Modeling Clustered Data with Very Few Clusters
Modeling Clustered Data with Very Few Clusters
复制标题
DOI：
10.1080/00273171.2016.1167008
复制
发表时间：
2016-01-01
影响因子：
3.8
通讯作者：
Stapleton, Laura M.
中科院分区：
文献类型：
--
作者：
McNeish, Daniel;Stapleton, Laura M.
聚类数据的小样本推理最近在方法学文献中受到越来越多的关注，许多方法的小样本行为都有一些模拟研究。然而，几乎所有以前的研究都集中在单一类别的方法上（例如，只有多层次模型，只有三明治估计的校正），以及可以实现以适应具有非常少的聚类的聚类数据的各种方法的差异性能在很大程度上是未知的，这可能是由于严格的学科偏好。此外，这些研究中的大多数都集中在具有15个或更多聚类的场景上，并且具有不切实际的简单数据生成模型，预测因子很少。本文的动机是应用教育心理学集群随机试验，提出了一个模拟研究，同时解决了极端的小样本和差异性能（估计偏差，I型错误率和相对功率）的12种方法，以考虑集群数据与模型，具有更现实的预测数量。激励数据，然后与每种方法建模，并比较结果。结果表明，广义估计方程表现不佳;贝叶斯先验分布的选择影响性能;固定效应模型表现相当不错。应用的限制和影响进行了讨论。
Small-sample inference with clustered data has received increased attention recently in the methodological literature, with several simulation studies being presented on the small-sample behavior of many methods. However, nearly all previous studies focus on a single class of methods (e.g., only multilevel models, only corrections to sandwich estimators), and the differential performance of various methods that can be implemented to accommodate clustered data with very few clusters is largely unknown, potentially due to the rigid disciplinary preferences. Furthermore, a majority of these studies focus on scenarios with 15 or more clusters and feature unrealistically simple data-generation models with very few predictors. This article, motivated by an applied educational psychology cluster randomized trial, presents a simulation study that simultaneously addresses the extreme small sample and differential performance (estimation bias, Type I error rates, and relative power) of 12 methods to account for clustered data with a model that features a more realistic number of predictors. The motivating data are then modeled with each method, and results are compared. Results show that generalized estimating equations perform poorly; the choice of Bayesian prior distributions affects performance; and fixed effect models perform quite well. Limitations and implications for applications are also discussed.