I'm helping a friend design his research. He'll use a Google Forms questionnaire with several questions (from 0 to 5, Likert scale) to assess anxiety
of students' before taking two different tests
. The questionnaire has about 10 questions, all underlie anxiety and all go from 0 (strongly disagree) to 5 (strongly agree), and students will take either test A or test B (the tests are the same, but in different languages). The question is to verify if anxiety
can be explained by type of test
. Hence, I'm thinking about performing an Ordinal regression (using R's clm
) to test it in a second moment.
The thing is, after we have the data, what would be an appropriate approach to combine all results into a single "anxiety" measure? in order to be able to fit the model afterwards?
Hypothetical data:
ID test Q1 ... Q10
PART1 A 1 3
PART2 A 2 4
PART3 B 5 1
PART4 B 4 2
### We'd like to model "Anxiety" taking into consideration Q1 to Q10:
clm(ANXIETY ~ test)
Notes:
- sts will take either test A or test B, so this is not a repeated-measures design
- I've thought of running a pilot study with a few sts and run a Cronbach's alpha to assess reliability/consistency, but I still don't know what to do afterwards to combine all questions into a single variable.
- This is more of a theoretical question, but R tips would be much appreciated. Thanks!
I've seen many similar questions here, hopefully this isn't a duplicate, but I still don't know what path would be the most adequate. like this from 2015, this from 2012,this from 2017 and this from 2015