Simplification of epistemic networks using parsimonious removal with interpretive alignment

Yeyu Wang, Zachari Swiecki, A. R. Ruis, David Williamson Shaffer

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Quantitative Ethnography
Subtitle of host publicationSecond International Conference, ICQE 2020, Proceedings
EditorsAndrew R. Ruis, Seung B. Lee
Place of PublicationCham Switzerland
Number of pages15
ISBN (Electronic)9783030677886
ISBN (Print)9783030677879
Publication statusPublished - 2021
Externally publishedYes
EventInternational Conference on Quantitative Ethnography 2020 - Malibu, United States of America
Duration: 1 Feb 20213 Feb 2021
Conference number: 2nd (Website) (Proceedings)

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer Nature Switzerland
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceInternational Conference on Quantitative Ethnography 2020
Abbreviated titleICQE 2020
Country/TerritoryUnited States of America
Internet address


  • Epistemic network analysis (ENA)
  • Interpretive alignment
  • Model comparison
  • Model refinement
  • Unified methods

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