Between-Groups ANOVA

Chapter 12

> When to use an F distribution•Working with more than two samples

> ANOVA•Used with two or more nominal

independent variables and an interval dependent variable

> The problem of too many t tests• Fishing for a finding• Problem of Type I error

Why not use multiple t-tests?

> Analyzing variability to compare means

• F = variance between groups variance within groups

> That is, the difference among the sample means divided by the average of the sample variances

The F Distribution

Types of Variance

> Between groups: estimate of the population variance based on differences among group means

> Within groups: estimate of population variance based on differences within (3 or more) sample distributions

Check Your Learning

> If between-groups variance is 8 and within-groups variance is 2, what would F be?

Types of ANOVA

> One-Way: hypothesis test including one nominal variable with more than two levels and a scale DV

> Within-Groups: more than two samples, with the same participants; also called repeated-measures

> Between-Groups: more than two samples, with different participants in each sample

Assumptions of ANOVAs

> Random selection of samples> Normally distributed sample> Homoscedasticity: samples come from

populations with the same variance

One-Way Between-Groups ANOVA

> Everything about ANOVA but the calculations> 1. Identify the populations, distribution, and

assumptions.> 2. State the null and research hypotheses.> 3. Determine the characteristics of the

comparison distribution.> 4. Determine the critical value, or cutoff.> 5. Calculate the test statistic.> 6. Make a decision.

Step 3. Characteristics

•What are the degrees of freedom?> If there are three levels of the independent

variable?> If there are a total of 20 participants in each of

the three levels?

1 groupsbetween Ndf

lastwithin dfdfdfdfdf ...321

111 ndf

> Step 4: Critical Values

Determine Cutoffs for an F Distribution (Step 4)

Formulae

2)( GMXSStotal

2)( MXSSwithin

2)( GMXSSbetween

betweenwithintotal SSSSSS

between

betweenbetween df

SSMS

within

withinwithin df

SSMS

within

between

MSMSF

> Quantifies overlap> Two ways to estimate population

variance• Between-groups variability•Within-groups variability

Logic behind the F Statistic

The Logic of ANOVA

> Presents important calculations and final results in a consistent, easy-to-read format

The Source Table

> What is the ANOVA telling us to do about the null hypothesis?

> Do we reject or accept the null hypothesis?

Bringing it All Together

An F Distribution

Here the F statistic is 8.27 while the cutoff is 3.86. Do we reject the null hypothesis?

Making a Decision

> Step 1. Compare the variance (MS) by diving the sum squares by the degrees of freedom.

> Step 2. Divide the between-groups MS by the within-groups MS value.

> Step 3. Compare the calculated F to the critical F (in Appendix B).• If calculated is bigger than critical, we have

a significant difference between means

Calculating Effect Size

> R2 is a common measure of effect size for ANOVAs.

total

between

SSSSR 2

Post-Hoc Tests to Determine Which Groups Are Different

> When you have three groups, and F is significant, how do you know where the difference(s) are?• Tukey HSD• Bonferonni

> A priori (planned) comparisons

Tukey HSD Test

> Widely used post hoc test that uses means and standard error

MsMMHSD 21

NMSs within

M

The Bonferroni Test

> A post-hoc test that provides a more strict critical value for every comparison of means.

> We use a smaller critical region to make it more difficult to reject the null hypothesis. •Determine the number of comparisons we

plan to make. > Divide the p level by the number of

comparisons.