Why does SPSS keep missing data?

There are two types of missing values in SPSS: 1) system-missing values, and 2) user-defined missing values. We will demonstrate reading data containing each kind of missing value. Both data sets are identical except for the coding of the missing values.

How do you deal with missing completely at random?

Listwise deletion is preferred when there is a Missing Completely at Random case. In Listwise deletion entire rows(which hold the missing values) are deleted. It is also known as complete-case analysis as it removes all data that have one or more missing values.

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

In SPSS, you should run a missing values analysis (under the “analyze” tab) to see if the values are Missing Completely at Random (MCAR), or if there is some pattern among missing data. If there are no patterns detected, then pairwise or listwise deletion could be done to deal with missing data.

What is the preferred way of dealing with missing values?

Deletion. Listwise deletion (complete-case analysis) removes all data for an observation that has one or more missing values. Particularly if the missing data is limited to a small number of observations, you may just opt to eliminate those cases from the analysis.

How do you treat missing values in data analysis?

Imputing the Missing Value

1. Replacing With Arbitrary Value.
2. Replacing With Mode.
3. Replacing With Median.
4. Replacing with previous value – Forward fill.
5. Replacing with next value – Backward fill.
6. Interpolation.
7. Impute the Most Frequent Value.

What are the options available for treatment of missing values?

There are various statistical methods like regression techniques, machine learning methods like SVM and/or data mining methods to impute such missing values.