Interpretation & Results

This page provides resources for interpreting and communicating findings in Quantitative Ethnography (QE). Because QE integrates qualitative, quantitative, and computational approaches, interpreting results requires more than reading a visualization or reporting a statistical test. Researchers must connect computational outputs back to the underlying data, theoretical framework, research questions, and broader context of meaning-making.

The resources on this page are designed to help researchers make sense of QE outputs, explain analytic findings clearly, and write results sections that are rigorous, transparent, and accessible.

On this page, you will find resources on:

• Reading ENA visualizations
  Guidance on how to interpret network graphs, node size, edge thickness, layout, placement, and visual differences across groups or conditions.
• Interpreting ENA scores and means
  Resources for understanding group means and differences across participants, groups, or contexts.
• Statistical testing in ENA
  Introduction to common statistical approaches used in ENA, including tests of network differences, confidence intervals, permutation tests, effect sizes, and model comparisons.
• Closing the interpretive loop
  Guidance on returning to the original data, selecting illustrative excerpts, comparing cases, and using qualitative evidence to explain computational patterns.
• Writing QE findings
  Examples and templates for reporting QE results, including how to describe analytic choices, interpret visualizations, report statistical findings, and connect claims to evidence.
• Visual storytelling and communication
  Resources for designing clear figures, explaining results to different audiences, and communicating complex QE findings in manuscripts, presentations, posters, and workshops.
• Validity, inference, and interpretation
  Readings on closing the interpretive loop, making warranted claims, documenting uncertainty, and ensuring that interpretations are grounded in both theory and data.

Guiding questions

As you interpret QE findings, consider:

• What does the visualization show, and what does it not show?
• How do the results answer the research question?
• What patterns are statistically meaningful, qualitatively meaningful, or theoretically important?
• How do the computational findings connect back to the original data?
• What excerpts, cases, or examples help explain the observed patterns?
• What alternative interpretations should be considered?
• What claims are warranted based on the data, methods, and analysis?

Closing the interpretive loop

A central goal of QE interpretation is to close the interpretive loop. This means moving between computational representations and the original qualitative data to ensure that findings are not only statistically or visually compelling, but also meaningful, contextually grounded, and theoretically defensible.

Strong QE results sections do more than present networks or statistical comparisons. They explain what the patterns mean, why they matter, how they are supported by the data, and what they reveal about meaning-making across people, groups, settings, or time.

Articles

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