Missing Data
Values absent from a dataset, arising from skipped questions, dropout, or errors, which can bias results if handled carelessly.
Missing data occur when values that should have been recorded are absent: respondents skip questions, participants drop out of longitudinal studies, equipment fails, or records are incomplete. What matters is the pattern. Data may be missing essentially at random, or missingness may be related to the value itself, as when people with sensitive incomes decline to report them. Common responses include deleting incomplete cases, substituting estimated values through imputation, or using analysis methods that tolerate gaps, and each choice carries assumptions.
In a thesis, report how much data were missing, on which variables, and how you handled the gaps. Silently deleting cases shrinks your sample and can bias results if the missingness is systematic. A short, honest paragraph on missing data, with your handling rule stated before results, is a hallmark of careful quantitative work that examiners look for.
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