US: One way Between Independent Groups / Subjects Analysis of Crosstabulation Between Subjects Variance Kruskal-Wallis with Design Chi-square Test Skewed: Kruskal-Wallis Matched Participants or Repeated Measures Friedman ANOVA Advanced Within-subjects / analyses Repeated Measures Design If this familiar, it should. It’s the same table presented in Lecture 9 on Two group comparisons, except that it now covers comparisons of two groups. or more One-Way Between-subjects Analysis of Variance Comparing the means of 3 or more groups. Suppose are three groups Group A, Group B, and Group C. – Why not just perform multiple t-tests. t-test comparing Mean of Group A with Mean of Group B t-test comparing mean of Group A with Mean of Group C t-test comparing mean of Group B with Mean of Group C The above 3 t-tests exhaust the possible comparisons between 3 groups. Problem with the above method: It’s very difficult to for the tests, which makes compute the correct p-value it difficult to use in hypothesis testing. What is needed is a single omnibus test. Such a test was provided by Sir R. A. Fisher. It’s based on the following idea Consider 3 populations whose means are all equal:
Independent Groups / Between Subjects Design US: One way Between Subjects Analysis of Variance Skewed: Kruskal-Wallis Kruskal-Wallis Crosstabulation with Chi-square Test Matched Participants or Within-subjects / Repeated Measures Design Repeated Measures Friedman ANOVA Advanced analyses If this familiar, it should. It’s the same table presented in Lecture 9 on Two group comparisons, except that it now covers comparisons of two or more groups. One-Way Between-subjects Analysis of Variance Comparing the means of 3 or more groups. Suppose are three groups – Group A, Group B, and Group C. Why not just perform multiple t-tests. t-test comparing Mean of Group A with Mean of Group B t-test comparing mean of Group A with Mean of Group C t-test comparing mean of Group B with Mean of Group C The above 3 t-tests exhaust the possible comparisons between 3 groups. Problem with the above method: It’s very difficult to compute the correct p-value for the tests, which makes it difficult to use in hypothesis testing. What is needed is a single omnibus test. Such a test was provided by Sir R. A. Fisher. It’s based on the following idea Consider 3 populations whose means are all equal:
p5100_lecture10_single_factor_designs.docx