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CSUN PSY 524 - Profile Analysis and Doubly Manova

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Profile Analysis and Doubly ManovaComparisons on mains effectsEqual levelsFlatnessTesting interactions - Simple Effects, Simple Comparisons and Interaction ContrastsSimple effect and Simple ComparisonsInteractionsSlide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Interaction ContrastsSlide 16Slide 17Doubly MANOVASlide 19Doubly MANOVASlide 21Slide 22Slide 23Profile Analysis andProfile Analysis andDoubly ManovaDoubly ManovaComps in PAComps in PAand Doubly Manovaand Doubly ManovaPsy 524Psy 524Andrew AinsworthAndrew AinsworthComparisons on mains effectsComparisons on mains effectsIf the equal levels or flatness If the equal levels or flatness hypotheses are rejected and there are hypotheses are rejected and there are more than levels you need to break more than levels you need to break down the effect to see where the down the effect to see where the differences lie.differences lie.Equal levelsEqual levelsFor a significant equal levels test simply For a significant equal levels test simply use the compute function in SPSS to use the compute function in SPSS to create averages over all of the DVs.create averages over all of the DVs.Use this new variable as a DV in a Use this new variable as a DV in a univariate ANOVA where you can use univariate ANOVA where you can use post hoc tests or implement planned post hoc tests or implement planned comparisons using syntax.comparisons using syntax.FlatnessFlatnessIf the multivariate test for flatness is rejected If the multivariate test for flatness is rejected than you turn to interpreting comparisons in a than you turn to interpreting comparisons in a univariate within subjects ANOVA.univariate within subjects ANOVA.You can rerun the analysis removing the You can rerun the analysis removing the between subjects variables and implement between subjects variables and implement post hoc tests on the within subjects variable post hoc tests on the within subjects variable or use syntax to use planned comparisons.or use syntax to use planned comparisons.Testing interactions - Simple Testing interactions - Simple Effects, Simple Comparisons and Effects, Simple Comparisons and Interaction ContrastsInteraction ContrastsSimple effect and Simple ComparisonsSimple effect and Simple ComparisonsInteractionsInteractionsWhenever the parallelism hypothesis is Whenever the parallelism hypothesis is rejected you need to pull apart the data rejected you need to pull apart the data to try and pinpoint what parts of the to try and pinpoint what parts of the profile are causing the interactionprofile are causing the interactionInteractionsInteractionsParallelism and Flatness significant, Parallelism and Flatness significant, equal levels not significantequal levels not significant-Simple effects would be used to compare Simple effects would be used to compare the groups while holding each of the DVs the groups while holding each of the DVs constantconstantInteractionsInteractionsParallelism and Flatness significant, Parallelism and Flatness significant, equal levels not significantequal levels not significant-This is the same as doing a separate This is the same as doing a separate ANOVA between groups for each DV ANOVA between groups for each DV -A Scheffe adjustment is recommended if A Scheffe adjustment is recommended if doing this post hocdoing this post hoc•Fs=(k – 1)F(k – 1), k(n – 1)Fs=(k – 1)F(k – 1), k(n – 1)•K is number of groups and n is number of K is number of groups and n is number of subjectssubjectsInteractionsInteractionsParallelism and Flatness Parallelism and Flatness significant, equal levels not significant, equal levels not significantsignificant-If any simple effect is significant than it If any simple effect is significant than it should be followed by simple contrasts that should be followed by simple contrasts that can be implemented through syntax if can be implemented through syntax if planned or by post hoc adjustment.planned or by post hoc adjustment.InteractionsInteractionsParallelism and Equal levels significant, Parallelism and Equal levels significant, flatness not significantflatness not significant-This happens “rarely because if parallelism This happens “rarely because if parallelism and levels are significant, flatness is and levels are significant, flatness is nonsignificant only if profiles for different nonsignificant only if profiles for different groups are mirror images that cancel each groups are mirror images that cancel each other out”.other out”.InteractionsInteractionsParallelism and Equal levels significant, Parallelism and Equal levels significant, flatness not significantflatness not significant-This is done by doing a series of one-way This is done by doing a series of one-way within subjects ANOVAs for each group within subjects ANOVAs for each group separately.separately.InteractionsInteractionsParallelism and Equal levels significant, Parallelism and Equal levels significant, flatness not significantflatness not significant-A Scheffe adjustment is recommended if A Scheffe adjustment is recommended if doing this post hocdoing this post hoc•Fs=(p – 1)F(p – 1), k(p – 1)(n – 1)Fs=(p – 1)F(p – 1), k(p – 1)(n – 1)•P is number of repeated measures, n is number P is number of repeated measures, n is number of subjectsof subjects-If any are significant, follow up with simple If any are significant, follow up with simple contrasts on the within subjects variable.contrasts on the within subjects variable.InteractionsInteractionsIf all effects are significantIf all effects are significant-Perform interaction contrasts by separating Perform interaction contrasts by separating the data into smaller two by two the data into smaller two by two interactionsinteractionsInteraction ContrastsInteraction ContrastsInteractionsInteractionsIf all effects are significantIf all effects are significant-This can be done by using the select cases This can be done by using the select cases function in SPSS, selecting two groups and function in SPSS, selecting two groups and doing a mixed ANOVA with just two of the doing a mixed ANOVA with just two of the DVs; this will break down the interaction DVs; this will break down the interaction into smaller interactions that are easier to into smaller interactions that are easier to interpret.interpret.InteractionsInteractionsIf all effects are significantIf all effects are significant-It can also be


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