Integrative data analyses have recently been shown to be an effective tool for researchers interested in synthesizing datasets from multiple studies in order to draw statistical or substantive conclusions. The actual process of integrating the different datasets depends on the availability of some common measures or items reflecting the same studied constructs. However, exactly how many common items are needed to effectively integrate multiple datasets has to date not been determined. This study evaluated the effect of using different numbers of common items in integrative data analysis applications. The study used simulations based on realistic data integration settings in which the number of common item sets was varied. The results provided insight concerning the optimal numbers of common items sets to safeguard estimation precision. The practical implications of these findings in view of past research in the psychometric literature concerning the necessary number of common item sets are also discussed.