Dummy variables are numerical variables that represent categorical data. They are typically coded as 0 or 1, where 1 indicates the presence of a category and 0 indicates its absence. This technique allows us to incorporate categorical predictors into regression models, which typically require numerical input.

Your challenges seem to stem from issues with the recoding process in SPSS, particularly when handling categorical variables for regression. Here's a systematic way to address your problem:

1. Issue: Using the "Create Dummy Variable" Menu

The error message suggests a problem with SPSS's external program execution (e.g., Python integration or script handling). To fix this:

Check Python Integration

Go to Edit > Options > File Locations and ensure Python is properly configured.

If needed, reinstall SPSS and include Python during installation.

Alternative Approach for Dummy Variables

Instead of relying on the "Create Dummy Variable" menu:

Use the Transform > Recode into Different Variables option to manually create dummy variables.

Recode each category of the demographic variable into a new variable:

E.g., for a variable Gender with categories Male and Female:

Create a new variable (Gender_Male) and set:

Male = 1

Female = 0

Repeat this for other categories, ensuring that for

𝑘

k categories, you create

𝑘

−

1

k−1 dummy variables.

2. Issue: Recode into Different Variables Shows Zeros

If the recoding results in only zeros:

Verify Input Values: Double-check the original variable's values in Data View. Ensure the categories (e.g., 1, 2, Male, Female) match those you're recoding.

Use Automatic Recode:

Go to Transform > Automatic Recode.

Select the categorical variable.

Choose to "Create new variable."

SPSS will assign numeric values to each category automatically, which can be used for further processing.

3. Ensure Correct Data Preparation

Handle Missing Values: Check for missing values in your data that could interfere with the recoding process. Replace or exclude missing data appropriately.

Confirm Variable Type: Go to Variable View and ensure the variable is set to "String" for text categories or "Numeric" for numeric categories.

4. Use Syntax for Precision

Using SPSS syntax is often more reliable than the GUI. For example, if Gender has categories Male and Female, use the following syntax to create dummy variables:

spss

DO IF (Gender = 'Male').

COMPUTE Gender_Male = 1.

ELSE IF (Gender = 'Female').

COMPUTE Gender_Male = 0.

END IF.

EXECUTE.

Adapt this for other variables and categories.

5. Regression Analysis with Dummy Variables

Once dummy variables are created:

Include them as independent variables in your regression model.

Remember to omit one category from each set of dummy variables to avoid multicollinearity (the reference category).

Additional Tips:

SPSS Help Logs: Check SPSS output logs for detailed error messages.

Tutorials: Refer to SPSS tutorials on dummy variables and regression for further clarity.

Seek IT Support: If the external program issue persists, reach out to your IT team for help configuring SPSS.

Let me know if successful

Isuru Navod and Rajendra K. Gupta - Thank you, I am grateful.

Ibiyemi Sadare is your issue resolved. Can you share how it was resolved

Rajendra K. Gupta, thank you. I tried re-installing but it did not work. I coded as 1 and 0 for the analysis.

In the past, I did the rocode into different variable and it worked well, but now I don't know what happened. It just did not work.