My variables are psychological satisfaction measured with the PreSS (questionnaire using likert scales), reward satisfaction divided in pay structure, pay level, pay raises and advantages, measured with the PSQ (questionnaire using likert scales), turnover intention using ATS (questionnaire using a seven-point-scale). However I also have information about age (ratio scale), sex (standard m or f question), education degrees (multiple choice), management position or not (yes/no question) and net pay (nominal scale).

The sample size is 86 at the present moment. However, I'm studying 4 different companies and 2 still need to participate. (So I now have 2 groups at 74 and 12).

Like you already assumed I'm trying to model turnover intention which results in a score. So I could do square regression or rank regression (one model)? And for questions 1 & 2 an upper tail test?

I would probably wait for the rest of your data to come in to avoid maybe influencing how you proceed with analysis (i.e. if you see something in a prelim analysis you might (consciously or subconsciously) alter the analysis plan when the rest of the data come in later.

Would you mind clarifying the actual number of independent variables that you'd plan to use in trying to model intention to turnover? I'm trying to make sure I understood what you wrote above. Depending on this number, it may call for some creativity or careful selection of what terms to include in the model.

As for my comment on using a multiple linear regression (same as ordinary least squares): if you fit a preliminary model, you can investigate the model assumptions. If they're reasonably satisfied, you might be able to use this method, provided it checks out after you go through some of the modeling steps. If it doesn't work, or if you want to check the robustness of your conclusions, you could use a rank regression (predicting the rank of the intention to turnover score, think of it as a nonparametric analysis). Either of these would allow you to test for interactions as you want.

In terms of the "tailedness" of the test, you can disregard my comment before, as it wasn't very clear. Realistically, you can just test the coefficient at that time to see if the interaction coefficient meshes with your hypothesis.

Education is defined by using 3 groups. professional bachelors are 1, academic bachelors are 2 and masters are 3. It is still possible to use K categories here, right? Because I have tried it and it resulted in some strange graphs... (i.e. turnover intention increases when psychological satisfaction increased with academic bachelors and master's degrees.)

Kind Regards

In general, it's much easier to interpret model coefficients (for linear regression, for example) when you fit the intercept and use k-1 dummy variables for a variable with k levels. Using 1/0 coding will create a unique permutation of 0/1 for each of the k-levels.