Int. J. Environ. Res. Public Health 2012, 9 946 for scoring purposes): (1) “How would you evaluate the amount of your daily food consumption?” (if high/low = 0, if moderate = 1); (2) “How many times do you eat during the day?” (if ≥3 = 1, if <3 = 0); (3) “How many days per week do you eat red meat?” (if ≤1 = 1, if else = 0); (4) “How many days per week do you eat legumes?” (if ≥1 = 1, if else = 0); (5) “How many days per week do you eat fruits?” (if ≥7 = 1, if else = 0); (6) “How many days per week do you eat vegetables?” (if ≥7 = 1, if else = 0). Total diet score was calculated by summing up answers (range 0–6), with higher scores indicating healthier diet patterns. The 33.3% percentiles were used for categorization into low (score ≤3), moderate (score 4) and high (score 5+) healthy dieters. Similarly, sleep quality was evaluated by five questions: (1) “How would you evaluate your sleep?” (if deep = 2, if superficial = 1, if bad = 0); (2) “Is it easy for you to fall asleep?” (if yes = 1, if no = 0); (3) “Do you sleep during midday?” (if always = 2, if sometimes = 1. if never = 0); (4) “Do you feel rest in the morning after awakening?” (if yes = 1, if no = 0); (5) “Are you satisfied with your sleep?” (if yes = 1, if no = 0). Total sleep score was calculated by summing up answers (range 0–7), with higher scores indicating higher sleep quality. The 33.3% percentiles were used for categorization into low (score ≤4), moderate (score 5) and high (score 6+) sleep quality. Religiosity was assessed by two simple scored questions: (1) “Do you pray regularly?” (if yes = 1, if no = 0); (2) “How often do you attend church ceremonies?” (if at least once per week = 2, if 3 or less per month = 1, if never = 0). Total religiosity score was calculated by summing up answers (range 0–3), with higher scores indicating higher religiosity. Three religiosity categories were formed: low (score 0 or 1), moderate (score 2) and high (score 3). 2.3. Statistical Analyses Statistical calculations were performed using SPSS for Windows (version 18.0.3) statistical software [18]. Univariate analyses of the independent variables categories with SRH categories was performed using chi-square statistics. Table 1 presents descriptive statistics (absolute and percentage values) of the independent variables across SRH categories, the p values of chi-square tests and Kendall's tau b to assist directionality interpretation. Sociodemographic and disease-related variables significant at the p level of <0.1 were first separately and then as a group entered in multinomial regression model with SRH as dependent variable. In general multinomial regression analysis helps to assess the “distance” between very good to poor and good to poor. Statistical significance of a factor in both these comparisons reveals its contribution mainly to negative SRH ratings, whereas significance in very good vs. poor only reveals its significance to positive SRH ratings. In other words, factors of the first paradigm could represent already attained or granted situations that are lost, while factors of the second paradigm could represent less granted situations that are gained in the benefit of the individual. Unadjusted and adjusted odds ratios (OR) and 95% confidence intervals (95% CI) are presented in Table 2. Nagelkerke R square represents the amount of SRH variability explained by the sociodemographic and disease-related variables. Finally, using multinomial regression analyses with SRH as dependent variable, each lifestyle variable was adjusted for the effects of the main model (Table 3). The p level of significance was set at 0.05 for all analyses.