Cramming Sam's top tips from chapter 15
Click on the topic to read Sam's tips from the book
One-way repeated-measures designs
- One-way repeated-measures designs compares several means, when those means come from the same entities; for example, if you measured people’s statistical ability each month over a year-long course.
- When you have three or more repeated-measures conditions there is an additional assumption: sphericity.
- You can test for sphericity using Mauchly’s test, but it is better to always adjust for the departure from sphericity in the data.
- The table labelled Tests of Within-Subjects Effects shows the main F-statistic. Other things being equal, always read the row labelled Greenhouse–Geisser (or Huynh–Feldt, but you’ll have to read this chapter to find out the relative merits of the two procedures). If the value in the column labelled Sig. is less than 0.05 then the means of the conditions are significantly different.
- For contrasts and post hoc tests, again look to the columns labelled Sig. to discover if your comparisons are significant (i.e., the value is less than 0.05).
Factorial repeated-measures designs
- Two-way repeated-measures designs compare means when there are two predictor/independent variables, and the same entities have been used in all conditions.
- You can test the assumption of sphericity when you have three or more repeated-measures conditions with Mauchly’s test, but a better approach is to routinely interpret F-statistics that have been corrected for the amount by which the data are not spherical.
- The table labelled Tests of Within-Subjects Effects shows the F-statistics and their p-values. In a two-way design you will have a main effect of each variable and the interaction between them. For each effect, read the row labelled Greenhouse–Geisser (you can also look at Huynh–Feldt, but you’ll have to read this chapter to find out the relative merits of the two procedures). If the value in the column labelled Sig. is less than 0.05 then the effect is significant.
- Break down the main effects and interactions using contrasts. These contrasts appear in the table labelled Tests of Within-Subjects Contrasts. If the values in the column labelled Sig. are less than 0.05 the contrast is significant.