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Intro to Statistics with R: Analysis of Variance (ANOVA)

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4 hours
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Course Description

Analysis of Variance (ANOVA) is probably one of the most popular and commonly used statistical procedures. In this course, Professor Conway will cover the essentials of ANOVA such as one-way between groups ANOVA, post-hoc tests, and repeated measures ANOVA.
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  1. 1

    An introduction to ANOVA

    Free

    In this first chapter you will learn the basic concepts of ANOVA based on the working memory training example. The difference and benefits compared to t-tests is explained, and you will see how you can compare two or more group means by engaging in ANOVA. Furthermore, you will get a deep understanding on F-tests and the corresponding distribution.

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    Introduction to ANOVA
    50 xp
    Working memory experiment
    100 xp
    Difference between t-tests and ANOVA
    50 xp
    Exploration of the F-test
    50 xp
    Generate density plot of the F-distribution
    100 xp
    Why is the F-distribution always positive?
    50 xp
    F-ratio
    50 xp
    Between group sum of squares
    100 xp
    Within groups sum of squares
    100 xp
    Calculating the F-ratio
    100 xp
    ANOVA table
    50 xp
    A faster way: ANOVA in R
    100 xp
    Significance of the F-ratio
    50 xp
    Levene's test
    100 xp
    Does the assumption hold?
    50 xp
  2. 2

    Post-hoc analysis

    Free

    The F-ratio you calculated in the previous chapter tells you if there is a significant effect somewhere across your groups, but it does not tell you which pairwise comparisons are significant. That is what the post-hoc tests explained in this chapter will do for you. Post-hoc tests such as Tukey’s and Bonferroni’s procedure allow for multiple comparisons without inflating the probability of a type I error.

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    An introduction to post-hoc tests
    50 xp
    The necessity of post-hoc tests
    50 xp
    NHST table
    50 xp
    Calculate and interpret the results of Tukey
    100 xp
    Bonferroni adjusted p-values
    100 xp
  3. 3

    Between groups factorial ANOVA

    Free

    In this final chapter on ANOVA the different concepts behind a factorial ANOVA are explained. In a Factorial ANOVA you have two independent variables and one dependent continuous variable. This allows you to look at main effects, interaction effects, and simple effects. Special attention goes to effect size, post-hoc tests, simple effects analyses and the homogeneity of variance assumption.

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    Introduction to factorial ANOVA
    50 xp
    Starting off
    50 xp
    Data exploration with a barplot
    100 xp
    Barplot interpretation
    50 xp
    Hypotheses, F-ratios and effects
    50 xp
    The homogeneity of variance assumption
    100 xp
    Homogeneity of variance?
    50 xp
    Groups and subjects
    50 xp
    The factorial ANOVA
    100 xp
    Interpreting the summary table
    50 xp
    Post-hoc tests and effect sizes
    50 xp
    The interaction effect (1)
    100 xp
    The interaction effect (2)
    50 xp
    The interaction effect (3)
    50 xp
    The effect sizes
    100 xp
    Interpreting etaSquared output (1)
    50 xp
    Interpreting etaSquared output (2)
    50 xp
    Pairwise comparisons
    100 xp
    Tukey test for the easy driving simulator
    50 xp
    Tukey test for the difficult driving simulator
    50 xp

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