- Email: [email protected]
Prevention programs for reducing adolescent problem behaviors: Implications of the co-occurrence of problem behaviors in adolescence
Journal of Adolescent Health 36 (2005) 82– 86
Review article
Prevention programs for reducing adolescent problem behaviors: Implications of the co-occurrence of problem behaviors in adolescence Vincent Guilamo-Ramos, Ph.D.a,b,*, Harold A. Litardo, Ph.D.c, and James Jaccard, Ph.D.c,d a School of Social Work, Columbia University, New York, New York Department of Population and Family Health, Columbia University, New York, New York c Department of Psychology, University at Albany, State University of New York, Albany, New York d School of Social Welfare, University at Albany, State University of New York, Albany, New York Manuscript received June 17, 2003; manuscript accepted December 16, 2003 b
Abstract
Purpose: To examine the correlations between multiple risk behaviors in adolescent populations to document the extent to which problem behaviors are intercorrelated and to identify factors associated with variations in these correlations. Methods: Studies from 1977 through the end of 1999 that included two or more problem behaviors in adolescents were identified by literature searches using the PsychLit database, Social Sciences Citation Index, manual journal searches and “ancestry” approaches. The behaviors studied were alcohol use, marijuana use, illicit drug use, cigarette smoking, general deviant behavior, and sexual activity. Included studies reported correlation coefficients between variables. Results: Across all studies, the mean correlation between any two pairs of problem behaviors was 0.35, with a standard deviation of 0.28. This suggests that, on average, about two-thirds of the variation in problem behavior is the result of unique rather than common causes. The magnitude of the correlations varied as a function of the age of the adolescent, with lower correlations being evident for older adolescents. In addition, the magnitude of the correlation varied as a function of when the study was conducted, with studies of past generations showing stronger connections between risk behaviors than current generations. Conclusions: The data suggest that there is considerably more unique variation in classic adolescent problem behaviors than common variation. © 2005 Society for Adolescent Medicine. All rights reserved.
Keywords:
Adolescent problem behavior; Prevention; Intervention design
Health researchers and practitioners have long been interested in the reasons why some adolescents abuse substances, commit delinquent acts, smoke cigarettes, or engage in unsafe sexual practices. An important theoretical emphasis has been the identification of factors that influence multiple risk behaviors. Probably the most notable of these frameworks is Jessor and Jessor’s Problem Behavior Theory (PBT) [1]. According to PBT, the performance of problem behaviors should be highly correlated because they share many common causes. Research has shown that problem *Address correspondence to: Dr. Vincent Guilamo-Ramos, School of Social Work, Columbia University, New York, NY 10027. E-mail address: [email protected].
behaviors do indeed tend to be correlated and conventional wisdom within the field of adolescent health is that problem behaviors are linked. Most of these studies have emphasized significance tests of the correlations between problem behaviors, which amounts to testing the hypothesis that the correlation between the variables is not zero. Of greater interest should be the magnitude of the correlations, because this provides a sense of the extent to which the variables may have common causes versus unique causes. Statistically significant but small correlations suggest that unique causes for each behavior may be dominant. Large correlations suggest the pervasiveness of common causes and affirm the viability of interventions that emphasize causes affecting multiple problem behaviors simultaneously.
1054-139X/05/$ – see front matter © 2005 Society for Adolescent Medicine. All rights reserved. doi:10.1016/j.jadohealth.2003.12.013
V. Guilamo-Ramos et al. / Journal of Adolescent Health 36 (2005) 82– 86
The issue of common versus unique determinants of behavior is important because of different formats that adolescent-based health interventions may take. To date, most interventions have been behavior specific. For example, there are school-based interventions aimed at preventing sexual risk-taking [2,3], preventing smoking [4,5], preventing drug use [6], and preventing alcohol use [7]. These programs are based, in part, on a philosophy that one must consider the unique determinants of a given problem behavior when developing health education strategies. An alternative philosophy focuses not on problem behaviors per se, but rather addresses positive features of adolescent development [8 –11]. Known as Positive Youth Development (PYD) programs, these interventions assume that the same individual, family, school, and community factors that influence positive outcomes in youth also affect youth problem behaviors. PYD interventions focus on promoting bonding, resilience, spirituality, social skills, moral competence, self-efficacy, belief in the future, prosocial norms and other general orientations toward life. To the extent that problem behaviors share such common causes, they should exhibit relatively high correlations with each other. This article samples research studies that have examined the correlations between multiple risk behaviors in adolescent populations. We present perspectives on the magnitude (as opposed to statistical significance) of these correlations so as to provide a sense of the amount of common variance that exists among them. We also explore factors that may be associated with variations in these correlations. One such factor is gender. It is well known that adolescent females are more strongly discouraged from engaging in risk activities than males and that differential socialization practices create greater opportunities for risk-taking on the part of boys as opposed to girls [12]. As such, it may be easier for a male who has engaged in one problem behavior to engage in another, relative to females, resulting in higher correlations between problem behaviors for males than females. A second factor that may be predictive of correlation differences is the age of the adolescent. Opportunities for risk-taking increase with greater levels of independence and cognitive skills that older adolescents have. Older adolescents may therefore be more prone to engage in multiple problem behaviors, yielding stronger correlations among them. We also compared research conducted in the 1970s, 1980s, and 1990s to explore possible generational differences in the correlations between risk behaviors. More recent adolescent cultural milieus have encouraged increasing levels of freedom and autonomy [13]. Accordingly, one might expect the correlations among risk behaviors to be higher within more recent generations. Finally, within a given behavioral domain (e.g., sexual activity) there are multiple categories of risk-taking behaviors (e.g., having sex often, failing to use protection, having a large number of sexual partners). Categories of risk be-
83
haviors within a content domain may be more highly correlated than risk behaviors across content domains (e.g., use of protection will be more highly correlated with the number of sexual partners as compared with its correlation with smoking cigarettes). To the extent that a common cause (e.g., fear of AIDS) influences behaviors within a domain, but not across domains, then the correlations between risk behaviors within domains will tend to be higher. Methods Inclusion criteria The current review summarizes data in studies published between 1977 and 1999. Only studies that contained measures of the following problem behaviors were included: (a) alcohol use, (b) marijuana use and the use of other illicit drugs, (c) cigarette smoking, (d) delinquency, and (e) sexual behavior. Studies were excluded if they only reported frequencies of these behaviors or if the data were not reported in the form of indices reflecting statistical association among problem behaviors. Literature search Four methods to locate studies were used. First, a computer-based search in the PsychLit database was conducted. Searches began with the keyword problem behavior, which was then crossed with the following words: marijuana (search revealed 33 studies), illicit drug (search revealed 13 studies), delinquency (search revealed 279 studies), sex (search revealed 319 studies), smoking (search revealed 56 studies), cigarette (search revealed 20 studies), drinking (search revealed 112 studies) and alcohol (search revealed 176 studies). Next, a search of the Social Sciences Citation Index from 1978 to the present located all articles citing Jessor and Jessor’s [1] classic book on multiple problem behaviors, as well as the more recent classic work by Jessor, Donovan, and Costa [14]. Third, a manual search was conducted of the following journals: Adolescence, International Journal of the Addictions, Journal of Adolescent Health, Journal of Child and Adolescent Substance Abuse, Journal of Consulting and Clinical Psychology, Journal of Research on Adolescence, Journal of Social and Personality Psychology, and Journal of Youth and Adolescence. Fourth, a citation search (“ancestry approach”) as suggested by White [15], was performed in which all of the reference sections from previously gathered articles were examined. Statistical analyses A large number of articles were excluded because they did not provide the necessary information to summarize correlations. In the final analysis, a total of 43 studies reported sufficient information to gain perspectives on the magnitude of correlations between problem behaviors. The studies included appear in the reference section [16 –51].
