Problems With Listwise Deletion
Listwise deletion affects statistical power of the tests conducted. Statistical power relies in part on high sample size. Because listwise deletion excludes data with missing values, it reduces the sample which is being statistically analysed.
Listwise deletion is also problematic when the reason for missing data may not be random (i.e. questions in questionnaires aiming to extract sensitive information). Due to the method much of the subjects' data will be excluded from analysis leaving a bias in data findings. For instance, a questionnaire may include questions about respondents current earnings and sexual persuasions as well as their views on a certain subject. Many of the subjects in the sample may not answer these questions due to the intrusive nature of the questions but may answer all other questions. Listwise deletion will exclude these respondents from analysis. This may create a bias as participants who do divulge this information may have different characteristics than participants who do not. Multiple imputation is an alternate technique for dealing with missing data that attempts to eliminate this bias.
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