The development of a goodness-of-fit test for high level binary multilevel models

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.