Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)



First Advisor

Colleen A. Redding


Bulgaria has recently emerged as one of the countries characterized by strikingly high death rates due to stroke, heart disease and different types of cancer. No serious attempt at dynamic analysis of the behavioral factors contributing to these high disease rates exists. It is clear that in order for this trend to be changed, the group within the age range of onset of most unhealthy behaviors needs to receive special attention. These facts and the lack of systematic exploration of the behavioral health risks of adolescents underline the importance of the proposed study.

The project had three goals: 1/ Measurement development and validation of smoking cessation, smoking prevention and stress related measures for Bulgarian adolescents; 2/ exploration of factors associated with smoking cessation and prevention in the same population; 3/ applied comparison of logistic regression analysis and discriminant function analysis for models with binary outcomes. In the total sample recruited from 12 high schools in Bulgaria (N=673), 276 (41.0%) participants were classified as smokers and quitters and 369 (54.8%) were nonsmokers. Measures with good psychometric properties were developed for decisional balance (DB) and selfefficacy (SE) for smoking cessation and prevention among ever smokers and nonsmokers respectively. The stage distributions of all measures confirmed theoretical predictions. Thus the validity of these TTM constructs for the Bulgarian adolescent population was supported. Two stress measures were also validated in the sample. These validated measures can be used with confidence in future research.

A series of logistic regression and discriminant function analyses were performed to explore the factors associated with smoking behavior. Smoking status was operationalized in a variety of ways in an attempt to differentiate between the factors related to smoking initiation, progression to regular smoking and smoking cessation. Attitude towards smoking bans was the single predictor that was retained across all models. In addition factors that differentiated between current smokers and ex-smokers were age, smoking status of family members and temptation to smoke. Nonsmokers at risk were differentiated from committed nonsmokers by scores on pros of staying smoke free, temptations and belief that smoking is harmful to health. Variables that distinguished between smokers and nonsmokers were age, GP A, smoking status of sibling and friends and beliefs that smoking is harmful to health. These data failed to provide evidence for a relationship between levels of perceived stress and smoking behavior, contrary to expectations. These results provide some insight into the factors that need to be considered when smoking cessation and prevention programs for this population are developed.

Logistic regression and discriminant function analysis on data with binary outcomes resulted in models with comparable overall classification rates. For models with very different group sample sizes and equal prior probabilities, however, the logistic regression models had lower sensitivity. The logistic regression procedure demonstrated more sensitivity to the choice of classification threshold than DF A did in these data. Researchers should take this characteristic into account when selecting a method for analysis, since it strongly influences classification results.