On the Unnecessary Ubiquity of Hierarchical Linear Modeling
On the Unnecessary Ubiquity of Hierarchical Linear Modeling
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DOI：
10.1037/met0000078
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发表时间：
2017-03-01
影响因子：
7
通讯作者：
Silverman, Rebecca D.
中科院分区：
文献类型：
--
作者：
McNeish, Daniel;Stapleton, Laura M.;Silverman, Rebecca D.
在心理学和行为科学中，使用分层线性模型（HLM）及其对离散结果的扩展是对聚类数据建模的流行方法。然而，HLM及其离散结果扩展肯定不是唯一可用于对聚类数据进行建模的方法。虽然其他方法存在，并广泛应用于其他学科，但心理学家似乎还没有在实质性研究中考虑这些方法。本文比较和对比HLM与其他方法，包括广义估计方程和集群稳健的标准误差。这些替代方法不对随机效应进行建模，因此假设数量较少，并且与单水平方法相同，其优点是估计值经过调整以反映观察结果的聚类。这些替代方法可能是有利的情况下进行了讨论，包括随机效应是和不需要的研究问题，当随机效应可以改变回归系数的解释，离散结果的随机效应建模的挑战，以及使用HLM可能受益于使用替代方法发表的心理学文章的例子。说明性的例子提供和讨论，以证明替代方法的优点，也当HLM将是首选的方法。
In psychology and the behavioral sciences generally, the use of the hierarchical linear model (HLM) and its extensions for discrete outcomes are popular methods for modeling clustered data. HLM and its discrete outcome extensions, however, are certainly not the only methods available to model clustered data. Although other methods exist and are widely implemented in other disciplines, it seems that psychologists have yet to consider these methods in substantive studies. This article compares and contrasts HLM with alternative methods including generalized estimating equations and cluster-robust standard errors. These alternative methods do not model random effects and thus make a smaller number of assumptions and are interpreted identically to single-level methods with the benefit that estimates are adjusted to reflect clustering of observations. Situations where these alternative methods may be advantageous are discussed including research questions where random effects are and are not required, when random effects can change the interpretation of regression coefficients, challenges of modeling with random effects with discrete outcomes, and examples of published psychology articles that use HLM that may have benefitted from using alternative methods. Illustrative examples are provided and discussed to demonstrate the advantages of the alternative methods and also when HLM would be the preferred method.