Skip to main content

Marginal Effects and Adjusted Predictions

FOUNDATION
By: Dustin S. Stoltz & Richard A. Williams | Edited by: Paul Atkinson, Sara Delamont, Alexandru Cernat, Joseph W. Sakshaug & Richard A. Williams Published: 2020 | Length:   4 | DOI: |
+- LessMore information
Download PDF

Marginal effects and adjusted predictions are means for providing insights into how important effects really are. Adjusted predictions are expected values of a dependent variable computed from the results of a regression, where all independent variables are held at specified values. A marginal effect is the change in the predicted value of a dependent variable after changing one independent variable—either a discrete change in categorical variables or an instantaneous change in continuous variables—while all other variables are held at specified values. Comparing predicted values and marginal effects is a tool for summarizing, interpreting, and testing the significance of independent variables.

While the coefficients of the simplest linear models tend to be easy to understand in substantive terms, the models’ underlying assumptions are often not met by ...

Looks like you do not have access to this content.

Copy and paste the following HTML into your website