Questionnaire data analysis using information geometry.
Sci Rep
; 10(1): 8633, 2020 05 25.
Article
en En
| MEDLINE
| ID: mdl-32451420
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_desigualdade_iniquidade
Tipo de estudio:
Observational_studies
/
Prognostic_studies
Aspecto:
Equity_inequality
Idioma:
En
Revista:
Sci Rep
Año:
2020
Tipo del documento:
Article
País de afiliación:
Países Bajos