Exploring Risk in Early Adolescent African American ... - Springer Link

P1: JLS American Journal of Community Psychology [ajcp]

pp1104-ajcp-480113

January 26, 2004

17:38

Style file version May 31, 2002

C 2004) American Journal of Community Psychology, Vol. 33, Nos. 1/2, March 2004 (°

Exploring Risk in Early Adolescent African American Youth Thomas W. Farmer,1,4 LeShawndra N. Price,2 Keri K. O’Neal,1 Man-Chi Leung,1 Jennifer B. Goforth,1 Beverley D. Cairns,1 and Le’Roy E. Reese3

Two studies were conducted to explore the degree to which single- and multiple-risk profiles were evident in samples of African American early adolescents in low-income inner-city, rural, and suburban schools. Study 1 examined early adolescent risk status (i.e., single, multiple) in relation to later adjustment in a representative sample (70% European American, 30% African American). Youth who experienced a single risk in early adolescence had moderately increased levels of school dropout and criminal arrests, whereas youth with multiple risks (i.e., combination of 2 or more risks) had significantly increased levels of school dropout, criminal arrests, and teen parenthood. Study 2 examined the extent to which single- and multiple-risk profiles were evident in cross-sectional samples of African American youth from low-income inner-city and rural areas. About one fourth of both the inner-city and rural samples of African American youth were composed of youth in the single-risk category. A significantly greater proportion of boys in the inner-city sample (20%) than boys in the rural sample (13%) experienced multiple risks. Girls across the rural and inner-city samples did not differ in terms of risk. Overall, more than 60% of African American youth in these two low-income samples did not evidence risk for later adjustment problems. Implications for research and intervention are discussed. KEY WORDS: adolescence; configurations; adjustment; African American.

The concept of “at risk” is commonly used to characterize minority youth from low-income environments. While children from ethnic minorities are disproportionately more likely to live in high poverty areas and to have adjustment difficulties in adolescence and adulthood, many minority youth from poor communities do not appear to experience negative outcomes (Cairns & Cairns, 1994; Dryfoos, 1990). Moving beyond stereotypes that may deleteriously impact the growth of youth in low-income communities, it is necessary to explore the degree to which minority youth from such communities evidence risk

that is associated with later problems in adaptation. To this end, studies are needed that specifically examine risk for later adjustment problems in low-income minority youth. Information along these lines should help promote the development of more effective prevention programs. Much of the research on “at-risk” youth from low-income communities has focused on inner-city neighborhoods. American inner cities have been identified as having high levels of poverty and associated risks (Anderson, 1994; Burton, 1991). Regardless of ethnic or racial background, youth who grow up in inner-city neighborhoods and experience persistent poverty are more likely to have problematic outcomes (McLoyd, 1998; Safyer, 1994). Although many rural communities have elevated rates of poverty that parallel inner-city neighborhoods, there is much less information about developmental risk in poor rural environments. It is possible that impoverished rural youth are susceptible to the same types of risks and outcomes as those of their inner-city counterparts. McLoyd’s

1 Center

for Developmental Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. 2 National Institute of Mental Health, Developmental Psychopathology and Prevention Research Branch, Rockville, Maryland. 3 Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Atlanta, Georgia. 4 To whom correspondence should be addressed at Center for Developmental Science, C.B. #8115, University of North Carolina, Chapel Hill, North Carolina 27599-8115; e-mail: [email protected].