84
V. Guilamo-Ramos et al. / Journal of Adolescent Health 36 (2005) 82– 86
Table 1 Mean correlations between pairs of problem behaviors as a function of characteristics of the study population and characteristics of the study
Across all studies and all behaviors Studies with male adolescents Studies with female adolescents Studies of early adolescence Studies of middle adolescence Studies of late adolescence Studies in the 1970s Studies in the 1980s Studies in the 1990s Within category correlations Across category correlations
Mean
SD
25th
50th
75th
0.35 0.39 0.38 0.37 0.41 0.16 0.48 0.34 0.28 0.37 0.35
0.28 0.16 0.16 0.16 0.31 0.17 0.39 0.20 0.21 0.22 0.29
0.17 0.31 0.28 0.28 0.22 0.00 0.24 0.21 0.14 0.26 0.21
0.33 0.39 0.39 0.38 0.36 0.15 0.38 0.34 0.27 0.37 0.34
0.47 0.46 0.47 0.46 0.51 0.26 0.59 0.46 0.42 0.54 0.44
The correlations that were reported were diverse in character in terms of the statistical indices used to reflect the association between variables. Some were Pearson correlations, others were tau correlations, some were biserial correlations, and some were polychoric correlations of varying structure. Some of the data were within-subjects in character and others were between-subjects in character. In our judgment, there was sufficient variability in the statistical indices, the design features, and the nature of the measures used so as to mitigate against the application of formal metaanalytic statistical tools. Our approach was descriptive, in that we characterized the mean correlations across studies as a function of different variables. The means were calculated by transforming correlations to Fisher’s Z, calculating a weighted mean according to the sample size of the study, and then transforming the mean Z back to correlation units. The reported means should be viewed as providing a general sense of the magnitude of the correlations among problem behaviors and a general sense of the trends in these correlations. Results Table 1 presents the mean correlation across studies between any two pairs of problem behavior as a function of different characteristics of the study population and the different facets of the study. Information about the standard deviation of the correlations and the 25th, 50th and 75th quantiles are reported. The mean correlation across all pairs of problem behaviors across all studies was 0.35. This suggests that there is considerably more unique variation in the behaviors than there is common variation, but that the common variation is not trivial. For studies that reported analyses separately for males and females (n ⫽ 19), the mean correlation for any two problem behaviors for males was 0.39, whereas for females it was 0.38. These results suggest trivial gender differences. We analyzed studies that could unambiguously provide information about three age groups, early adolescence (12 to 14 years), middle adolescence (15 to 17 years), and late
adolescence (18 to 25 years). For early adolescence, there were 8 studies, for middle adolescence there were 30 studies, and for later adolescence there were 5 studies. For early adolescence, the mean correlation between problem behaviors was 0.37, for middle adolescence, it was 0.41, and for late adolescence it was 0.16. The most striking trend in these data is the decidedly lower correlation for late adolescence as opposed to early and middle adolescence. For generational differences, we categorized studies into three time periods, studies published in the 1970s (n ⫽ 5), studies published in the 1980s (n ⫽ 13), and studies published in the 1990s (n ⫽ 25). For studies published in the 1970s, the mean correlation between problem behaviors was 0.48, for studies published in the 1980s the mean correlation was 0.34, and for studies published in the 1990s the mean correlation was 0.28. The trend in these data is for the correlations to be decreasing over time (1970 mean ⫽ 0.48, 1980 mean ⫽ 0.34, 1990 mean ⫽ 0.28). Finally, for the analysis of within versus across category correlations, the mean correlation for behaviors that fell within the same content domain (e.g., frequency of alcohol use and typical quantity consumed on a given occasion) was 0.37. The mean correlation for behaviors across categories was 0.35. The differences in these correlations are not substantial. We examined whether the between-category correlations were substantially higher for some pairs of behaviors (e.g., alcohol use paired with drug use) than others (e.g., alcohol use paired with sexual activity) but did not find evidence for such meaningful differences. Discussion The results of this study affirm the conventional wisdom that problem behaviors in adolescence across diverse domains are correlated and probably share common causes. However, the magnitude of the correlation between behaviors is not strong, typically being around 0.35. This suggests that roughly two-thirds of the variance in problem behaviors is owing to unique rather than common causes. (Note: If one assumes that the common cause of two variables is equally
V. Guilamo-Ramos et al. / Journal of Adolescent Health 36 (2005) 82– 86
