Course Overview

A hands-on, methodology-focused program covering experimental design principles, data preparation, statistical analysis, and interpretation. Includes ANOVA, regression, factorial designs, mixed models, and power analysis to ensure valid, robust findings.

Learning Outcomes

• Differentiate experimental designs: randomized block, factorial, repeated measures, Latin square, and between-/within-subjects layouts
• Identify experimental units, control for variability, and prevent pseudo-replication
• Apply strategies to reduce bias and enhance reproducibility (randomization, blocking, replication) and choose optimal designs based on research objectives
• Estimate and manage variability, understand statistical power and significance, and calculate appropriate sample sizes
• Conduct hypothesis testing using t-tests, ANOVA (one-way, two-way), ANCOVA, and mixed models
• Execute regression analysis (simple, multiple, interaction effects) and interpret outcomes (coefficients, diagnostics)
• Explore advanced modeling techniques like contrasts, mixed-effects models, SEM and repeated measures analysis
• Analyze categorical data using Chi-square tests and logistic regression
• Handle exploratory data analysis (graphical summaries, correlation, cross-tabulation) to understand data structure

Tools & Resources

SPSS , R or Minitab, Smart PLS, AMOS, Excel (visualization), plus AI assistants for design recommendations and analysis validation.