51

C 2004 Plenum Publishing Corporation 0091-0562/04/0300-0051/0 °


52

pp1104-ajcp-480113

January 26, 2004

17:38


Farmer, Price, O’Neal, Leung, Goforth, Cairns, and Reese

model of how poverty and economic loss affect African American children suggests that economic hardship diminishes the capacity for supportive, consistent, involved parenting, increases chronic stress within the family, and affects children indirectly through the impact of parental behaviors and parent– child interactions (McLoyd, 1990). The mechanisms and processes by which poverty impacts children and families may be universal rather than context-specific. Despite differences between rural and inner-city environments, poverty may influence inner-city and rural families in similar ways. Studies using person-oriented procedures have shown that developmental risks tend to come in “correlated packages” (Cairns & Cairns, 1994). The increase in the likelihood of poor outcomes is moderate for children who evidence only a single risk. Instead, later problems in adaptation are associated with multiple-risk profiles that include a combination of factors such as aggression, academic problems, and social problems (Bergman & Magnusson, 1997; Cairns & Cairns, 1994). On the basis of such findings, we decided to explore outcomes of single- and multiplerisk profiles in the Carolina Longitudinal Study (CLS; Cairns & Cairns, 1994) and to examine the degree to which these a priori profiles were evident in samples of inner-city and rural African American youth. This analysis was facilitated by the fact that the Interpersonal Competence Scale—Teacher (ICS-T; Cairns, Leung, Gest, & Cairns, 1995) was used across each of the different data sets employed in the current study. This measure yields factors pertaining to aggression, academic competence, and popularity. Two distinct studies were conducted. Study 1 involved identifying negative outcomes associated with single-risk (i.e., aggression, academic problems, social problems) and multiple-risk profiles (i.e., combinations of two or more of these factors) in the CLS. The outcomes we examined were school dropout, teenage parenthood, and adult criminality. On the basis of prior use of configural analyses with this sample, we expected that single-risk factors would be associated with only moderate increases in the level of negative outcomes whereas multiple risks would be associated with much greater increases in problems of adaptation. Using a subsample of African American youth embedded in the CLS, we examined the extent to which there was consistency between African American and European American youths’ risk profiles and later outcomes. Study 2 involved exploring the degree to which these risk profiles were evident in samples of inner-

city and rural African American early adolescents. We expected that most youth in these communities would not be characterized by risk profiles, and that most of the youth who were at risk would evidence single-risk profiles that are associated with only moderate increases in poor outcomes. In addition, we expected few differences between the inner-city and rural samples. STUDY 1 Method Sample A sample of 475 participants (248 girls and 227 boys) was drawn from Cohort II of the CLS. The CLS sample has been described in detail elsewhere (Cairns & Cairns, 1994). At the beginning of the study in 1982– 83, these participants were enrolled in the seventh grade of three middle schools in three communities of North Carolina. Participants were assessed and interviewed annually until the cohort finished high school in 1988–89. Further assessments and interviews were done at ages 20 and 24. The mean age of the sample was 13.24 years (SD = 0.67) when the participants were assessed in seventh grade. The mean family socioeconomic status at that time was 31.43 (SD = 17.79) on the Duncan scale (Featherman revision), and the full range of occupations was represented in the sample (range from 12 to 88). Among the participants, 27% of male and 32% of female participants were minority status. All students in the classrooms were invited to participate. Those whose parent or legal guardian signed a statement of informed consent were included in the study. Materials/Measures Interpersonal Competence Scale—Teacher Form. The ICS-T (Cairns, Leung, Gest, & Cairns, 1995) is an 18-item inventory that pertains to aggression, popularity, affiliation, academic competence, and sports competence. Each item requires a teacher to classify a student’s characteristics at one point in time on a 7-point unidimensional scale. The items of the ICS-T can be divided into five factors: aggression (ex: “argues,” “gets in trouble at school,” “gets in fights”), academic competence (ex: “good at spelling,” “good at math”), popularity (ex: “popular with boys,” “popular with girls,” “lots of friends”), olympian (ex: “good at