correlated with each behavior, then under traditional assumptions of factor analysis, the proportion of unique variance associated with either behavior will equal one minus the correlation between behaviors). Although some might argue that the average correlation is attenuated because of random measurement error, one could argue equally that the correlation is inflated owing to common method variance. Interventions that ignore the unique determinants of problem behaviors thus may be limited in the success rates that they can achieve. To be sure, the idea that a single intervention will affect multiple target behaviors is appealing and such efforts are worthy of pursuit. However, the large literatures in social psychology that have shown the utility of topic-specific theories [52,53] coupled with the results of the present analysis strongly implicate the need to consider domain specific intervention strategies. At the least, such components should be included in PYD programs. The fact that correlations between behaviors were only moderate even when the outcome behaviors occurred within the same content domain suggests that unique determinants of within-domain behaviors also must be considered. For example, although a fear of AIDS might affect the initiation of sex, the frequency of sex, the use of protection, and the number and nature of the partners that an adolescent has, each of these behaviors has unique factors that affect variation in them, more so than the fear of AIDS. For example, if one is studying the number of sexual partners that someone has, it makes sense to look at how much the person dates, how attractive the individual is, how large the individual’s social network is, how extroverted the individual is, and so on. None of these variables, however, would be expected to have much of an impact on, say, whether someone uses a condom or whether someone smokes cigarettes. These unique factors need to be taken into account in intervention strategies. We did not observe significant gender differences in the co-occurrence of problem behaviors, but we did observe a tendency for correlations to be weaker in older adolescence. Perhaps this is because such adolescents are further along in the transition to adulthood and are beginning to leave behind the more rebellious periods of early and middle adolescence. We also observed a generational trend suggesting that the correlations between problem behaviors may be decreasing over time. Perhaps this is owing to the increasing number of school-based intervention programs with passing generations, interventions that have tended to target only a single problem behavior at a time. Alternatively, it might reflect changes in society that have accentuated determinants of problem behaviors within domains rather than across domains. For example, the emergence of AIDS in the late 1980s would affect sexual risk-taking but probably not alcohol use or cigarette smoking. Lowering the legal age of alcohol use and zero-tolerance drunk driving laws may affect alcohol-related behaviors but not smoking-related be-
85
haviors. Future research is needed to explore these issues in greater depth. As with any study, the results of the current one must be interpreted within the limitations of the research methodology. First, estimates of correlations are affected by measurement error and conclusions should be tempered accordingly (random measurement error would tend to attenuate the correlations but systematic measurement error would, in many cases, tend to inflate the correlations). Second, the studies upon which we based our mean estimates may not be representative of all studies conducted with multiple problem behaviors, though we can think of no compelling reason why they are not. Third, any meta-analysis of summary statistics across studies as diverse as the ones in our analysis glosses over and obscures subtle measurement differences in constructs across the studies. Despite these limitations, the results of the present study suggest that the majority of variance in problem behavior is owing to unique causes rather than common causes and interventions should take this into account accordingly.
References [1] Jessor R, Jessor SL. Problem Behavior and Psychosocial Development: A Longitudinal Study of Youth. New York, NY: Academic Press, 1977. [2] Jemmott JB, Jemmott L. HIV risk reduction behavioral interventions with heterosexual adolescents. AIDS 2000;14:40 –52. [3] Jemmott JB, Jemmott L, Fong GT. Abstinence and safer sex HIV risk reduction interventions for African American adolescents. JAMA 1998;270:1529 –36. [4] Sussman S, Dent C, Burton D, et al. Developing School-based Tobacco Use Prevention and Cessation Programs. Newbury Park, CA: Sage, 1995. [5] Sussman S, Dent C, Stacy A, et al. Project Towards No Tobacco Use: One-year behavior outcomes. Am J Public Health 1993;83:1245–50. [6] Flay BR. Approaches to substance abuse prevention utilizing school curriculum plus environment change. Addict Behav 2000;25:861– 85. [7] Chou CP, Montgomery S, Pentz MA, et al. Effects of a communitybased prevention program in decreasing drug use in high-risk adolescents. Am J Public Health 1998;6:944 – 8. [8] Catalono R, Berglund L, Ryan J, et al. Positive youth development in the United States: Research findings on evaluations of positive youth development programs (Prev Treat [serial on the internet]). Available at: http://journals.apa.org/prevention/volume5/toc-jun24-02.htm. Accessed April 14, 2003. [9] Flay BR. Positive youth development requires comprehensive health promotion programs. Am J Health Behav 2002;26:407–24. [10] Flay BR, Allred CG, Ordway N. Effects of positive action program on achievement and discipline: Two matched-control comparisons. Prev Sci 2001;2:71– 89. [11] Kirby D. Sexuality Education: An Evaluation of Programs and Their Effects. Santa Cruz, CA: ETR/Network Publications, 1984. [12] Marín BV, Coyle KK, Gomez CA, et al. Older boyfriends and girlfriends increase the risk of sexual initiation in young adolescents. J Adolesc Health 2000;27:409 –18. [13] Flannery DJ, Williams LL, Vazsonyi AT. Who are they with and what are they doing? Delinquent behavior, substance abuse, and early adolescents’ after-school time. Am J Orthop 1999;69:247–53.
