BACKGROUND & AIMS:
:In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.
背景与目标:
: 在普通的最小二乘回归中,研究人员通常对知道一组参数是否与零不同感兴趣。使用完整的数据,可以使用预测测试中的增益,分层多元回归或综合f检验来实现。然而,在实质性研究场景中,经常存在缺失的数据。在当前最先进的缺失数据策略之一的多重插补的背景下,对一组参数的联合显著性有几种不同的类比多参数检验,这些多参数检验统计量可以参考各种分布进行统计推断。然而,对这些测试的性能知之甚少,并且几乎没有研究研究在行为科学数据的典型场景 (例如,小到中等样本等) 中比较了这些测试的类型1错误率和统计能力。本文使用蒙特卡洛模拟技术来检验这些多参数测试统计量在多种现实条件下的多次插补的性能。我们根据模拟结果为实质性研究人员提供了一些实用的建议,并通过一个实证示例说明了这些测试统计量的计算。