Repeated Measures ANOVA and Non-parametric Statistics is the final course in the Statistics for the Health Professions MicroBachelors series, designed to help learners handle more complex and practical data analysis challenges often found in real healthcare settings. Building on earlier coursework in Correlations and t-tests , this course focuses on comparing outcomes across multiple time points or conditions and introduces techniques for analyzing non-normally distributed or ordinal data.
You will begin by exploring paired sample t-tests and repeated measures ANOVA, statistical methods used to analyze data collected from the same individuals across different times or treatments. This technique is essential for evaluating changes over time such as patient recovery, symptom severity, or clinical progress. You will learn how to conduct repeated measures analysis in R, assess within-subject variability, and interpret key outputs such as F-values and p-values.
Next, the course introduces non-parametric tests like the Wilcoxon signed-rank test, Mann-Whitney U test, and Kruskal-Wallis test. These tools are particularly useful when data violate the assumptions of normality or involve ranked/ordinal measures, which are common in survey responses, symptom scales, and clinical ratings.
This course emphasizes application in healthcare, providing learners with hands-on experience using R and real datasets to make meaningful, data-informed decisions in professional practice or research.