pp1104-ajcp-480113

January 26, 2004

17:38


Risk in African American Youth

53

sports,” “good looking,” “wins”), and affiliation (ex: “smiles,” “shy,” “friendly”). The first three factors, aggression, academic competence, and popularity, were used to determine risk status of participants in this study. Psychometric properties of the ICS-T have been assessed in the CLS longitudinal sample (Cairns et al., 1995). Three-week test–retest reliability coefficients are moderately high (.80–.92) and 1-year coefficients are moderately strong (i.e., .40–.50). School Dropout. School dropout was determined by information from schools and annual interviews of participants. The schools at which participants were enrolled during seventh grade were visited every year to obtain information on whether the participants were currently enrolled. If a participant was not enrolled at the school, we obtained information regarding where his or her record had been forwarded and checked with that school to determine whether the participant was currently enrolled. Each participant was also interviewed annually. If the interview was not conducted in a school, the participant was asked whether and where he or she was enrolled. A research team member then visited the school to confirm the participant’s report. Teenage Parenthood. Information about childbirth was gathered from participant interviews. In the interview, participants were asked whether or not they had children. Specific information such as the name, date, and place of birth of each child was collected. These reports were then confirmed by other sources (e.g., newspaper announcements, official birth records, participants’ parents). In this study, teenage parenthood was defined as being a biological parent before age 20. Police Arrest. Records of arrests were obtained from North Carolina State Bureau of Investigation (SBI). The SBI records were based upon arrest reports from local police stations. The data were collected in 1996 when participants were approximately 25 years old. Coding of arrests followed the Federal Bureau of Investigation’s (FBI) 1992 Uniform Crime Report (UCR; FBI, 1992). UCR is a nationwide standardized system for crime classification. SBI records were further broken down into three

broad categories: violent offenses (e.g., criminal homicide, forcible rape), property offenses (e.g., burglarybreaking or entering, motor vehicle theft, fraud), and other offenses (e.g., prostitution and commercialized vice, drug abuse violations, gambling). The average number of arrests was 0.93 (SD = 3.55, range = 0–39) for male participants and 0.12 (SD = 0.50, range = 0– 4) for female participants. Given that the distribution was highly skewed, participants were dichotomized into two categories—has been arrested (35 boys and 15 girls) or has never been arrested (178 boys and 225 girls). The same was done regarding the three categories of offenses. Among male participants, 22 had been arrested for violent crimes, 24 had been arrested for property crimes, and 15 had been arrested for other crimes. Among female participants, 4 had been arrested for violent crimes, 12 had been arrested for property crimes, and 2 had been arrested for other crimes.

Results Risk Status Configurations Three ICS-T factors (aggression, academic competence, and popularity) were used to determine the risk status of the participants. A participant was considered to be in the “risk group” of a factor if he or she had an extreme score in the unfavorable end of that factor. Specifically, a participant with a score of 1 SD above the mean in aggression would be in the aggression risk group, whereas a participant with a score of 1 SD below the mean in academic competence and popularity would be in the academic risk group and popularity risk group, respectively. The mean scores of these three ICS-T factors and their cutoff points for risk group are shown in Table I. The next step was to identify participants who had multiple risks. A participant who had an extreme score in the unfavorable end of two or all three ICST factors (i.e., aggression, academic competence, and popularity) was considered to have multiple risks. The criterion used for extreme score here was 0.75 SD

Table I. Mean Scores and Cutoff Points for Risk in Three ICS-T Factors Male ICS-T factor

Mean

Aggression Academic Popularity

3.36 4.16 4.39

SD 1.58 1.48 1.12

Female Risk cutoff

Mean

SD

Risk cutoff

4.94 2.68 3.27

2.60 4.93 4.70

1.51 1.47 1.21

4.11 3.46 3.49


pp1104-ajcp-480113

January 26, 2004

54

17:38


Farmer, Price, O’Neal, Leung, Goforth, Cairns, and Reese Table II. Distribution of Participants in Risk Status by Gender and Race Adaptation Male African American European American Total Female African American European American Total

Multiple risks

Single risk

No risk

15 (24.6%) 24 (14.9%) 39 (17.6%)

7 (11.5%) 29 (18.0%) 36 (16.2%)

39 (63.9%) 108 (67.1%) 147 (66.2%)

9 (11.7%) 15 (9.1%) 24 (9.9%)

15 (19.5%) 29 (17.6%) 44 (18.2%)

