Why Diversity Measures Are Not Comparable (and How to Fix It): Normalizing Multitype Team Diversity

Research on team diversity increasingly adopts multitype and multidimensional designs, yet the measurement of diversity remains methodologically fragmented. Although existing frameworks distinguish theoretically meaningful diversity types, the indices used to operationalize them are expressed on heterogeneous and non-comparable scales. As a result, researchers face persistent difficulties in interpreting coefficients, comparing diversity between attributes or types, and integrating diversity measures into multivariate, longitudinal, or optimization-based models. To address this limitation, we propose a systematic normalization of multitype team diversity indices. Building directly on Dawson’s measurement framework, we derive the theoretical maximum of each diversity index according to its corresponding assumptions regarding diversity type, attribute scale, and team size. Using these maxima, all indices are rescaled to the unit interval [0-1], preserving their substantive meaning while making them directly comparable. This normalization completes an essential but previously implicit step in multitype diversity measurement and provides a unified, interpretable metric that strengthens the methodological foundations of cumulative research on team diversity.