Identifying trajectories of adolescent smoking: An application of latent growth mixture modeling.

Colder, Craig R., Mehta, Paras, Balanda, Kevin, Campbell, Richard T., Mayhew, Kathryn, Stanton, Warren R., Pentz, Mary Ann, Flay, Brian R.·2001·Health Psychology
DOI 10.1037//0278-6133.20.2.127

Abstract

The goal of the current study was to identify discrete longitudinal patterns of change in adolescent smoking using latent growth mixture modeling. Five distinct longitudinal patterns were identified. A group of early rapid escalators was characterized by early escalation (at age 13) that rapidly increased to heavy smoking. A pattern characterized by occasional puffing up until age 15, at which time smoking escalated to moderate levels was also identified (late moderate escalators). Another group included adolescents who, after age 15, began to escalate slowly in their smoking to light (0.5 cigarettes per month) levels (late slow escalators). Finally, a group of stable light smokers (those who smoked 1-2 cigarettes per month) and a group of stable puffers (those who smoked only a few puffs per month) were also identified. The stable puffer group was the largest group and represented 25% of smokers.

Cites (2)

  1. Analysis of Incomplete Multivariate Data
    Schafer, J.L. · 1997
  2. Analyzing developmental trajectories: A semiparametric, group-based approach.
    Nagin, Daniel S. · 1999 · Psychological Methods

Show all 2 references

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CoChat. (2026). Citation check: Identifying trajectories of adolescent smoking: An application of latent growth mixture modeling. https://app.cochat.ai/papers/10.1037//0278-6133.20.2.127
The paper
Colder, C. R., Mehta, P., Balanda, K., Campbell, R. T., Mayhew, K., Stanton, W. R., Pentz, M. A., & Flay, B. R. (2001). Identifying trajectories of adolescent smoking: An application of latent growth mixture modeling. Health Psychology. https://doi.org/10.1037//0278-6133.20.2.127

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.