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"The first step is to understand which of our more than 250 compensable factors are important when it comes to pricing a job and how that job's pay is affected by these compensable factors." says to me that they do not use the same set of 250 for each occupation. For example statistician/data scientist or similar the computer languages known are important but are not at all for a human resources graduate, so wouldn't be asked.

I expect that the way of obtaining data is to offer people who search the web for salaries the possibility to compare theirs to others by filling out a survey. So it is biased towards people who are either looking for an increase in salary or a new job. Also people have a tendency to ask hypothetical questions, for example if I got a Masters would an extra $20,000 per year be typical.


Maybe they use regularization to not add even all the potential main effects. Maybe they are smarter than your naive strawman?


Chris: There are no technical solutions to the first problem, which is unreliable input. As for the second problem, they explicitly use an interaction effect as an example of value add so they clearly have *significant* interaction effects in their model. You can use regularization but if you don't have enough data, either your effects are artifacts of your model, or your effects are shrunk to zero. How do you think regularization help?

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