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The ANOVA test family is similar to the T-test family in that we use it to compare groups to determine if any significant differences exist between those groups. The ANOVA is appropriate when you're comparing more than two groups. Here are some examples of what types of research questions and/or hypotheses may indicate an ANOVA is appropriate:
One-Way ANOVA:
Two-Way ANOVA:
Repeated Measures ANOVA:
*NOTE: each participant experiences each type of chocolate and provides a test performance score for each.*
Suggested Order to Learn About ANOVA Concepts
Learning about and becoming competent in conducting an ANOVA involves several steps and a variety of different skills. Below is the suggested order by the ASC statistics coaches along with resources to assist you in learning these concepts. All of the concepts below can be discussed with a statistics coach in an individual session. Individual sessions can be scheduled by using ASC Chat at the hours listed in ASC Contact Information on the left-hand side of this page. Additionally, students can learn more about conducting an ANOVA during the Inferential Statistics group session Fridays at 4:00 p.m. PST. Students can self-schedule for this session. For more information, see the Relevant FAQs at the bottom of this page.
Topic | Resource |
Introduction to ANOVA from Dr. Jeffry White, Academic Success Center The F ratio test and concept of analysis of variances |
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Dr. Jeffry White, Difference in the F and t distributions The F-statistic, F distribution, and probability |
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Degrees of freedom numerator and degrees of freedom denominator | |
Dr. Jeffry White, centrality of the assumptions of ANOVA The assumptions of ANOVA and the implications for violation Levels of measures Balanced designs (equal/unequal sample sizes) Normality of the response variability
Dr. Jeffry White, more on the normality assumption Dr. Jeffry White, more on the homogeneity of variances assumption Equality of variances: Levene’s test |
Introduction: Centrality of Assumptions Measurement Levels: What and why? Choosing Between the Kolmogorov-Smirnov and the Shapiro-Wilk Tests of Normality using SPSS
Homogeneity of Variances Assumption Conducting and Interpreting a Levene's Test in SPSS
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The null and alternative hypotheses for 1-way ANOVA 2-way (factorial) ANOVA |
Hypothesis testing with One-Way Between Groups ANOVA: Part 1 Hypothesis Testing with One-Way Between Groups ANOVA: Part 2 |
Simple main and interaction effects Ordinal and disordinal interactions |
Tests of simple effects in a two-way ANOVA |
Using SPSS to compute 1-way ANOVA (between groups) Using SPSS to compute 1-way ANOVA (between subjects) Using SPSS to compute 1-way ANOVA (repeated measures) Interpreting the 1-way ANOVA output in SPSS |
One-Way Between Groups ANOVA: SPSS |
Using SPSS to compute 2-way (factorial) ANOVA Interpreting the factorial ANOVA table in SPSS |
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Calculating effect sizes for ANOVA | Significance vs. Effect Size for One-Way ANOVA Using SPSS |
Calculating observed power for ANOVA | Understanding and Calculating Power after Two-Way ANOVA Using SPSS |
Post hoc and multiple comparison tests in ANOVA | Post-Hoc Tests for One-Way ANOVA Using SPSS |
Probability of Type I/II errors Dr. Jeffry White, more about the concept of alpha error inflation The concept of alpha error inflation and familywise error (e.g. multiple hypothesis testing) - alpha error adjustment strategies |
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Dr. Jeffry White, an alternative approach when the normality assumption has been violated Nonparametric alternatives for ANOVA (Kruskal-Wallis test) |
ANOVA vs. Kruskal-Wallis Test in SPSS with Assumption Testing |
Dr. Jeffry White, introduction to an alternative approach when the homogeneity of variances assumption has been violated Alternative when homogeneity of variances have been violated (Brown-Forsythe & Welch tests) |
Recommended Reading
The articles below will assist you in understanding several of the concepts presented above including but not limited to assumptions, equal variances, and normality.