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

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• 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 and Topics

• 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

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Torres Munguía, Juan Armando
• Event: Veröffentlichung
• (who): Universidad Pablo de Olavide
• (where): Sevilla
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/113873
• Last update: 10.03.2025, 11:46 AM CET