[R-sig-ME] variance estimates of main effects change dramatically depending on interactions permitted

[Mike Lawrence]

I am interested in estimating the variance in the effect V1 across
levels of my random effect ("id"). I also happen to have another
variable, V2, crossed with V1. The model:

lmer(
	data = my_data
	, formula = dv ~ V1*V2 + ( V1 + V2 | id )
)

yields a random effects variance estimate for V1 of about 10. However,
the model:

lmer(
	data = my_data
	, formula = dv ~ V1*V2 + ( V1*V2 | id )
)

yields a random effects variance estimate for V1 of about 100.

Any idea what would cause such a large difference, and which variance
estimate is more appropriate?

Cheers,

Mike

--
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University

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~ Certainty is folly... I think. ~