Acta Oeconomica Pragensia 2014, 22(6):66-78 | DOI: 10.18267/j.aop.459

Problem of Missing Data in Questionnaire Surveys

Iva Pecáková
Vysoká škola ekonomická v Praze, Fakulta informatiky a statistiky, katedra statistiky a pravděpodobnosti (e-mail: iva.pecakova@vse.cz).

Almost any data set can be encountered to the problem of missing data; it is well known in the phenomena relating to people populations and researched in sample surveys. In recent decades, the issue of missing data received considerable attention, because the simple omission of units, for which data are lacking, from the analysis may lead to erroneous conclusions. The approach that accepts the existence of missing data through the modification of the probabilities of units selection with probabilities of obtaining data on them, leads to the construction and use of the weights. Different solution lies in filling in missing data. Using the arithmetic mean or a regression function, recommended for this purpose before, leads at the relevant variables at least to an underestimation of variability; furthermore, it is applicable only for measurable variables. Alternative approaches to missing data are based on the likelihood of collected data assuming some model. Two directions of their development can be distinguished again, estimating population parameters without imputation of missing data on the one hand (EM algorithm) and multiple imputation methods on the other.

Keywords: sample surveys, missing data, categorical data, data imputation
JEL classification: C10, C18, C83

Published: October 1, 2014  Show citation

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Pecáková, I. (2014). Problem of Missing Data in Questionnaire Surveys. Acta Oeconomica Pragensia22(6), 66-78. doi: 10.18267/j.aop.459
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References

  1. AGRESTI, A. 2002. Categorical Data Analysis. 2. vyd. New Jersey : Wiley, 2002. ISBN 0-471-36093-7. Go to original source...
  2. ALLISON, P D. 2001. Missing Data. SAGE Publications, 2001. ISBN 978-14-1298-507-9.
  3. ALLISON, P D. 2009. Missing Data. In MILLSAP R. - MAYDEU-OLIVARES, A. (ed.). Sage Handbook of Quantitative Methods in Psychology. SAGE Publications, 2009.
  4. COCHRAN, W. G. 1997. Sampling Techniques. 3. vyd. New Jersey : Wiley, 1977. ISBN 978-0-47116240-7.
  5. FIENBERG, E. S. 1970. An Alternative Procedure for Estimation in Contingency Table. The Annals of Mathematical Statistics 1970, roč. 41, 907-917. Go to original source...
  6. JOBSON, J. D. 1992. Applied Multivariate Data Analysis. Volume II: Categorical and Multivariuate Analysis. New York : Springer-Verlag, 1992. ISBN 0-387-97804-6.
  7. LITTLE, R. 1988. A test of MCAR for Multivariate Data With Missing Values. Journal of the American Statistical Association 1988, roč. 83, 1198-1202. Go to original source...
  8. LITTLE, R.; RUBIN, D. 2002. Statistical Analysis with Missing Data. New Jersey : Wiley, 2002. ISBN 0-471-18386-5. Go to original source...
  9. PECÁKOVÁ, I. 2011. Statistika v terénních průzkumech. 2. vyd. Praha : Professional Publishing 2011. ISBN 978-80-7431-039-3.
  10. PIGOTT, T. 2001. A Review of Methods for Missing Data. Educational Research and Evaluation 2001, roč. 7, 353-383. Go to original source...
  11. RUBIN, D. B. 1976. Inference and missing data. Biometrika 1976, roč. 63, 581-590. Go to original source...
  12. SCHAFER, J. L.; OLSEN, M. K. 2014. Multiple Imputation for Missing-data problems: a Data Analyst's Perspective [online, cit. 2014-09-10]. http://webdocs.cs.ualberta.ca/~ajit/impute.pdf
  13. SPSS, 2007. SPSS - Missing Value Analysis. Chicago : SPSS Inc., 2007.

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