ABSTRACT
In Chapter 2 we examined the index of linear correlation between two variables, the Pearson product moment r and 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 demonstration of the significance of the departure of the coefficients from zero; the unbiased estimation of their magnitudes in the population; and the assertion, in the case of the regression coefficient, that one variable was, in part, dependent on or causally affected by the other. When we move to the situation with more than one independent variable, however, the inferential possibilities increase more or less exponen tially. Therefore, it always behooves the investigator to make the underlying rationale and goals of the analysis as explicit as possible. Fortunately, an appa ratus for doing so has been developed in the form of the analysis of causal models.1 Because the authors advocate the employment of these models in virtually all investigations conducted for the purpose of understanding phe nomena (as opposed to simple prediction), this chapter begins with an introduc tion to the use of causal models.
