In hierarchical multiple regression (not to be confused with hierarchical linear models that account for variance components), you add model terms by block. The fit of the new model is measured by the difference in multiple R-squared values and an F-test involving the residual sum of squares of each model.
My question is: Why do papers report the difference in multiple R-squared values instead of the difference in adjusted R-squared values? The F-test is not directly testing the difference between R-squared values. Furthermore, the adjusted R-squared accounts for the bias in multiple R-squared estimates. All this considered, why are people reporting multiple R-squared values?
Thanks in advance.