Yost et al. BMC Public Health 2010, 10:465 http://www.biomedcentral.com/1471-2458/10/465 weight and reports of engaging in exercise as a weight- loss method among U.S. female adolescents. Upon determining subjects were trying to lose weight, they were identified as engaging in exercise to try to lose weight if they answered “yes” to the “Exercise” response to the question, “During the past seven days, which of the following did you do in order to lose weight or keep from gaining weight?” Data Analysis Data were analyzed using STATA, a statistical software package, to account for the Add Health study’s cluster sampling with unequal probability design and using Add Health sampling weights for cross-sectional analyses, which allowed for a nationally representative sample of U.S. female adolescents [38]. Results were considered statistically significant if the p-value was less than of 0.05. Initial analyses included descriptive statistics for sample characteristics and a summary statistics of vari- ables. Chi square statistics determined relationships between dichotomous variables. After determining the presence of low to modest correlations between all inde- pendent variables (ranging from -0.2232 to 0.4889) in each model, survey-weighted logistic regression was per- formed to determine the effect of a dichotomous or continuous independent variable on the probability of a dichotomous dependent variable. Wald tests, when appropriate, were performed to determine the signifi- cance between the independent variable and dependent variable [39]. For each of the current study’s research questions, three models were used to predict the depen- dent variable. The first model contained only demo- graphic variables. The second model contained demographic variables as well as BMI and weight per- ception as independent variables. The third model con- tained demographic variables and variables indicating subjects’ weight perception accuracy as independent variables. Results Sample Description Of the 2,510 subjects in the Add Health public-use data core sample, a total of 2,216 subjects were eligible for secondary analysis in the current study, after applying inclusion and exclusion criteria. Subjects were 13 to 18 years old (M = 15.8, SD = 1.5), with the majority between 15 and 17 years old. As shown in Table 2, which depicts the sample’s demographic variables, most subjects were non-Hispanic (88%) and White (69%); however, other ethnic (Hispanic) and race categories (Black or African American, American Indian or Native American, and Asian or Pacific Islander) were also represented in the sample. Although 85.1% of the sub- jects’ mothers reported having graduated from high Page 5 of 11 Table 2 Descriptive Statistics for Demographic Variables (N = 2216) Variable (n) Age at Wave II (2216) Mean (Standard Deviation) Ethnicity (2208) Hispanic non-Hispanic Race (2210) White Black or African American American Indian or Native American Asian or Pacific Islander Maternal Educational Attainment (1977) 8th grade or less > 8th grade, not high school graduate Business/trade, not high school graduate High school graduate Completed a General Equivalency Diploma Business/trade after HS Non-college graduate College graduate Post-graduate Total Household Income (1742)a Mean (Standard Deviation) Median Range Note. a in thousands of dollars n 15.80 263 1945 1531 425 44 73 90 193 11 560 94 187 359 282 201 48.90 40.00 0-900 % (1.46) 11.91 88.09 69.28 19.23 1.99 3.30 4.55 9.76 0.56 28.33 4.75 9.46 18.16 14.26 10.17 (56.5) school or earned a General Equivalency Diploma, this percentage was slightly lower than the 1994 percentage (91.6%) of educational achievement rates for a high school diploma or greater in the U.S. for females 24 to 64 years old [40]. In addition, the subjects’ median total household income ($40,000) was slightly higher than that of U.S. households in 1994 ($32,264) [41]. Weight Related Variables Body-Mass Index The majority of female adolescents in this study sample were not overweight or obese (72.63%) (see Table 3). However, significant racial differences existed in the sample’s weight status. Black/African American and American Indian/Native American female adolescents had the highest prevalence of overweight and obesity. Chi-square statistics indicated Black/African American female adolescents were significantly more likely to be overweight or obese than female adolescents who were White, Asian/Pacific Islander, or in the race category of “Other” (p < 0.01). American Indian/Native American female adolescents were significantly more likely to be overweight or obese than White (p < 0.05) or Asian/ Pacific Islander (p < 0.01) female adolescents. Asian/