Artikel

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

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.

Language: Englisch

Bibliographic citation: 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

Classification: Wirtschaft
Methodological Issues: General
Data Collection and Data Estimation Methodology; Computer Programs: General
Survey Methods; Sampling Methods

Subject: imputation methods
hot-deck
polytomous regression
random forests
smoking habits
missing categorical data

Event: Geistige Schöpfung

(who): Torres Munguía, Juan Armando

Event: Veröffentlichung

(who): Universidad Pablo de Olavide

(where): Sevilla

(when): 2014

Handle: http://hdl.handle.net/10419/113873

Last update: 10.03.2025, 11:46 AM CET

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Torres Munguía, Juan Armando
Universidad Pablo de Olavide

Time of origin

2014

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

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Other Objects (12)

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