<figure><img src="../.gitbook/assets/image (136).png" alt="" width="267"><figcaption></figcaption></figure>

1. Click on _**Factor Analysis**_ in the _**Statistics**_ category

<figure><img src="../.gitbook/assets/image (137).png" alt="" width="563"><figcaption></figcaption></figure>

2. _**Install Package**_: You can automatically _**Import**_ the necessary packages for factor analysis.

3. _**Data**_: Select the data for factor analysis. You can also choose specific conditions using the [_**Subset**_](../data-analysis/5.-subset.md) option.

4. _**Variable**_: Choose the variables from the selected data for factor analysis.

5. _**Rotation**_: Select a rotation method to analyze which factors best explain the data.

6. _**Method**_: Choose a factor analysis method:

* _**Principal**_: Principal Factor Analysis

* _ML_: Maximum Likelihood Factor Analysis

* _Minres_: Minimum Residual Factor Analysis

7. _**Impute**_: Select a method for handling missing values:

* **Drop**: Remove rows with missing values.

* **Mean / Median**: Replace missing values with the mean or median.

8. _**Extract**_: Decide on the criteria for extracting factors and specify the number of factors to extract.

9. _**Display**_: Visualize the results.

10. _**Code View**_: Preview the generated code.

11. _**Data View**_: Preview the resulting data.

12. _**Run**_: Execute the code.