Collinearity, Power, and Interpretation of Multiple Regression Analysis by Charlotte H. Mason and William D. Perreault, Jr. reviews the impact of collinearity on multiple regression analysis in marketing research. The authors conducted a Monte Carlo simulation to assess how collinearity affects the accuracy of regression coefficients and standard errors, as well as the likelihood of Type II errors. They found that while collinearity can distort regression coefficients and standard errors, its effects are often exaggerated. The study shows that collinearity should not be viewed in isolation but in conjunction with other factors such as sample size, R², and the magnitude of coefficients. For example, a bivariate correlation of .95 has little effect on coefficient recovery if the sample size is 250 and R² is at least .75, but can lead to high Type II error rates if the sample size is 30 and R² is .25. The authors also discuss methods for detecting and coping with collinearity, including dropping variables, transforming data, and using biased estimators like ridge regression. They conclude that while collinearity can be problematic, its impact is often less severe than other factors like sample size and model fit. The study emphasizes the importance of considering collinearity within the broader context of power and highlights the need for further research on how collinearity diagnostics interact with other power-related factors.Collinearity, Power, and Interpretation of Multiple Regression Analysis by Charlotte H. Mason and William D. Perreault, Jr. reviews the impact of collinearity on multiple regression analysis in marketing research. The authors conducted a Monte Carlo simulation to assess how collinearity affects the accuracy of regression coefficients and standard errors, as well as the likelihood of Type II errors. They found that while collinearity can distort regression coefficients and standard errors, its effects are often exaggerated. The study shows that collinearity should not be viewed in isolation but in conjunction with other factors such as sample size, R², and the magnitude of coefficients. For example, a bivariate correlation of .95 has little effect on coefficient recovery if the sample size is 250 and R² is at least .75, but can lead to high Type II error rates if the sample size is 30 and R² is .25. The authors also discuss methods for detecting and coping with collinearity, including dropping variables, transforming data, and using biased estimators like ridge regression. They conclude that while collinearity can be problematic, its impact is often less severe than other factors like sample size and model fit. The study emphasizes the importance of considering collinearity within the broader context of power and highlights the need for further research on how collinearity diagnostics interact with other power-related factors.