Missing data were imputed where possible followed by listwise deletion.

In statistics, listwise deletion is a method for handling missing data.

After listwise deletion of missing data, 231 completed surveys were analysed.

Respondents with a missing value for one or more variables were excluded from the analysis (listwise deletion).

All values are based on data remaining after listwise deletion of cases with missing values.

Listwise deletion was used, omitting respondents with information missing for one or more variables.

Using listwise deletion, the researcher would remove subjects 3, 4 and 8 from the sample before performing any further analysis.

Listwise deletion affects statistical power of the tests conducted.

Listwise deletion will exclude these respondents from analysis.

Listwise deletion of missing data was undertaken for each of the statistical analyses.

After listwise deletion of missing data, 81, 56, and 406 completed surveys respectively from the three grades were analyzed.

Sample size is 945 after listwise deletion.

Listwise deletion was employed in the analysis.

Because listwise deletion excludes data with missing values, it reduces the sample which is being statistically analysed.

Listwise deletion is also problematic when the reason for missing data may not be random (i.e., questions in questionnaires aiming to extract sensitive information).

By far, the most common means of dealing with missing data is listwise deletion, which is when all cases with a missing value are deleted.

Because missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with listwise deletion of cases that have missing values.

If the data are missing completely at random, then listwise deletion does not add any bias, but it does decrease the power of the analysis by decreasing the effective sample size.

Choose from four methods, Listwise Deletion, Pairwise Deletion, EM or Regression to estimate the means, correlation matrix or covariance matrix.