Subgroup analysis
When and why should you run a subgroup analysis?
If your organization has diverse pay structures across different regions or departments, it can be beneficial to conduct a subgroup analysis. For instance, in a global organization, pay structures may vary significantly between countries. In Country A, salaries might be heavily based on experience, or education and skills, whereas in Country B, the job family might play a more significant role in determining pay.
By running a subgroup analysis, you can generate separate models for each country (or any other subgroup), providing unique analysis results for each one. This approach allows you to understand how different variables impact pay structures in each subgroup, offering unique insights at a local level.
With subgroup analysis, you obtain individual compensation models for each subgroup, showing how the variables affect salaries differently across the subgroups. Each model will have its own health indicators such as the R² value, reflecting the proportion of variability explained by the model in that specific subgroup.
Instead of evaluating a single, overarching model, you will have multiple models corresponding to each subgroup, enabling a more nuanced and accurate analysis of pay equity within your organization.
Running a subgroup analysis
The subgroup analysis feature allows you to run the same analysis on subsets of the data, for example for each business unit, each department, or each country.
To run a subgroup analysis, proceed as follows:
Enter the run analysis form.
Check the Run a subgroup analysis checkbox.
Select the group Variable.
Choose the specific Subgroups for which to run the analysis.
Run the analysis.
After the analysis has completed running, an overview panel is presented at the top of the page and the overview page is shown. The overview graph has one point for each subgroup:
the size represents the number of employees in the subgroup,
the location indicates the unadjusted and adjusted pay gap,
the color indicates which group is underpaid for the unadjusted and the adjusted pay gap.
This information is also displayed in the table on the left.
We also include an overview summary, which combines the results of all the subgroup analyses. As the point of a subgroup analysis is to run independent models for each subgroup, the overview of the subgroup analyzes presents the pay gap as the weighted average of the pay gap in the individual subgroups. The weighted average is the subgroups' pay gaps weighted by the number of employees in each subgroup.
The pay gaps shown in the overview analysis for a subgroup analysis are therefore different from the results you will get if you would run one analysis on the entire dataset. In that case the pay gaps are based on the analysis from a single regression for the all employees.
To view the results of a specific subgroup analysis, click either on the dot in the graph, or on the row in the table. This brings up the results from the corresponding subset analysis, displayed in the same manner as single level analysis. Clicking on the Compensation Model Results tab will bring up the compensation model for the selected subgroup, and similarly the Reports tab will highlight the selected subgroup.
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For more information about the compensation model, see: Understanding & analyzing the compensation model.
You can use the drop-down to switch between subgroups or click the x icon next to it to go back to the summary for all of the groups. If you want to have a quick look at the subgroup overview graph and table click Display subgroup overview.
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Editing a suggested raise in the subgroup will also edit the raise for that employee in the overview analysis for all employees.
When running subgroup analysis, the pay gap is measured and closed separately in each subgroup, meaning that there may be some groups where raises are suggested for women and others where raises are suggested for men (assuming we run the analysis for gender).
Given the often small size of the analysis groups, the system automatically selects to apply backwards elimination with a default p-value threshold of 0.25. In other words, from the analysis of each subgroup, variables that are not significant are removed from the model. As a result, the variables included in each model may differ from one subgroup to another. Further, we note that the coefficients for the variables in each subgroup will be different - for example, in some job roles education may play a major role in determining compensation, while in others experience may be more important. To use another p-value for the backwards elimination for subgroups you can check the optional backwards elimination check box and apply a new threshold number. Please note that selecting this option also applies backwards elimination to the overall analysis.
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In case your dataset involves large subgroups and your analysis takes long to run, you can consider manually disabling backwards elimination, to speed the analysis up. We do however recommend checking the significance of variables in the compensation results tab, and manually removing any that do not significantly contribute to your model.