Context
I have a survey of 16 questions, each with four possible responses. The purpose of the survey is to measure the respondent's propensity towards four categories (which we will denote A, B, C, D). Each of the four responses per question are representative of an aspect of the four categories, A, B, C, D.
The respondent rank orders each of the four responses (we will denote the first response by "4", the second by "3", etc).
To score the categories, we add the responses up based on the coding above. There are 16 x (4 + 3 + 2 + 1) = 160 total points. The sums for each category are computed, and the maximum score is deemed the respondent's dominant category.
Therefore each survey looks like the following (in CSV format)
question_num, A, B, C, D
1, 4, 3, 1, 2
2, 3, 4, 1, 2
3, 3, 4, 2, 1
4, 4, 3, 1, 2
5, 4, 3, 1, 2
6, 4, 3, 2, 1
7, 4, 3, 1, 2
.
.
.
16, 3, 4, 1, 2
sums, 64, 48, 24, 24
I have about 325 surveys completed.
Aim
I want to remove possible redundant items in the survey so I can reduce the burden on future respondents.
Questions
- My first strategy was to do a multi-logistic regression with the response as the dominant category (described above). Is this a good idea?
- Would PCA be helpful?
- Are there any other strategies for identifying redundant items?
