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 Repjournal_title
Scientific reportsauthors
Har-Shemesh O,Quax R,Lansing JS,Sloot PMAdoi
10.1038/s41598-020-63760-8subject
Has Abstractpub_date
2020-05-25 00:00:00pages
8633issue
1issn
2045-2322pii
10.1038/s41598-020-63760-8journal_volume
10pub_type
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