Before specific interventions for college student smokers can be developed and tested, research needs to characterize patterns of smoking in this population and examine whether evidence exists for a classification of smokers that goes beyond the traditional ��current smoker�� or ��daily versus nondaily�� taxonomy. In this study, we used latent class analysis Belnacasan (VX-765) (LCA) to identify subgroups (classes) of college smokers with similar smoking patterns (Lazarsfeld & Henry, 1968; McCutcheon, 1987). LCA is an empirically based statistical method for explaining heterogeneity in response patterns in terms of underlying classes.

We aimed to (a) use LCA to characterize patterns of smoking in a sample of college students; (b) estimate the prevalence of these patterns of smoking; and (c) better understand the variation in smoking patterns by examining the associations with demographic variables, health risk variables, and other aspects of smoking behavior, including efficacy to quit, nicotine dependence, and perceived health effects. Methods Population and sample In fall 2006, a random sample of undergraduate students attending 10 universities (eight public and two private) in North Carolina were invited to complete a Web-based survey as part of a randomized group trial of an intervention to prevent high-risk drinking behaviors and their consequences (Wolfson et al., 2007). Students from each campus were selected randomly from undergraduate enrollment lists provided by each school. The goal was to have 416 students (104 each of freshmen, sophomores, juniors, and seniors) from each university to complete the survey (n=4,160).

The number of students selected to participate was based on the expectation from previous studies and previous waves of the survey that approximately 30%�C35% of the students would complete the survey within the allowed time period (Reed, Wang, Shillington, Clapp, & Lange, 2006). The Web site was shut down shortly after the target numbers from the 10 schools were achieved. The response rate across all 10 schools was 21.0% and varied quite a bit across campuses (9.3%�C34.0%). Variation in the response rates across schools may reflect varying levels of technological capabilities (Mitra, Jain-Shukla, Robbins, Champion, & DuRant, 2007). The response rate was likely affected by the survey link being deactivated after the quota (4,160 students) was reached (i.

e., a higher response rate would have been achieved if a quota system had not been used). All the students selected to participate were sent an E-mail inviting them to participate in a Web-based survey, which provided a link to a secured Web site where the survey could be completed. The E-mail notification protocol, GSK-3 including multiple, frequent reminders for the Web-based survey, was based on the Dillman (2000) approach (Mitra et al., 2007).