Certainly! I can guide you on how to perform Principal Component Analysis (PCA) in SPSS to assess the unidimensionality of your questionnaire and potentially improve its reliability. Here are the steps:

1. Prepare Your Data: Make sure your data is properly organized in SPSS, with each question represented by a separate variable and each participant's responses in corresponding rows.

2. Open the Data: Open your data file in SPSS.

3. Run the PCA: Go to "Analyze" in the menu bar, select "Dimension Reduction," and then choose "Factor."

4. Select Variables: In the "Factor" dialog box, move all the variables/questions from the left pane to the right pane. This indicates that you want to include all the variables in the analysis.

5. Choose Extraction Method: Under the "Extraction" tab, select the extraction method you want to use. In this case, choose "Principal Components" as the extraction method.

6. Review Output Options: Under the "Options" tab, you can choose additional options such as labeling factor solutions, saving factor scores, or suppressing small factor loadings. Make your selections based on your specific needs.

7. Run the Analysis: Click "OK" to run the PCA analysis. SPSS will generate output tables, including the "Component Matrix" table, which shows the factor loadings for each question.

8. Interpret the Results: Look at the factor loadings in the "Component Matrix" table. High factor loadings (close to 1 or -1) indicate that the question is strongly related to the underlying factor, while low loadings (close to 0) suggest a weak relationship. Assess whether the questions primarily load on a single factor (unidimensionality) or if they load on multiple factors.

9. Assess Reliability: After identifying the factor structure, you can assess the reliability of the scale by calculating Cronbach's alpha for the identified factor(s). If a particular factor has low internal consistency, you may consider removing or revising the questions that load poorly on that factor to improve the reliability.

10. Further Analysis: Depending on your research goals, you may perform additional analyses such as factor rotation or confirmatory factor analysis (CFA) to refine the scale further.

Remember, PCA is just one method for assessing unidimensionality and improving reliability. It's important to consider other factors, such as the theoretical framework and content validity of your questionnaire, when making decisions about item retention, modification, or removal.

Additionally, consider consulting with a statistician or a research methodology expert who can provide personalized guidance based on your specific study and data.