A second advantage of IDA's approach to pooling data at the item level is increased sample heterogeneity. Although an initial temptation when embarking on IDA is to minimize between-study heterogeneity (i.e.., to carefully select contributing studies that are as similar as possible), the presence of certain types of between-study differences can facilitate our ability to distinguish within-study and between-study variation in our findings. For example, many studies in clinical psychology use sampling methods that result in the under-representation of potentially important subgroups in the population of interest (e.g., groups based on gender, race, socioeconomic status, age). By pooling participants across such samples, the representation of these subgroups may be increased, allowing for distinct groups to be simultaneously considered. Similarly, given adequate sample representation within studies, group comparisons may be possible within IDA that are not possible due to small samples sizes within the individual studies. This in turn increases the external validity of the IDA findings relative to those of the individual contributing studies.
