# Lesson Archives

1. ## Repeated Measures ANOVA in R

The repeated-measures ANOVA is used for analyzing data where same subjects are measured more than once. This chapter describes the different types of repeated measures ANOVA, including: 1) One-way repeated measures ANOVA, an extension of the paired-samples t-test for comparing the means of three or more levels of a within-subjects variable. 2) two-way repeated measures ANOVA used to evaluate simultaneously the effect of two within-subject factors on a continuous outcome variable. 3) three-way repeated measures ANOVA used to evaluate simultaneously the effect of three within-subject factors on a continuous outcome variable.
2. ## ANOVA in R

The ANOVA test (or Analysis of Variance) is used to compare the mean of multiple groups. This chapter describes the different types of ANOVA for comparing independent groups, including: 1) One-way ANOVA: an extension of the independent samples t-test for comparing the means in a situation where there are more than two groups. 2) two-way ANOVA used to evaluate simultaneously the effect of two different grouping variables on a continuous outcome variable. 3) three-way ANOVA used to evaluate simultaneously the effect of three different grouping variables on a continuous outcome variable.
3. ## Sign Test in R

The Sign test is used to compare the medians of paired or matched observations. It is an alternative to the paired-samples t-test and the Wilcoxon signed-rank test in the situation, where the distribution of differences between paired data values is neither normal (in t-test) nor symmetrical (in Wilcoxon test). In this chapter, you will learn how to compute paired-samples sign test in R
4. ## Wilcoxon Test in R

This chapter describes how to compute and interpret the wilcoxon test in R. This test is a non-parametric alternative to the t-test for comparing two means. You will learn how to compute the different types of Wilcoxon tests in R, including: One-sample Wilcoxon signed rank test, Wilcoxon rank sum test and Wilcoxon signed rank test on paired samples. We will also show how to check the assumptions and compute effect size.
5. ## T-test in R

This chapter describes how to compute and interpret the different t-test in R including: one-sample t-test, independent samples t-test and paired samples t-test.
6. ## Inter-Rater Reliability Analyses: Quick R Codes

This article describes how to compute the different inter-rater agreement measures using the irr packages.