53 (68.8%) 121 (73.3%) 174 (71.9%)

from the mean. This lower criterion was employed because there were very few participants who scored beyond 1 SD in more than and factor. If 1 SD was used, we would be focusing on a very small extreme group of participants and would fail to capture a larger group who were at risk in multiple domains. This lower criteria has been used in cluster-analytic studies to identify risk profiles in youth (e.g., Rodkin, Farmer, Pearl, & Van Acker, 2000; Xie, Cairns, & Cairns, 1999) and is consistent with a holistic developmental analysis of correlated constraints (Cairns & Cairns, 1994). On the basis of the criteria for single- and multiple-risk factors described above, participants were divided into three categories. The distribution of participants in these risk categories is shown in Table II. Two thirds of male participants had no risk, whereas the other boys were split quite evenly between multiple risks and single risk. About 10% of female participants had multiple risks, 18% had single risk, and 72% had no risk. African American boys tended to have a higher proportion of multiple risks and a smaller proportion of single or no risk than did European American boys, but the difference was not significant. The distribution of African American girls and European American girls was very similar.

Risk Status and Later Adaptations Later adaptations of participants with different risk status (i.e., multiple, single, none) were compared. All analyses were conducted separately for

males and females. Significant differences were found in the rate of school dropout (χ 2 = 25.42, p < .001), teenage parenthood (χ 2 = 9.77, p < .01), and police arrest (χ 2 = 16.97, p < .01) among male participants (Table III). Boys with multiple-risk factors had the highest rate of problematic outcomes in all three measures. The rate was about two to four times higher than boys with single or no risk. Boys who had a single-risk factor had a higher rate of school dropout (25.0%) than did boys who had no-risk factor (10.9%). The same analyses were run for females. Significant differences were found in school dropout and teen parenthood (Table IV). Similar to boys, girls with multiple risks had the highest rates of problematic outcomes. The rate difference was most extreme in school dropout, where 66.7% of multirisk female participants dropped out of school, compared to 15.9% of single risk and 5.2% of no risk. In teenage parenthood, the rate for female participants with multiple-risk factors was about 2.5 times as high as other girls. Although girls with single and multiple risks had higher arrest rates than did nonrisk girls, the difference was not statistically significant. The outcomes of African American and European American participants were examined. European American boys were about twice as likely to drop out of school as African American boys, regardless of risk status (Table V). However, African American boys had a higher rate of teen parenthood than European American boys with the same risk status. African American boys also had a higher arrest rate except for those with multiple risks.

Table III. Later Adaptation Problems of Males as a Function of Risk Status Adaptation measure School dropout Teen parenthood Police arrest ∗∗ p

< .01. ∗∗∗ p < .001.

Multiple risk factors (n = 39)

Single risk factor (n = 36)

No risk (n = 147)

χ2

46.2% 20.5% 35.9%

25.0% 5.6% 13.9%

10.9% 5.4% 9.5%

25.42∗∗∗ 9.77∗∗ 16.97∗∗


pp1104-ajcp-480113

January 26, 2004

17:38



55

Table IV. Later Adaptation Problems of Females as a Function of Risk Status Adaptation measure

Multiple risk factors (n = 24)

Single risk factor (n = 44)

No risk (n = 174)

χ2

66.7% 45.8% 12.5%

15.9% 18.2% 11.4%

5.2% 16.1% 4.0%

69.84∗∗∗ 11.97∗∗ 5.07

School dropout Teen parenthood Police arrest ∗∗ p

< .01. ∗∗∗ p < .001.

Because of the relatively small sample size and low prevalence rates, no statistical tests were run for these comparisons. A similar pattern was found with girls (Table VI). European American girls showed a higher rate of dropout. Overall, African American girls showed a higher rate in teen parenthood. However, a lower percentage of African American girls with a singlerisk factor became teenage mothers than European American girls. Similar to the boys, African American girls with single- or no-risk factor had a higher arrest rate than European American girls with the same risk status.

in two counties. All students in selected grades in each school were invited to participate, and only those African American children who agreed and had signed parent consents were included. Measures Interpersonal Competence Scale—Teacher Form. Same as Study 1, the ICS-T was used for teachers to rate the behavioral characteristics of the participants. Again, three factors, aggression, academic competence, and popularity, were used to determine risk status.

