EEOPay is the latest version of DCI’s comprehensive and advanced salary equity analysis software that has been leading the industry since 2001. EEOPay enables an organization to identify potential pay equity problems related to race and/or sex, and to make necessary salary adjustments. The software was developed with human resources professionals in mind, empowering them to conduct advanced statistical analyses and use the results to make sound decisions– all without being a statistician. EEOPay utilizes the most advanced statistical analyses that are accepted by the courts, EEOC, and OFCCP, and can be used to conduct both proactive analyses and analyses within the scope of an OFCCP or EEOC investigation. This software equips employers in their ongoing efforts to avoid costly compensation settlements and ensure non-discrimination in their pay practices.
EEOPay is frequently updated and supported by DCI’s experienced, in-house software development team to reflect the methodologies employed by both EEOC and OFCCP, and recognized by the courts. Analyses and features included in EEOPay are:
- Functionality to follow OFCCP’s Directive 2018-05
- Run a report showing the ratio of employees to control variables in each Pay Analysis Group (PAG) or SSEG to compare against OFCCP’s 10:1 guideline
- Automatically use the race/ethnicity group with the highest average pay in each PAG or SSEG as the reference group for statistical comparisons
- Separately analyze base pay, total compensation, and other components of compensation (e.g., bonus, commission, overtime, shift differentials) using a single dataset
- Automatically transform salary to the log of salary in the regression model
- Analyze statistical outliers for indicators of potentially inappropriate PAGs
- Automatically evaluate the effect of sex or race in separate regression models
- Automatically apply the 30-and-5 sample size rule for regression analyses
- Pattern of Disparity reports by location and across the entire organization
- These reports include a unique set of pattern analysis tests developed by DCI’s Ph.D. statisticians to provide a succinct summary of the overall patterns resulting from the comparison of race/ethnicity and sex groups across PAGs/SSEGs and locations. The reports include three different test results that identify the overall size of the difference between each pair of compared groups, and whether or not the pattern of differences is statistically significant.
- Wage Gap Report showing differences based on sex and race/ethnicity across your entire organization
- Race Comparison Significance Matrix (by Location and Organization) summarizing the number of cases where each race/ethnicity group is the lower- or higher-paid group when statistically significant differences exist
- Non-Statistical Comparison report summarizing pay differences between men and women in PAGs or SSEGs that do not include enough employees to perform statistical significance tests
- Multiple Regression Analysis
- Explain the percentage of variability in pay for a PAG or SSEG
- Predict employee salaries from a regression model and compare to current salaries
- Calculate potential salary adjustments by individual and group
- Statistical Significance Tests (t-tests, Fisher’s Exact test)
- Multiple options for comparing pay of race/ethnicity groups
- Compare each group to the highest-paid group in each PAG
- Compare each group to a selected reference group (e.g., Black vs. White, Asian vs. White)
- Compare White vs. Minority (i.e., all non-White employees)
- Compare any two custom-defined Race Groups
- Factor Pattern Analysis showing group differences in merit variables
- Correlation Analysis showing how merit variables are related to pay and to each other
- Cohort Analysis including both statistical (large PAGs) and non-statistical (small groups) comparisons of similarly situated employees
- Summary reports to easily identify PAGs requiring further research and provide overall results for the organization’s analysis
- Automatically create vector-coded variables from existing categorical data for use in regression analyses
- Automatically create time-based variables (e.g., years since hire) from existing dates for use in regression analyses
- Automatically create potential PAGs from existing organizational-level data
- Export reports to PDF, Excel, or Word formats
- Import data from Excel workbooks in the current format (.xlsx) or legacy format (.xls)
- Export updated compensation roster table (e.g., after adding vector-coded and time-based variables) to Excel
