Discuss importance of data cleaning in preliminary data analysis
In most surveys, it takes place so that respondent has either provided ambiguous response or response has been improperly recorded. In such cases, missing value analysis is conducted for cleaning the data. If proportion of missing values is more than 10%, it poses greater problems. There are four options for treating missing values: (a) substituting missing value with a neutral value (generally mean value for variable); (b) substituting an imputed response by following a pattern of respondent's other responses; (c) casewise deletion, in which respondents with any missing responses are discarded from analysis and (d) pairwise deletion, wherein only respondents with complete responses for that specific variable are included. Different procedures for data cleaning may yield different results and hence, researcher must take utmost care when cleaning the data. Data cleaning must be kept at a minimum if possible.