 Missingness, Its Reasons and TreatmentChapter 7 in Survey Methodology and Missing Data, 2018, pp 99-110 from Springer Abstract: Abstract 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. Keywords: European Statistical System (ESS); Responder Units; Estimated Response Probabilities; Response Propensity; Domain Test Data (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-79011-4_7 Ordering information: This item can be ordered from DOI: 10.1007/978-3-319-79011-4_7 Access Statistics for this chapter More chapters in Springer Books from Springer |
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