A regression analysis is the OFCCP’s preferred method of comparing employee salaries to detect if significant differences in pay between gender or race/ethnicity groups exist. PayEval makes conducting a regression analysis simple and straightforward.
“[I]f a contractor performs a multiple regression analysis, which reasonably meets the standards outlined in the voluntary guidelines and the analysis finds no discrimination, OFCCP will consider the contractor’s compensation practices to be in compliance with Executive Order 11246.”
- OFCCP Voluntary Guidelines for Self-Evaluation of Compensation.
The detailed report will include a statistical test of the factors used in the regression model. For this analysis, a table is included with the number of observations used in the analysis, the degrees of freedom for error and the percent of the variation explained by the model. For the overall analysis of gender impacts on compensation, the selected model with the included factors explains 86.5% of the variation in compensation. In the subsequent table, note that service, EEO job code and job title have a significant impact on compensation at a 95% confidence level, as indicated by the highlighted standard deviation values of 5.20, 3.56, and 7.30.
Another option is to display only significant regression results. This would only include the items from the standard analysis that produced significant results. This also provides information such as reference groups and controls used in the regression model. Note that all the resulting estimated impact for this specified report would be highlighted in red or bolded since only results considered significant based on typical OFCCP guidelines are included. For this regression analysis, the “Black” employees appears to have a pay rate that is 71.35% less than the reference race within the “white” job group. However, note that none of these are in qualified comparison groups since there are only 20 employees in the group (based on OFCCP’s 30/5 rule).
