Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models
Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models
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DOI：
10.1037/0893-3200.21.4.726
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发表时间：
2007-12-01
影响因子：
2.7
通讯作者：
Gallop, Robert J.
中科院分区：
文献类型：
--
作者：
Atkins, David C.;Gallop, Robert J.
婚姻和家庭研究人员经常研究不频繁的行为。这些强大的心理变量，如虐待、批评和吸毒，对家庭和社会以及用于研究它们的统计模型都有重要的影响。对于这些类型的数据，大多数研究人员仍然依赖于普通最小二乘（OLS）回归，但是对于这些计数数据，OLS回归的估计和推断可能存在严重偏差。本文介绍了正偏斜事件数据的统计方法，包括泊松、负二项、零膨胀泊松和零膨胀负二项回归模型。本文通过一个婚姻承诺实例介绍了这些统计方法，并在在线补充资料中提供了在R、SAS、SPSS和Mplus中运行示例分析的数据和计算机代码。扩展和实用的建议，以协助研究人员使用这些工具与他们的数据。
Marital and family researchers often study infrequent behaviors. These powerful psychological variables, such as abuse, criticism, and drug use, have important ramifications for families and society as well as for the statistical models used to study them. Most researchers continue to rely on ordinary least-squares (OLS) regression for these types of data, but estimates and inferences from OLS regression can be seriously biased for count data such as these. This article presents a tutorial on statistical methods for positively skewed event data, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. These statistical methods are introduced through a marital commitment example, and the data and computer code to run the example analyses in R, SAS, SPSS, and Mplus are included in the online supplemental material. Extensions and practical advice are given to assist researchers in using these tools with their data.