Discuss the pros and cons of the various options for the treatment of missing responses

What will be an ideal response?

1. Substitute a Neutral Value. If the mean is substituted for the missing value, the mean of the variable remains unchanged and other statistics, such as correlations, are not affected much. Although this approach has some merit, the logic of substituting a mean value (say 4 ) for respondents who, if they had answered, might have used either high ratings (6 or 7 ) or low ratings (1 or 2 ) is questionable.
2. Substitute an Imputed Response. This approach requires considerable effort and can introduce serious bias. Sophisticated statistical procedures have been developed to calculate imputed values for missing responses.
3. Casewise Deletion. Because many respondents may have some missing responses, this approach could result in a small sample. Throwing away large amounts of data is undesirable, because it is costly and time consuming to collect data. Furthermore, respondents with missing responses could differ from respondents with complete responses in systematic ways. If so, casewise deletion could seriously bias the results.
4. Pairwise Deletion. Different calculations in an analysis may be based on different sample sizes. This procedure may be appropriate when (1 ) the sample size is large, (2 ) there are few missing responses, and (3 ) the variables are not highly related. Yet, this procedure can produce results that are unappealing or even infeasible.

The different procedures for the treatment of missing responses may yield different results, particularly when the responses are not missing at random and the variables are related. Hence, missing responses should be kept to a minimum. The researcher should carefully consider the implications of the various procedures before selecting a particular method for the treatment of nonresponse.