CoChat
Sign Up
Cited by 41Substance Abuse Treatment and OutcomesCannabis and Cannabinoid ResearchSmoking Behavior and Cessation

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

The Interplay Between Marijuana-Specific Risk Factors and Marijuana Use Over the Course of Adolescence

Open access
Guttmannova, Katarina, Skinner, Martie L., Oesterle, Sabrina, White, Helene R., Catalano, Richard F., Hawkins, J. David·2019·Prevention Science
DOI 10.1007/s11121-018-0882-9PMC PMC6123306
Open this paper in CoChat — free account

Abstract

Permissive attitudes and norms about marijuana use and perceptions of low harm from use are considered risk factors for adolescent marijuana use. However, the relationship between risk and use may be reciprocal and vary across development and socializing domains. We examined the bidirectional relationships between marijuana-specific risk factors in individual, parent, peer, and community domains and adolescent marijuana use. Longitudinal data came from a sample of 2002 adolescents in 12 communities. Controlling for sociodemographic covariates and communities in which the individuals resided, autoregressive cross-lagged models examined predictive associations between the risk factors and marijuana use. After accounting for concurrent relationships between risk and use and stability in behavior over time, early adolescence and the transition to high school were particularly salient developmental time points. Specifically, higher risk in all four domains in grades 7 and 9 predicted greater use 1 year later. Moreover, youth's perception of lax community enforcement of laws regarding adolescent use at all time points predicted increases in marijuana use at the subsequent assessment, and perceived low harm from use was a risk factor that prospectively predicted more marijuana use at most of the time points. Finally, greater frequency of marijuana use predicted higher levels of risk factors at the next time point in most socializing domains throughout adolescence. Prevention programs should take into account developmental transitions, especially in early adolescence and during the transition to high school. They also should focus on the reciprocal relationships between use and risk across multiple socializing domains.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s11121-018-0882-9

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Perceiving the drug as harmless was an independent risk factor that could prospectively predict future marijuana abuse.

    “perceiving the drug as harmless was an independent risk factor that could prospectively predict future marijuana abuse [27].”

    From Risk and protective factors of drug abuse among adolescents: a systematic review (2021) · cites this paper as [27]

    The Discussion reports that adolescents’ perception of low harm prospectively predicted more marijuana use at most time points, after accounting for concurrent associations and prior behavior. But the Methods measure past-month use frequency, not marijuana abuse; the Discussion also cautions that the associations are not necessarily causal. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Rebellious traits are positively associated with marijuana drug abuse.

    “Another longitudinal study by Guttmannova et al. showed that rebellious traits are positively associated with marijuana drug abuse [27].”

    From Risk and protective factors of drug abuse among adolescents: a systematic review (2021) · cites this paper as [27]

    The Results (Cross-lagged Models; page unavailable) report that rebelliousness was positively associated with marijuana use in Grade 8 adolescents. The Methods (Measures; page unavailable) define the outcome as past-month use frequency, not marijuana abuse, so the claim's reference to abuse goes beyond what was measured. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Low parental educational level predicted a greater risk of future drug abuse by reducing the youth's perception of harm.

    “low parental educational level predicted a greater risk of future drug abuse by reducing the youth’s perception of harm [27, 43].”

    From Risk and protective factors of drug abuse among adolescents: a systematic review (2021) · cites this paper as [27]

    The Results (Cross-lagged Models) associate lower parental education with lower perceived harm and greater marijuana use in Grade 8. The Discussion says lower perceived harm predicted later marijuana use, but the paper does not establish that perceived harm mediated the parental-education association. It studies marijuana use rather than future drug abuse generally, and its Limitations section cautions that the associations are not necessarily causal. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 59