STUDY 2 Results Method ICS-T Factor Scores of Rural and Inner-City Samples Sample A total of 1,487 participants were recruited from inner-city communities in Chicago, IL, and Birmingham, AL, and rural communities in Alabama. The residents in these communities were predominately African Americans, and all of the participants in the study were African Americans. The inner-city sample included 335 fourth to sixth graders (155 males and 180 females) from five public schools located within school districts inside the central city area. The rural sample included 1152 fifth and sixth graders (573 males and 579 females) from six public schools

The mean and standard deviation of the three ICS-T factors of the rural and inner-city samples are shown in Tables VII and VIII. A multivariate analysis of variance was run to test effects of grade, gender, and location (rural vs. inner city). Main effects were found for gender, Wilks’s lambda = .97, F(3, 1245) = 12.35, p < .001), and location, Wilks’s lambda = .98, F(3, 1245) = 7.85, p < .001. Boys were rated significantly higher in aggression, F(1, 1247) = 72.61, p < .001, but lower in academic, F(1, 1247) = 38.08, p < .001, than girls. Inner-city youth had a higher mean rating in aggression, F(1, 1247) = 35.07, p < .001, and academic,

Table V. Later Adaptation Problems of Males by Race and Risk Status

Table VI. Later Adaptation Problems of Females by Race and Risk Status

Risk status African American boys Multiple risks Single risk No risk European American boys Multiple risks Single risk No risk

n

School Teenage Police dropout parenthood arrest

15 7 39

26.7% 14.3% 7.7%

26.7% 14.3% 7.7%

33.3% 42.9% 12.8%

24 29 108

58.3% 27.6% 12.0%

16.7% 3.4% 4.6%

37.5% 6.9% 8.3%

Risk status African American girls Multiple risks Single risk No risk European American girls Multiple risks Single risk No risk

n

School Teenage Police dropout parenthood arrest

9 15 53

44.4% 6.7% 3.8%

55.6% 13.3% 24.5%

0.0% 26.7% 7.5%

15 29 121

80.0% 20.7% 5.8%

40.0% 20.7% 12.4%

20.0% 3.4% 2.5%


pp1104-ajcp-480113

January 26, 2004

56

17:38


Farmer, Price, O’Neal, Leung, Goforth, Cairns, and Reese

ICS-T factor

Mean

SD

Mean

SD

inner-city samples were compared on individual risk factors (i.e., ICS-T aggression, popularity, and academic scores) and profiles. No significant differences were found for either boys or girls.


3.46 4.08 4.54

1.73 1.51 1.24

3.82 4.48 4.62

1.62 1.80 1.04

Comparison With the Sample in Study 1

Table VII. ICS-T Factor Scores of Rural and Inner-City Samples, Male Participants (n = 573 for Rural and 155 for Inner City) Rural

Inner city

F(1, 1247) = 12.84, p < .05, than rural youth. No grade effect was found. Risk Status of Rural and Inner-City Youth As in Study 1, a score of 1 SD above the mean in aggression and 1 standard deviation below the mean in academic and popularity was used to identify risk participants. Risk was determined with male and female participants separated but rural and inner-city youth combined. The percentage of participants who were in the risk group was compared between rural and the inner-city participants in each ICS-T factor (Table IX). Inner-city and rural boys had about the same percentage of risk participants on each measure. Inner-city female participants had a higher percentage of risk in aggression than their rural counterparts (22.8% vs. 14.0%, χ 2 = 6.84, p < .01). No other significant differences were found. Risk status (multiple risks, single risk, and no risk) was determined in the same way as in Study 1 (Table X). Inner-city and rural boys had a significant difference in the percentage of participants in these categories (χ 2 = 6.56, p < .05). About one fourth of boys in both locations were single-risk participants. Inner-city boys tended to have a higher percentage in the multiple-risk category and thus, a lower percentage of participants in the no-risk category than rural boys. No significant differences were found for female participants. It was possible that there were differences in the Chicago and Alabama inner-city samples with regard to risk status. Therefore to examine this, the two Table VIII. ICS-T Factor Scores of Rural and Inner-City Samples, Female Participants (n = 579 for Rural and 180 for Inner City) Rural

