Whether it relies on primary or secondary data, issues with missing data are constant and even significant in any form of research. (S1) Irrespective of the environment, subject matter and database used, missing values occur. Missing values are those situations where no data is stored for a variable in a given observation. (S2) Missing data are variables without observations or simply questions without answers. (S4) If any data on any variable from any participant is not present, the researcher is dealing with missing or incomplete data. (IS1) Some of the prominent causes of missing data can include data recordation (for instance miscodes), corrupted raw data fields and human error (S2)
Fisher and Waclawski explain, that philosophically, the fact that missing data even exist is seen as analytically “unpleasant” (S2). But why is this really an issue is research? Missing data can be seen as an issue because even a small percent of missing data can contribute to large problems with an analysis leading to the
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Jacob Gross et al give mention to the fact that it is not a common trend for missing data to be rectified; in fact they stated that social science researches often ignore the issues surrounding missing data. They stressed on the need for researchers to not only develop a deep knowledge of why data are missing but to also be made aware of how to deal with missing data issues (S1).
The inevitability of missing data has been mentioned in that; unplanned missing data inevitably introduces ambiguity into the inferences that can be drawn from the study (IS4). There are ways to rectify such; however, it is important to know the types of missing data that the research design has encountered. There may be and probably are multiple reasons for missing data in a given data set and any type of missing data can threaten validity of analyses