Software & Reporting

Salary Equity Software - HR Equator


Salary Equity Software

HR Equator is the most comprehensive and advanced salary equity software tool on the market. This proprietary software program enables a company to identify potential pay equity problems in its organization and, in turn, can assist in making salary adjustments if necessary. The software was developed with human resources professionals in mind. Basically, the software enables the non-technical HR professional to conduct advanced statistical analyses without being a statistician. HR Equator utilizes the most advanced statistical analyses that are accepted by the courts, EEOC, and the OFCCP, and the reports can be used to conduct comprehensive proactive or reactive analyses as part of an OFCCP or EEOC investigation. This software tool has been recognized as exactly what employers need to avoid costly compensation settlements and ensure non-discrimination in their pay practices.

Mirroring the methodologies employed by both the EEOC and the OFCCP, and recognized by the courts, these analyses and features include:

  • Multiple Regression Analysis
  • Program to Help Create Pay Analysis Groups
  • Generation of Item 11 Report and Excel file
  • Statistical Significance Tests (t-tests, Fisher’s exact test)
  • Analyses Comparing Race/Ethnicity Sub-Groups as well as Minority-Nonminority Comparisons
  • Factor Pattern Analysis
  • Correlation Analysis
  • Cohort Analysis (small group and large group)
  • Back Pay Calculator
  • Descriptive Statistics and Frequencies


DCI Announces Strategic Partnership with Savina Consulting

September 8, 2015 – Washington, DC

DCI Consulting Group, a leading HR risk management firm, announces a strategic partnership with Savina Consulting, an expert litigation support firm, to provide seamless EEO litigation support and expert witness services to current and new clients.

See the full Press Release here.

Recent Blog Posts

Minuscule Shortfalls: When Statistical Significance Alone Falls Short

We have written several blogs (e.g., regarding the recent Apsley v. Boeing case) discussing the influence of sample size on the likelihood of observing statistically significant indicators in adverse impact analyses. As covered in those blogs, the larger the samples being analyzed, the higher the likelihood of observing statistically significant indicators of protected class subgroup differences, regardless of how different the pass rates are in practical terms. However, even in cases of small sample sizes, the differences between protected class subgroups may be statistically significant, and it is equally important (as it is with large samples) to evaluate the practical significance of the difference. In the following paragraphs, we discuss interpreting analysis results based on small

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