Overview-Summary of Missing Values

Completeness of data is the degree to which a dataset has all the relevant and necessary information. A complete dataset must not have missing, duplicate, or irrelevant values since they can affect the analysis. Hence, a correct and complete dataset makes sure that the analysis and reports are meaningful.

Incomplete data includes questions without answers or variables without observations. Even a small percentage of missing data can cause serious problems. It can produce skewed results, affect the analysis, and lead to wrong inferences.

One important feature of the Overview tab is the ‘Summary of Missing Values’. The pie chart in this tab displays the complete versus incomplete data for variables, cases, and values. These charts are available in terms of both percentages and counts.

Click the ‘%’ icon to view the summary of missing values in percentage terms, as shown in the following image.

To view the charts for counts, click the ‘N’ icon.

Several methods are available for handling missing data and the method of imputation is one among them.

For more information on missing values and imputation, click here