  1. Agrawal, A., Madden, P. A., Bucholz, K. K., Heath, A. C., & Lynskey, M. T. (2014). Initial reactions to tobacco and cannabis smoking: A twin study. Addiction, 109, 663–671. · DOI
  2. Agresti, A. (2013). Categorical data analysis (3rd ed.). Hoboken: Wiley.
  3. Arthur, M. W., Hawkins, J. D., Pollard, J. A., Catalano, R. F., & Baglioni Jr., A. J. (2002). Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors: The Communities That Care Youth Survey. Evaluation Review, 26, 575–601.
  4. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs: Prentice-Hall.
  5. Becker, S. J., & Curry, J. F. (2014). Testing the effects of peer socialization versus selection on alcohol and marijuana use among treated adolescents. Substance Use & Misuse, 49, 234–242. · DOI
  6. Bentler, P. M. (1990). Comparative fix indexes in structural models. Psychological Bulletin, 107, 238–246. · DOI · Paper page
  7. Beyers, J. M., Toumbourou, J. W., Catalano, R. F., Arthur, M. W., & Hawkins, J. D. (2004). A cross-national comparison of risk and protective factors for adolescent substance use: The United States and Australia. Journal of Adolescent Health, 35, 3–16.
  8. Bronfenbrenner, U. (1994). Ecological models of human development. In: International encyclopedia of education (2nd ed., Vol. 3, pp. 1643–1647). Oxford: Elsevier.
  9. Brooks-Russell, A., Levinson, A., Li, Y., Roppolo, R. H., & Bull, S. (2017). What do Colorado adults know about legal use of recreational marijuana after a media campaign? Health Promotion Practice, 18, 193–200. · DOI
  10. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park: Sage.
  11. Cambron, C., Guttmannova, K., & Fleming, C. B. (2017). State and national contexts in evaluating cannabis laws: A case study of Washington State. Journal of Drug Issues, 47, 74–90. · DOI
  12. Catalano, R. F., & Hawkins, J. D. (1996). The social development model: A theory of antisocial behavior. In J. D. Hawkins (Ed.), Delinquency and crime: Current theories (pp. 149–197). New York: Cambridge University Press.
  13. Catalano, R. F., Oxford, M. L., Harachi, T. W., Abbott, R. D., & Haggerty, K. P. (1999). A test of the social development model to predict problem behaviour during the elementary school period. Criminal Behaviour and Mental Health, 9, 39–56. · DOI
  14. Catalano, R. F., Fagan, A. A., Gavin, L. E., Greenberg, M. T., Irwin, C. E., Ross, D. A., & Shek, D. T. L. (2012). Worldwide application of the prevention science research base in adolescent health. The Lancet, 379, 1653–1664. · DOI · Paper page
  15. Cicchetti, D. (1990). A historical perspective on the discipline of developmental psychopathology. In J. E. Rolf, A. S. Masten, D. Cicchetti, K. H. Nuechterlein, & S. Weintraub (Eds.), Risk and protective factors in the development of psychopathology (pp. 2–28). New York: Cambridge University Press. · DOI
  16. Deutsch, A. R., Chernyavskiy, P., Steinley, D., & Slutske, W. S. (2015). Measuring peer socialization for adolescent substance use: A comparison of perceived and actual friends’ substance use effects. Journal of Studies on Alcohol and Drugs, 76, 267–277. · DOI
  17. Dever, B. V., Schulenberg, J. E., Dworkin, J. B., O'Malley, P. M., Kloska, D. D., & Bachman, J. G. (2012). Predicting risk-taking with and without substance use: The effects of parental monitoring, school bonding, and sports participation. Prevention Science, 13, 605–615. · DOI
  18. Dishion, T. J., & Owen, L. D. (2002). A longitudinal analysis of friendships and substance use: Bidirectional influence from adolescence to adulthood. Developmental Psychology, 38, 480–491. · DOI
  19. Donovan, J. E. (2004). Adolescent alcohol initiation: A review of psychosocial risk factors. Journal of Adolescent Health, 35, 529.e527–529.e518. · DOI
  20. Ellickson, P. L., Tucker, J. S., Klein, D. J., & Saner, H. (2004). Antecedents and outcomes of marijuana use initiation during adolescence. Preventive Medicine, 39, 976–984. · DOI
  21. Epstein, M., Hill, K. G., Nevell, A. M., Guttmannova, K., Bailey, J. A., Abbott, R. D., … Hawkins, J. D. (2015). Trajectories of marijuana use from adolescence into adulthood: Environmental and individual correlates. Developmental Psychology, 51, 1650–1663. · DOI · Paper page
  22. Epstein, M., Hill, K. G., Roe, S. S., Bailey, J. A., Iacono, W. G., McGue, M., … Haggerty, K. P. (2017). Time-varying effects of families and peers on adolescent marijuana use: Person-environment interactions across development. Development and Psychopathology, 29, 887–900. · DOI
