I am preparing to analyze some data and I have a question about how to go about it. It is a 39 question survey with 5-item Likert scale responses. I will be using the responses to separate the participants into the category that they score highest in for further analysis. The problem is that Category A corresponds to 14 questions, Category B has 11 questions, and Category C has 14 questions. What is the best way to go about determining which one the participant scores highest in, since the # of questions is not equal? Will it work to take their total possible points in each category and divide it by the number of points possible, then compare their percentages across the three categories to see which is the highest?
I suppose your suggestion would be OK, but if all questions have 5 point response scales, why not just average the responses for each person over all the questions in each category? I wonder if you might be concerned about using the mean with ordinal data? That's a common concern, but I think it's generally overblown. With multiple questions, the subscale scores should be sufficiently equal-interval for your purposes. This topic has been discussed several times on CV; you might be interested in reading this question and especially the answer by @JeromyAnglim.
We can push past this strategy, however. I'm not sure you need to categorize your participants as being in either A, or B, or C. I'm typically against categorizing in this manner (for more on that, you might want to read this question and my answer there). They each certainly have some level of membership in each, and categorizing them amounts to throwing that information away. Moreover, even for those participants who are all more in A than elsewhere (for example), their level of A-ness will vary from participant to participant. Thus, simply calling each of them an 'A' throws away the information about their within-category variability. Why not keep those 3 scores for each participant, and use them as 3 covariates in a multiple regression model when it comes time to do your final analysis?
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