Hi everyone,
I am trying to identify relevant predictors of an outcome in multi-level data. I am wondering what the best approach is here - how do you go about the selection process? By significance in the overall model, recalculate, select again, recalculate again etc.? Using a normal regression stepwise, forwards or backwards procedure? LASSO (do I need to model the multi-level structure if ICC is low)?
Thank you so much!
It is recommended that a multi-level model be used to account for the hierarchical structure, even in cases where the intraclass correlation coefficient (ICC) is low.
Variable Selection:
LASSO: This method is useful for selecting predictors, but it is essential to ensure that the multi-level structure is considered.
Stepwise: This method is less ideal for multi-level data, but it can be used with caution.
Significance: It is important to evaluate the significance of predictors in the overall model, but it is crucial to avoid overfitting.
Focus on modeling the hierarchical structure, careful selection, and validation to ensure accuracy.
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