# Random intercept turns insignificant - Interpretation

I am running a intercept model with intercepts varying across people in R. My independent variables are all numeric variables.

My question is a general one: I saw already that it can happen that my random intercept turns insignificant when I add explanatory variables.

Does anybody know how to interpret this?

• How many observations per engineer? Also, how many explanatory variables? Do they vary at the "engineer" level? – probabilityislogic Feb 25 '15 at 13:03

In principle having only 15 random-effects levels should not be a problem, even for maximum-likelihood procedures (e.g. package lme4). However, if the variance of your random-effects is exactly zero, it is possible that your model is somehow overfitted, or that in your case 15 levels are too few (these nice simulations here illustrates the problem). If that is the case, check on this page, for example they suggest to use some informative prior on the random-effects variance.