Among data pooling techniques, relatively unique advantages of IDA primarily result from the level of data pooling; in IDA, pooling is based on raw data (e.g., item responses) from individual participants rather than summary statistics at the level of completed studies (as in meta-analysis). This approach yields larger sample sizes than typical single-study designs, a particularly important advantage for examining low-base rate behaviors that are commonly of interest in clinical psychology. Although the pooled data set will have an average base-rate that remains within the range of contributing studies (i.e., most simply, each contributing study may have 5% of the sample reporting some form of psychopathology or heavy drug use and thus so will the pooled data set), the absolute number of individuals engaging in the behavior will necessarily be greater in the pooled sample relative to the individual contributing studies. As a result, the stability of model estimation is improved, the influence of extreme observations is reduced, and more complicated models can be fitted than would otherwise be possible within the individual studies.
