Course 12 — Statistical Analysis with SPSS

💻📈 Statistical Analysis with SPSS

Welcome to Statistical Analysis with SPSS, a practical course designed to help undergraduate and postgraduate researchers confidently use SPSS for research data preparation, statistical analysis, interpretation, and academic reporting.

The course takes learners from the SPSS Data View and Variable View through data cleaning, descriptive statistics, reliability analysis, assumption testing, correlation, t-tests, ANOVA, regression, non-parametric tests, chi-square analysis, factor analysis, and interpretation of statistical output.

The focus is not simply on learning SPSS menus. Learners will develop the ability to determine which statistical procedure is appropriate, why it is appropriate, how to interpret the output, and how to present the result in a dissertation.

This reflects the GDG manual’s central principle that statistical software can perform calculations and generate outputs, but the researcher remains responsible for appropriate test selection, assumption checking, interpretation, and conclusions supported by the data.

📘 What Learners Will Explore:

• Introduction to SPSS
• Understanding the SPSS interface
• Data View and Variable View
• Creating and importing datasets
• Variable names and labels
• Value labels
• Measurement levels
• Data coding
• Data cleaning
• Missing data
• Invalid values
• Duplicate cases
• Outliers
• Frequencies and percentages
• Mean, median, and mode
• Standard deviation
• Descriptive charts
• Reliability analysis
• Cronbach’s alpha
• Assumption testing
• Normality
• Q-Q plots and histograms
• Homogeneity
• Pearson and Spearman correlation
• One-sample t-tests
• Independent-samples t-tests
• Paired-samples t-tests
• Effect sizes
• One-way ANOVA
• Post-hoc testing
• Welch ANOVA
• Two-way ANOVA
• Simple and multiple regression
• R² and regression coefficients
• VIF and multicollinearity
• Non-parametric tests
• Chi-square and crosstabs
• Cramér’s V
• Factor analysis
• KMO and Bartlett’s test
• Factor loadings and explained variance
• Reading SPSS output
• Statistical decision-making
• APA statistical reporting

The course content follows the SPSS progression already established in the GDG Online Research Academy library.

🧠 SPSS Skills in Action:

• Data Management: Prepare a properly structured SPSS dataset.
• Statistical Analysis: Conduct appropriate statistical procedures.
• Interpretation: Understand tables, coefficients, significance values, and effect sizes.
• Academic Reporting: Convert SPSS results into dissertation-ready statistical findings.

🎯 By the End of This Course, You Will Be Able To:

• Create and organize a research dataset in SPSS.
• Code and clean research data.
• Produce descriptive statistics.
• Conduct reliability analysis.
• Check statistical assumptions.
• Perform correlation analysis.
• Conduct t-tests.
• Conduct ANOVA and post-hoc analyses.
• Perform regression analysis.
• Conduct selected non-parametric procedures.
• Perform chi-square analysis.
• Understand the fundamentals of factor analysis.
• Interpret SPSS statistical output.
• Make appropriate statistical decisions.
• Report statistical findings academically.

💻 Practical Analysis Workshop

Learners will work through realistic research datasets and follow the complete SPSS workflow from data preparation to statistical reporting.

The emphasis is on doing the analysis, not merely watching demonstrations. Learners will practice selecting procedures, running analyses, interpreting outputs, and translating statistical results into appropriate dissertation language.