Abstract:
Before making inferences about a population using a fitted model, it
is necessary to determine whether the fitted model describes the
data well. A poorly fitted model may lead to biased and invalid conclusions,
resulting in incorrect inferences. Recent studies show the
necessity of goodness-of-fit tests for high level binary multilevel
models. The focus here was to develop a goodness-of-fit test to use
in the model adequacy testing of high level binary multilevel models
and to examine, whether the type I error and power hold for the
newly developed goodness-of-fit test considering a three-level random
intercept model.