Inner city

ICS-T factor

Mean

SD

Mean

SD


2.74 4.42 4.75

1.54 1.50 1.20

3.28 4.87 4.57

1.44 1.51 1.02

For the purpose of cross-study comparison, the mean and standard deviation of the ICS-T factors in Study 1 were applied to the African American sample in Study 2 to determine risk status. The distribution of multiple-risk, single-risk, and no-risk participants was reported in Table XI. Compared to the sample in Study 1, rural and inner-city boys were about 7– 8% higher in the proportion of single risk and about 6–11% lower in the proportion of no risk, but the differences were not statistically significant. Rural girls were 5.4% higher in multiple risks, 4.5% higher in single risk, and 9.9% lower in no risk (χ 2 = 7.60, p < .05). Inner-city girls were 14.1% higher in multiple risks, 2.5% higher in single risk, and 16.6% lower in no risk (χ 2 = 15.98, p < .01) than the sample in Study 1.

Discussion As expected, most African American youth from inner-city and rural low-income communities did not evidence risk profiles that have been linked to problematic outcomes in a representative longitudinal sample. Over 60% of these youth had academic, behavioral, and social characteristics indicative of positive adaptation in early adolescence. About one quarter of the youth in both the inner-city and rural samples were characterized by a single risk in either the academic, behavioral, or social domains. Fifteen percent of the African American youth from lowincome inner-city and rural areas had multiple-risk profiles that have been linked to increased levels of school dropout, teen parenthood, and involvement in adult criminality. The level of risk in the inner-city and rural African American samples were fairly consistent with that of a subsample of African American youth who were embedded within the longitudinal sample. There were relatively few differences across the inner-city and rural samples. Although the inner-city youth tended to be slightly higher than rural youth on most of the risk scores, the only significant difference was for girls on the aggressive factor. Twenty-three


pp1104-ajcp-480113

January 26, 2004

17:38



57

Table IX. Percentage of Risk Participants in Each ICS-T Factor by Gender and Location Male

Female

ICS-T factor

Rural

Inner city

χ2


17.7% 20.0% 12.8%

22.6% 22.1% 11.1%

1.73 0.28 0.31

∗∗ p

Rural

Inner city

χ2

14.0% 17.5% 11.9%

22.8% 14.9% 15.0%

6.84∗∗ 0.65 1.18

< .01.

percent of inner-city girls were rated high on aggression by teachers compared to 14% of rural girls. In terms of risk profiles, a significantly higher percentage of inner-city boys had multiple-risk profiles than rural boys. There were no significant differences between inner-city and rural girls on the risk profiles. As others have observed (e.g., Cairns & Cairns, 1994; Dryfoos, 1990), the results of this study indicate that the majority of African American youth from low-income environments do not appear to have the types of early adolescent risk that are linked to later problems. These findings suggest that we must be careful about characterizing African American youth as being at risk simply because of their ethnic status and their low-economic background. Although the majority of results were as expected, our findings suggest that the relationship between early adolescent profiles and later outcomes may be different for European American and African American youth. As a component of the first study, outcomes of African American and European American youth were compared by risk status. Because of the relatively small sample size and low prevalence rates in the risk profiles, it was not appropriate to make direct statistical comparisons. The trends indicated that European American boys and girls, regardless of risk status, had about twice the school dropout rate as African American boys and girls. Conversely, African American youth tended to have higher rates of teenage parenthood and police arrests than their European American counterparts with the same risk status. Further work is needed to examine whether these results can be replicated in a larger sample. Nonetheless, the possible differences in the comparisons between African American and European

