Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships

TLDR

The corruption study’s outcome variable contained about 13 % zeros, a common feature in social‑science data. The paper investigates the use of zero‑inflated generalized linear mixed models in social science, offering detailed estimation guidelines for nested data. The authors apply the method to a corruption database of 149 countries, extending a prior mixed‑model analysis and outlining its advantages. Results show that accounting for country‑level random effects and zero inflation improves model fit compared to a simpler framework.

Abstract

Our article explores an underused mathematical analytical methodology in the social sciences. In addition to describing the method and its advantages, we extend a previously reported application of mixed models in a well-known database about corruption in 149 countries. The dataset in the mentioned study included a reasonable amount of zeros (13.19%) in the outcome variable, which is typical of this type of research, as well as quite a bit of social sciences research. In our paper, present detailed guidelines regarding the estimation of models where the data for the outcome variable includes an excess number of zeros, and the dataset has a natural nested structure. We believe our research is not likely to reject the hypothesis favoring the adoption of mixed modeling and the inflation of zeros over the original simpler framework. Instead, our results demonstrate the importance of considering random effects at country levels and the zero-inflated nature of the outcome variable.