Adverse Impact and Test Validation Book Series: Multiple Regression. Introduction. Comparison of Compensation using

Adverse Impact and Test

Validation Book Series:

Multiple Regression

Using Multiple Regression to Examine Compensation Practices

Introduction

• Reasons for Investigating Pay Equity:

– The Equal Pay Act of 1963

– Title VII of the Civil Rights Act of 1964

• Employers may wish to examine Compensation

between groups – Tools available:

oIndependent Samples t-Test (t-Test) oMultiple Regression (MR)

Comparison of Compensation using

t

_-Test:

– Simple to conduct – Easy to understand • Cons:

– Doesn’t account for any legitimate reasons for differences in pay

Comparison of Compensation using MR:

– Requires much more data

– Requires deeper understanding of statistics/statistical analyses

• Pros:

– Includes explanatory variables

– Better reflects real-world compensation structure – Endorsed by OFCCP

Steps for Conducting MR Analyses

– Proactive:

oTypically Employer-initiated oData build is simpler – Reactive:

oTypically externally-initiated (Plaintiff or Government Agency) oData build must be thorough

Step 1 – Identify and Review Available Data

Step 1 – Identify and Review Available Data

– Employee ID

– Job Grouping Information (e.g. Job Title, Job Group, other Similarly Situated Employee Group (SSEG) information)

– Race/Ethnicity – Gender

– Date of Last Degree Earned – Highest Degree Earned – Date of Birth – Time in Company

– Time in Current Position (Time in Job)

Step 1 – Identify and Review Available Data

– Current compensation (Annual Salary or Hourly Wage) – Part-time vs. Full-time status

– Exempt vs. Non-exempt status – Job Title

– Grade Level or Salary Band – Employee Location

– Prior Experience data (many types) – Performance Ratings – Etc.

Step 1 – Identify and Review Available Data

compensation decisions

– Are variables Categorical or Continuous? oE.g. Education: “Any Degree?” vs. “Highest Degree”

• Choose Variable Formats appropriately

– Text – Date – Number – Currency – Etc.

Step 2 – Create and Verify Variable

Coding

Step 2 – Create and Verify Variable

Coding

• Focal Group should be coded as “1”; Reference

Group should be coded as “0” – e.g. “Female”“1,” “Male”“0” – e.g. “Minority”“1,” “White”“0” – e.g. “African American”“1,” “White”“0”

Step 3 – Conduct Preliminary Data

Analysis

• Generate Correlation Matrix

–In SPSS

oClick: Analyze Correlate Bivariate… oIdentify variables

–In Excel

oData Analysis ToolPak: Select Correlation oIdentify range containing variables

Step 3 – Conduct Preliminary Data

Analysis

• Examine Correlation Matrix

– Is there a statistically significant correlation between Gender/Race and Compensation?

– What other variables are correlated with Compensation? – Are there any large correlations between any of the

explanatory variables? (This could lead to multicollinearity, discussed later)

Note: This is done without accounting for SSEG’s, and as such is informative rather than analytical.

Step 4 – Create Groups of Employees for

Analysis

• Size of Employee Groups plays a large role in Statistical Power (i.e., the ability to detect a statistically significant difference if it exists to be found)

– Larger Groups favor those who seek statistically significant differences (e.g. Plaintiffs, OFCCP, EEOC)

– Smaller Groups favor those who seek no statistically significant differences (e.g. Defendants, Employers)

Step 4 – Create Groups of Employees for

Analysis

• Groups should represent Employees that are similarly situatedwith respect to:

– Work performed

– Levels of responsibility required – Skills needed

Step 5 – Conduct MR Analysis

• Using SPSS

Step 5 – Conduct MR Analysis Using

Excel

• Verify the Analysis ToolPak is Installed

Step 5 – Conduct MR Analysis Using

Excel

• Prepare Data worksheet

– Dependent (Compensation) Variable in Column A – Independent variables in Columns B and on

– Grouping variable (Gender or Race) in the column following all explanatory variables

Step 5 – Conduct MR Analysis Using

Excel

• To bring up the Data Analysis Menu:

– For Excel 2007/2010, select the “Data” tab on the Ribbon, then click “Data Analysis” (at the right end of the ribbon) – For Excel 2003, click on the “Tools” menu, then the “Data

Analysis” menu option

• On the Data Analysis Menu, Select “Regression”

Step 5 – Conduct MR Analysis Using

Excel

• Select Range containing Compensation

information for “Input Y Range”

• Select Range containing explanatory information

up to Gender/Race for “Input X Range”

• Check the “Confidence Interval” Box • If the data has field names, also check the

“Labels” box

• Click “OK”

Step 5 – Conduct MR Analysis Using

Excel: Interpret Initial Results

• Under “Regression Statistics,” check the R-Square

value

• In the ANOVA section, check the “Significance F” value for Regression

• Evaluate the p-Values for the independent variables

Step 5 – Conduct MR Analysis Using

SPSS

• Run Analysis

– Regression Diagnostics and supplemental statistical procedures

Step 5 – Conduct MR Analysis Using

SPSS

• Interpret Initial Results

– Review the model summary – Review the ANOVA Report – Review the Coefficients Report – Multicollinearity

– The key to the interpretation

– Interaction Terms: When two variables create a third

Step 6 – Conduct a Cohort Analysis

differences, investigate whether there is additional information which may also contribute to Compensation decisions.

• Examples include:

– Previous Salary – Prior Experience – Publishing History