  23. Fergusson, D. M., Horwood, L. J., Lynskey, M. T., & Madden, P. A. F. (2003). Early reactions to cannabis predict later dependence. Archives of General Psychiatry, 60, 1033–1039. · DOI
  24. Finkel, S. E. (1995). Causal analysis with panel data. Thousand Oaks: Sage. · DOI
  25. Fleary, S. A., Heffer, R. W., McKyer, E. L. J., & Newman, D. A. (2010). Using the bioecological model to predict risk perception of marijuana use and reported marijuana use in adolescence. Addictive Behaviors, 35, 795–798. · DOI
  26. Fleming, C. B., Guttmannova, K., Cambron, C., Rhew, I. C., & Oesterle, S. (2016). Examination of the divergence in trends for adolescent marijuana use and marijuana-specific risk factors in Washington State. Journal of Adolescent Health, 59, 269–275. · DOI
  27. Grant, J. D., Scherrer, J. F., Lyons, M. J., Tsuang, M., True, W. R., & Bucholz, K. K. (2005). Subjective reactions to cocaine and marijuana are associated with abuse and dependence. Addictive Behaviors, 30, 1574–1586. · DOI
  28. Guttmannova, K., Wheeler, M. J., Hill, K. G., Evans-Campbell, T. A., Hartigan, L. A., Jones, T. M., … Catalano, R. F. (2017). Assessment of risk and protection in Native American youth: Steps toward conducting culturally relevant, sustainable prevention in Indian Country. Journal of Community Psychology, 43, 346–362. · DOI
  29. Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64–105. · DOI · Paper page
  30. Hawkins, J. D., Catalano, R. F., Arthur, M. W., Egan, E., Brown, E. C., Abbott, R. D., & Murray, D. M. (2008). Testing communities that care: The rationale, design and behavioral baseline equivalence of the community youth development study. Prevention Science, 9, 178–190. · DOI
  31. Hawkins, J. D., Oesterle, S., Brown, E. C., Monahan, K. C., Abbott, R. D., Arthur, M. W., & Catalano, R. F. (2012). Sustained decreases in risk exposure and youth problem behaviors after installation of the Communities That Care prevention system in a randomized trial. Archives of Pediatrics and Adolescent Medicine, 166, 141–148. · DOI
  32. Hawkins, J. D., Oesterle, S., Brown, E. C., Abbott, R. D., & Catalano, R. F. (2014). Youth problem behaviors 8 years after implementing the Communities That Care prevention system: A community-randomized trial. JAMA Pediatrics, 168, 122–129. · DOI · Paper page
  33. Hill, K. G., Hawkins, J. D., Bailey, J. A., Catalano, R. F., Abbott, R. D., & Shapiro, V. (2010). Person-environment interaction in the prediction of alcohol abuse and alcohol dependence in adulthood. Drug & Alcohol Dependence, 110, 62–69. · DOI
  34. Hughes, A., Lipari, R. N., & Williams, M. (2015). The CBHSQ Report: State estimates of adolescent marijuana use and perceptions of risk of harm from marijuana use: 2013 and 2014. Rockville: Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality.
  35. Jaccard, J., Blanton, H., & Dodge, T. (2005). Peer influences on risk behavior: An analysis of the effects of a close friend. Developmental Psychology, 41, 135–147. · DOI
  36. Jessor, R., & Jessor, S. L. (1977). Problem behavior and psychological development: A longitudinal study of youth. New York: Academic Press.
  37. Kilmer, J. R., Geisner, I. M., Gasser, M. L., & Lindgren, K. P. (2015). Normative perceptions of non-medical stimulant use: Associations with actual use and hazardous drinking. Addictive Behaviors, 42, 51–56. · DOI
  38. Kosterman, R., Bailey, J. A., Guttmannova, K., Jones, T. A., Eisenberg, N., Hill, K. G., & Hawkins, J. D. (2016). Marijuana legalization and parents’ attitudes, use, and parenting in Washington State. Journal of Adolescent Health, 59, 450–456. · DOI
  39. Lipari, R., Kroutil, L. A., & Pemberton, M. R. (2015). Risk and protective factors and initiation of substance use: Results from the 2014 National Survey on Drug Use and Health. Rockville: Substance Abuse and Mental Health Services Administration.
  40. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130–149. · DOI · Paper page
  41. Marsh, H. W., & Hau, K.-T. (1996). Assessing goodness of fit: Is parsimony always desirable? The Journal of Experimental Education, 64, 364–390. · DOI
  42. Martens, M. P., Page, J. C., Mowry, E. S., Damann, K. M., Taylor, K. K., & Cimini, M. D. (2006). Differences between actual and perceived student norms: An examination of alcohol use, drug use, and sexual behavior. Journal of American College Health, 54, 295–300. · DOI
  43. Mason, W. A., Fleming, C. B., & Haggerty, K. P. (2016). Prevention of marijuana misuse: School-, family-, and community-based approaches. In M. T. Compton (Ed.), Marijuana and mental health (pp. 199–225). Arlington: American Psychiatric Association Publishing.