86
V. Guilamo-Ramos et al. / Journal of Adolescent Health 36 (2005) 82– 86
[14] Jessor R, Donovan JE, Costa F. Beyond Adolescence: Problem Behavior and Young Adult Development. New York, NY: Cambridge University Press, 1991. [15] White HD. Scientific communication and literature retrieval. In: Cooper HM, Hedges LV (eds). The Handbook of Research Synthesis. New York, NY: Russell Sage Foundation, 1994:41–55. [16] Allen JP, Leadbeater BJ, Aber JL. The development of problem behavior syndromes in at-risk adolescents. Dev Psychopathol 1994; 6:323– 42. [17] Bailey SL, Rachal JV. Dimensions of adolescent problem drinking. J Stud Alcohol 1993;54:555– 65. [18] Barnes GM. Adolescent alcohol abuse and other problem behaviors: Their relationships and common parental influences. J Youth Adolesc 1984;13:329 – 48. [19] Barnes GM, Welte JW. Adolescent alcohol abuse: Subgroup differences and relationships to other problem behaviors. J Adolesc Res 1986;1:79 –94. [20] Biglan A, Metzler CW, Wirt R, et al. Social and behavioral factors associated with high-risk sexual behavior among adolescents. J Behav Med 1990;13:245– 61. [21] Block JR, Goodman N. Illicit drug use and consumption of alcohol, tobacco, and over-the-counter medicine among adolescents. Int J Addict 1978;13:933– 46 (This study contributed two data sets). [22] Devine D, Long P, Forehand R. A prospective study of adolescent sexual activity: Description, correlates, and predictors. Adv Behav Res Ther 1993;15:185–209. [23] Donovan JE. Young adult drinking-driving: Behavioral and psychosocial correlates. J Stud Alcohol 1993;54:600 –13. [24] Donovan JE, Jessor R. Structure of problem behavior in adolescence and young adulthood. J Consult Clin Psychol 1985;53:890 –904 (This study contributed three data sets). [25] Donovan JE, Jessor R, Costa FM. Syndrome of problem behavior in adolescence: A replication. J Consult Clin Psychol 1988;56:762–5 (This study contributed two data sets). [26] Duncan SC, Stryker LA, Duncan TE. Exploring associations in developmental trends of adolescent substance use and risky sexual behavior in a high-risky sexual behavior in a high-risk population. J Behav Med 1999;22:21–34 (This study contributed three data sets). [27] Farrell AD, Danish SJ, Howard CW. Relationship between drug use and other problem behaviors in urban adolescents. J Consult Clin Psychol 1992;60:705–12 (This study contributed three data sets). [28] Fortenberry JD, Costa FM, Jessor R, Donovan JE. Contraceptive behavior and adolescent lifestyles: A structural modeling approach. J Res Adolesc 1997;7:307–29. [29] Gillmore MR, Hawkins D, Catalano RF, et al. Structure of problem behaviors in preadolescence. J Consult Clin Psychol 1991;59:499 – 506. [30] Gould LC, Berberian RM, Kasl SV, et al. Sequential patterns of multiple-drug use among high school students. Arch Gen Psychiatry 1977;34:216 –22. [31] Hays RD, Widaman KF, DiMatteo MR, Stacy AW. Structural-equation models of current drug use: Are appropriate models so simple(x)? J Pers Soc Psychol 1987;52:134 – 44. [32] Huba GJ, Wingard JA, Bentler PM. A comparison of two latent variable causal models for adolescent drug use. J Pers Soc Psychol 1981;40:180 –93.
[33] Hundleby JD. Adolescent drug use in a behavioral matrix: A confirmation and comparison of the sexes. Addict Behav 1987;12:103–12. [34] Hundleby JD, Carpenter RA, Ross RJ, Mercer GW. Adolescent drug use and other behaviors. J Child Psychol Psychiatry 1982;23:61– 8. [35] Jessor R, Chase JA, Donovan JE. Psychosocial correlates of marijuana use and problem drinking in a national sample of adolescents. Am J Public Health 1980;70:604 –13. [36] Kandel DB, Kessler RC, Margulies RZ. Antecedents of adolescent initiation into stages of drug use: A developmental analysis. J Youth Adolesc 1978;7:13– 40. [37] Kleinman PH, Wish ED, Deren S, Rainone G, Morehouse E. Daily marijuana use and problem behaviors among adolescents. Int J Addict 1988;23:87–107. [38] Lang SW, Waller PF, Shope JT. Adolescent driving: Characteristics associated with single-vehicle and injury crashes. J Safety Res 1996; 27:241–57. [39] Leadbeater BJ, Hellner I, Allen JP, Aber JL. Assessment of interpersonal negotiation strategies in youth engaged in problem behaviors. Dev Psychol 1989;3:465–72. [40] McGee L, Newcomb MD. General deviance syndrome: Expanded hierarchical evaluations at four ages from early adolescence to adulthood. J Consult Clin Psychol 1992;60:766 –76. [41] Rosenbaum E, Kandel DB. Early onset of adolescent sexual behavior and drug involvement. J Marriage Fam 1990;52;783–98. [42] Single E, Kandel DB, Faust R. Patterns of multiple drug use in high school. J Health Soc Behav 1974;15:344 –57. [43] Stanton WR, Lowe JB, Silva PA. Antecedents of vulnerability and resilience to smoking among adolescents. J Adolesc Health 1995;16: 71–7. [44] Tang CS, Wong CS, Schwarzer R. Psychosocial differences between occasional and regular adolescent users of marijuana and heroin. J Youth Adolesc 1996;25:219 –39. [45] Tildesley EA, Hops H, Ary D, Andrews JA. Multitrait-multimethod model of adolescent deviance, drug use, academic, and sexual behaviors. J Psychopathol Behav Assess 1995;17:185–215. [46] Vingilis E, Adlaf E. The structure of problem behavior among Ontario high school students: A confirmatory-factor analysis. Health Educ Res 1990;5:151– 60. [47] Watts WD, Wright LS. The relationship of alcohol, tobacco, marijuana, and other illegal drug use to delinquency among MexicanAmerican, Black, and White adolescent males. Adolescence 1990;25: 171– 81. [48] Webb JA, Baer PE, McLaughlin RJ, et al. Risk factors and their relation to initiation of alcohol use among early adolescents. J Am Acad Child Adolesc Psychiatry 1991;30:563– 8. [49] Windle M. A longitudinal study of stress buffering for adolescent problem behaviors. Dev Psychol 1992;28:522–30. [50] Windle M. Substance use, risky behaviors, and victimization among a US national adolescent sample. Addiction 1994;89:175– 82. [51] Zimmerman MA, Maton KI. Life-style and substance use among male African-American urban adolescents: A cluster analytic approach. Am J Community Psychol 1992;20:121–38. [52] Ajzen I, Fishbein M. Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychol Bull 1977;84:888 – 918. [53] Ajzen I, Fishbein M. Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice Hall, 1980.