American youth in Study 1 indicate a need for research that more directly examines ecological risk and protective factors (e.g., parenting behaviors, teachers’ attitudes and beliefs, community supports and collective monitoring) that may be associated with the risk indicators (i.e., academic performance, aggression, popularity) that are examined here. For example, the present results suggest that there may be protective factors in African American communities that promote completing school regardless of individual risk factors. Future work needs to investigate how individual protective factors (i.e., beliefs, values, bonding to school) and social factors (e.g., parental beliefs, involvement in supportive peer and community networks) may come together to reduce the negative impact of individual risk for school dropout in African American youth. On the other hand, there is also a need to better examine the higher rates of arrest for African American youth as compared to European American youth with similar risk profiles. These results suggest that there may be differences in how similar youth of different races are treated by law enforcement. For example, future work is needed to examine whether African American youth experience a greater level of surveillance than European American youth, whether there are differences in how those who violate laws are treated, or whether there are additional community support mechanisms that protect at-risk European American youth from becoming involved in criminal activity. The results on the relationship between early adolescent risk status and subsequent adjustment problems is consistent with a growing body of work that indicates that adjustment problems tend to reflect the correlated contributions of multiple factors (Bergman & Magnusson, 1997; Cairns & Cairns, 1994;

Table X. Risk Status by Gender and Location Male

Female

Location

Multiple risks

Single risk

No risk

Multiple risks

Single risk

No risk

Rural Inner city

13.4% 22.4%

25.9% 22.4%

60.7% 55.2%

10.0% 16.7%

24.7% 19.6%

64.3% 63.7%


pp1104-ajcp-480113

January 26, 2004

58

17:38


Farmer, Price, O’Neal, Leung, Goforth, Cairns, and Reese Table XI. Risk Status by Gender and Location, With Risk Criteria From Study 1 Applied Male

Female

Location

Multiple risks

Single risk

No risk

Multiple risks

Single risk

No risk

Rural Inner city

16.3% 21.1%

23.3% 24.1%

60.4% 54.9%

15.3% 24.0%

22.7% 20.7%

62.0 % 55.3%

O’Donnell, Hawkins, & Abbott, 1995). Likewise, prevention programs have been developed to address multiple factors including academic problems, peer problems, behavior problems, and difficulties in the home and community (e.g., Eron et al., 2002; Reid, Eddy, Fetrow, & Stoolmiller, 1999). However, the results here suggest that such efforts may be enhanced by distinguishing between youth who experience single and multiple risks. On the basis of the concept of correlated constraints and a systems view of development, it follows that the goals of preventive interventions for singlerisk and multiple-risk youth may be distinct. When the developmental system of a youth centers primarily around positive factors (e.g., personal strengths, supportive relationships, sufficient resources) but a single risk (e.g., academic problems, behavior problems, social problems) is present, the focus of intervention should be on preventing the negative reorganization of the youth’s system (Farmer & Farmer, 2001). This involves supporting the positive factors while simultaneously working to ameliorate the risk (see Farmer, Quinn, Hussey, & Holahan, 2001). However, when the developmental system of a youth is composed primarily of negative factors, prevention efforts should focus on promoting the positive reorganization of the system. This involves examining interconnections of the various problematic factors, identifying how the different factors support and sustain each other, and developing individualized plans to systematically address the factors as a collective unit. It also involves developing natural supports to help sustain new gains and to promote positive change in other problem factors (see Farmer et al., 2001).

search focusing specifically on each of these populations. As ethnic minority youth from high poverty areas move into adolescence, it is possible they are confronted with constraints that increase their risks for problems that are not experienced by European American youth and youth from more affluent backgrounds. Risk profiles generated from the representative sample in this report may not be relevant to the inner-city and rural African American youth in this study. Further, the risk profiles are based on teacher ratings of student behavioral characteristics. As such, our measurement of risk does not include family, environmental, or peer group influences. The importance of such factors has been demonstrated in the literature (e.g., Gorman-Smith, Tolan, Zelli, & Huesmann, 1996; Reese, Vera, Thompson, & Reyes, 2001), and future measurements of overall risk would benefit from their inclusion. This work does not afford the most complete picture of risk in African American youth. However, it does provide an important first step to help direct future research. Specifically, this study demonstrates a strong need for research that examines patterns of adaptation in African American youth and that investigates the interplay between individual and ecological factors. Following African American and other minority youth from a variety of backgrounds across childhood and adolescence and into adulthood should help to clarify the developmental needs and trajectories of such youth. Such research should elucidate developmental processes associated with adaptive patterns and further help to clarify how configurations of factors may promote or protect against later maladjustment.

