Melanie posted on Tuesday, April 02, 2013 - 5:09 pm
I want to examine the factor structure of a questionnaire amongst a group of raters. The raters are clustered into groups based on who they have rated. Iím not interested in the ratees' scores at all at this point. I just want to know if the questionnaire factors well across the raters, and reviewers have told me I need to take into account the fact that they are grouped based on who they have rated.
So Iím confused as to whether Iím ok just using the cluster and type=complex or whether I need to use multi-level modelling and specify within and between models (which presumably will be the same as Iím not looking at/entering the ratee data at this point).
Do you have 351 ratees and 110 raters. If not, what is cluster? Aren't ratees nested in raters such that you would have more ratees that raters?
Melanie posted on Wednesday, April 03, 2013 - 8:34 pm
I have 110 people (ratees) who have been rated by a total of 351 raters.
I am clustering on the the person they rated (so Rater 1, 2 and 3 might rate ratee A. Rater 4, 5, 6 and 7 might rate ratee B and so on - the raters never rate more than one person)
So the average cluster size (no. of raters who rate one person) is 3.19
and I'm just looking to see if the questionnaire factors well across the rater's responses, taking into account that several raters may be rating the same person on that questionnaire. I don't want to look at ratee responses on that questionnaire.
Its essentially a 360 degree feedback process and I just want to look at whether the 360 version of the questionnaire factors well. I'm not interested in the self-rated version of the questionnaire.