This blog post will propose a new Regulatory Compliance Scale (RCS)(Fiene, 2022) which should help in making comparisons between regulatory compliance and program quality systems, such as Environmental Rating Scales and Quality Rating & Improvement systems. The proposed scale builds off of a familiar 1-7 Likert scale that has been used a good deal in the early care and education field within program quality instruments/tools. This scale is based upon 40+ years of research into regulatory compliance data distributions which have been reported in this blog (RIKINotes) over the years.

The proposed scale (see RCS Table below) has the following structure of full compliance, substantial compliance, mediocre compliance, and low/non-optimal compliance. Numerically it is proposed that full compliance = 0 no rule violations; substantial compliance = 1-3 rule violations; mediocre compliance = 4-9 rule violations; and low/non-optimal compliance = 10+ rule violations. The transformation to a 1-7 Likert scale is as follows: full compliance = 7; substantial compliance = 5; mediocre compliance = 3; and low/non-optimal compliance = 1.

When the above regulatory compliance scale is utilized it substantially reduces the skewness and kurtosis in the regulatory compliance data distribution which is a major problem with all regulatory compliance data distributions and has been reported repeatedly in the human services licensing research literature. The revised or transformed data distribution begins to approach a more normally distributed data set; albeit, not as normally distributed as the various Environmental Rating Scales but significantly better when straight frequency counts are used in determining regulatory compliance. This has been the preferred means of data recording since the introduction of Instrument-based Program Monitoring (IPM) in the 1980’s. It is being proposed that the above Regulatory Compliance Scale (RCS)(Fiene, 2022) be used in place of this frequency based data system.

This newly proposed scale should go a long way in making future analyses in utilizing regulatory compliance data more useful and meaningful when making comparisons with the various program quality initiatives present in the early care and education field, such as the Environmental Rating Scales and Quality Rating & Improvement Systems.