Measures of Difference and Association

Lectures

Measures of Difference and Association (Part 1)

Sean Roberts discusses the use of quantitative methods, their utility in research, and their importance in the scientific method. Also briefly touches on basic pitfalls commonly associated with the use of quantitative methods, such as Galton’s Problem and issues in data structure.

Measures of Difference and Association (Part 2)

Discusses the conceptual basis of null hypothesis significance testing and illustrates these concepts using data simulated in the R programming language. Also touches on a classic—and simple—statistical test: the binomial test.

Measures of Difference and Association (Part 3): Advanced Null Hypothesis Significance Testing

Discusses null hypothesis significance testing in detail—introducing key concepts such as p-values, permutations, and statistical distributions—and introduces the use of chi-squared tests of association.

Measures of Difference and Association (Part 4): Correlations

Discusses basic tests of association between continuous and ordered variables—including Pearson’s correlations, Spearman’s correlations, and Kendall’s correlations. Also discusses the effects of outliers and non-standard methods for assessing the relationship between two variables, such as permutation, bootstrapping, and simulation.