1. PROFILE ANALYSIS

2. Profile Analysis Main Point: Repeated measures multivariate analysis One/Several DVs all measured on the same scale

3. Profile Analysis Main Point: Most commonly used as a time series design Measured several times on the same DV

4. Profile Analysis Main Point: Doubly multivariate – several different DVs are measured over time  “Doubly” because there are double layers, or multiple DVs measured a couple times

5. Research questions: Mainly: Do people have different “profiles” on a set of measures

6. One Issue Measures much have the same range of scores with the values having the same meaning Because test of profiles measure the differences in adjacent DVs for that “time” measurement Difference scores are called segments

7. Profile Parts Parallelism profiles Do the different groups have different parallel profiles ANOVA comparison = interaction

8. Profile Parts Levels: Overall group differences – regardless of parallelism, does one group on average have a higher score on the collected set of measures?  Between subjects ANOVA analysis

9. Profile Parts Flatness – similarity of responses on the DV independent of group Do all the DVs (or times of the DV) elicit the same average response?

10. Profile Parts Contrasts after profile – if you get differences then you have to follow up with a type of contrast analysis

11. Examples and Follow Ups Example data – I have a class I’ve taught a couple times Class 1 Quiz 1 Quiz 2 Quiz 3 Quiz 4 Quiz 5 Quiz 6 Quiz 7 Class 2 Quiz 1 Quiz 2 Quiz 3 Quiz 4 Quiz 5 Quiz 6 Quiz 7

12. Example

13. Class 1 Quiz 1 Quiz 2 Quiz 3 Quiz 4 Quiz 5 Quiz 6 Quiz 7 Class 2 Quiz 1 Quiz 2 Quiz 3 Quiz 4 Quiz 5 Quiz 6 Quiz 7 Parallelism = interaction – do the lines cross? Levels – between these two are they different? Flatness – are these the same over time?

14. Limitations - Theoretical Choice of DV Limited to scales that are the same Easy to use when you are repeated the same scale over and over

15. Limitations - Theoretical Choice of DV If the units are not the same you can convert to z- score Differences in profiles attributed to the differences in group treatments  Causal if you have manipulated them.

16. Limitations – Practical Sample size – use a between subjects anova analysis if you don’t have a program that will run multivariate program More people in the smallest group than there are DVs Rule of thumb is 10 cases to 1 on DVs

17. Limitations - Practical Repeated measures ANOVA has more power Collecting more data points from the same people, so that reduces error Error is controlled with in person, instead of with in group Still need more people than a univariate analysis

18. Power Usually a little stronger – you have to deal less with Sphericity With g*power – you can do this as a regular repeated measures – but you will need to run more people than regular repeated measures with very small effect sizes

19. Limitation - Practical Unequal N isn’t a big deal Also harder to have because you measure people several times, ends up being missing instead of unequal

20. Missing Data Special imputation because it’s missing See page 345 Basically involves summing and averaging the scores that you do have for the person, and then averaging the other scores from everyone else Or you can do a HLM (hierarchical linear model) if imputing scores is not a good idea (cancer study)

21. Normality Robust! Check! Unless there are fewer cases in a cell than there are DVs

22. Outliers All DVs get outlier analysis Could do it for each time segment

23. Homogeneity If sample sizes are equal, homogeneity of variance is not necessary since all scores came from the same person Box’s M still is applicable p<.001

24. Linearity For parallelism and flatness, you are assuming linearity since you are checking if the lines are flat or cross You use bivariate charts to get combos of the DV

25. Multicollinearity – Singularity But we want our DVs to be correlated because they are all measured from the same people?! Statistically will not run when R2 value research.999

26. Issues Univariate versus multivariate Sphericity – the correlation between each time measurement must be the same With a multivariate test you will never meet this assumption With only two levels of the IV, not a big deal

27. Fixes Greenhouse-Geisser or Huynh-Feldt – are adjustments given automatically for violations  Adjusts the significance values to be more conservative  Or you could lower your alpha rate (so you need a lower p value) but then you lose power

28. Issues Univariate versus Multivariate Do both! If they give you same result, then report univariate (much easier!) Trend analyses – do this instead if it makes sense with your data