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Adolescent Risk Behavior Subgroups: An Empirical Assessment

  • Empirical Research
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Abstract

Theories and prior research have outlined a constellation of adolescent risk behaviors that tend to co-occur, reflecting a general pattern. Although their generality has largely been supported, there is some question about how to best study and portray the relationship among these behaviors. This study used data from a survey administered to high school youth (n = 2549, 38 schools). The general population sample comprised an even split between boys and girls, averaged roughly 16 years of age, and was 59% White and 10% Hispanic/Latino. Using latent class analysis, four subgroups, comprised of varying types and degrees of risky behavior, were identified. Specifically, there were two groups that “abstained” and “experimented” with risky behaviors and two others that had higher, but somewhat distinct, patterns of such activities. These groups were then examined in relation to youth characteristics (e.g., mental and physical health, school performance) and socio-environmental factors (e.g., social support, parental monitoring) that may be useful for better understanding “problem behavior syndrome” and development of prevention strategy.

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Notes

  1. We generally use the phrase “risky behaviors” in our study as we have a broad pool of measures that go past some extant definitions of problem behaviors. Nevertheless, there is considerable overlap between the two phrasings in the individual behaviors that are indicated.

  2. Of course, there are other problems in application (e.g., measurement error, use of appropriate levels of measurement) that can impact observed levels of explained variance in factor analysis of general problem behavior.

  3. These measures were dichotomized in an attempt to limit the number of parameters to be estimated in the models presented here. These items were chosen in part because they had limited variation beyond a “yes–no” distinction and, substantively, they were more suitable to such scaling than other measures included in the analysis (e.g., gambling, frequency of substance use). Some have argued against dichotomization in such analyses (MacCallum et al. 2002; cf. Farrington and Loeber 2000), so future replication of these results with less circumscribed measures is warranted.

  4. Cronbach’s alpha should be interpreted with caution in the current study due to the noncontinuous nature of many of the included variables.

  5. The Chi-Square may be problematic as a measure of fit in models with a high prevalence of sparse cells, particularly when coupled with the inclusion of a high volume of items (Nylund et al. 2007). In this case, the volume of items precluded the program’s computation of the Model Chi-Square value.

  6. A preliminary investigation of these model specifications suggests that the four class LCA model discussed here has a better fit than factor analysis specifications (e.g., BIC of 42873.27 compared to 43686.65 for the three component EFA). There is some evidence to suggest that a hybrid Factor Analysis/LCA model might provide a comparatively good fit, however (BIC = 42564.32). Further study of these alternative approaches to assessing risky behavior in adolescence is necessary.

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Authors and Affiliations

  1. School of Criminal Justice, University of Cincinnati, P.O. Box 210389, 600 Dyer Hall, Cincinnati, OH, 45221-0389, USA

    Christopher J. Sullivan

  2. Department of Psychology, University of New Orleans, New Orleans, LA, USA

    Kristina K. Childs

  3. University of Delaware, Newark, DE, USA

    Daniel O’Connell

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  1. Christopher J. Sullivan
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  3. Daniel O’Connell
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Correspondence to Christopher J. Sullivan.

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Table 6 Descriptives for key study variables
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Sullivan, C.J., Childs, K.K. & O’Connell, D. Adolescent Risk Behavior Subgroups: An Empirical Assessment. J Youth Adolescence 39, 541–562 (2010). https://doi.org/10.1007/s10964-009-9445-5

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