First, frequency statistics were computed for the overall sample, males, and females. Estimated weighted frequencies were further stratified by age, race/ethnicity, annual household income, employment status, educational attainment, sexual orientation, and four geographical regions- classified by the U.S. Census Bureau [28]. Next, a frequency analysis was conducted by each of the eight ACE categories using mean ACE scores, stratified by the same sociodemographic variables previously mentioned. Both sets of frequency analyses are weighted with corresponding 95% confidence intervals (CI). Data analyses were conducted using SPSS software, version 24 [29]. Lastly, post-estimation F-tests were used to detect ACEs differences in mean scores (number of ACE exposures) and also differences within types of ACE exposure. We use the method outlined by Cumming [30] in which the 95% confidence intervals of two coefficients are compared. In the event that the confidence intervals overlap by less than half the length of one confidence interval arm, then the p-value between the confidence intervals is at least below the level of significance (i.e., less than .05). Previous studies show this method to be sufficiently accurate when
