Multicollinearity refers to the presence of highly intercorrelated independent variables in structural equation models, that is, models estimated by using techniques such as least squares regression and maximum likelihood. There is a problem of multicollinearity in both the natural and social sciences where theory formulation and estimation is in the mathematical model building tradition. The variety of ways in which analysts can cope with the condition of multicollinearity is presented. The measure of multicollinearity developed is a nonlinear transformation of the maximum variance inflation factor (Vmax) which, like the correlation coefficient, gives zero for no collinearity and one for perfect collinearity. Three ridge regression estimating methods are described and their advantages and disadvantages compared. The first three disadvantages of ridge regression stem from the use of the total mean square error (MSE) criterion. The last two chapters of the report address the theory and practice of normalization ridge regression analysis. It is shown that if a new criterion called the variance normalization criterion is used, several of the inherent disadvantages based on using the MSE criterion are correctable. Results indicate that normalization ridge regression analysis has advantages (in terms of the performance indices) over seven other methods. (Author/RL)