Combining explanatory variables into factors instead of using individual variables in an achievement production function is advocated in several of the articles in this special issue. This article provides a brief overview of factor analysis explaining and illustrating the reasoning for this technique. There is a linchpin: Factor analysis is an aspect of regression analysis which is used to estimate the relationships between an outcome and the explanatory variables of a production function. While different in purpose, factor analysis and regression analysis share similarities. Regression estimates the relationships between an outcome and several explanatory variables, taking into consideration the relationships among the explanatory variables. Factor analysis, in contrast, estimates the relationships only among combinations of explanatory variables. (Contains 4 tables and 4 endnotes.)