Review article
Prevention programs for reducing adolescent problem behaviors: Implications of the co-occurrence of problem behaviors in adolescence Vincent Guilamo-Ramos, Ph.D.a,b,*, Harold A. Litardo, Ph.D.c, and James Jaccard, Ph.D.c,d a School of Social Work, Columbia University, New York, New York Department of Population and Family Health, Columbia University, New York, New York c Department of Psychology, University at Albany, State University of New York, Albany, New York d School of Social Welfare, University at Albany, State University of New York, Albany, New York Manuscript received June 17, 2003; manuscript accepted December 16, 2003 b
Abstract
Purpose: To examine the correlations between multiple risk behaviors in adolescent populations to document the extent to which problem behaviors are intercorrelated and to identify factors associated with variations in these correlations. Methods: Studies from 1977 through the end of 1999 that included two or more problem behaviors in adolescents were identified by literature searches using the PsychLit database, Social Sciences Citation Index, manual journal searches and “ancestry” approaches. The behaviors studied were alcohol use, marijuana use, illicit drug use, cigarette smoking, general deviant behavior, and sexual activity. Included studies reported correlation coefficients between variables. Results: Across all studies, the mean correlation between any two pairs of problem behaviors was 0.35, with a standard deviation of 0.28. This suggests that, on average, about two-thirds of the variation in problem behavior is the result of unique rather than common causes. The magnitude of the correlations varied as a function of the age of the adolescent, with lower correlations being evident for older adolescents. In addition, the magnitude of the correlation varied as a function of when the study was conducted, with studies of past generations showing stronger connections between risk behaviors than current generations. Conclusions: The data suggest that there is considerably more unique variation in classic adolescent problem behaviors than common variation. © 2005 Society for Adolescent Medicine. All rights reserved.
Keywords:
Adolescent problem behavior; Prevention; Intervention design
Health researchers and practitioners have long been interested in the reasons why some adolescents abuse substances, commit delinquent acts, smoke cigarettes, or engage in unsafe sexual practices. An important theoretical emphasis has been the identification of factors that influence multiple risk behaviors. Probably the most notable of these frameworks is Jessor and Jessor’s Problem Behavior Theory (PBT) [1]. According to PBT, the performance of problem behaviors should be highly correlated because they share many common causes. Research has shown that problem *Address correspondence to: Dr. Vincent Guilamo-Ramos, School of Social Work, Columbia University, New York, NY 10027. E-mail address: [email protected].
behaviors do indeed tend to be correlated and conventional wisdom within the field of adolescent health is that problem behaviors are linked. Most of these studies have emphasized significance tests of the correlations between problem behaviors, which amounts to testing the hypothesis that the correlation between the variables is not zero. Of greater interest should be the magnitude of the correlations, because this provides a sense of the extent to which the variables may have common causes versus unique causes. Statistically significant but small correlations suggest that unique causes for each behavior may be dominant. Large correlations suggest the pervasiveness of common causes and affirm the viability of interventions that emphasize causes affecting multiple problem behaviors simultaneously.
1054-139X/05/$ – see front matter © 2005 Society for Adolescent Medicine. All rights reserved. doi:10.1016/j.jadohealth.2003.12.013
V. Guilamo-Ramos et al. / Journal of Adolescent Health 36 (2005) 82– 86
The issue of common versus unique determinants of behavior is important because of different formats that adolescent-based health interventions may take. To date, most interventions have been behavior specific. For example, there are school-based interventions aimed at preventing sexual risk-taking [2,3], preventing smoking [4,5], preventing drug use [6], and preventing alcohol use [7]. These programs are based, in part, on a philosophy that one must consider the unique determinants of a given problem behavior when developing health education strategies. An alternative philosophy focuses not on problem behaviors per se, but rather addresses positive features of adolescent development [8 –11]. Known as Positive Youth Development (PYD) programs, these interventions assume that the same individual, family, school, and community factors that influence positive outcomes in youth also affect youth problem behaviors. PYD interventions focus on promoting bonding, resilience, spirituality, social skills, moral competence, self-efficacy, belief in the future, prosocial norms and other general orientations toward life. To the extent that problem behaviors share such common causes, they should exhibit relatively high correlations with each other. This article samples research studies that have examined the correlations between multiple risk behaviors in adolescent populations. We present perspectives on the magnitude (as opposed to statistical significance) of these correlations so as to provide a sense of the amount of common variance that exists among them. We also explore factors that may be associated with variations in these correlations. One such factor is gender. It is well known that adolescent females are more strongly discouraged from engaging in risk activities than males and that differential socialization practices create greater opportunities for risk-taking on the part of boys as opposed to girls [12]. As such, it may be easier for a male who has engaged in one problem behavior to engage in another, relative to females, resulting in higher correlations between problem behaviors for males than females. A second factor that may be predictive of correlation differences is the age of the adolescent. Opportunities for risk-taking increase with greater levels of independence and cognitive skills that older adolescents have. Older adolescents may therefore be more prone to engage in multiple problem behaviors, yielding stronger correlations among them. We also compared research conducted in the 1970s, 1980s, and 1990s to explore possible generational differences in the correlations between risk behaviors. More recent adolescent cultural milieus have encouraged increasing levels of freedom and autonomy [13]. Accordingly, one might expect the correlations among risk behaviors to be higher within more recent generations. Finally, within a given behavioral domain (e.g., sexual activity) there are multiple categories of risk-taking behaviors (e.g., having sex often, failing to use protection, having a large number of sexual partners). Categories of risk be-
83
haviors within a content domain may be more highly correlated than risk behaviors across content domains (e.g., use of protection will be more highly correlated with the number of sexual partners as compared with its correlation with smoking cigarettes). To the extent that a common cause (e.g., fear of AIDS) influences behaviors within a domain, but not across domains, then the correlations between risk behaviors within domains will tend to be higher. Methods Inclusion criteria The current review summarizes data in studies published between 1977 and 1999. Only studies that contained measures of the following problem behaviors were included: (a) alcohol use, (b) marijuana use and the use of other illicit drugs, (c) cigarette smoking, (d) delinquency, and (e) sexual behavior. Studies were excluded if they only reported frequencies of these behaviors or if the data were not reported in the form of indices reflecting statistical association among problem behaviors. Literature search Four methods to locate studies were used. First, a computer-based search in the PsychLit database was conducted. Searches began with the keyword problem behavior, which was then crossed with the following words: marijuana (search revealed 33 studies), illicit drug (search revealed 13 studies), delinquency (search revealed 279 studies), sex (search revealed 319 studies), smoking (search revealed 56 studies), cigarette (search revealed 20 studies), drinking (search revealed 112 studies) and alcohol (search revealed 176 studies). Next, a search of the Social Sciences Citation Index from 1978 to the present located all articles citing Jessor and Jessor’s [1] classic book on multiple problem behaviors, as well as the more recent classic work by Jessor, Donovan, and Costa [14]. Third, a manual search was conducted of the following journals: Adolescence, International Journal of the Addictions, Journal of Adolescent Health, Journal of Child and Adolescent Substance Abuse, Journal of Consulting and Clinical Psychology, Journal of Research on Adolescence, Journal of Social and Personality Psychology, and Journal of Youth and Adolescence. Fourth, a citation search (“ancestry approach”) as suggested by White [15], was performed in which all of the reference sections from previously gathered articles were examined. Statistical analyses A large number of articles were excluded because they did not provide the necessary information to summarize correlations. In the final analysis, a total of 43 studies reported sufficient information to gain perspectives on the magnitude of correlations between problem behaviors. The studies included appear in the reference section [16 –51].
