Hi,
I am utilizing a questionnaire to measure self reported technology proficiency of individuals. This particular questionnaire has 34 items representing 6 sub scales (Containing 5, 5, 5, 5, 8, and 6 items respectively). I have a single output variable also. I am trying to make a correlation table with these 6 sub scales against the output variable and check for significance. My questions is what is correct approach to do this. As it has two layers of variable (6 dimensions representing 34 items) and i am trying to map them against one output variable. How can an aggregate correlation be calculated?
The best approach would be to construct a Confirmatory Factor Analysis for the assignment of variables to scales, and then extend that into a Structural Equation Model that treats one of these scales as a dependent variable.
But that approach involves a complex statistical analysis. Instead, you could assess the reliability of each of your scales using coefficient alpha. If the alphas are acceptable (preferably .8 but possibly .7), then you can add together the respective sets of items to form the 6 scales.
From there, you can calculate pairwise correlations. To find out the relationship between the dependent variable and all 5 of the predictor scales, you can use a multiple regression -- but I would work about multicollinearity if the predictors are highly intercorrelated.
What do you mean "check for significance"? Please say what your research question(s) is (are).
Hi Daniel Wright , sorry if i wasnt clear. Attached is the screenshot of the reference study. I am trying to repeat this calculation, although in a different context. Notice the authors have calculated correlation of 6 subscales from a questionnaire and checked if they have correlation with 3 different outcome variables. My question is - how we can do this practically, as each of these 6 subscales have in turn 5 to 8 questions under each of them. Hope it is more clear now
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