of importance inherent in VCA and note other metrics have been used in social science research For example, researchers in other areas often discuss importance of a variable as the expected change in the dependent variable for a one standard deviation change in the independent variable (the standardized beta rather than VCA's explained variance. Brush and Bromiley(1997)also argue that under many circumstances, VCA lacks the power to find effects even when they are imposed to be present in the data. Another issue revolves around the interpretation of variance component effects-according to Brush and Bromiley(1997)the square root of variance components should be used when interpreting the relative importance of effects. ANOVA presents difficulties because corporate effects must be entered into the model before business unit ffects which gives an upper bound on the relative importance of corporation (Rumelt, 1991) For example, Rumelt(1991)finds a substantial corporate effect when he enters corporation before business unit. In addition to the debate over corporate, industry, and business unit effects, the underlying issue of estimating importance of an effect has wide impact in business research Brush, Bromiley and Hendrickx(forthcoming)use a simultaneous equation model to assess relative importance. They claim this method provides reliable estimates of effects and solves some of the difficulties posed by assumptions in the other estimation approaches(Brush, Bromiley and Hendrikx, forthcoming). While Brush, Bromiley and Hendrickx(forthcoming) focus on the use of continuous performance variables to estimate corporate and industry effects, they still use dummy variables to control for business unit effects We use Monte Carlo simulation to compare ANOVA, VCA and simultaneous equations techniques in their ability to estimate the relative importance of effects. The comparisons evaluate both bias and precision of the estimators