84
V. Guilamo-Ramos et al. / Journal of Adolescent Health 36 (2005) 82– 86
Table 1 Mean correlations between pairs of problem behaviors as a function of characteristics of the study population and characteristics of the study
Across all studies and all behaviors Studies with male adolescents Studies with female adolescents Studies of early adolescence Studies of middle adolescence Studies of late adolescence Studies in the 1970s Studies in the 1980s Studies in the 1990s Within category correlations Across category correlations
Mean
SD
25th
50th
75th
0.35 0.39 0.38 0.37 0.41 0.16 0.48 0.34 0.28 0.37 0.35
0.28 0.16 0.16 0.16 0.31 0.17 0.39 0.20 0.21 0.22 0.29
0.17 0.31 0.28 0.28 0.22 0.00 0.24 0.21 0.14 0.26 0.21
0.33 0.39 0.39 0.38 0.36 0.15 0.38 0.34 0.27 0.37 0.34
0.47 0.46 0.47 0.46 0.51 0.26 0.59 0.46 0.42 0.54 0.44
The correlations that were reported were diverse in character in terms of the statistical indices used to reflect the association between variables. Some were Pearson correlations, others were tau correlations, some were biserial correlations, and some were polychoric correlations of varying structure. Some of the data were within-subjects in character and others were between-subjects in character. In our judgment, there was sufficient variability in the statistical indices, the design features, and the nature of the measures used so as to mitigate against the application of formal metaanalytic statistical tools. Our approach was descriptive, in that we characterized the mean correlations across studies as a function of different variables. The means were calculated by transforming correlations to Fisher’s Z, calculating a weighted mean according to the sample size of the study, and then transforming the mean Z back to correlation units. The reported means should be viewed as providing a general sense of the magnitude of the correlations among problem behaviors and a general sense of the trends in these correlations. Results Table 1 presents the mean correlation across studies between any two pairs of problem behavior as a function of different characteristics of the study population and the different facets of the study. Information about the standard deviation of the correlations and the 25th, 50th and 75th quantiles are reported. The mean correlation across all pairs of problem behaviors across all studies was 0.35. This suggests that there is considerably more unique variation in the behaviors than there is common variation, but that the common variation is not trivial. For studies that reported analyses separately for males and females (n ⫽ 19), the mean correlation for any two problem behaviors for males was 0.39, whereas for females it was 0.38. These results suggest trivial gender differences. We analyzed studies that could unambiguously provide information about three age groups, early adolescence (12 to 14 years), middle adolescence (15 to 17 years), and late
adolescence (18 to 25 years). For early adolescence, there were 8 studies, for middle adolescence there were 30 studies, and for later adolescence there were 5 studies. For early adolescence, the mean correlation between problem behaviors was 0.37, for middle adolescence, it was 0.41, and for late adolescence it was 0.16. The most striking trend in these data is the decidedly lower correlation for late adolescence as opposed to early and middle adolescence. For generational differences, we categorized studies into three time periods, studies published in the 1970s (n ⫽ 5), studies published in the 1980s (n ⫽ 13), and studies published in the 1990s (n ⫽ 25). For studies published in the 1970s, the mean correlation between problem behaviors was 0.48, for studies published in the 1980s the mean correlation was 0.34, and for studies published in the 1990s the mean correlation was 0.28. The trend in these data is for the correlations to be decreasing over time (1970 mean ⫽ 0.48, 1980 mean ⫽ 0.34, 1990 mean ⫽ 0.28). Finally, for the analysis of within versus across category correlations, the mean correlation for behaviors that fell within the same content domain (e.g., frequency of alcohol use and typical quantity consumed on a given occasion) was 0.37. The mean correlation for behaviors across categories was 0.35. The differences in these correlations are not substantial. We examined whether the between-category correlations were substantially higher for some pairs of behaviors (e.g., alcohol use paired with drug use) than others (e.g., alcohol use paired with sexual activity) but did not find evidence for such meaningful differences. Discussion The results of this study affirm the conventional wisdom that problem behaviors in adolescence across diverse domains are correlated and probably share common causes. However, the magnitude of the correlation between behaviors is not strong, typically being around 0.35. This suggests that roughly two-thirds of the variance in problem behaviors is owing to unique rather than common causes. (Note: If one assumes that the common cause of two variables is equally
V. Guilamo-Ramos et al. / Journal of Adolescent Health 36 (2005) 82– 86
