The Prevalence of Missing Data in Survey Research

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Hamzeh Mohd Dodeen

Abstract

The credibility of surveys relies significantly on the completeness of the data collected from representative samples. Missing data is a serious problem in survey research. The existence of variables with missing information negatively affects the research results and findings. This study examines the prevalence of missing data in surveys, and additionally compares its incidence between genders. A total of 119 relevant surveys from different countries represented the sample of this study. Results indicated that, on average, 38% of data was lost in the surveys analyzed. Males and females were very similar with respect to the extent of missing data, with an average of 37% and 38% respectively. Overall, results show that only 62% of the initial sample size was available at the end of the data collection stage.

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How to Cite
Dodeen, H. (2018). The Prevalence of Missing Data in Survey Research. International Journal for Innovation Education and Research, 6(3), 83-90. Retrieved from http://ijier.net/index.php/ijier/article/view/978
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References

Acock, A. C. (2005). Working with missing data. Journal of Marriage and Family, 67,1012-1028.
Bodner, T. E. (2006). Missing data: Prevalence and reporting practices. Psychological Reports, 99(3), 675-680.
Cool, A. (2000). A review of methods for dealing with missing data. Paper presented at the Annual Meeting of Southwest Educational Research Association, Dallas, TX.
Hardy, S. E., Allore, H., & Studenski, S. (2009). Missing data: A special challenge in aging research. Journal of the American Geriatrics Society, 57(4), 722-729.
Horton, N. J., & Kleinman, K. P. (2007). Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. The American Statistician, 61 (1), 79-90.
Howell, D. C. (2007). Treatment of missing data. Retrieved from http://www.uvm.edu/~dhowell/StatPages/StatHomePage.html
Light, R., Singer, J., & Willett, B. (1990). By design: Planning research on higher education. London, England: Harvard University Press.
Little, R. J., & Rubin, D. B. (2002). Statistical analysis with missing data. 2nd Edition, New York: Wiley & Sons.
Marie, L. (1997). The application of item response theory to employee attitude survey data Using Samejima’s graded response model. (Unpublished Doctoral Thesis).The University of Connecticut, Connecticut, USA.
McKnight, P. E., McKnight, K. M., Sidani, S., & Figueredo, A. J. (2007). Missing data: A gentle introduction. The Guilford Press: New York.
Raaijmakers, Q. (1999). Effectiveness of different missing data treatments in survey with Likert-type data: Introducing the relative mean substitution approach. Educational and Psychological Measurement, 59, 725-748.
Raymond, M. R. (1987). Missing data in evaluation research. Evaluation & The Health Professions, 9, 395-420.
Wayman, J. (2003). Multiple imputation for missing data: What is it and how can I use it? Paper presented on the Annual Conference of the American Educational Research Association (AERA), Chicago, IL.
Wilkinson, L., & The APA Task Force on Statistical Inference (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604.
Witta, E. L. (1994). Are values missing randomly in survey research? Paper presented at the Annual Conference of Mid-South Educational Research Association, Nashville, TN.