IDEAS home Printed from https://ideas.repec.org/a/pab/rmcpee/v17y2014i1p101-120.html
   My bibliography  Save this article

Comparison of Imputation Methods for Handling Missing Categorical Data with Univariate Pattern|| Una comparación de métodos de imputación de variables categóricas con patrón univariado

Author

Listed:
  • Torres Munguía, Juan Armando

    (Instituto Tecnológico y de Estudios Superiores de Monterrey (México))

Abstract

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. || El presente estudio examina la estimación de proporciones muestrales en la presencia de valores faltantes en una variable categórica. Se utiliza una encuesta de consumo de tabaco (Encuesta Nacional de Adicciones de México 2011) para crear bases de datos simuladas pero reales con 5% y 15% de valores perdidos para cada mecanismo de no respuesta MCAR, MAR y MNAR. Se evalúa el desempeño de seis métodos para tratar la falta de respuesta: listwise, imputación de moda, imputación aleatoria, hot-deck, imputación por regresión politómica y árboles de clasificación. Los resultados de las simulaciones indican que los métodos más efectivos para el tratamiento de la no respuesta en variables categóricas, bajo los escenarios simulados, son hot-deck y la regresión politómica.

Suggested Citation

  • Torres Munguía, Juan Armando, 2014. "Comparison of Imputation Methods for Handling Missing Categorical Data with Univariate Pattern|| Una comparación de métodos de imputación de variables categóricas con patrón univariado," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 17(1), pages 101-120, June.
    • Handle: RePEc:pab:rmcpee:v:17:y:2014:i:1:p:101-120
    as

    Download full text from publisher

    File URL: http://www.upo.es/RevMetCuant/pdf/vol17/art91.pdf
    Download Restriction: no
    File URL: http://www.upo.es/RevMetCuant/bibtex.php?id=91
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cristina Barceló, 2008. "The impact of alternative imputation methods on the measurement of income and wealth: Evidence from the Spanish survey of household finances," Working Papers 0829, Banco de España.
    2. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    3. G. Chauvet & J.-C. Deville & D. Haziza, 2011. "On balanced random imputation in surveys," Biometrika, Biometrika Trust, vol. 98(2), pages 459-471.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raymundo M. Campos-Vázquez, 2013. "Efectos de los ingresos no reportados en el nivel y tendencia de la pobreza laboral en México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 23-54, November.
    2. Paul T. von Hippel, 2013. "Should a Normal Imputation Model be Modified to Impute Skewed Variables?," Sociological Methods & Research, , vol. 42(1), pages 105-138, February.
    3. Siedschlag Iulia & Kaitila Ville & McQuinn John & Zhang Xiaoheng, 2014. "International Investment and Firm Performance: Empirical Evidence from Small Open Economies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(6), pages 662-687, December.
    4. Miller, Elizabeth A. & Paschall, Katherine W. & Azar, Sandra T., 2017. "Latent classes of older foster youth: Prospective associations with outcomes and exits from the foster care system during the transition to adulthood," Children and Youth Services Review, Elsevier, vol. 79(C), pages 495-505.
    5. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    6. Nancy, Jane Y. & Khanna, Nehemiah H. & Arputharaj, Kannan, 2017. "Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 63-79.
    7. Frick, Joachim R. & Grabka, Markus M. & Groh-Samberg, Olaf, 2012. "Dealing With Incomplete Household Panel Data in Inequality Research," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 41(1), pages 89-123.
    8. Thomas Masterson & Kijong Kim & Fernando Rios-Avila, 2016. "Simulations of Employment for Individuals in LIMTCP Consumption-poor Households in Tanzania and Ghana, 2012," Economics Working Paper Archive wp_871, Levy Economics Institute.
    9. McDonough, Ian K. & Millimet, Daniel L., 2017. "Missing data, imputation, and endogeneity," Journal of Econometrics, Elsevier, vol. 199(2), pages 141-155.
    10. Hamrick, Karen S., 2012. "Nonresponse Bias Analysis of Body Mass Index in the Eating and Health Module," Technical Bulletins 184303, United States Department of Agriculture, Economic Research Service.
    11. Lingyun Lyu & Yu Cheng & Abdus S. Wahed, 2023. "Imputation‐based Q‐learning for optimizing dynamic treatment regimes with right‐censored survival outcome," Biometrics, The International Biometric Society, vol. 79(4), pages 3676-3689, December.
    12. Yijie Xue & Nicole Lazar, 2012. "Empirical likelihood-based hot deck imputation methods," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 629-646.
    13. Marcello D’Orazio, 2015. "Integration and imputation of survey data in R: the StatMatch package," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 57-68, June.
    14. Zhong, Hua & Hu, Wuyang, 2015. "Farmers’ Willingness to Engage in Best Management Practices: an Application of Multiple Imputation," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196962, Southern Agricultural Economics Association.
    15. Thomas Masterson, 2014. "Quality of Statistical Match and Employment Simulations Used in the Estimation of the Levy Institute Measure of Time and Income Poverty (LIMTIP) for South Korea, 2009," Economics Working Paper Archive wp_793, Levy Economics Institute.
    16. Yanqing Sun & Li Qi & Fei Heng & Peter B. Gilbert, 2020. "A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 791-814, August.
    17. Młodak Andrzej, 2021. "An application of a complex measure to model–based imputation in business statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 1-28, March.
    18. Thomas Masterson, 2012. "Simulations of Full-Time Employment and Household Work in the Levy Institute Measure of Time and Income Poverty (LIMTIP) for Argentina, Chile, and Mexico," Economics Working Paper Archive wp_727, Levy Economics Institute.
    19. Chiara Elena Dalla & Menon Martina & Perali Federico, 2019. "An Integrated Database to Measure Living Standards," Journal of Official Statistics, Sciendo, vol. 35(3), pages 531-576, September.
    20. Xinran Cui & Hao Gu & Chongshi Gu & Wenhan Cao & Jiayi Wang, 2023. "A Novel Imputation Model for Missing Concrete Dam Monitoring Data," Mathematics, MDPI, vol. 11(9), pages 1-24, May.

    More about this item

    Keywords

    imputation methods; hot-deck; polytomous regression; random forests; smoking habits; missing categorical data; métodos de imputación; hot-deck; regresión politómica; árboles de clasificación; hábitos de consumo de tabaco; valores perdidos en variables categóricas;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pab:rmcpee:v:17:y:2014:i:1:p:101-120. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Publicación Digital - UPO (email available below). General contact details of provider: https://edirc.repec.org/data/dmupoes.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.