The article by Charlotte H. Mason and William D. Perreault, Jr., titled "Collinearity, Power, and Interpretation of Multiple Regression Analysis," published in the *Journal of Marketing Research* in 1991, explores the impact of collinearity on multiple regression analysis in marketing research. The authors address the common concern of correlated predictor variables and their potential effects on regression estimates. Despite extensive literature on coping with collinearity, the study aims to clarify the conditions under which collinearity affects estimates and the severity of these effects. The authors conduct a Monte Carlo simulation to investigate how different levels of collinearity among predictors affect the accuracy of estimated regression coefficients, their standard errors, and the likelihood of Type II errors. They compare these effects with other factors such as sample size, the strength of the true population relationship (R²), and the pattern of regression coefficients. Key findings include: - Collinearity alone cannot be viewed in isolation; its impact must be evaluated in conjunction with other factors. - High bivariate correlations (e.g., .95) have minimal effect on coefficient recovery and inference if the sample size is large (250+) and R² is at least .75. - In contrast, a bivariate correlation of .95 with a small sample size (30) and a low R² (.25) results in Type II error rates of 88% or more. - The interactions of collinearity with sample size, R², and the magnitude of coefficients are significant and important. - The problem of Type II errors is severe even with little collinearity if the sample size is small or the model fit is weak. - High levels of collinearity can be largely offset with sufficient power. The study recommends caution in relying on diagnostics and rules of thumb for collinearity, emphasizing the need to consider the broader context of power. Future research should assess how various collinearity diagnostics are affected by power-related factors.The article by Charlotte H. Mason and William D. Perreault, Jr., titled "Collinearity, Power, and Interpretation of Multiple Regression Analysis," published in the *Journal of Marketing Research* in 1991, explores the impact of collinearity on multiple regression analysis in marketing research. The authors address the common concern of correlated predictor variables and their potential effects on regression estimates. Despite extensive literature on coping with collinearity, the study aims to clarify the conditions under which collinearity affects estimates and the severity of these effects. The authors conduct a Monte Carlo simulation to investigate how different levels of collinearity among predictors affect the accuracy of estimated regression coefficients, their standard errors, and the likelihood of Type II errors. They compare these effects with other factors such as sample size, the strength of the true population relationship (R²), and the pattern of regression coefficients. Key findings include: - Collinearity alone cannot be viewed in isolation; its impact must be evaluated in conjunction with other factors. - High bivariate correlations (e.g., .95) have minimal effect on coefficient recovery and inference if the sample size is large (250+) and R² is at least .75. - In contrast, a bivariate correlation of .95 with a small sample size (30) and a low R² (.25) results in Type II error rates of 88% or more. - The interactions of collinearity with sample size, R², and the magnitude of coefficients are significant and important. - The problem of Type II errors is severe even with little collinearity if the sample size is small or the model fit is weak. - High levels of collinearity can be largely offset with sufficient power. The study recommends caution in relying on diagnostics and rules of thumb for collinearity, emphasizing the need to consider the broader context of power. Future research should assess how various collinearity diagnostics are affected by power-related factors.