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  • The paper deals with the comparison of data dimensionality reduction methods with emphasis on ordinal data. Categorical and especially ordinal data we frequently obtain from questionnaire surveys. A questionnaire usually includes a big amount of questions (variables). For applications of multivariate statistical methods, it is useful to reduce the number of these questions and create new latent variables, which represent groups of original questions. Some dimensionality reduction methods are applicable to ordinal data (latent class models), some methods must be improved (categorical principal component analysis). Other methods are based on a distance matrix, so it is possible to use an appropriate distance measure for ordinal data (multidimensional scaling). In this paper, dimensionality reduction methods are applied to real datasets including ordinal data in the form of Likert scales. Various techniques for the comparison of these methods are used. They are aimed to investigate goodness of the data structure in original and reduced space. In this paper the goodness is evaluated by Spearman rank correlation coefficient.
  • The paper deals with the comparison of data dimensionality reduction methods with emphasis on ordinal data. Categorical and especially ordinal data we frequently obtain from questionnaire surveys. A questionnaire usually includes a big amount of questions (variables). For applications of multivariate statistical methods, it is useful to reduce the number of these questions and create new latent variables, which represent groups of original questions. Some dimensionality reduction methods are applicable to ordinal data (latent class models), some methods must be improved (categorical principal component analysis). Other methods are based on a distance matrix, so it is possible to use an appropriate distance measure for ordinal data (multidimensional scaling). In this paper, dimensionality reduction methods are applied to real datasets including ordinal data in the form of Likert scales. Various techniques for the comparison of these methods are used. They are aimed to investigate goodness of the data structure in original and reduced space. In this paper the goodness is evaluated by Spearman rank correlation coefficient. (en)
Title
  • Comparison of dimensionality reduction methods applied to ordinal data
  • Comparison of dimensionality reduction methods applied to ordinal data (en)
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  • Comparison of dimensionality reduction methods applied to ordinal data
  • Comparison of dimensionality reduction methods applied to ordinal data (en)
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  • RIV/71226401:_____/13:#0000108!RIV15-MSM-71226401
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S, Z(MSM6138439910)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
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  • 66181
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  • RIV/71226401:_____/13:#0000108
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  • dimension reduction, principal component analysis, multidimensional scaling, latent class models (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [23434F7EB84B]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Praha
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  • The 7th International Days of Statistics and Economics Conference Proceedings
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Prokop, Martin
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • Melandrium
https://schema.org/isbn
  • 978-80-86175-87-4
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