  44. Mercken, L., Steglich, C., Knibbe, R., & Vries, H. (2012). Dynamics of friendship networks and alcohol use in early and mid-adolescence. Journal of Studies on Alcohol and Drugs, 73, 99–110. · DOI
  45. Miech, R. A., Johnston, L. D., O'Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2015). Monitoring the Future national survey results on drug use, 1975–2014: Volume I, Secondary school students. Ann Arbor: Institute for Social Research, The University of Michigan.
  46. Monahan, K. C., Oesterle, S., & Hawkins, J. D. (2010). Predictors and consequences of school connectedness: The case for prevention. The Prevention Researcher, 17, 3–6.
  47. Muthén, L. K., & Muthén, B. O. (1998-2013). Mplus user’s guide. Los Angeles: Muthén & Muthén.
  48. Pacula, R. L., & Sevigny, E. L. (2014). Marijuana liberalization policies: Why we can’t learn much from policy still in motion. Journal of Policy Analysis and Management, 33, 212–221. · DOI
  49. Pacula, R. L., Powell, D., Heaton, P., & Sevigny, E. L. (2015). Assessing the effects of medical marijuana laws on marijuana use: The devil is in the details. Journal of Policy Analysis and Management, 34, 7–31. · DOI
  50. Perkins, H. W., Meilman, P. W., Leichliter, J. S., Cashin, J. R., & Presley, C. A. (1999). Misperceptions of the norms for the frequency of alcohol and other drug use on college campuses. Journal of American College Health, 47, 253–258. · DOI
  51. Rogosa, D. (1980). A critique of cross-lagged correlation. Psychological Bulletin, 88, 245–258. · DOI
  52. Sieving, R. E., Perry, C. L., & Williams, C. L. (2000). Do friendships change behaviors, or do behaviors change friendships? Examining paths of influence in young adolescents’ alcohol use. Journal of Adolescent Health, 26, 27–35. · DOI
  53. Simons-Morton, B., & Chen, R. S. (2006). Over time relationships between early adolescent and peer substance use. Addictive Behaviors, 31, 1211–1223. · DOI
  54. Simons-Morton, B. G., & Farhat, T. (2010). Recent findings on peer group influences on adolescent smoking. The Journal of Primary Prevention, 31, 191–208. · DOI
  55. Slater, M. D. (2003). Sensation-seeking as a moderator of the effects of peer influences, consistency with personal aspirations and perceived harm on marijuana and cigarette use among younger adolescents. Substance Use & Misuse, 38, 865–880. · DOI
  56. Tang, Z., & Orwin, R. G. (2009). Marijuana initiation among American youth and its risks as dynamic processes: Prospective findings from a national longitudinal study. Substance Use & Misuse, 44, 195–211. · DOI
  57. Thornberry, T. P. (1996). Empirical support for interactional theory: A review of the literature. In J. D. Hawkins (Ed.), Delinquency and crime: Current theories (pp. 198–235). New York: Cambridge University Press.
  58. Wu, L.-T., Swartz, M. S., Brady, K. T., & Hoyle, R. H. (2015). Perceived cannabis use norms and cannabis use among adolescents in the United States. Journal of Psychiatric Research, 64, 79–87. · DOI
  59. Zaff, J. F., Donlan, A., Gunning, A., Anderson, S. E., McDermott, E., & Sedaca, M. (2016). Factors that promote high school graduation: A review of the literature. Educational Psychology Review, 29, 447–476. · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Guttmannova, K., Skinner, M. L., Oesterle, S., White, H. R., Catalano, R. F., & Hawkins, J. D.
Affiliations
  • University of Washington (US)
  • Rutgers, The State University of New Jersey (US)
ORCID
Venue
Prevention Science
Publisher
Springer Science and Business Media LLC
Published
2018-03-05
Volume
20
Issue
2
Pages
235-245
ISSN
  • 1389-4986 (ISSN-L)
  • 1573-6695
DOI
10.1007/s11121-018-0882-9
PMC
PMC6123306
OpenAlex
W2790151012
Cited by
41
References
4
Citations by year
2026: 5 · 2025: 4 · 2024: 3 · 2023: 7 · 2022: 8 · 2021: 7 · 2020: 3 · 2019: 4
FWCI
2.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Prevention Science (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute on Drug Abuse (R01 DA015183-12)
Topics
  • Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
  • Health Sciences › Medicine › Pharmacology (medical) › Cannabis and Cannabinoid Research
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Smoking Behavior and Cessation
Keywords
adolescent cannabis use, parental influence, peer influence, early adolescence, transition to high school, substance use prevention
MeSH terms
Marijuana Use, Adolescent, Adolescent Behavior, Alcohol Drinking, Marijuana Abuse, Female, Humans, Male, Marijuana Smoking, Peer Group, Risk Factors, Social Behavior, Violence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review