A comparison of missing data handling methods in linear structural relationship model: evidence from BDHS2007 data

Authors

  • A.S.M.A. Mamun Department of Statistics University of Rajshahi, Bangladesh
  • Y. Z. Zubairi 1Mathematics Division, Centre for Foundation Studies in Science, University of Malaya, Kuala Lumpur, Malaysia.
  • A. G. Hussin Faculty of Defence Science and Technology, National Defence University of Malaysia, Kuala Lumpur, Malaysia
  • Sohel Rana Department of Mathematics/Institute for Mathematical research, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

DOI:

https://doi.org/10.1285/i20705948v9n1p122

Keywords:

Errors-in-variable model, Imputation method, EM-algorithm, Performance indicator, Demographic health survey

Abstract

Missing observations in dependent variable is a common feature in survey research. A number of techniques have been developed to impute missing data. In this article, we have evaluated the performance of several impu- tation methods namely mean-before method, mean-before-after method and expectation-maximization algorithm in linear structural relationship model. On the basis of mean absolute error and root mean square error for both simulated and real data sets, we have shown that expectation-maximization algorithm is the most effective method than the other two imputation meth- ods to analyze the missing data in linear structural relationship model.


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Published

26-04-2016

Issue

Vol. 9 No. 1 (2016): Electronic Journal of Applied Statistical Analysis

Section

Original Paper

License

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