Size does matter: How varying group sizes in a sample affect the most common measures of group diversity

Work group diversity can be conceptualized in different ways (i.e., variety, separation, and disparity), and the appropriate operationalization of a diversity dimension depends on which of these diversity types researchers have in mind. Based on prior work on the measurement of the different types of diversity, we show that the most common diversity indexes (i.e., Blau’s index, Teachman’s index, standard deviation, mean Euclidean distance [MED], Gini coefficient, and coefficient of variation) are systematically biased whenever they are used in field studies in which the overall sample comprises groups of varying sizes. Using simulated data, we illustrate this bias inherent in all of the common diversity measures. This bias can lead to erroneous conclusions concerning the impact of group size and the relationship between group diversity and group outcomes. We offer bias-corrected formulas and suggest that diversity researchers henceforth use these adjusted versions when investigating the effects of group diversity in organizational settings

Strike When the Force Is with You: Optimal Stopping with Application to Resource Equilibria

Optimal investment in a nonrenewable resource project occurs when the rate of increase of the project's forward value falls to the force of interest. This stopping rule yields a financial interpretation of resource quality as being a property of the project rather than of individual units of reserves. It also leads to re-interpretations of (a) rent as the present value of the project rather than of units of reserves and (b) Hotelling's insight as, not a rule for the path of rents, but an equilibrium algorithm for price. The analysis is extended to sequential development of pesticides, antibiotics, and forests. Copyright 2007, Oxford University Press.