Simplification of Epistemic Networks Using Parsimonious Removal with Interpretive Alignment

Abstract

A key goal of quantitative ethnographic (QE) models, and statistical models more generally, is to produce the most parsimonious model that adequately explains or predicts the phenomenon of interest. In epistemic network analysis (ENA), for example, this entails constructing network models with the fewest number of codes whose interaction structure provides sufficient explanatory power in a given context. Unlike most statistical models, however, modification of ENA models can affect not only the statistical properties but also the interpretive alignment between quantitative features and qualitative meaning that is a central goal in QE analyses. In this study, we propose a novel method, Parsimonious Removal with Interpretive Alignment, for systematically identifying more parsimonious ENA models that are likely to maintain interpretive alignment with an existing model. To test the efficacy of the method, we implemented it on a well-studied dataset for which there is a published, validated ENA model, and we show that the method successfully identifies reduced models likely to maintain explanatory power and interpretive alignment.