<p>While <code>multilevel</code> and <code>multilevel-analysis</code> are probably easily mergeable, some of the others are genuine terminology differences.</p>

<ol>
<li>"Random effects" to economists is the GLS estimator of the random intercept model in the <code>panel-data</code> models. The "fixed effect" is a conditional estimator that only uses the within-cluster variability. I am sure that whatever is done with the <code>random-effects-model</code> tag will create confusion. Sometimes, economists do say that (what they understand as) the random effect model is valid when you have the random sample of clusters, but generally what they worry about is the correlation between the regressors and the random effects, which rather has to do with how complete your model is. So some of the <code>random-effects-model</code> entries may in fact have to be moved to <code>panel-data</code> if we are to clean this. May be  there should be <code>random-effects-model-econometrics</code> and <code>fixed-effects-model-econometrics</code> subtags.</li>
<li>GLMM stands for generalized linear mixed models, and while related to the GLM (generalized linear model, not the terrible "general linear model acronym" coined by SAS, I believe), it is different enough that these tags should live independently of one another.</li>
<li>R packages have somewhat different functionality. Since CV is very heavily R-oriented, it is probably fine that these tags exist as they are, but if you are subscribed to the multilevel list, you will see way many more questions about software packages like HLM, Mplus and Stata (in particular, the <a href="http://www.gllamm.org/" rel="nofollow">GLLAMM</a> package that beats most other ones except for speed). Social scientists tend to go to these lists rather than to some geeky website ;). So to be generalists, should we go ahead and create the tags for all of these other packages???</li>
<li>There is also <code>hierarchical-analysis</code> tag. The phrase is again used in a number of different contexts. Social scientists who use the HLM package would mean the multilevel analysis/mixed models that we are discussing here, while Bayesian statisticians would mean a model with priors and hyperpriors, which may or may not be involve the random effects of the multilevel models.</li>
</ol>

<p>There was an earlier <a href="http://rads.stackoverflow.com/amzn/click/1441903178" rel="nofollow">Pinheiro &amp; Bates</a> book, so may be there is a published version of that quote, if needed.</p>

<p>A good course of action might be to elect a mixed-models officer who'd be cleaning these tags from time to time as they see fit. Otherwise, we'd be just lost in discussing what needs to be done.</p>
