Course

Unit 1

Multiple Comparisons Problem

Introduction to multiple comparisons. Familywise error rate and Type I error inflation. Bonferroni correction, Holm’s procedure, False Discovery Rate (FDR). Applications in psychological research. Hands-on activity: Use Jamovi/JASP to perform multiple comparison corrections and visualise familywise error effects.

Unit 2

Analysis of Variance (ANOVA)

One-way ANOVA, assumptions, and effect size. Post-hoc comparisons and planned contrasts. Factorial ANOVA and interaction effects. Repeated measures ANOVA. Hands-on activity: Perform ANOVA and post hoc analyses in Jamovi/JASP, and generate graphs of interaction effects.

Unit 3

Regression Analyses

Simple and multiple linear regression. Assumptions, model building, and multicollinearity. Logistic regression for categorical outcomes. Interpretation of regression coefficients. Hands-on activity: Use RStudio Cloud or Jamovi to conduct regression analysis and visualise regression lines.

Unit 4

Meta-analysis

Introduction, purpose, and benefits of meta-analysis. Effect sizes: Cohen’s d, odds ratio, correlation coefficient. Forest plots and funnel plots. Fixed-effect vs. random-effects models. Hands-on activity: Simulate small meta-analysis datasets using Jamovi/JASP and create forest plots.

Unit 5

Statistical Decision-Making and Software Application

Choosing the right statistical test: decision trees and flowcharts. Overview and hands-on training in SPSS, Jamovi, and/or R. Data entry, cleaning, and visualisation. Reporting results in APA format for psychological research. Hands-on activity: Create visualisations (bar charts, histograms, scatterplots) and export outputs for reports using beginner-friendly software.

Text Books / References

Textbooks

1. Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). SAGE Publications.
2. Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Cengage Learning.
3. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
4. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Wiley.
5. Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the behavioral sciences (10th ed.). Cengage Learning.

Suggested Readings

1. Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). Open University Press.
2. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
3. American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). American Psychological Association.

This course is designed to deepen students’ understanding of advanced statistical methods used in psychological and psychotherapeutic research. Building upon the foundations of introductory statistics, students will explore complex analytical techniques such as ANOVA, regression analyses, and meta-analysis. The course addresses the multiple comparisons problem and guides students in selecting appropriate statistical tests for different research questions. Students will also gain hands-on experience with beginner-friendly statistical and visualization tools such as Jamovi, JASP, and RStudio Cloud to perform analyses, visualize results, and simulate data for practice. These tools provide an accessible, low-code environment for learning and applying statistical concepts without requiring extensive programming knowledge. A significant emphasis is placed on practical application using statistical software for data analysis. Students will also be trained to accurately interpret statistical results in the context of both basic and applied research, enabling them to become critical consumers and producers of psychological data.

Course Objectives

• To provide an understanding of advanced statistical techniques relevant to psychological research.
• To introduce students to the principles and problems associated with multiple comparisons.
• To enable students to conduct and interpret ANOVA and regression analyses for both categorical and continuous variables.
• To familiarise students with the methods and applications of meta-analysis.
• To develop skills in using statistical software for data entry, analysis, and result interpretation.
• To train students to select appropriate statistical tests based on research design and hypotheses.
• To prepare students to apply statistical methods in both basic and applied psychological research.
• To introduce beginner-friendly software tools (Jamovi, JASP, RStudio Cloud) for conducting statistical analyses and creating visualisations.

Course Outcomes

• CO1: Understand and apply the concept of multiple comparisons and associated control procedures.
• CO2: Conduct one-way and factorial ANOVA, interpret outputs, and assess assumptions.
• CO3: Perform linear and logistic regression analyses and interpret their results.
• CO4: Understand the process and purpose of meta-analysis in psychological research.
• CO5: Use statistical software (e.g., SPSS, Jamovi, R) to analyse and visualise psychological data.

CO-PO Mapping