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PROFILE ANALYSIS
Profile AnalysisMain Point:
Repeated measures multivariate analysisOne/Several DVs all measured on the same
scale
Profile AnalysisMain Point:
Most commonly used as a time series designMeasured several times on the same DV
Profile AnalysisMain Point:
Doubly multivariate – several different DVs are measured over time“Doubly” because there are double layers, or
multiple DVs measured a couple times
Research questions:Mainly:
Do people have different “profiles” on a set of measures
One IssueMeasures much have the same range of
scores with the values having the same meaningBecause test of profiles measure the
differences in adjacent DVs for that “time” measurement
Difference scores are called segments
Profile PartsParallelism profiles
Do the different groups have different parallel profiles
ANOVA comparison = interaction
Profile PartsLevels:
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
Profile PartsFlatness – similarity of responses on the
DV independent of groupDo all the DVs (or times of the DV) elicit the
same average response?
Profile PartsContrasts after profile – if you get
differences then you have to follow up with a type of contrast analysis
Examples and Follow UpsExample data – I have a class I’ve taught a
couple times
Class 1
Quiz1
Quiz 2
Quiz 3
Quiz 4
Quiz 5
Quiz6
Quiz 7
Class 2
Quiz 1
Quiz 2
Quiz 3
Quiz 4
Quiz 5
Quiz6
Quiz 7
Example
1 2 3 4 5 6 70
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Series1Series2
Example
Class 1
Quiz1
Quiz 2
Quiz 3
Quiz 4
Quiz 5
Quiz6
Quiz 7
Class 2
Quiz 1
Quiz 2
Quiz 3
Quiz 4
Quiz 5
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Quiz 7
Parallelism = interaction – do the lines cross?
Levels – between these two are they
different?
Flatness – are these the same
over time?
Limitations - TheoreticalChoice of DV
Limited to scales that are the sameEasy to use when you are repeated the same
scale over and over
Limitations - TheoreticalChoice of DV
If the units are not the same you can convert to z-score
Differences in profiles attributed to the differences in group treatmentsCausal if you have manipulated them.
Limitations – PracticalSample size – use a between subjects
anova analysis if you don’t have a program that will run multivariate programMore people in the smallest group than there
are DVsRule of thumb is 10 cases to 1 on DVs
Limitations - PracticalRepeated measures ANOVA has more
powerCollecting more data points from the same
people, so that reduces errorError is controlled with in person, instead of
with in groupStill need more people than a univariate
analysis
PowerUsually a little stronger – you have to deal
less with SphericityWith 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
Limitation - PracticalUnequal N isn’t a big deal
Also harder to have because you measure people several times, ends up being missing instead of unequal
Missing DataSpecial imputation because it’s missing
See page 345Basically 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)
Normality Robust! Check!
Unless there are fewer cases in a cell than there are DVs
OutliersAll DVs get outlier analysis
Could do it for each time segment
HomogeneityIf sample sizes are equal, homogeneity of
variance is not necessary since all scores came from the same personBox’s M still is applicable p<.001
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
Multicollinearity – SingularityBut 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
IssuesUnivariate 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
FixesFixes
Greenhouse-Geisser or Huynh-Feldt – are adjustments given automatically for violations Adjusts the significance values to be more
conservativeOr you could lower your alpha rate (so you need
a lower p value) but then you lose power
IssuesUnivariate 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