If the respondent has an excessive amount of missing data, then you are better off just deleting that respondent from the overall data. If a respondent failed to answer the last few questions, you need to determine if this amount of missing data is sufficiently acceptable to retain the respondent’s other answers.
These incomplete rows are then subject to deletion.Hence, you could see if the respondent dropped out of the survey and stopped answering questions. The quickest and easiest way to see if respondent abandonment has occurred is simply to sort the last few columns of the data in ascending order.After forming an ID column, it is a good idea to initially examine if you have any respondent misconduct. This is done to make it easier to find a specific case, especially if you have sorted on different columns.I usually do this on the first column of the data, and it is simply an increasing number from 1 (on the first row) to the last row of the data. Once your data has been keyed into a data software program like Excel, SAS, or SPSS, the first thing you need to do is set up an “ID” column.We also need to assess if you have any missing data. The first step before analyzing your model is to examine your data to make sure there are no errors, outliers, or respondent misconduct.
