I'm interested in investigating what individual factors contribute to the choice of three outcome variables. Additionally, I want to test whether there are pairwise interactions between the predictor variables under investigation. Is it valid to build two seperate models, given that there are two research questions? I would appreciate it if you could recommend books or articles related to this issue. Thank you.
Yes, it's valid to build separate models to address different research questions. In your case, one model could focus on identifying the individual factors that contribute to the choice of outcome variables, while the other model could examine pairwise interactions between predictor variables.
For the first research question, you might consider using techniques such as multiple regression analysis or logistic regression analysis, depending on the nature of your outcome variables (continuous or categorical, respectively). These techniques can help identify which individual predictor variables significantly contribute to the outcome variables.
For the second research question, exploring pairwise interactions between predictor variables, you could use techniques like interaction terms in regression models or more advanced methods like structural equation modeling (SEM) or generalized linear models (GLMs) with interaction effects.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate Data Analysis (8th ed.). Cengage Learning.
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). Sage Publications.
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