I can use some help with project design and choosing the correct method to answer this question. I have a dataset that contains associate ID, associate specialization, hours worked, revenue, period, project ID, a flag that tells me if the project was training, and years associate has been with the company. I was asked to answer the following question:

Does hours spent training early in an Associates career impact the amount of revenue they generate in the future?

Here are my issues answering this question:

  1. Some associates work on training projects throughout their career, not just once in the beginning. While other associates never do training.
  2. The specialization matters; if the associate trained in specialization X then we would want to know the impact of work done in specialization X, but some associates may switch specializations throughout their career.

I've been looking into GLMM and linear mixed models and cannot seem to find the best method or the correct way to set up the data for this question.



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