Abstract
Youths growing up in low-income inner-city neighborhoods are at substantial risk for initiating substance use, violent behavior, and sexual intercourse at early ages; these risk behaviors continue at comparatively high rates through adolescence. Hopelessness has been implicated as a risk factor for these behaviors. In this paper, we consider how race influences this process. African Americans form a demographic minority within the United States, but they are often the majority within inner-city neighborhoods. For Caucasians, the opposite typically holds. Mixed-race populations form a minority within both contexts. Using longitudinal data, we examine the relationship between race and risk behaviors in several impoverished inner-city neighborhoods where African Americans form the distinct majority and Caucasians and people of mixed racial heritage form a small minority. We also consider how race moderates the relationship between hopelessness and risk behavior. Our findings show that compared to Caucasian or mixed-race adolescents, African American adolescents are less likely to engage in risk behaviors, and that hopelessness has a less important impact on their behaviors.
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Notes
The one type of risk behavior that has generated inconsistent findings in this regard is substance use and abuse. For example, Johnston et al. (2003), reporting on the 2002 Monitoring the Future (MTF) data, conclude that adolescents living in the lowest socioeconomic strata are no more likely to use drugs and alcohol than adolescents living in more affluent households. Yet, a careful analysis of the 2002 MFT data (Johnston et al. 2005) shows that 12-month prevalence of marijuana use among eighth graders varied considerably by parental education (24.2% for the lowest education category versus 9.7% for the highest education category; Table D7). Trends were similar although less pronounced among 10th graders (33.5% vs. 25.8%; Table D8); however, among 12th graders the trend was reversed (30.8% vs. 36.1%; Table D-9). Similar findings emerged for 30-day alcohol use (Tables D63–D65) and 12-month cocaine use (Tables D25–D27). In general, studies (including MTF) suggest that African American adolescents use alcohol and drugs at lower levels than do Caucasian adolescents. This is paradoxical since African Americans are overrepresented in drug-related institutional samples (e.g., Kandel 1995). These results, coupled with those noted above, suggest the possibility of a race × class interaction in substance use. At any rate, the results point out how limited our understanding of class, race, and substance use really is, and the importance of further investigation in these areas (cf., Biafora and Zimmerman 1998).
In 1998, 92.8% of respondents were actively recruited and 7.2% of respondents were passively recruited; during subsequent years, all the passively recruited youths became part of the active recruitment sample. A linear mixed model analysis, comparing the 1998 targeted sample with the 1998 untargeted sample on behaviors listed in Table 3 measured in 1998–2003, found a significant difference only for sexual intercourse. The 1999 cohort had a somewhat larger passively recruited sample. A comparison of their responses with the new actively recruited sample across the nine risk behaviors in Table 3 between 1999 and 2003 showed no significant differences. Thus, it seems reasonable to combine the passive recruitment sample with the active recruitment sample for subsequent analyses.
Inconsistency over time in racial self-designation may simply reflect inconsistency, or it may reflect personal confusion on the part of the respondent. When we look more closely at those youths whose race we could not classify based on the coding rules specified in the text, we find that they are younger (13.53 compared with 13.73 years of age for respondents who could be classified) and more likely to be male (77.68% compared with 51.15% for respondents who could be classified). Moreover, unclassified respondents were more inconsistent in their behavioral responses than those who could be classified (1.41 vs. .90; see note 4). Thus, the former explanation (simple inconsistency) seems more likely than the latter (confusion about racial identity), and the decision to eliminate these respondents from the analysis seems warranted.
