Building a statistical classification: A new tool for classification development and testing

Abstract

Statistical classifications are essential for collecting consistent data that can be compared over space and time. However, a publicly-documented body of practice concerning how to undertake the development and testing of a statistical classification is currently lacking. What aspects of the classification should be tested during the development process? How do we judge whether the classification is fit-for-purpose? How should problems and shortcomings be identified so that they can be remedied? To fill this gap, we drew on existing, authoritative sources to develop an analytic structure for use in the development and testing of statistical classifications. It consists of two components: (1) a statistical classification development and testing framework reflecting the required features of a statistical classification; and (2) a 4-tier model representing the main elements that make up a statistical classification, to use as a heuristic structure within which to locate issues identified and consider how they can be addressed. In this paper, we outline the development of the framework and model, and reflect on their application in testing a draft classification of health interventions. We propose this analytic structure as a new tool to support those engaged in the development of statistical classifications.