correlated with each behavior, then under traditional assumptions of factor analysis, the proportion of unique variance associated with either behavior will equal one minus the correlation between behaviors). Although some might argue that the average correlation is attenuated because of random measurement error, one could argue equally that the correlation is inflated owing to common method variance. Interventions that ignore the unique determinants of problem behaviors thus may be limited in the success rates that they can achieve. To be sure, the idea that a single intervention will affect multiple target behaviors is appealing and such efforts are worthy of pursuit. However, the large literatures in social psychology that have shown the utility of topic-specific theories [52,53] coupled with the results of the present analysis strongly implicate the need to consider domain specific intervention strategies. At the least, such components should be included in PYD programs. The fact that correlations between behaviors were only moderate even when the outcome behaviors occurred within the same content domain suggests that unique determinants of within-domain behaviors also must be considered. For example, although a fear of AIDS might affect the initiation of sex, the frequency of sex, the use of protection, and the number and nature of the partners that an adolescent has, each of these behaviors has unique factors that affect variation in them, more so than the fear of AIDS. For example, if one is studying the number of sexual partners that someone has, it makes sense to look at how much the person dates, how attractive the individual is, how large the individual’s social network is, how extroverted the individual is, and so on. None of these variables, however, would be expected to have much of an impact on, say, whether someone uses a condom or whether someone smokes cigarettes. These unique factors need to be taken into account in intervention strategies. We did not observe significant gender differences in the co-occurrence of problem behaviors, but we did observe a tendency for correlations to be weaker in older adolescence. Perhaps this is because such adolescents are further along in the transition to adulthood and are beginning to leave behind the more rebellious periods of early and middle adolescence. We also observed a generational trend suggesting that the correlations between problem behaviors may be decreasing over time. Perhaps this is owing to the increasing number of school-based intervention programs with passing generations, interventions that have tended to target only a single problem behavior at a time. Alternatively, it might reflect changes in society that have accentuated determinants of problem behaviors within domains rather than across domains. For example, the emergence of AIDS in the late 1980s would affect sexual risk-taking but probably not alcohol use or cigarette smoking. Lowering the legal age of alcohol use and zero-tolerance drunk driving laws may affect alcohol-related behaviors but not smoking-related be-
85
haviors. Future research is needed to explore these issues in greater depth. As with any study, the results of the current one must be interpreted within the limitations of the research methodology. First, estimates of correlations are affected by measurement error and conclusions should be tempered accordingly (random measurement error would tend to attenuate the correlations but systematic measurement error would, in many cases, tend to inflate the correlations). Second, the studies upon which we based our mean estimates may not be representative of all studies conducted with multiple problem behaviors, though we can think of no compelling reason why they are not. Third, any meta-analysis of summary statistics across studies as diverse as the ones in our analysis glosses over and obscures subtle measurement differences in constructs across the studies. Despite these limitations, the results of the present study suggest that the majority of variance in problem behavior is owing to unique causes rather than common causes and interventions should take this into account accordingly.
References [1] Jessor R, Jessor SL. Problem Behavior and Psychosocial Development: A Longitudinal Study of Youth. New York, NY: Academic Press, 1977. [2] Jemmott JB, Jemmott L. HIV risk reduction behavioral interventions with heterosexual adolescents. AIDS 2000;14:40 –52. [3] Jemmott JB, Jemmott L, Fong GT. Abstinence and safer sex HIV risk reduction interventions for African American adolescents. JAMA 1998;270:1529 –36. [4] Sussman S, Dent C, Burton D, et al. Developing School-based Tobacco Use Prevention and Cessation Programs. Newbury Park, CA: Sage, 1995. [5] Sussman S, Dent C, Stacy A, et al. Project Towards No Tobacco Use: One-year behavior outcomes. Am J Public Health 1993;83:1245–50. [6] Flay BR. Approaches to substance abuse prevention utilizing school curriculum plus environment change. Addict Behav 2000;25:861– 85. [7] Chou CP, Montgomery S, Pentz MA, et al. Effects of a communitybased prevention program in decreasing drug use in high-risk adolescents. Am J Public Health 1998;6:944 – 8. [8] Catalono R, Berglund L, Ryan J, et al. Positive youth development in the United States: Research findings on evaluations of positive youth development programs (Prev Treat [serial on the internet]). Available at: http://journals.apa.org/prevention/volume5/toc-jun24-02.htm. Accessed April 14, 2003. [9] Flay BR. Positive youth development requires comprehensive health promotion programs. Am J Health Behav 2002;26:407–24. [10] Flay BR, Allred CG, Ordway N. Effects of positive action program on achievement and discipline: Two matched-control comparisons. Prev Sci 2001;2:71– 89. [11] Kirby D. Sexuality Education: An Evaluation of Programs and Their Effects. Santa Cruz, CA: ETR/Network Publications, 1984. [12] Marín BV, Coyle KK, Gomez CA, et al. Older boyfriends and girlfriends increase the risk of sexual initiation in young adolescents. J Adolesc Health 2000;27:409 –18. [13] Flannery DJ, Williams LL, Vazsonyi AT. Who are they with and what are they doing? Delinquent behavior, substance abuse, and early adolescents’ after-school time. Am J Orthop 1999;69:247–53.
