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概述：在社会心理学和行为学的研究中，记录某些健康或行为结果发生频率的计数中（如在一段时间内无防护措施的性行为的次数）往往含有大量的零，这是因为当某些对象对于某种研究行为没有危险时就会产生“结构性零”。不像随机零（结果可以是大于零，但是也可能由于样本变异性而成为零），结构性零在统计和临床上通常是非常不同的。如果两种类型零的差异被忽略，就可能会导致对结果和研究发现的错误解释。然而在实践中，结构性零经常会没有被观察到而这种潜在性使数据分析复杂化了。在这篇文章中，我们专注于一种模式，即通常用于解决零膨胀数据的零膨胀泊松（Zero-inflated Poisson，ZIP）回归模型。首先，我们对结构性零和ZIP模型做一个简要概述。然后我们以一项青春期少女艾滋病高危性行为的研究数据来阐述ZIP模型。文中还附有SAS和Stata的示例代码，以帮助运行和解释ZIP分析。“,”Summary:In psychosocial and behavioral studies count outcomes recording the frequencies of the occurrence of some health or behavior outcomes (such as the number of unprotected sexual behaviors during a period of itme) otfen contain a preponderance of zeroes because of the presence of ‘structural zeroes’ that occur when some subjects are not at risk for the behavior of interest. Unlike random zeroes (responses that can be greater than zero, but are zero due to sampling variability), structural zeroes are usually very different, both staitsitcally and clinically. False interpretaitons of results and study ifndings may result if differences in the two types of zeroes are ignored. However, in pracitce, the status of the structural zeroes is otfen not observed and this latent nature complicates the data analysis. In this aritcle, we focus on one model, the zero-inlfated Poisson (ZIP) regression model that is commonly used to address zero-inlfated data. We ifrst give a brief overview of the issues of structural zeroes and the ZIP model. We then given an illustraiton of ZIP with data from a study on HIV-risk sexual behaviors among adolescent girls. Sample codes in SAS and Stata are also included to help perform and explain ZIP analyses.