CORRELATION AND REGRESSION

Description of the Procedure, and An Illustration of the Use of the Procedure in An Application

Correlation and regression procedures are described in this section. This description is followed by an illustration of the use of the procedures in an application.

In least-squares regression analysis, the emphasis is placed on an analysis of the joint interaction of the variations in dependent and independent variables. Because of this analytical emphasis in least squares regression analysis, both the independent and the dependent variables must be measured on an interval scale.

Simple regression analysis involves the analysis of the relationship between one dependent variable and one explanatory, or independent, variable. Multiple regression, on the other hand, is a general statistical technique through which one can analyze the relationship between a dependent or criterion variable and a set of independent or predictor variables. Multiple regression may be viewed either as a descriptive tool by which the linear dependence of one variable on o

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to correlation analysis, is that regression coefficients permit the projection of movements in one variable based on movement in another variable or in a set of other variables. Correlation coefficients establish both the strength of relationships between variables and the nature of such relationshipsł-positive or negative. What a simple correlation coefficient does not do, however, is to establish a causal relationship between the variables. Regression coefficients, either from simple or multiple regression analysis, however, can establish such causal relationships. Illustration of Use in An Application Both correlation and regression analysis procedures were used in an investigation of the relationships between role ambiguity and role conflict, as independent or explanatory variables, on job performance, job satisfaction, and organizational commitment as dependent variables (Dubinsky, Michaels, Kotabe, Lim, & Moon, 1992, pp. 77-99). Additionally, job performance also served as an independent variable in the equation in which job satisfaction was the dependent variable, and job satisfaction served as an independent variable in the equation in which organizational commitment was the dependent variable. Coefficients o
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