IN A STRUCTURAL EQUATION MODEL, EACH EQUATION REPRESENTS A CAUSAL LINK RATHER THAN A MERE EMPIRICAL ASSOCIATION. IN A REGRESSION MODEL, ON THE OTHER HAND, EACH EQUATION REPRESENTS THE CONDITIONAL MEAN OF A DEPENDENT VARIABLE AS A FUNCTION OF EXPLANATORY VARIABLES. IT IS THIS DISTINCTION THAT MAKES CONVENTIONAL REGRESSION ANALYSIS AN INADEQUATE TOOL FOR ESTIMATING STRUCTURAL EQUATION MODELS. THE INADEQUACY OF LEAST SQUARES REGRESSION ANALYSIS IS DEMONSTRATED IN THREE CASES, INVOLVING UNOBSERVABLE VARIABLES (ERRORS OF MEASUREMENT), SIMULTANEITY (RECIPROCAL CAUSATION), AND OMITTED VARIABLES (INADEQUATE CONTROL). WITH REGARD TO STRUCTURAL PARAMETERS, THE ISSUE OF IDENTIFYING OBSERVABLE AND UNOBSERVABLE VARIABLES IS CRUCIAL. TRANSFORMATIONS OR ROTATIONS ARE WIDELY USED IN CONVENTIONAL EXPLORATORY FACTOR ANALYSIS, BUT THIS TYPE OF ANALYSIS MAY HAVE LIMITED APPEAL AS A BASIS FOR STRUCTURAL MODELING. WHEN THE MOMENTS OF OBSERVABLE VARIABLES PROVIDE MORE THAN ENOUGH INFORMATION TO DETERMINE STRUCTURAL PARAMETERS, A MODEL IS OVERIDENTIFIED. IN THIS CASE, THERE ARE RESTRICTIONS OR CONSTRAINTS ON THE OBSERVABLE MOMENTS. FOR ESTIMATING A CONVENTIONAL OVERIDENTIFIED SIMULTANEOUS EQUATION MODEL (NO MEASUREMENT ERROR), ECONOMISTS FREQUENTLY USE THE TWO-STAGE LEAST SQUARES PROCEDURE INITIATED BY THEIL. IN A VARIETY OF OVERIDENTIFIED SITUATIONS, EFFICIENT ESTIMATING PROCEDURES CAN BE VIEWED AS DEVICES FOR RECONCILING CONFLICTING MOMENT EQUATIONS. THE STRUCTURAL EQUATION APPROACH IS APPLICABLE TO ALL SOCIAL SCIENCE FIELDS. (DEP)