Purpose -This paper seeks to examine the relationship between board composition and firm performance using a board-level aggregation variable.Design/methodology/approach -This study uses linear regression to analyze the relationship between board role typology and firm performance using a panel data set of 277 non-financial listed Malaysian firms over the period 2002-2007.Findings -The empirical results show that firm-boards with a high representation of outside and foreign directors are associated with better performance compared to those firm-boards that have a majority of insider executive and affiliated non-executive directors.Research limitations/implications -The findings seem to imply that in widely owned firms a higher proportion of outsiders on the board reduces under-investment and agency problems, which has significant economic implications.Originality/value -This is the first study to use a board-level aggregation variable to demonstrate the impact of boards' resourcefulness on firm performance.

Inaccurate and invalid statistical inferences in regression analysis may be caused by multicollinearity due to the presence of high leverage points (HLP) in a data set. Therefore, it is important that high leverage point which is a form ofoutlier be detected because its existence can lead to misfitting of a regression model, thus resulting in inaccuracy of regression results. In this paper, several methods have been proposed to identify HLP in a financial accounting data set prior to conducting further analysis of regression and other multivariate analysis. The Pearson scorrelation coefficient and variance inflation factors (VIF) were used to measure the success of a detection method. Numerical analysis showed that common diagnostics like the twice-mean and thrice-mean rules failed to detect HLP in the given data set whilst robust approaches such as the potentials and diagnostic-robust generalized potentials (DRGP) methods were found to be successful in identifying high leverage point as indicated by lower values of the Pearson s correlation coefficient and variance inflation factors.