Sage-Tips

Can you do logistic regression with missing data?

Missing data handling techniques for a logistic regression, which is often used to model a choice among alternatives, need more investigation. The default procedure normally deletes cases with missing data on the variables of interest, which known as listwise deletion.

How do you handle missing data in regression analysis?

Techniques for Handling the Missing Data

1. Listwise or case deletion.
2. Pairwise deletion.
3. Mean substitution.
4. Regression imputation.
5. Last observation carried forward.
6. Maximum likelihood.
7. Expectation-Maximization.
8. Multiple imputation.

How do you deal with null values in logistic regression?

Code the missing values as zero and construct a new predictor which is one if the value is missing and zero otherwise. Then make sure you always include them both together in the model and test them together.

How does SPSS handle missing data in logistic regression?

SPSS removes cases list-wise by default, and in my experience this is the case for the majority of statistical procedures. So if a case is missing data for any of the variables in the analysis it will be dropped entirely from the model.

How do you deal with missing data?

Best techniques to handle missing data

1. Use deletion methods to eliminate missing data. The deletion methods only work for certain datasets where participants have missing fields.
2. Use regression analysis to systematically eliminate data.
3. Data scientists can use data imputation techniques.

How do you handle missing data?

Popular strategies to handle missing values in the dataset

1. Deleting Rows with missing values.
2. Impute missing values for continuous variable.
3. Impute missing values for categorical variable.
4. Other Imputation Methods.
5. Using Algorithms that support missing values.
6. Prediction of missing values.

How would you deal with missing data?

How do I replace missing categorical data in SPSS?

Impute missing values.

1. From the menus choose:
2. In the Categorical Regression dialog box, click Missing.
3. Select the variable(s) for which you want to change the method of handling missing values and choose the method(s).
4. Click Change.
5. Repeat until all variables have the method you want.
6. Click Continue.

What do you do with missing data 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.