12  Summary

In this module, we explored the complete process of data analysis using SPSS, covering both fundamental and advanced statistical techniques. Beginning with an introduction to the software and data input methods, we progressed through essential preparatory steps such as data transformation and descriptive statistics. Key diagnostic tests, including normality, outliers, and reliability assessments, were discussed to ensure the robustness of analyses.

We then delved into specific statistical techniques, from exploratory factor analysis to hypothesis testing methods such as t-tests and ANOVA, enabling comparisons and inferences across data sets. Finally, we examined correlation and regression analyses, providing tools for understanding relationships and predictive modeling.

By following this structured approach, users are equipped to handle various data analysis challenges effectively. This module serves as a practical guide for leveraging SPSS to extract meaningful insights, ensuring a comprehensive understanding of data and its implications.