changes in domain-specific self-concepts over the course of adolescence and the determinants of such change.
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