[R-sig-ME] Standard Error of a coef. in a 2-level model vs. 2 OLS models
@|m@h@rme| @end|ng |rom gm@||@com
Mon Sep 14 01:50:38 CEST 2020
Just a clarification.
For `ols1` model, I can approximate its SE of the sector coefficient by
using the within and between variance components from the HLM model:
sqrt(( 6.68 + 39.15 )/45)/(160*.25))
BUT For `ols2` model, how can I approximate its SE of the sector
coefficient by using the within and between variance components from the
On Sun, Sep 13, 2020 at 6:37 PM Simon Harmel <sim.harmel using gmail.com> wrote:
> Dear All,
> I have fit two ols models (ols1 & ols2) and an mixed-effects model (m1).
> ols1 is a simple lm() model that ignores the second-level. ols2 is a simple
> lm() model that ignores the first-level.
> For `ols1` model, `sigma(ols1)^2` almost equals sum of variance
> components in the `m1` model: 6.68 (bet.) + 39.15 (with.)
> For `ols2` model, I wonder what does `sigma(ols2)^2` represents when
> compared to the `m1` model?
> Here is the fully reproducible code:
> hsb <- read.csv('
> hsb_ave <- hsb %>% group_by(sch.id) %>% mutate(math_ave = mean(math)) %>%
> slice(1) # data that only considers grouping but ignores lower level
> ols1 <- lm(math ~ sector, data = hsb)
> m1 <- lmer(math ~ sector + (1|sch.id), data = hsb)
> # `sigma(ols1)^2` almost equals 6.68 (bet.) + 39.15 (with.) from lmer
> But if I fit another ols model that only considers the grouping structure
> (ignoring lower level):
> ols2 <- lm(math_ave ~ sector, data = hsb_ave)
> Then what does `sigma(ols2)^2` should amount to when compared to the `m1`
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