The use of stepwise regression has been criticized for both interpretive misunderstandings and statistical aberrations. A major statistical problem with stepwise regression and other procedures that involve multiple significance tests is the inflation of the Type I error rate. General approaches to control the family-wise error rate such as the Bonferroni correction are based on the assumptions of independent tests which typically reduces power. Because correlated predictors is a more realistic situation, other algorithms based on the average correlation in the predictor matrix have been proposed. The present study proposes an algorithm based on the maximum eigenvalue and the determinant of the predictor matrix for controlling the family-wise Type I error rate for multiple corrected tests. A Monte Carlo simulation with 5,000 replications was performed to demonstrate the effectiveness of the proposed algorithm. Three tables and one figure are included. (Contains 19 references.) (Author)