REML vs ML in lmerTest

Hi everyone,

I'm a little confused about the use REML and ML. I fit models in lmer,
which were pretty straightforward (a few continuous and a few nominal
predictors, plus random intercepts for clusters of data). I tested the
fixed effects using the lmerTest anova function with Kenward-Roger df (I
have no interest in testing random effect significance). I get the same F
values, df, and p values regardless of whether the models were fit with
REML or ML, but the actual sums of squares in the anova output differ
modestly. Given that it didn't matter at all for the results, it doesn't
seem I should particularly care whether I use REML and ML in the lmer. But,
I want to report which I used.

So my questions:

-Why do I get the same statistical values except for SS with REML and ML?
-Which would be more appropriate - REML or ML? I'm thinking REML because I
have an unbalanced sample sizes for each level of the random effect (based
on Bolker et al. 2008), but I wanted to double check that this makes sense.

Thank you!
Brad

Bradley Evan Carlson
Assistant Professor of Biology
Wabash College, Crawfordsville IN

Email: *carlsonb at wabash.edu* <+carlsonb at wabash.edu>
Website: https://sites.google.com/site/bradleyecarlson/home

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