Missingness, Its Reasons and Treatment
We have described many things relating to missing values in the previous chapters but have not described them precisely. The focus of this chapter is how to deal with missing values. The outcomes of this are then used in the remaining chapters, particularly as they concern reweighting, imputation, and survey analysis. It therefore would be good to come back and look at this chapter if something is not clear when one is reading the later chapters. The examples here are mainly taken from the ESS, which includes missingness information. They thus have been calculated from the fieldwork data available. Other considerations and examples can be found in many sources and often in conference papers. We do not give many references, nonetheless it is good, for example, to compare the two ESSs (the European Statistical System, abbreviated to ESS). Stoop (2017) makes a useful comparison between the two. Gideon (2012) is also a good book to read, particularly the chapter by Stoop and Harrison (2012). Koch, Halbherr, Stoop, and Kappelhof (2004) focus on quality comparisons.
- Gideon, L. (Ed.). (2012). Handbook of survey methodology for the social sciences. New York: Springer.Google Scholar
- Koch, A., Halbherr, V., Stoop, I., & Kappelhof, J. (2004). Assessing ESS sample quality by using external and internal criteria. Working Paper. Retrieved January 2017, from https://www.researchgate.net/publication/316043128_Assessing_ESS_sample_quality_by_using_external_and_internal_criteria
- Stoop, I. (2017). The other ESS: The European social survey and the European statistical system. New Techniques and Technologies for Statistics (NTTS). Retrieved December 2017, from https://www.conference-service.com/NTTS2017/documents/agenda/data/abstracts/abstract_238.html
- Stoop, I., & Harrison, E. (2012). Classification of surveys. In L. Gideon (Ed.), Handbook of survey methodology for the social sciences. Heidelberg: Springer.Google Scholar