The format of the behavior questions allows us to check consistency of responses. For the set of questions used to assess any given behavior, patterns of responses were categorized as consistent or inconsistent. A response pattern is inconsistent if the respondent indicated that he or she had never engaged in the behavior in response to one question but had engaged in the behavior during at least one recent time period in response to other questions. Overall, the questionnaire asked about 20 different behaviors, although only nine are considered in this paper. For any given behavior, only a small percentage of respondents are inconsistent (between 1.4% for tobacco use in 2001 and 11.7% for encouraging others to fight in 1999), with an overall mean across behaviors and years equal to 4.77%. Across the 20 behaviors, the mean number of inconsistencies ranged between .63 (2001) and 1.44 (1999). Inconsistencies are correlated between consecutive years, with r ranging between .26 and .31. Inconsistent responses may be an indication that respondents were impaired or fatigued, or that they misinterpreted the questions, or that they did not take the task seriously. Any of these explanations would raise questions about the validity of the data of respondents who were consistently inconsistent in their response patterns across behaviors. Thus, we chose to exclude respondents with inconsistent responses from the analyses. In other analyses (e.g., Bolland 2003) we conservatively chose to exclude all cases with more than two inconsistent response patterns during any given year from that year’s analyses. Given the small number of Caucasian and mixed-race respondents in the sample, however, we relaxed that criterion somewhat and have excluded all cases with more than four inconsistent response patterns during any given year from that year’s analysis. This resulted in the exclusion of 6.5% of respondents in 1998, 14.4% of respondents in 1999, 8.2% of respondents in 2000, 3.5% of respondents in 2001, 6.3% of respondents in 2002, and 8.0% of respondents in 2003. Across years, the mean age of retained cases was 13.73, compared with 13.84 for excluded cases; 50.4% of retained cases were females, compared with 25.0% of excluded cases. Mean level of hopelessness for retained cases was 1.40, compared with 3.06 for excluded cases. Also across years, 86.0% of excluded cases were African Americans, compared with 95.2% of retained cases; 1.1% of excluded cases were Caucasians, compared with 0.6% of retained cases; and 12.9% of excluded cases were mixed-race respondents, compared 4.1% of retained cases. The correlation between number of inconsistencies and Stanford Achievement Test reading scores provided by the School District is modest but statistically significant, ranging between −.133 and −.202 across years.
These results suggest that loss to followup is largely related to residential movement that we were not able to track during the short course of the survey each year (3 months during the summer). Youths who did not participate during any given year, because we were unable to find them during the summer, were no less willing to participate subsequently when we did locate them.
We checked for bias that might have been created as a result of differential attrition by running 15 separate MANOVAS (e.g., 1999–2000, 1999–2001, 1999–2002) on the nine risk behavior variables listed in Table 3, with retention, race, and gender as independent variables and age as a covariate. Only one of the 15 omnibus analyses showed a significant main effect (Wilks’ λ) for retention (p < .05), and only one of the analyses showed a significant retention × race interaction (Wilks’ λ). These rates approximate what would be expected by chance. Only 11 retention main effects for individual behaviors (out of nine behaviors × 15 ANOVAS = 135 comparisons) produced significant univariate effects for attrition, and 15 (out of 135) analyses of individual behaviors produced significant univariate attrition × race interactions; again, both results approximate what would be expected by chance. Moreover, the results of the univariate analyses were inconsistent. Whereas nine of the 11 former analyses showed retained cases reported higher levels of risk behavior than dropouts, two of the analyses showed opposite results. In the analyses or attrition × race interactions, five of the 15 statistically significant results showed that retained Caucasians reported higher rates of risk behavior than dropped Caucasians, but three other significant results showed that retained Caucasians had lower rates of risk behavior than dropped Caucasians; in addition, 10 of the 15 statistically significant results showed that retained mixed-race respondents reported lower rates of risk behavior than dropped mixed race respondents, but one other result showed the opposite pattern. Attrition rates (discounting cases that aged out of the study) for African American respondents (averaging 33.2% between contiguous years) were lower than those for Caucasian (47.4%) and mixed-race (51.6%) respondents. In general, attrition bias seems to present little risk to the data.
Degrees of freedom for these and subsequent analyses were estimated using a procedure developed by Satterthwaite (1941) coupled with an inflation of the estimated variance–covariance matrix of the fixed and random effects, as detailed by Kenward and Roger (1997). This adjustment is most useful for small samples. Thus, we would expect it to make little difference here, and indeed it does not: Other methods for estimating degrees of freedom produce substantively identical results.
For these multiple comparison analyses, three means were compared, yielding three tests. Probability adjustments for these three comparisons were made as follows: p* = 1 − (1 − p)3, where p* is the adjusted probability of a Type I error given multiple comparisons and p = the probability of a Type I error associated with each individual t-test.
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Acknowledgements
The research reported here was partially supported by the National Institutes of Health Office for Research on Minority Health through a cooperative agreement administered by the National Institute for Child Health and Human Development (HD30060); by a grant from the Center for Substance Abuse Treatment, Substance Abuse and Mental Health Services Administration (TI13340); by a grant from the National Institute on Drug Abuse (DA017428); by a grant from the Centers for Disease Control and Prevention (CE000191); by the Cities of Mobile and Prichard; by the Mobile Housing Board; and by the Mobile County Health Department.
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Bolland, J.M., Bryant, C.M., Lian, B.E. et al. Development and Risk Behavior Among African American, Caucasian, and Mixed-race Adolescents Living in High Poverty Inner-city Neighborhoods. Am J Community Psychol 40, 230–249 (2007). https://doi.org/10.1007/s10464-007-9132-1
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DOI: https://doi.org/10.1007/s10464-007-9132-1