PROFILE ANALYSIS. Profile Analysis Main Point: Repeated measures multivariate analysis One/Several DVs…

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

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Class 2

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Example

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Series1Series2

Example

Class 1

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Class 2

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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