In Chapter 2 we examined the index of linear correlation between two variables, the Pearson product moment correlation rand the regression equation for estimating Y from X. Because of the simplicity of the two-variable problems, we did not need to go into detail regarding the interpretive use of these coefficients to draw substantive inferences. The inferences were limited to the unbiased estimation of their magnitudes in the population; the assertion, in the case of the regression coefficient, that one variable was, in part, related to or dependent on the other; and the demonstration of the significance of the departure of the coefficients from zero. When we move to the situation with more than one independent variable , however, the inferential possibilities increase more or less exponentially. Therefore, it always behooves the investigator to make the underlying theoretical rationale and goals of the analysis as explicit as possible. Fortunately, an apparatus for doing so has been developed in the form of the analysis of causal models. Because the authors advocate the employment of these models in virtually all investigations conducted for the purpose of understanding phenomena (as opposed to simple prediction), this chapter begins with an introduction to the use of causal models. A more complete presentation is found in Chapter 12.