I have 11 motivations (M1-M11) in my questionnaire for ocean swimming. After running an EFA, I detected 4 underlying latent factors (relaxation, socialization, etc.) which explained about 58% of the variance.
- Relaxation: M1,M2,M3
- Socialization: M4,M6
- Factor 3: M5,M7,M8
- Factor 4:M9,M10,M11
Now, I am planning to model these motivations as independent variables to explain my binomial response variable (willingness to pay entry fee - yes/no), alongside other sociodemographic variables.
I am wondering if I should using the raw likert scores of each motivation as my explanatory variables, or use factor/construct mean likert scores. So either
response variable (yes/no) <- age+gender+education+M1+M2+M3+M4+M5+M6+M7+M8+M9+M10
response variable (yes/no) <- age+gender+education+factor1 mean score+ factor2 mean score + factor3 mean score + factor4 mean score
whereby factor1 mean score == M1+M2+M3/3 = 5+3+4/3 = 4 and so on.
Just to add as well that after running both models, the results I get is rather similar anyway. In the first final model, M5 is marginally significant (p=0.052) while age is significant (p<0.05), but this swaps around for the second final model (the factor mean to which M5 belongs to becomes significant, while age becomes marginally significant).
Thank you very much!