Limitations and Future Directions

ACKNOWLEDGMENTS

This study cannot and should not take the place of longitudinal research with minority youth. Although the work here provides insight into the early adolescent adaptation of inner-city and rural African American youth, we recognize that it is not an optimal design and that there is a need for longitudinal re-

This research was supported by Grants U81CCU416369 and R49CCR419824 from the Centers for Disease Control and Prevention to Thomas W. Farmer (Principal Investigator) and in part by National Institute of Mental Health Grant MH52429 to the Center for Developmental Science.


pp1104-ajcp-480113

Risk in African American Youth The views expressed in this paper are ours and do not represent the granting agencies.

REFERENCES Anderson, E. (1994). The code of the street. The Atlantic Monthly, 275, 81–84. Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Developmental Psychopathology, 9, 291–319. Burton, L. M. (1991). Caring for children. The American Enterprise, 2, 34–37. Cairns, R. B., & Cairns, B. D. (1994). Lifelines and risks: Pathways of youth in our time. New York: Cambridge University Press. Cairns, R. B., Leung, M.-C., Gest, S. D., & Cairns, B. D. (1995). A brief method for assessing social development: Structure, reliability, stability, and developmental validity of the Interpersonal Competence Scale. Behavioral Research and Therapy Incorporating Behavioral Assessment, 33, 725–736. Dryfoos, J. G. (1990). Adolescents at risk: Prevalence and prevention. New York: Oxford University Press. Eron, L., Huesmann, R., Spindler, A., Guerra, N., Henry, D., & Tolan, P. (2002). A cognitive–ecological approach to preventing aggression in urban settings: Initial outcomes for high-risk children. Journal of Consulting and Clinical Psychology, 70, 179–194. Farmer, T. W., & Farmer, E. M. Z. (2001). Developmental science, systems of care, and prevention of emotional and behavioral problems in youth. American Journal of Orthopsychiatry, 71, 171–181. Farmer, T. W., Quinn, M. M., Hussey, W., & Holahan, T. (2001). The development of disruptive behavior disorders and cor-

January 26, 2004

17:38


59 related constraints: Implications for intervention. Behavioral Disorders, 7, 117–130. Federal Bureau of Investigation. (1992). Uniform crime report: Crime in the United States. Washington, DC: U.S. Department of Justice. Gorman-Smith, D., Tolan, P. H., Zelli, A., & Huesmann, L. R. (1996). The relation of family functioning to violence among inner-city minority youths. Journal of Family Psychology, 10, 115–129. McLoyd, V. C. (1990). The impact of economic hardship on Black families and children: Psychological distress, parenting, and socioemotional development. Child Development, 61, 311– 346. McLoyd, V. C. (1998). Socioeconomic disadvantage and child development. American Psychologist, 53, 185–204. O’Donnell, J., Hawkins, J., & Abbott, R. (1995). Predicting serious delinquency and substance use among aggressive boys. Journal of Consulting and Clinical Psychology, 63, 529–537. Reese, L. E., Vera, E. M., Thompson, K., & Reyes, R. (2001). A qualitative investigation of perceptions of violence risk factors in low-income African American Children. Journal of Clinical Child Psychology, 30, 161–171. Reid, J. B., Eddy, M., Fetrow, R. A., & Stoolmiller, M. (1999). Description and immediate impacts of a preventive intervention for conduct problems. American Journal of Community Psychology, 27, 483–517. Rodkin, P., Farmer, T. W, Pearl, R., & Van Acker, R. (2000). The heterogeneity of popularity in boys: Antisocial and prosocial configurations. Developmental Psychology, 36, 14–24. Safyer, A. W. (1994). The impact of inner-city life on adolescent development: Implications for social work. Smith College Studies in Social Work, 64, 153–167. Xie, H., Cairns, R. B., & Cairns, B. D. (1999). Social network centrality and social competence among inner-city children. Journal of Emotional and Behavioral Disorders, 7, 147–155.