CONSTRUCT VALIDITY IN EMPIRICAL RESEARCH IS DEFINED AS THE 'FIT BETWEEN EMPIRICAL MEASURES AND THE THEORETICAL CONCEPTS REPRESENTED.' THIS PRESENTATION EXAMINES THE QUALITY OF CONCEPTUALIZATIONS, MEASUREMENTS, AND THEIR CORRESPONDENCE IN THE USE OF SUMMARY STATISTICS. THE DESCRIPTIVE USE OF TWO COVARIANCE-BASED MEASURES IS EXAMINED: THE PEARSON PRODUCT MOMENT CORRELATION AND THE UNSTANDARDIZED REGRESSION COEFFICIENT. USING ILLUSTRATIVE DATA, IT IS ARGUED THAT THE ALGEBRA NECESSARILY INVOLVED IN CORRELATIONAL MEASURES DISPORPORTIONATELY WEIGHTS SCORES FARTHER FROM THE MEAN, DESPITE IMPLICIT ASSUMPTIONS THAT ALL CASES IN THE SAMPLE OR POPULATION ARE EQUALLY IMPORTANT. SINCE REGRESSION COEFFICIENTS AND CORRELATION COEFFICIENTS HAVE THE SAME NUMERATOR, THE REGRESSION COEFFICIENT IS EXAMINED TO SEE IF SIMILAR PROBLEMS EXIST. FROM THIS INVESTIGATION, IT IS CONCLUDED THAT SLOPE MEASURES MAY TYPICALLY HAVE HIGHER CONSTRUCT VALIDITY THAN CORRELATIONAL STATISTICS. IF COVARIANCE CAN BE USEFULLY INTERPRETED AS MOMENTS OF FORCE, IT IS ARGUED THAT AN ALGEBRA WHICH BUILDS ON MORE DEVIANT CASES IS APPROPRIATE. USING THE REGRESSION COEFFICIENT AS A CONSTANT OF PROPORTIONALITY BETWEEN X AND Y LOGICALLY REQUIRES THE MULTIPLICATION PROCESS PRODUCING UNEQUAL WEIGHTING. IT IS CONCLUDED, THEREFORE, THAT FOR REGRESSION COEFFICIENTS THERE IS A GOOD FIT BETWEEN THE ALGEBRA AND THE AGGREGATE PROCESSES DESCRIBED. ILLUSTRATIVE DATA ARE INCLUDED. (RCB)