Questionnaire data analysis using information geometry.

Abstract:

:The analysis of questionnaires often involves representing the high-dimensional responses in a low-dimensional space (e.g., PCA, MCA, or t-SNE). However questionnaire data often contains categorical variables and common statistical model assumptions rarely hold. Here we present a non-parametric approach based on Fisher Information which obtains a low-dimensional embedding of a statistical manifold (SM). The SM has deep connections with parametric statistical models and the theory of phase transitions in statistical physics. Firstly we simulate questionnaire responses based on a non-linear SM and validate our method compared to other methods. Secondly we apply our method to two empirical datasets containing largely categorical variables: an anthropological survey of rice farmers in Bali and a cohort study on health inequality in Amsterdam. Compare to previous analysis and known anthropological knowledge we conclude that our method best discriminates between different behaviours, paving the way to dimension reduction as effective as for continuous data.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Har-Shemesh O,Quax R,Lansing JS,Sloot PMA

doi

10.1038/s41598-020-63760-8

subject

Has Abstract

pub_date

2020-05-25 00:00:00

pages

8633

issue

1

issn

2045-2322

pii

10.1038/s41598-020-63760-8

journal_volume

10

pub_type

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