This work addresses the problem of metric diversity in meta-analysis for Software Engineering by clustering studies using input-output tables and by vote-counting. Diversity arises when researchers, measuring same phenomena, use different, and typically "incomparable" metrics making impossible a direct analysis of the effects and their sizes. Additionally, this work discusses an application of proposed approach to the case of Software Reuse.