Cramming Sam's top tips from chapter 12
Click on the topic to read Sam's tips from the book
- If the F for the overall model is significant you need to find out which groups differ.
- When you have generated specific hypotheses before the experiment, use planned contrasts.
- Each contrast compares two ‘chunks’ of variance. (A chunk can contain one or more groups.)
- The first contrast will usually be experimental groups against control groups.
- The next contrast will be to take one of the chunks that contained more than one group (if there were any) and divide it in to two chunks.
- You repeat this process: if there are any chunks in previous contrasts that contained more than one group that haven’t already been broken down into smaller chunks, then create new contrasts that breaks them down into smaller chunks.
- Carry on creating contrasts until each group has appeared in a chunk on its own in one of your contrasts.
- The number of contrasts you end up with should be one less than the number of experimental conditions. If not, you’ve done it wrong.
- In each contrast assign a ‘weight’ to each group that is the value of the number of groups in the opposite chunk in that contrast.
- For a given contrast, randomly select one chunk, and for the groups in that chunk change their weights to be negative numbers.
- Breathe a sigh of relief.
Post hoc tests
- When you have no specific hypotheses before the experiment, follow up the model with post hoc tests.
- When you have equal sample sizes and group variances are similar use REGWQ or Tukey.
- If you want guaranteed control over the Type I error rate then use Bonferroni.
- If sample sizes are slightly different then use Gabriel’s, but if sample sizes are very different use Hochberg’s GT2.
- If there is any doubt that group variances are equal then use the Games–Howell procedure.
One-way independent ANOVA
- One-way independent ANOVA compares several means, when those means have come from different groups of people; for example, if you have several experimental conditions and have used different participants in each condition. It is a special case of the linear model.
- When you have generated specific hypotheses before the experiment use planned contrasts, but if you don’t have specific hypotheses use post hoc tests.
- There are lots of different post hoc tests: when you have equal sample sizes and homogeneity of variance is met, use REGWQ or Tukey’s HSD. If sample sizes are slightly different then use Gabriel’s procedure, but if sample sizes are very different use Hochberg’s GT2. If there is any doubt about homogeneity of variance use the Games–Howell procedure.
- You can test for homogeneity of variance using Levene’s test, but consider using a robust test in all situations (the Welch or Browne–Forsythe F) or Wilcox’s t1way() function.
- Locate the p-value (usually in a column labelled Sig.). If the value is less than 0.05 then scientists typically interpret this as the group means being significantly different.
- For contrasts and post hoc tests, again look to the columns labelled Sig. to discover if your comparisons are significant (they will be if the significance value is less than 0.05).