I am interested in the moderating effect of a voter's political knowledge on the effect of a voter's candidate evaluations on vote choice. I am trying to estimate conditional logit models to analyze this. At least that is what they are called in political science, I have already noticed that the terminology here is not entirely consistent across different disciplines.
All voters in my dataset have the same choice set of 6 parties. For my analysis I am using a stacked data matrix where every row constitutes a voter*party dyad. I would like to estimate an interaction effect between political knowledge (individual-specific) and candidate evaluation (alternative-specific) on vote choice. My problem is that, at least to my knowledge from trying to do this with the mlogit package in R, it is not possible to include variables like political knowledge in these models, as there is no intra-individual variation. Political knowledge of voter A is the same for party 1, party 2, ..., party 6. It is constant per individual, same as is age, gender, etc.
Of course, I am now asking myself how I can properly model this. I have read some papers that get around this issue by regressing vote choice for every single party upon the individual-specific variable and using the predicted probabilities (y-hats) from this binary logistic model as an independent variable in the conditional logistic regression (see for example Gattermann & De Vreese 2017; Giebler & Wagner 2015). However, these papers do not use y-hats as a constituent factor of an interaction effect, and I have no idea if this would even be valid or how I would interpret such an interaction effect.
I have also found a paper that calculates only the interaction effect and excludes from the model the main effect of one of the factors constituting the interaction effect. I know that best practice suggests to include all variables in the model (especially since it makes sense to expect a direct effect of political knowledge on vote choice) but maybe this is negligible in this case.
I would really appreciate feedback or guidance in this matter, as I am a bit stuck right now and I don't feel confident enough to choose one of the above mentioned alternatives. Maybe someone has a good argument for one of these alternatives, or maybe there are even other ways to solve this.
Feel free to let me know if you want me to describe the problem in more detail. Thank you!
EDIT1, 2022-03-11: I was asked to further specify my question. The dependent variable "vote choice" is a dummy, coded 1 when a respondent indicated voting for this party and 0 otherwise. Example data:
The vote choice is the only choice asked of the respondents and every respondent in the dataset made this choice. There are no cases who did not choose a party. The parties are not ranked, respondents were simply asked what party they would vote for (1 vote for 1 party). For every party, the evaluation of their one lead candidate was asked. There was no choice of candidates within a party.
Apart from the proposed interaction of political knowledge and candidate evaluation, I would like to include standard predictors of vote choice, i.e. an individual's party identification (dummy variable 0 if not feeling close to respective party, 1 if feeling close to party), distance between the individual's position on the left-right dimension and the party position on the left-right dimension (continuous variable), both predictors are alternative-specific. I would also like to include controls for sex (dummy m/f), age (continuous) and income (continuous), all individual-specific.