From Nuisance to Novel Research Questions: Using Multilevel Models to Predict Heterogeneous Variances
From Nuisance to Novel Research Questions: Using Multilevel Models to Predict Heterogeneous Variances
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DOI：
10.1177/1094428119887434
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发表时间：
2019-11
影响因子：
9.5
通讯作者：
H. Lester;Kristin L. Cullen‐Lester;R. Walters
中科院分区：
文献类型：
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作者：
H. Lester;Kristin L. Cullen‐Lester;R. Walters
反映变异性差异的结构引起了许多研究工作场所现象的研究人员的兴趣。通常用于调查“基于变异性”的结构的聚合方法受到几个限制，包括无法包括1级预测因子和未能考虑变异性估计中的不确定性。我们演示了如何混合效应的位置规模（MELS）和异质方差模型，这是传统的混合效应（或多层次）模型的直接扩展，可以用来测试平均值（位置）和变异性（规模）相关的假设同时。本文的目的是展示（a）如何用嵌套的横截面和纵向数据估计MELS和异质方差模型，以回答组织研究人员感兴趣的结构的新研究问题，（B）贝叶斯方法如何在预测变异性和平均水平时允许包含随机截距和斜率，最后（c）研究人员如何使用多水平方法来预测组间异质性方差。在这样做的时候，这篇文章强调了在组织研究中将可变性视为不仅仅是统计上的麻烦的附加值。
Constructs that reflect differences in variability are of interest to many researchers studying workplace phenomena. The aggregation methods typically used to investigate “variability-based” constructs suffer from several limitations, including the inability to include Level 1 predictors and a failure to account for uncertainty in the variability estimates. We demonstrate how mixed-effects location-scale (MELS) and heterogeneous variance models, which are direct extensions of traditional mixed-effects (or multilevel) models, can be used to test mean (location)- and variability (scale)-related hypotheses simultaneously. The aims of this article are to demonstrate (a) how the MELS and heterogeneous variance models can be estimated with both nested cross-sectional and longitudinal data to answer novel research questions about constructs of interest to organizational researchers, (b) how a Bayesian approach allows for the inclusion of random intercepts and slopes when predicting both variability and mean levels, and finally (c) how researchers can use a multilevel approach to predict between-group heterogeneous variances. In doing so, this article highlights the added value of viewing variability as more than a statistical nuisance in organizational research.