In the last 15 years, there has been a surge of writing about new forms of transnational governance constituted by various mixtures of state and non-state actors. It has been argued and shown that network methods are a fruitful tool for studying the structure and functioning of these transnational governance arrangements. However, empirically measuring large transnational networks that include various types of public and private actors is a methodologically challenging task that can only be insufficiently addressed by using standard tools of network data collection, such as surveys and archival sources. In this paper, I present a multiple-sources and multiple-measurement procedure for measuring large transnational networks constituted by different types of actors that helps scholars to collect the data needed to apply the methodological toolkit of network analysis in the study of transnational governance. Using original datasets from different transnational governance networks, I describe the procedure and the network data it yields. I then discuss the validity of the data and how robust the measurements obtained from it are with respect to missing data on network ties. Using simulation methods and statistical analysis, I examine various network measures that have received the attention of political scientists (e.g. centrality, brokerage, centralization, fragmentation) and investigate how stable they are with respect to missing data on network ties. Results indicate that some measures are more stable than others, and that stability of network measures with respect to missing data on network ties is also a function of network and study properties.