BEHL 2005/2019 (UO) Introductory Research Methods PDF

BEHL 2005 / BEHL 2019 (UO) Introductory Research Methods NON-PARAMETRIC ALTERNATIVES Professor Hannah Keage What are we going to cover? Non-parametric tests Spearman correlation Wilcoxon test Content from this lecture references: Why would be use a non-parametric test? • If we violate any assum...

BEHL 2005 / BEHL 2019 (UO) Introductory Research Methods NON-PARAMETRIC ALTERNATIVES Professor Hannah Keage What are we going to cover? Non-parametric tests Spearman correlation Wilcoxon test Content from this lecture references: Why would be use a non-parametric test? • If we violate any assumptions for parametric tests, e.g. our data is not normal. • Most researchers aim to run parametric tests, as they generally have more statistical power, but sometimes we need to use nonparametric tests. • Non-parametric tests have none or very few assumptions. Spearman correlation • Spearman correlation coefficient is referred to as rho (ρ) or as rs. • Works on ranks of data (unlike Pearson correlation). • Can use same effect size cut-offs as Pearson correlation. • Assesses strength of monotonic relationship between two variables, linear or non-linear. • Assumptions that apply to Spearman correlation: • At least one variable needs to be continuous. • The other variable can be continuous or dichotomous. • The relationship between the two variables is monotonic. What does monotonic mean? If you order pairs of data, the constantly increase or consistently decrease. Spearman correlation Reporting examples: rs (38) = .34, p=.009 There was a statistically significant and weak positive relationship between hours of TV watched and fatigue rs (196) = -.75, p<.001 There was a statistically significant and strong negative relationship between hours of TV watched and sleep duration rs (degrees of freedom) = esimate, p=x Wilcoxon tests • Like the t-test, the Wilcoxon test comes in two forms, one-sample and two-sample. • Can handle any type of data, where you want compare two groups. • Mann-Whitney U Wilcoxon test for between-subjects. • One sample Wilcoxon test for within-subjects. • We won’t go onto effect sizes for Wilcoxon tests in this course. Wilcoxon tests Reporting examples: W = 110, p<.001 W = 54, p=.037 Those in the heavy TV watching group were statistically significantly more fatigued than the low TV watching group Those in the heavy TV watching group had statistically significantly less sleep than those in the low TV watching group W = esimate, p=x BEHL 2005 / BEHL 2019 (UO) Introductory Research Methods NON-PARAMETRIC ALTERNATIVES Professor Hannah Keage