Background: In the context of international large scale assessments, it is often not feasible to implement a complete survey of all relevant populations. For example, the OECD Program for International Student Assessment surveys both students and schools, but does not obtain information from teachers. In contrast the OECD Teaching and Learning International Survey assesses teachers and schools but does not assess students. Clearly, important information is missing from both assessments. One approach to obtaining information from both surveys is through data fusion--a variety of methods that can be used to create a synthetic data set containing information from both surveys. Methods: This paper presents an experimental evaluation of a representative group of data fusion methods using data from Iceland--the only OECD country that implemented both PISA and TALIS to all members of the relevant populations. Results: On the basis of a set of validity criterion we find that Bayesian bootstrap predictive mean matching and the EM-bootstrap methods perform best with respect to creating a usable synthetic data file for research purposes.