I have discussed licensing measurement a great deal in this blog. Today, I want to introduce a new term to basically describe what I have been discussing with licensing measurement, called Regulatory Metrics or RegalMetrics for short. The reason for doing this is to be better positioned within the burgeoning new science called Regulatory Science. Licensing may be too delimited in its scope while regulatory science is more all encompassing and I feel will be the new science of rules, regulations, and standards.

The same issues are still present within regulatory metrics as they were in licensing measurement, such as how regulatory compliance data distributions are dramatically skewed with intense kurtosis. How best to deal with nominal measurement data? Do we transform the nominal data to ordinal scales as has been proposed in this blog (January 9th Post) into a Regulatory Compliance Scale to make it more similar to other more normally distributed program quality data distributions? Another way of thinking about this is in having “Licensing Buckets” for “Full, Substantial, Mid, and Low” regulatory compliance levels (see the Post of January 9th). The need for dichotomization of data is warranted because of the skewed data distributions. How best to minimize false positives and false negative decisions regarding the issuing of licenses based upon regulatory compliance scores. And lastly and probably most significant is how to deal with the introduction of mediocrity into fully compliant programs.

This last issue is a major issue for regulatory science regardless of discipline in how best to address the plateau of quality as programs move from substantial to full regulatory compliance. By not addressing this issue will continue to lead to frustration by consumers and the various industries we regulate in not being able to fully reward our outstanding performers because based upon regulatory compliance scores it is difficult to distinguish between these top performers and the mediocre performers. Regulatory science modeling is excellent at distinguishing between fully compliant programs and those that are having real difficulty with regulatory compliance. Where the models break down is distinguishing between programs that are in substantial compliance and full compliance when it comes to any quality dimension. This is what leads to the public wanting deregulation because the rules just don’t seem to make a difference. And then when there is a tragedy, the push for more regulations in order to protect all individuals so that they do not have the same tragedy repeat itself. It is this constant deregulation versus over-regulation mentality that is so counter productive and not driven by good public policy nor empirical data.