Licensing Measurement

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Enhanced Dichotomization Model for GeneratingLicensing Key IndicatorsThe licensing key indicator methodology has beenevolving over the past decade in making it moresensitive to the selection process of the specific rules tobe included as key indicators. Some of the enhancementscan occur because of state licensing data systems beingable to provide population data rather than having toselect sample data. Because of the nominal nature oflicensing data and the severe skewness of the datadistributions, non-parametric statistical approaches needto be employed in the analysis of the data.A key component in the analysis of the licensing datadistributions is to dichotomization of the data which isgenerally not warranted but is acceptable with veryskewed data distributions. The dichotomization that hasbeen most successful is a H25/M50/L25 distribution inwhich H25 represents the High Group of regulatorycompliance, M50 which represents the Mediocre orMiddle Group of regulatory compliance, L25 whichrepresents the Lowest Group of regulatory compliance.In the past, the methodology allowed for full andsubstantial compliance within the High Group. Thisdecision is no longer recommended. Rather, in order todecrease the number of False Negatives, it is now119
recommended that only Full (100%) regulatorycompliance is used in defining the High Group. Thiseliminates the possibility of False Negatives.By making this above change and in using the fulldistribution of licensing data, it enhances the results forgenerating the licensing key indicator rules. Foradditional information on this modeling please see:Fiene, Richard (2018), “ECPQIM National Data Base”,Mendeley Data, V1.http://dx.doi.org/10.17632/kzk6xssx4d.1 This data baseprovides the detailed ECPQIM data distributions for theabove changes. The enhancements increase the phicoefficients and reliability in either moving or notmoving from abbreviated inspections to fullcomprehensive inspections. This data base also containsclear demonstrations of the efficacy of the ECPQIM –Early Childhood Program Quality Improvement andIndicator Model as a vehicle for improving early careand education programs.120
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RIKI/NARALicensingMeasurementand Mo
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Permission is granted to copy and/o
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seemed to have more and more impact
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The field of regulatory science is
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can always check out any of the pub
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Dr Fiene is regarded as a leading i
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consider. This is a very important
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inferential monitoring. All these t
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for the future. Caring for Our Chil
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approaches can be utilized in any h
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It also became of interest for the
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National Resource Center for Health
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for early care and education: Carin
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for Children and Families published
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programs were not increasing in qua
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course to make certain you continue
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organizational element in how rules
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signed by a loved one. In a differe
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on regulatory compliance/licensing
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Notes:_____________________________
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recommended as the sole means for t
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Statistical Methods and Data BasesT
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Notes:_____________________________
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presented here has these two system
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No Ceiling effect. 7) Gatekeeper ve
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increases the robustness of the dat
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elements and principles in how lice
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The lecture from the NARA licensing
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Page 89 and 90: • Enhanced Dichotomization Model
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Page 93 and 94: case. Now it is possible to explore
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Licensing Measurement

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