Hi all,
I have a statistical modelling question. I am sure a lot of people have done similar things but I am missing the correct technical terms to find examples on my own.
The observations in my data are comparable to job applications. There are several different jobs and one group of applications for each job. From these competing applications one is picked for the job.
Now I want to determine, which characteristics of an application have a significant influence on the probability that the application is getting picked - across all jobs.
So I considered a logit or probit model with robust standard errors clustering on job level as well as including random-effects or fixed-effects for the jobs.
My problem is: how do I correct for the differences of the characteristics between jobs?
Let's say one characteristic is that people ask for a desired income in their application. So I have jobs where applicants ask for an income of 1000$ on average and groups where they ask for $ 1 million. Could I just calculate the income asked for relative to the group average or should apply a z-transformation? Are there standard procedures for this case or more advanced models?
I would greatly appreciate any help or hints on the technical terms I have to look for.
Thank you,
Tom
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