Dear,
We conducted in an experiment four force tests (at slow and fast speed and the dominant side separated from the non-dominant side) in 60 persons which where based on age categorized in three age-groups.
My data is as follows
We want to establish: the mean % difference between the Dom and Non-dom slow F as between the Dom and Non-dom fast F, further more we want to know if these mean % difference is different between low, mid and the high age-groups.
For this we used a general linear model -> repeated measures:
Via a syntax we can get a post-hoc table with the mean difference per variable.
But, our question is if this is the right way to analyze this data?
Because, if we calculate the dom vs. non-dom mean %difference by making for instance a third column %difference slow F and calculate the means per age group these means are way different from for instance the means %difference slow F calculated by the post-hoc general linear model -> repeated measures.
Much appreciation for your help.
Greetings,
Thomas
We conducted in an experiment four force tests (at slow and fast speed and the dominant side separated from the non-dominant side) in 60 persons which where based on age categorized in three age-groups.
My data is as follows
We want to establish: the mean % difference between the Dom and Non-dom slow F as between the Dom and Non-dom fast F, further more we want to know if these mean % difference is different between low, mid and the high age-groups.
For this we used a general linear model -> repeated measures:
- within-subject factor: side difference
- between-subject factor: age group
Via a syntax we can get a post-hoc table with the mean difference per variable.
But, our question is if this is the right way to analyze this data?
Because, if we calculate the dom vs. non-dom mean %difference by making for instance a third column %difference slow F and calculate the means per age group these means are way different from for instance the means %difference slow F calculated by the post-hoc general linear model -> repeated measures.
Much appreciation for your help.
Greetings,
Thomas