Analyzing likert-type data using penalized non-linear principal components analysis

• Aisouda Hoshiyar ^[1]
  1. [1] Helmut Schmidt University

     Helmut Schmidt University

     Hamburg, Freie und Hansestadt, Alemania

     
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 346-349
• Idioma: inglés
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• Resumen

  • We consider a survey on animal ethics and sustainability consisting of various Likert-type items. Although this kind of (ordinal) data often occurs in the social sciences, in case of principal components analysis (PCA) those data are either treated as numeric implying linear relationships between the variables at hand, or nonlinear PCA is applied where the obtained coecients are sometimes hard to interpret. We therefore revisit penalized nonlinear PCA for ordinal variables as an intermediate between the mentioned methods used so far. The new approach o ers both better interpretability as well as better performance on validation data.