English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We describe a data-driven discovery method that leverages Simpson's paradoxto uncover interesting patterns in behavioral data. Our method systematicallydisaggregates data to identify subgroups within a population whose behaviordeviates significantly from the rest of the population. Given an outcome ofinterest and a set of covariates, the method follows three steps. First, itdisaggregates data into subgroups, by conditioning on a particular covariate,so as minimize the variation of the outcome within the subgroups. Next, itmodels the outcome as a linear function of another covariate, both in thesubgroups and in the aggregate data. Finally, it compares trends to identifydisaggregations that produce subgroups with different behaviors from theaggregate. We illustrate the method by applying it to three real-worldbehavioral datasets, including Q\&A site Stack Exchange and online learningplatforms Khan Academy and Duolingo.

Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data