How to deal with missing data in spss

1. How can we handle the missing values in SPSS?
2. Which of the following is the best way to deal with missing data in SPSS?
3. How do you handle missing data?

How can we handle the missing values in SPSS?

You can specify the missing=listwise subcommand to exclude data if there is a missing value on any variable in the list. By default, missing values are excluded and percentages are based on the number of non-missing values.

Which of the following is the best way to deal with missing data in SPSS?

Regression is useful for handling missing data because it can be used to predict the null value using other information from the dataset. There are several methods of regression analysis, like Stochastic regression.

How do you handle missing data?

When dealing with missing data, data scientists can use two primary methods to solve the error: imputation or the removal of data. The imputation method develops reasonable guesses for missing data. It's most useful when the percentage of missing data is low.