[R-sig-ME] Is it kosher to use random-intercept estimates as explanatory variables in another model?

[Christos Hatzis]

She could do this in a single step using a multilevel model that includes
group-level predictors to model part of the variation associated with the
intercept.

Gelman & Hill include a nice example and discussion of the effect that
group-level predictors have on the estimates of observation-level parameters
in Section 12.6 of their book.

-Christos  

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Jeremy Koster
Sent: Monday, June 06, 2011 1:55 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Is it kosher to use random-intercept estimates as
explanatory variables in another model?

I'm reviewing a paper for a colleague, and I haven't seen this done before.

Imagine that she has a sample of 100 houses, all of which include children
who raise chickens.  She includes a random term for household and finds that
there is substantial household-level variance in chicken husbandry by kids.

She then takes the household-level estimates (i.e., plus/minus relative to
the model intercept) and uses them as an explanatory variable in an OLS
model with households as the sampling unit.  For example, she would predict
something like household-level income while using the random-intercept
estimates from the chicken analysis (and other covariates).

At first glance, this might seem relatively straightforward, but I haven't
encountered similar analyses, and I'm wondering about potential pitfalls . .
. particularly given the variable number of kids in each house.

Any thoughts?

Thanks!

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