et al., 2012). Not surprisingly, these methods may share little in their analytics beyond the common goal of data pooling. Together, however, they form a toolkit for researchers interested in analyzing pooled data. We offer to this toolkit an approach which we call Integrative Data Analysis (IDA). IDA is a framework for conducting the simultaneous analysis of raw data pooled from multiple studies. Our goals for this paper are to define the IDA framework for pooled data analysis; describe the advantages and limitations of this approach; discuss sampling, measurement and hypothesis testing within the IDA framework; and provide future directions for IDA applications and methodological development.
