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I have a dataset where each record is a purchase, with user, datetime, low-cost/high-cost option chosen (y, boolean), quality, and opportunity price (the lowest priced option) as variables. The goal is to predict whether someone will purchase a low-cost option or a high cost option.

However, some users have multiple records because they have made multiple purchases at different times (I don't care about time in this case).

What is a good model / approach for this sort of data? I am assuming I cant just leave the records in their as is for a standard Logistic Regression because it would break the assumption of iid.

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This is a typical application for mixed effects models - where you fit random effects, in particular, random intercepts, for user, and fixed effects for the other variables (hence "mixed" effects). Random intercepts takes into account non-independence, that choices by the same user will be more similar to each other than choices by other users. In this particular case, since it will be a logistic model it will be a generalised linear mixed model (GLMM). With this type of model you can make predictions for previous users or new users.

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  • $\begingroup$ curious, for hypothesis tests are there also frameworks to run 2-proportion z-tests and z-test for population means using mixed effects modeling? $\endgroup$
    – J Doe
    Commented Dec 13, 2019 at 3:06
  • $\begingroup$ The question was about prediction. If you have questions about inference in mixed models, please ask a new question, but in general you can do the same or similar tests as models without random effects $\endgroup$
    – Robert Long
    Commented Dec 13, 2019 at 8:12

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