Inference and Interval Estimation Methods for Indirect Effects With Latent Variable Models
Inference and Interval Estimation Methods for Indirect Effects With Latent Variable Models
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DOI：
10.1080/10705511.2014.935266
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发表时间：
2015-01-02
影响因子：
6
通讯作者：
Biesanz, Jeremy C.
中科院分区：
文献类型：
--
作者：
Falk, Carl F.;Biesanz, Jeremy C.
虽然最近的推断和区间估计方法的性能与观测变量的间接或介导的影响，鲜为人知的是，他们的潜变量模型的性能。本文介绍了一个广泛的蒙特卡洛研究11种不同的领先或流行的方法，适用于结构方程模型与潜变量。操纵变量包括样本量、每个潜变量的指标数量、每组指标的内部一致性以及潜变量之间的16种不同路径组合。结果表明，一些流行的或以前推荐的方法，如偏差校正的自助法和渐近标准误校准I型错误和覆盖率在某些条件下。基于似然的置信区间，分布的产品的方法，和百分位数引导出现作为领先的方法，区间估计和推理，而联合显著性检验和部分后验方法进行推理。
Although much is known about the performance of recent methods for inference and interval estimation for indirect or mediated effects with observed variables, little is known about their performance in latent variable models. This article presents an extensive Monte Carlo study of 11 different leading or popular methods adapted to structural equation models with latent variables. Manipulated variables included sample size, number of indicators per latent variable, internal consistency per set of indicators, and 16 different path combinations between latent variables. Results indicate that some popular or previously recommended methods, such as the bias-corrected bootstrap and asymptotic standard errors had poorly calibrated Type I error and coverage rates in some conditions. Likelihood-based confidence intervals, the distribution of the product method, and the percentile bootstrap emerged as leading methods for both interval estimation and inference, whereas joint significance tests and the partial posterior method performed well for inference.