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Prepare

A guide to the Prepare tab in the pay equity analysis, covering the factor plan and pay philosophy.

Written by Elise Laurent

This guide describes the Prepare tab in the pay equity analysis. Here you will find your factor plan and set up your pay philosophy – the factors you consider should affect employees' pay.


Factor plan – without the Job Architecture module

Read this section if you have not started the pay equity analysis based on the Job Architecture module.

Here you can see the factor plan used in the pay equity analysis. It shows all 10 factors, the number of levels per factor, points per level and weighting for each factor.

⚠️ Important: If you have already started the job valuation and make changes to the factor plan, it will affect the result of the job valuation. If you change the weighting of the factors, you cannot migrate the job valuation to Sysarb's Job Architecture. We recommend that you do not change factor scores or weightings.

💡 Tip: Do you want to read more about how the factor plan and the 10 factors work? See our guide:


Factor plan – with the Job Architecture module

Read this section if you have started the Pay equity analysis based on the Job Architecture module (sysarbs job architecture not external job architecture).

Here you can instead see the grades in your job architecture – these function as your factor plan in the pay equity analysis.


Pay philosophy

This is where you choose which factors should influence an employee's salary. The factors you select are used to explain part of the adjusted pay gap in the analysis. Below, we walk you through how to select factors and how to read the results.

How to select factors

The list on the left shows every factor available in the system. Each factor is marked as either Numerical (for example Grade or Age) or Categorical (for example Manager code or Locality).

Switch on the factors you want to use, the ones that match how your organisation believes pay should be set. For example: Grade (based on the job evaluation), Age (can indicate experience) and Company tenure.

⚠️ Important: You can only use a factor if data for it has been imported into the system.

💡 Tip: Choose factors that reflect what your organisation wants to influence an employee's salary. Sysarb recommends including Grade. Under the EU Pay Transparency Directive, it is the unadjusted pay gap that must be reported. The adjusted pay gap is a tool for understanding and explaining pay differences internally.

Reading the results per factor and the model fit

For each factor you switch on, the middle section shows how many percentage points of the pay gap the factor explains, with a plain-language explanation. It also shows whether the factor is Significant, meaning it has a statistically significant relationship with pay.

A factor marked Not reliable does not reliably explain the pay gap. If that happens, consider whether it belongs in your pay philosophy.

Above the list, you can see how well the model explains salaries, with one R² value for men and one for women. The marker on the colour scale and the heading (for example Strong model fit) show whether the analysis is reliable enough to give meaningful insights.

💡 Tip: With a weak model fit, the selected factors explain only a small part of the salaries. Review which factors are switched on, and check whether data is missing for any factor that drives pay in your organisation.

Detailed information on your regression model

The panel on the right brings the results together. It is based on a Blinder-Oaxaca decomposition, which means you see two regression lines: one for women and one for men.

Under Show insights, you can see the unadjusted pay gap, how much of it your pay factors explain, and what remains unexplained, which is your adjusted pay gap. For each factor, you can see what it tells you about your organisation: the distribution between men and women, how much salary increases per unit for each group, and whether the factor is significant for both.

Under Show details, you can also see the statistics behind each factor, broken down by women and men:

  • Mean: the factor's average in each group, for example average company tenure in years.

  • Coefficient (logspace): the regression model's coefficient for the factor. This is the basis for the plain-language explanation of how much salary increases per unit.

  • p-value: shows whether the relationship is statistically significant. A low p-value gives the factor the Significant label.

Click Export in the panel's header to download the underlying data.

⚠️ Important: The figures in the panel cover the employees who were analysed with the selected factors. Employees missing data for a selected factor may have been excluded, so the unadjusted pay gap here may differ from the one on the Dashboard, which includes all employees.

Previous result

If your audit has been upgraded to the new calculation model, you will see a Previous result button next to Export. Click it to see your audit's results from before the upgrade: the unadjusted pay gap, the adjusted pay gap and each factor's contribution. The window also explains what changed in the model and why.

⚠️ Important: All calculations have been redone with the new model, so your figures may differ from before. You cannot switch the audit back to the previous model.


Continue in the pay equity analysis guide series:

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