14b_Ch13_One-Factor_ANOVA_Independent_Outline - 1 2...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
One-Factor Analysis of Variance Assumptions, ANOVA Table, Effect Size, Post Hoc Tests, t and F for Two Independent Samples Assumptions ANOVA (Independent Samples) 1. Independent observations within each sample 2. The populations from which the sample were drawn must be normal 3. Homogeneity of Variance — the populations from which the samples are drawn must have equal variances ANOVA Summary Table Effect Size Eta Squared Post Hoc Tests Tukey’s HSD Scheffé Post Hoc Tests • If we reject H 0 , we know that not all of the means are the same Post Hoc Tests • But we don’t know which means are different Post Hoc Tests • When you have more than two population means, a test of multiple comparisons enables you to test a series of differences Post Hoc Tests • For example, a test of multiple comparisons would allow us to test the following differences: Why not just use a t test? t tests are designed for single comparisons • Multiple t tests increase the probability of a Type I error
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 08/22/2011 for the course PSY 207 taught by Professor Pfordesher during the Fall '07 term at SUNY Buffalo.

Page1 / 4

14b_Ch13_One-Factor_ANOVA_Independent_Outline - 1 2...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online