Licensing Measurement and Monitoring Systems Relationship: How Regulatory Science can inform Human Services Regulatory Administration

In a previous RIKINotes post, a matrix was presented which demonstrated how licensing measurement and monitoring systems were related at a micro level. In this post, a more macro level or theoretical level will be presented.

As stated in previous posts as well, licensing measurement is very different from other social science measurements in that it is a very skewed data distribution and not normally distributed. And when regulatory compliance results are compared to program quality results a clear “ceiling effect” is present. This has been documented in several previous posts and in the licensing measurement research literature (please see the Selected Publications web page for several examples from Georgia, Saskatchewan, Washington, and Head Start). Also see the ehandbook: Licensing Measurement and Monitoring Systems: Regulatory Science Applied to Human Services Regulatory Administration which contains additional details about this relationship.

These results from licensing measurement influence how best to design and implement a monitoring system. Because of these results, differential monitoring was proposed as an alternative to the existing paradigm of “One Size Fits All” monitoring. Differential monitoring which led to abbreviated licensing inspection reviews via risk assessment or key indicator methodological approaches were based upon specific risk aversion strategies employing mathematical models of weighting and prediction as outlined in the previous RIKINotes post.

As the earlier post presented the micro aspects of the relationship between licensing measurement and monitoring systems, this post presents the macro or theoretical aspects of this relationship for licensing researchers/scholars, regulatory scientists and licensing administrators to think about. Licensing measurement and monitoring systems are clearly driven by several regulatory science concepts, such as, the ceiling effect/diminishing returns, to do no harm, skewed data, and nominally based data. All these have an impact on human services regulatory administration and what an efficient and effective licensing system should look like.