86
V. Guilamo-Ramos et al. / Journal of Adolescent Health 36 (2005) 82– 86
[14] Jessor R, Donovan JE, Costa F. Beyond Adolescence: Problem Behavior and Young Adult Development. New York, NY: Cambridge University Press, 1991. [15] White HD. Scientific communication and literature retrieval. In: Cooper HM, Hedges LV (eds). The Handbook of Research Synthesis. New York, NY: Russell Sage Foundation, 1994:41–55. [16] Allen JP, Leadbeater BJ, Aber JL. The development of problem behavior syndromes in at-risk adolescents. Dev Psychopathol 1994; 6:323– 42. [17] Bailey SL, Rachal JV. Dimensions of adolescent problem drinking. J Stud Alcohol 1993;54:555– 65. [18] Barnes GM. Adolescent alcohol abuse and other problem behaviors: Their relationships and common parental influences. J Youth Adolesc 1984;13:329 – 48. [19] Barnes GM, Welte JW. Adolescent alcohol abuse: Subgroup differences and relationships to other problem behaviors. J Adolesc Res 1986;1:79 –94. [20] Biglan A, Metzler CW, Wirt R, et al. Social and behavioral factors associated with high-risk sexual behavior among adolescents. J Behav Med 1990;13:245– 61. [21] Block JR, Goodman N. Illicit drug use and consumption of alcohol, tobacco, and over-the-counter medicine among adolescents. Int J Addict 1978;13:933– 46 (This study contributed two data sets). [22] Devine D, Long P, Forehand R. A prospective study of adolescent sexual activity: Description, correlates, and predictors. Adv Behav Res Ther 1993;15:185–209. [23] Donovan JE. Young adult drinking-driving: Behavioral and psychosocial correlates. J Stud Alcohol 1993;54:600 –13. [24] Donovan JE, Jessor R. Structure of problem behavior in adolescence and young adulthood. J Consult Clin Psychol 1985;53:890 –904 (This study contributed three data sets). [25] Donovan JE, Jessor R, Costa FM. Syndrome of problem behavior in adolescence: A replication. J Consult Clin Psychol 1988;56:762–5 (This study contributed two data sets). [26] Duncan SC, Stryker LA, Duncan TE. Exploring associations in developmental trends of adolescent substance use and risky sexual behavior in a high-risky sexual behavior in a high-risk population. J Behav Med 1999;22:21–34 (This study contributed three data sets). [27] Farrell AD, Danish SJ, Howard CW. Relationship between drug use and other problem behaviors in urban adolescents. J Consult Clin Psychol 1992;60:705–12 (This study contributed three data sets). [28] Fortenberry JD, Costa FM, Jessor R, Donovan JE. Contraceptive behavior and adolescent lifestyles: A structural modeling approach. J Res Adolesc 1997;7:307–29. [29] Gillmore MR, Hawkins D, Catalano RF, et al. Structure of problem behaviors in preadolescence. J Consult Clin Psychol 1991;59:499 – 506. [30] Gould LC, Berberian RM, Kasl SV, et al. Sequential patterns of multiple-drug use among high school students. Arch Gen Psychiatry 1977;34:216 –22. [31] Hays RD, Widaman KF, DiMatteo MR, Stacy AW. Structural-equation models of current drug use: Are appropriate models so simple(x)? J Pers Soc Psychol 1987;52:134 – 44. [32] Huba GJ, Wingard JA, Bentler PM. A comparison of two latent variable causal models for adolescent drug use. J Pers Soc Psychol 1981;40:180 –93.
[33] Hundleby JD. Adolescent drug use in a behavioral matrix: A confirmation and comparison of the sexes. Addict Behav 1987;12:103–12. [34] Hundleby JD, Carpenter RA, Ross RJ, Mercer GW. Adolescent drug use and other behaviors. J Child Psychol Psychiatry 1982;23:61– 8. [35] Jessor R, Chase JA, Donovan JE. Psychosocial correlates of marijuana use and problem drinking in a national sample of adolescents. Am J Public Health 1980;70:604 –13. [36] Kandel DB, Kessler RC, Margulies RZ. Antecedents of adolescent initiation into stages of drug use: A developmental analysis. J Youth Adolesc 1978;7:13– 40. [37] Kleinman PH, Wish ED, Deren S, Rainone G, Morehouse E. Daily marijuana use and problem behaviors among adolescents. Int J Addict 1988;23:87–107. [38] Lang SW, Waller PF, Shope JT. Adolescent driving: Characteristics associated with single-vehicle and injury crashes. J Safety Res 1996; 27:241–57. [39] Leadbeater BJ, Hellner I, Allen JP, Aber JL. Assessment of interpersonal negotiation strategies in youth engaged in problem behaviors. Dev Psychol 1989;3:465–72. [40] McGee L, Newcomb MD. General deviance syndrome: Expanded hierarchical evaluations at four ages from early adolescence to adulthood. J Consult Clin Psychol 1992;60:766 –76. [41] Rosenbaum E, Kandel DB. Early onset of adolescent sexual behavior and drug involvement. J Marriage Fam 1990;52;783–98. [42] Single E, Kandel DB, Faust R. Patterns of multiple drug use in high school. J Health Soc Behav 1974;15:344 –57. [43] Stanton WR, Lowe JB, Silva PA. Antecedents of vulnerability and resilience to smoking among adolescents. J Adolesc Health 1995;16: 71–7. [44] Tang CS, Wong CS, Schwarzer R. Psychosocial differences between occasional and regular adolescent users of marijuana and heroin. J Youth Adolesc 1996;25:219 –39. [45] Tildesley EA, Hops H, Ary D, Andrews JA. Multitrait-multimethod model of adolescent deviance, drug use, academic, and sexual behaviors. J Psychopathol Behav Assess 1995;17:185–215. [46] Vingilis E, Adlaf E. The structure of problem behavior among Ontario high school students: A confirmatory-factor analysis. Health Educ Res 1990;5:151– 60. [47] Watts WD, Wright LS. The relationship of alcohol, tobacco, marijuana, and other illegal drug use to delinquency among MexicanAmerican, Black, and White adolescent males. Adolescence 1990;25: 171– 81. [48] Webb JA, Baer PE, McLaughlin RJ, et al. Risk factors and their relation to initiation of alcohol use among early adolescents. J Am Acad Child Adolesc Psychiatry 1991;30:563– 8. [49] Windle M. A longitudinal study of stress buffering for adolescent problem behaviors. Dev Psychol 1992;28:522–30. [50] Windle M. Substance use, risky behaviors, and victimization among a US national adolescent sample. Addiction 1994;89:175– 82. [51] Zimmerman MA, Maton KI. Life-style and substance use among male African-American urban adolescents: A cluster analytic approach. Am J Community Psychol 1992;20:121–38. [52] Ajzen I, Fishbein M. Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychol Bull 1977;84:888 – 918. [53] Ajzen I, Fishbein M. Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice Hall, 1980.