Social benefits likely play a role in young adult tobacco use.

Social benefits likely play a role in young adult tobacco use. 1.14], < .001) or Daily Smoker (odds ratio = 1.14, 95% confidence interval [1.07, 1.22], < .0001) compared to a Nonsmoker, controlling for demographics and other tobacco-related attitudes. The SPI and reduced SPI were independently related to young adult tobacco use. The steps brevity, ease of use, and strong association with tobacco use may make it useful to tobacco and other prevention researchers. = 5,455). Steps Demographics Demographic variables included age, sex (male/female), race/ethnicity, and educational status. Age 6138-41-6 manufacture 6138-41-6 manufacture was calculated using 6138-41-6 manufacture data collection date and self-reported birthday. Race/ethnicity was based on participants responses to two items: ethnicity (Hispanic or not) and to a single item where participants were asked, What is your race? and selected one category (Black, Asian, White, Hawaiian/Pacific Islander, American Indian/Alaskan Native, or more than one race). We recoded race/ethnicity into four categories (Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Other). Participants were also asked about their educational status (1 = to 5 = to 5 = (Muthn & Muthn, 2007). M< .0001) and daily smoking (OR = 1.28, 95% CI [1.20, 1.38], < .0001) compared to nonsmoking. In the subsequent model, we added demographic characteristics and found that the SPI was significantly related to smoking status, such that higher SPI scores were related to higher likelihood of being both a Nondaily (OR = 1.14, 95% CI [1.09, 1.19], < .0001) or Daily Smoker (OR = 1.25, Mouse monoclonal to Human Albumin 95% CI [1.17, 1.33], < .0001) compared to a Nonsmoker. Last, we joined the tobacco-related attitude variables to the model and found the SPI was still significantly related to smoking status (see Table 2), such that higher scores around the SPI were related to an increased probability of being 6138-41-6 manufacture a Nondaily (OR = 1.09, 95% CI [1.04, 1.14], < .001) or Daily Smoker (OR = 1.14, 95% CI [1.07, 1.22], < .0001) compared to a Nonsmoker. All models controlled for location. Race/ethnicity, sex, education, sexual orientation, stance against tobacco, peer smoking, and trends in smoking were also associated with daily and nondaily smoking. Table 2 Results for Logistic Regression. Exploratory Factor Analysis In an effort to examine if the full scale could be reduced to make it even more efficient all 13 items were factor analyzed and loadings were examined from the single-factor answer (Table 3). Items with the smallest loadings were decreased using an iterative approach until the Cronbachs alpha was no longer acceptable. Internal consistency for the full measure was.68, and after dropping items D8, D9, D10, D11, and D13, remained acceptable ( = .65) leaving eight total items (D1CD7 and D12). Table 3 Factor Loadings From Exploratory Factor Analysis of Single-Factor Answer. Predictive Validity: Logistic Regression Models With Reduced SPI We ran identical models as above using the reduced SPI to examine whether the measure would remain a significant predictor of smoking status in the single-predictor models and models including demographic and tobacco related variables. The reduced SPI was consistently found to be related to smoking status in all models. We will present results only from the final model with all the predictors (controlling for location). In the multinomial logistic regression model, the reduced SPI was significantly related to nondaily smoking (OR = 1.31, 95% CI [1.22, 1.40], < .0001) and daily smoking (OR = 1.50, 95% CI [1.36, 1.65], < .001) compared to nonsmoking. Discussion The present study examined the basic psychometric properties of the SPI in a bar-going emerging adult sample. We found that the SPI was associated with increased odds of daily and nondaily smoking impartial of demographic factors and other tobacco-related factors. In addition, we found evidence to support associations of demographic variables and their association with smoking status that has been found in other literature. Specifically, males, those who were not college-educated (Control & Prevention, 2010), non-Hispanic Whites compared to Hispanics and non-Hispanic Blacks (Stahre et al., 2010), and those who self-reported as gay or bisexual compared to straight (Balsam et al., 2012) were more likely to be smokers. The SPI was independently associated with smoking status when controlling for these demographic and other factors, suggesting the measure has utility to add to demographics and tobacco-related variables to more effectively identify high-risk young adults. An exploratory.