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I have a question regarding the use of Multilevel Modeling (aka Hierarchical Linear Modeling) with Sequential Linear Modeling.

I am trying to perform Sequential Linear Modeling (with a binary outcome) but I also want to take into account two levels of the data. Specifically, I have data taken from multiple schools and I want to have a second level for school - which would require me to use Multilevel Modeling.

My question is, is there a way to perform both analyses at the same time?

The alternative I have thought of is to perform Sequential Linear Modeling and separately perform Multilevel Modeling and report there was (not) nesting effects of school.

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  • $\begingroup$ What do you mean with Sequential Linear Modeling? $\endgroup$
    – kjetil b halvorsen ♦
    Commented Dec 28, 2022 at 17:15
  • $\begingroup$ By Sequential Linear Modeling, I mean the predictors are entered sequentially or in steps/blocks to determine the information gained by adding another predictor or sets of predictors. $\endgroup$
    – Sam16
    Commented Dec 28, 2022 at 18:08
  • $\begingroup$ So that is stepwise methods? Then maybe add the tag stepwise-regression $\endgroup$
    – kjetil b halvorsen ♦
    Commented Dec 28, 2022 at 18:45
  • $\begingroup$ Yes, and thank you I will add that now. $\endgroup$
    – Sam16
    Commented Dec 28, 2022 at 23:55

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Stepwise modelling is a fairly simple approach, so I don't see why it couldn't be done. Simply select your criterion for calling a model "better", and implement the stepwise procedure using a hierarchical model.

The question shouldn't be "can it be done" but rather "should it be done" and the evidence would suggest the answer would be: NO. You're free to search this site for threads on stepwise selection to see why this is so problematic.

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