IDA also offers several advantages for measurement, often resulting in a broader and more rigorous psychometric assessment of key constructs. In any single-study design, we typically assess such constructs using a discrete set of items that are shared across all members of the sample (e.g., all subjects respond to the same 10-item scale assessing depression) and selected based on the specific characteristics of a given sample (e.g., age, gender, ethnicity). A common challenge in many areas of psychological research is the need to reconcile the wide array of operationalizations of our constructs across studies and to evaluate the generalizability of our measures across populations of interest. This state of affairs is a clear stumbling block for study-to-study comparison but is in turn a distinct advantage for increased construct validity in IDA. Through data pooling and related measurement development, the psychometric assessment of a given construct can often be substantially broadened by incorporating the multiple methods of assessment that were used in each individual study and by examining performance of these measures across sub-populations within the pooled sample. This in turn results in much stronger psychometric properties of measures in the pooled design versus any single contributing study.
