Course overview
This course aims to equip students with essential statistical methods and analytical tools vital for human factors research. The focus is on developing robust understanding and skills in executing various statistical tests, from foundational concepts to advanced analysis techniques, including the General Linear Model. By covering topics such as Null Hypothesis Significance Testing, Panda programming tips, data visualization, parametric and non-parametric methods, and advanced topics like regression, latent variable analysis, and General Linear Model, students will be prepared to conduct rigorous and meaningful research in human factors, ensuring they can effectively design experiments, analyse data, and interpret results within real-world applications.
Course learning outcomes
- Describe fundamental statistical principles for human factor research
- Acquire skills in conducting and interpreting results from practical statistical tests including parametric and non-parametric tests, correlation, general linear model, latent variable anlysis, and regression analysis
- Develop proficiency in using Panda for statistical computing, including data manipulation, executing various statistical tests, and creating impactful visualisations
- Critically evaluate research designs and data quality, using techniques such as power analysis, normality testing, outlier detection, and inter-rater reliability measures to ensure rigorous research standards