Labeling Categories Within Each Level of Each Factor in Repeated Measures with Compare (instead of numbers)

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Labeling Categories Within Each Level of Each Factor in Repeated Measures with Compare (instead of numbers)

Postby bluelagoon » Sun Jul 10, 2016 1:32 pm

Hello there!

I have 8 columns of repeated measures data with within-subject factors represented by 2x2x2: Jump leg (left or right); land leg (left or right); jumpland (jumping or landing). (The rows include the subject and their attributes, such as whether the subject is injured or not). There are a number of different jumps for each subject.

Code: Select all

 GLM Force_Jump.1.1 Force_Land.1.1 Force_Jump.1.2 Force_Land.1.2 Force_Jump.2.1 Force_Land.2.1 Force_Jump.2.2 Force_Land.2.2 BY InjuredSide WITH Dominant
   /WSFACTOR=JumpLeg 2 Simple  LandLeg 2 simple  JumpLand 2 simple
I am using the COMPARE function to look at pairwise differences among the different levels of these factors. At a basic level, I can label within a particular variable and have it show up in the output, but not one of the factor levels that crosses multiple columns as a repeated measures design (the items I listed above) would. I am looking for a way to display (in the output) the items in parentheses above (left or right) - in other words, the actual values within each level of each factor.

Ideally I'd love to do something like this, but obviously this isn't real code that actually works:
/WSFACTOR=JumpLeg ["LeftJump" + "RightJump"] Simple LandLeg ["LeftLand" + "RightLand"] simple JumpLand ["Jumping" + "Landing"] simple
Example of a COMPARE use:

Code: Select all

  /EMMEANS=TABLES(Congruent*InjuredSide*JumpLand*JumpLeg*LandLeg) WITH(Dominant=MEAN) COMPARE(InjuredSide) ADJ(LSD)
Sample output is attached. Right now the only output I can get labeled is InjuredSide, as that is a variable I can directly alter the label/values for, unlike the repeated measures conditions.

This would be quite helpful as keeping track of all of the 1s and 2s can get a bit confusing, especially for longer takes - and for both for this particular analysis and for more complicated ones.

Thank you so much!
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