We study the effect of board independence and CEO duality on firm performance for a sample of stock-listed enterprises from Indonesia, Malaysia, South Korea and Thailand, applying quantile regression. Quantile regression is more powerful than classical linear regression since quantile regression can produce estimates for all conditional quantiles of the distribution of a response variable, whereas classical linear regression only estimates the conditional mean effects of a response variable. Moreover, quantile regression is better able to handle violations of the basic assumptions in classical linear regression. Our empirical evidence shows that the effect of board independence and CEO duality on firm performance is different across the conditional quantiles of the distribution of firm performance, something classical linear regression would leave unidentified. This finding suggests that estimating the quantile effect of a response variable can well be more insightful than estimating only the mean effect of the response variable. Additionally, we find a negative moderating effect of board size on the positive relationship between CEO duality and firm performance.