stimator with robust standard errors.
RESULTS
A traditional LCA of the six smoking indicators was first examined. These initial analyses ignored the clustering of students in communities. Table 1 presents the class solutions for one to six latent classes (see Model 1). The Bayesian Information Criterion (BIC) drastically declines (i.e., improves) from one to three classes and then begins to level off. Entropy is also best with the three-class model. Moreover, the four-class solution separates one of the classes from the three-class solution into two smaller groups, but the posterior probabilities indicate that there is substantial misclassification between these two smaller classes. For example, the posterior probabilities for the three-class solution are .99, .93, .98 and the posterior probabilities for the four-class solution are .79, .93, .98, and .79, with the first and fourth classes representing the separated classes from the three-class model. The low posterior probabilities for these two classes indicate that the model has difficulty distinguishing between people in the first and fourth class. Most important, the substantive interpretation of the three-class solution (as described in the next section) is theoretically meaningful, useful, and parsimonious. As such, we chose the three-class solution as the best model. The results are presented in Table 2.
In this three-class solution, the largest class represents nonsmokers and comprises 61.3% of the sample. Although some of these students had smoked a cigarette in their lifetime, none of them were current smokers or thought of themselves as a smoker. Moreover, they tended to associate with nonsmoking peers, believe that their parents would stop them from smoking, and perceive that smoking is harmful. The smallest class, described as the heavy smokers, represents 14.6% of the sample. Girls in this class tended to be regular smokers and viewed themselves as heavier smokers. They also tended to associate mostly with other peers who smoked cigarettes and were less likely than other girls to perceive that their parents would stop them from smoking and that smoking is harmful. The remaining students were classified as moderate or occasional cigarette smokers. Comprising 24.1% of the sample, these students were most likely to report occasional cigarette smoking and viewed themselves as light smokers. Just over half of them reported that most of their friends smoke. Nearly all believed that their parents would try to stop them from smoking and about three-quarters believed that smoking is harmful to one’s health.
Building on this three-class, Level 1 solution, we next specified a model that utilized the parametric approach to account for the nested structure of the data. The results of the model are presented in Table 1, Model 2. The BIC improves with the addition of the random effects and the entropy remains the same as for the fixed effects model. The estimated mean of the random effect (or random mean) for the heavy smoker class indicates that, for communities at the average random mean for both heavy smoking and moderate smoking, the average probability that a student would be classified as a heavy smoker is .13. The variance of the random mean describes the variation in the probability that a student will belong to the heavy