Missing data is a pragmatic fact that must be investigated and not a disaster to be mitigated. It is a property of the population to which we seek to generalize and can cause problems not only through its impact on the sample size available for analysis but also through its potential hidden biases. Making imputations without first analysing the randomness of the missing responses can even be worse than doing nothing, so care is needed while imputing missing values. This paper reflects on how to prevent, analyse and handle missing data and how effects of imputation can be checked.
KingCVFoggJR & DowneyR. Mean substitution for missing items: Sample size and the effectiveness of the technique. 13th Annual meeting for the society of industrial and organizational psychology, Dallas TX, 1998.
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HuismanM. Imputation of missing item responses: Some simple techniques. Quality and Quantity2000; 34: 331–351.
