It's strongly suggested that you not use the Wald p values for at least random effects in multilevel models (some suggest not using them for fixed effects either). Instead it is suggested you do a deviance (LR) test, testing one variable at a time (that is adding one to the model each time). II have found no guidance on what order makes the most sense. Two examples of this are below.
When you suspect a predictor has a fixed effect, and possibly a random one as well, do you have to first test the fixed effect with a LR then the random effect?
Do you have to test if there is a random intercept before you test if a slope is random? And if the intercept is not random, does it make sense to test for random slopes?