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## Profile Analysis Profile Analysis Profile Analysis

East Carolina University, US has reference to this Academic Journal, Profile Analysis Intro in addition to Assumptions Psy 524 Andrew Ainsworth Profile Analysis Profile analysis is the repeated measures extension of MANOVA where a set of DVs are commensurate (on the same scale). Profile Analysis The common use is where a set of DVs represent the same DV measured at multiple time points used in this way it is the multivariate alternative so that repeated measures or mixed ANOVA The choice often depends on the number of subjects, power in addition to whether the assumptions associated alongside within subjects ANOVA can be met (e.g. sphericity)

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Repeated Measures Data Profile Analysis The less common use is so that compare groups on multiple DVs that are commensurate (e.g. subscales of the same inventory) Current stat packages can be used so that perform more complex analyses where there are multiple factorial between subjects effects Commensurate Data

Questions asked by profile analysis There is one major question asked by profile analysis; Do groups have similar profiles on a set of DVs? Questions Usually in application of profile analysis a researcher is trying so that show that groups are not different, that is why most tests are named after the ?null? case. Questions Segments ? difference scores (or other linear combinations) between adjacent DV scores that are used in two of the major tests of profile analysis

Overview Overview Overview Overview Overview Overview Thank you

Questions Between Subjects ? (univariate) ? ?Equal Levels? On average does one group score higher than the other Averaging across DVs are the groups different This would be the between-groups main effect in mixed ANOVA Questions BS ? (univariate) ? ?Equal Levels? It is called the equal levels hypothesis in profile analysis Groups are different when the equal levels hypothesis is rejected

Questions Within Subjects (multivariate) ? ?Flatness? This is equivalent so that the within subjects main effect in repeated measures ANOVA In profile analysis terms this is a test in consideration of the flatness of the profiles ?Do all DVs elicit the same average response?? Questions WS (multivariate) ? ?Flatness? If flatness is rejected than there is a main effect across the DVs This is usually only tested if the test in consideration of parallel profiles is not rejected (we?ll talk about this in a second)

Questions Interaction (multivariate) ? Parallel Profiles Are the profiles in consideration of the two groups the same? This is a test in consideration of the interaction in repeated measures ANOVA This is usually the main test of interest in profile analysis An interaction occurs when the profiles are not parallel Questions If any of the hypotheses tested by profile analysis are significant than they need so that be followed by contrasts. Contrasts (on the main effects, alongside no interaction) Simple effects Simple contrasts Interaction contrasts (done when the interaction in addition to both main effects are significant) More on this later Interaction in addition to possibly one (but not both) main effect

Questions Estimating parameters Usually done through plots of the actual profiles If the flatness hypothesis is rejected than you would plot the average DV scores averaged across groups Questions Estimating parameters If equal levels hypothesis is rejected than you would plot the groups scores averaged across DVs Questions Estimating parameters And if the parallel profiles hypothesis is rejected you would plot the mean of each group on each DV

Questions Strength of association Calculated in the same way i.e. Eta squared in addition to Partial Eta squared Limitations Data must be on the same scale This means that any alterations done so that one variables need so that be applied so that the rest This is why it is used often alongside repeated measures since it is the same variable multiple times Limitations Data can be converted so that Z-scores first in addition to profile analysis can be applied Done by using the pooled within-subjects standard deviation so that standardize all scores Factor scores can also be used (more later) Dangerous since it is based on sample estimates of population standard deviation

Limitations Causality is limited so that manipulated group variables Generalizability is limited so that population used Limitations Assumptions should be tested on combined DVs but often difficult so screening on original DVs is used Assumptions Sample size needs so that be large enough; more subjects in the smallest cell than number of DVs This affects power in addition to the test in consideration of homogeneity of covariance matrices Data can be imputed

Assumptions Power is also determined on whether the univariate assumptions were met or not; profile analysis has more power than univariate tests adjusted in consideration of sphericity violations Assumptions Multivariate normality If there are more subjects in the smallest cell than number of DVs in addition to relatively equal n than PA is robust violations of multivariate normality If very small samples in addition to unequal n than look at the DVs so that see if any are particularly skewed Assumptions All DVs should be checked in consideration of univariate in addition to multivariate outliers

Assumptions Homogeneity of Variance-Covariance matrices If you have equal n than skip it If there are unequal n across cells interpret Box?s M at alpha equals .001. Assumptions Linearity It is assumed that the DVs are linearly related so that one another inspection of bivariate plots of the DVs is used so that assess this If symmetric DVs (normal) in addition to large sample this can also be ignored

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## Journal Ratings by East Carolina University

This Particular Journal got reviewed and rated by Limitations Causality is limited so that manipulated group variables Generalizability is limited so that population used Limitations Assumptions should be tested on combined DVs but often difficult so screening on original DVs is used Assumptions Sample size needs so that be large enough; more subjects in the smallest cell than number of DVs This affects power in addition to the test in consideration of homogeneity of covariance matrices Data can be imputed and short form of this particular Institution is US and gave this Journal an Excellent Rating.