🔵 Part I: Data Structure Issues and Design Technicalities
Objective: Equip students with foundational knowledge to prepare and structure data for valid and reliable mean-difference analysis in SPSS.

1. Introduction to Mean-Difference Techniques
▪️Overview of t-tests and ANOVA
▪️When and why to use mean-comparison methods

2. Research Design Essentials
▪️Independent vs. repeated measures designs
▪️Between-group vs. within-subject variables
▪️Randomization, control, and matching

3. Variables and Measurement
▪️Levels of measurement (nominal, ordinal, interval, ratio)
▪️Dependent and independent variable specification

4. Data Structure in SPSS
▪️Setting up the variable view and data view
▪️Labeling variables and values correctly
▪️Coding group membership (dummy coding, grouping variables)

5. Data Cleaning and Assumption Checking
▪️Handling missing data
▪️Testing for normality and outliers
▪️Homogeneity of variance and sphericity

Part II: Application of Mean-Difference Techniques in SPSS
Objective: Develop hands-on skills to conduct, interpret, and report t-tests and ANOVAs using SPSS.

6. Independent Samples t-test
▪️Use cases, assumptions
▪️Running the test in SPSS
▪️Interpreting SPSS output

7. Paired Samples t-test
▪️Use with repeated measures
▪️Applications and SPSS procedure
▪️Reporting effect sizes

8. One-Way ANOVA
▪️Assumptions and post-hoc tests
▪️Conducting and interpreting ANOVA in SPSS

9. Repeated Measures ANOVA
▪️Structure and assumptions
▪️Sphericity and corrections (Greenhouse-Geisser etc.)

10. Factorial ANOVA (Two-way ANOVA)
▪️Interaction effects
▪️Main vs. interaction effects interpretation
▪️Visualizing interaction plots

11. Reporting Results
▪️APA-style reporting of SPSS outputs
▪️Tables, figures, and interpretation

12. Common Mistakes