Covariate Regression Adjustment

We are often interested in outcome differences between two or more groups. Without accounting for the demographic/socioeconomic/etc. differences of the groups, we may falsely attribute outcome differences to group membership itself. In actuality, the differences may be attributable to the characteristics of group members, not group membership.

Covariate regression adjustment holds group characteristics (demographics, socioeconomic status, etc.) fixed before comparing outcomes across groups.

How it works

First, a mathematical model of the outcome variable is specified. The outcome variable and all variables that influence the outcome variable are included in the model. Second, multivariate regression techniques estimate the effect of each independent variable in the model, holding all other variables constant.

The regression-adjusted differences between groups are differences that exist after controlling for the demographic/socioeconomic/etc. disparities between each group.

When to use:

  • When the data are administrative or observational (not experimental)
    • Example: A large administrative dataset on the customers of job training programs and their subsequent labor market outcomes
  • For group comparisons
    • Example: For gender, race, age-group, etc., comparisons on the labor market outcomes for customers of the job training program


Further explanation on Covariate Regression Adjustment



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