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.

中文翻译：

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零膨胀广义线性混合模型：了解数据关系的更好方法

本文探讨了社会科学中未被充分利用的数学分析方法。除了描述该方法及其优点之外，我们还在一个已知的有关149个国家的腐败数据库中扩展了混合模型的先前报道的应用。提到的研究中的数据集在结果变量中包含合理数量的零（13.19％），这是此类研究以及相当多的社会科学研究的典型代表。在我们的论文中，提出了有关模型估计的详细指南，其中结果变量的数据包括过多的零，并且数据集具有自然的嵌套结构。我们认为，我们的研究不太可能会拒绝支持采用混合建模和零膨胀的假设，而不是原来的简单框架。反而，