Artikel

Comparison of imputation methods for handling missing categorical data with univariate pattern

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This paper examines the sample proportions estimates in the presence of univariate missing categorical data. A database about smoking habits (2011 National Addiction Survey of Mexico) was used to create simulated yet realistic datasets at rates 5% and 15% of missingness, each for MCAR, MAR and MNAR mechanisms. Then the performance of six methods for addressing missingness is evaluated: listwise, mode imputation, random imputation, hot-deck, imputation by polytomous regression and random forests. Results showed that the most effective methods for dealing with missing categorical data in most of the scenarios assessed in this paper were hot-deck and polytomous regression approaches.

Sprache
Englisch

Erschienen in
Journal: Revista de Métodos Cuantitativos para la Economía y la Empresa ; ISSN: 1886-516X ; Volume: 17 ; Year: 2014 ; Pages: 101-120 ; Sevilla: Universidad Pablo de Olavide

Klassifikation
Wirtschaft
Methodological Issues: General
Data Collection and Data Estimation Methodology; Computer Programs: General
Survey Methods; Sampling Methods
Thema
imputation methods
hot-deck
polytomous regression
random forests
smoking habits
missing categorical data

Ereignis
Geistige Schöpfung
(wer)
Torres Munguía, Juan Armando
Ereignis
Veröffentlichung
(wer)
Universidad Pablo de Olavide
(wo)
Sevilla
(wann)
2014

Handle
Letzte Aktualisierung
10.03.2025, 11:46 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
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Objekttyp

  • Artikel

Beteiligte

  • Torres Munguía, Juan Armando
  • Universidad Pablo de Olavide

Entstanden

  • 2014

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