Dickerson et al. BMC Public Health 2011, 11:926 http://www.biomedcentral.com/1471-2458/11/926 which were then used to calculate their BMI. They were then assigned to BMI classifications based on the meth- odology used by the WHO [2] (i.e., underweight (BMI < 18.5 kg/m2), normal weight (BMI of 18.5-24.99 kg/m2), overweight (BMI of 25.0-29.99 kg/m2), obese class I (BMI of 30.0-34.99 kg/m2), obese class II (BMI of 35.0- 39.99 kg/m2), or obese class III (BMI >= 40 kg/m2). WHO classifications of obesity were used because of the range of classifications (i.e., six categories) versus alter- native classification systems such as the one offered by the Centers for Disease Control and Prevention which only has four categories [22]. A wider range of classifica- tions allowed for more precise estimates using our sta- tistical model described below because it improved the within group homogeneity of subjects within each BMI classification. Independent variables The independent variables were chosen based on established literature indicating an association with BMI. Age, sex, and race/ethnicity were selected as independent variables based on research identifying their importance to obesity [23]. Age was measured as a continuous variable while sex and race/ethnicity were measured as categorical variables. The categories of race/ethnicity were non-Hispanic White, African- American, and Hispanic. Educational attainment was selected as an independent variable based on research examining its role as a covariate of obesity rates [1]. Educational attainment was measured as a continuous variable from one (first grade educational attainment) to 17 (graduate school educational attainment). Rural- ity was selected as an independent variable based on the differing food environments of urban and rural locales and the associated relationship with rates of obesity [24]. Rurality was measured as a categorical variable by county and then matched to an Urban Influence Code [25] which designated the respondent as living in a county that was either rural or urban. Hours per day spent watching television was selected as an independent variable to be representative of sedentary behavior as part of our research question. Hours per day spent watching television was mea- sured as a categorical variable using a six category response format (Question PAQ.710) in accordance with CDC’s validated NHANES instrument [20]. The categories in the BVHA were: less than 1 h, 1-2 h, 2-4 h, 4-6 h, and more than 6 h. Finally, minutes per week of moderate PA (e.g., fast walking) was selected as an independent variable as part of our research question. Minutes per week of moderate PA was mea- sured as a continuous variable. This question was based on the CDC’s BRFSS [21] survey to enhance validity of the resulting data. Page 3 of 10 Descriptive statistics Chi-square, t-test, and Kruskal-Wallis statistics were used to examine the relationships between independent variables and the dependent variable. Statistical signifi- cance was established as a = 0.05, but was then adjusted using the Bonferroni correction. Based on seven com- parisons in the descriptive statistics analysis, the new level of statistical significance was (0.05/7) = a = 0.007. Simultaneous quantile regression In studies using simultaneous quantile regression, the word “effect” is used to describe association, not causa- tion. To remain consistent with established literature, we are also using the term “effect” to imply association but not causality. The research question of the study required an estimation of homogeneity of effect between the independent variables and BMI classification. This required estimators that not only recognized effects at all BMI classifications, but estimators that could also recognize effects at specific points in the dataset. We selected simultaneous quantile regression because of its unique ability to meet our analytical requirements. Spe- cifically, we choose simultaneous quantile regression over quantile regression because it enabled us to test for the similarity of effect of our independent variables at different points in the dataset. This is a unique facet of simultaneous quantile regression relative to quantile regression [26,27]. We used the “sqreg” command in Stata Version 11 (StataCorp, College Station, TX) for our analysis and required the model to perform 100 repetitions of the analysis to provide more accurate bootstrapped standard errors. Any respondents contain- ing missing data in the analysis were listwise deleted from the analysis in Stata Version 11 (StataCorp, Col- lege Station, TX). Because simultaneous quantile regres- sion estimates the effect of the independent variables at multiple points in a dataset, it was important to know which points in the dataset to assign to each BMI classi- fication. We chose the median point of each classifica- tion. Thus, if those who were normal weight were represented between the zero and the 20th percentile in the dataset, we set the point of analysis for normal weight at the 10th percentile. Graphically representing effects by BMI classification The results of the simultaneous quantile regression model are represented graphically for ease of interpreta- tion (please see Figures 1 and 2). Each independent vari- able coefficient and respective p-value was plotted as an estimate of effect on its own line graph and included a 95% confidence interval. The vertical axes of the graphs represent the effect of the independent variable on BMI, while the horizontal axes represent the BMI classifica- tion. Data points representing p-values of statistical