Multiple Regression methods:

(Note that different terminology can be used by different authors)

Direct (Simple) Regression: All available predictor variable are put into the equation at once and they are assessed as if they had been entered last i.e., are assessed on the basis of the proportion of variance in the criterion variable (Y) they *uniquely* account for. (called *simple* regression in Bordens and Abbott)

Forward regression: sequentially add variables, one at a time based on the strength of their squared semi-partial correlations (or simple bivariate correlation in the case of the first variable to be entered into the equation)

Backward regression: start with them all then delete them on the bases of smallest change in the R^{2}

Stepwise Regression: a combination of forward and backward: at each step one can be entered (on basis of greatest improvement in R^{2} but one also may be removed if the change (reduction) in R^{2} is not significant. (In the Bordens and Abbott text, it sounds like they use this term to mean *Forward* regression.)

Hierarchical Regression: The researcher assumes control over the analyses. On basis of theory or practicality (e.g., economics). Note: this is equivalent to doing semi-partial correlations>