One thing that is ever present with regulatory compliance data distributions is that they are terribly skewed. See the previous post which provides a definition of skewed distributions and their implications. This post is going to attempt to provide a potential answer to why the data base is skewed.
At first, I was led to believe that potentially the skewness in the data was a result of the rules that being stringent enough, in other words, the health and safety standards were too easy to comply with. That could definitely be a contributing factor but this is not the case in all instances when one compares state human service rules and regulations and the Head Start Performance Standards. I think a much deeper structure may be operating that is more philosophical rather than practical.
The philosophy of regulatory compliance and rule formulation is one of risk aversion. In other words, how do we mitigate risk that potentially increases the chances of mortality or morbidity in the clients being served when a specific rule is out of compliance. This philosophy emphasizes the elimination of a risk, taking something away rather than adding to it. It is essentially, “Do No Harm”. It is interesting to note that generally regulatory compliance scoring is nominal in being either “Yes” or “No”; and a lower score is better than a higher score, there are fewer violations of rules. Not the way most assessment tools are designed.
For example, when one looks at program quality, this system is based upon the open-endedness adding to rather than taking away. It is all about, “Do Good” rather than “Do No Harm”. Generally when you look at the data distributions here, they are more normally distributed without the skewed nature of regulatory compliance data distributions. Generally program quality scoring is ordinal in nature on a Likert Scale. A higher score is better than a lower score. Makes sense in that when you have more of a good thing, the higher the score. And the philosophy of program quality is one of improvement with relatively little emphasis on risk aversion.
This is an alternate explanation to why regulatory compliance data distributions are so terribly skewed in comparison to other program quality measures.