Abstract: Local Influence and Robust Methods for Mediation Models

Mediation analysis investigates how certain variables mediate the effect of predictors on outcome variables. Existing studies of mediation models have been limited to normal theory maximum likelihood (ML) or least squares with normally distributed data. Because real data in the social and behavioral sciences are seldom normally distributed and often contain outliers, classical methods can result in biased and inefficient estimates, which lead to inaccurate or unreliable test of the meditated effect. The authors propose two approaches for better mediation analysis. One is to identify cases that strongly affect test results of mediation using local influence methods and robust methods. The other is to use robust methods for parameter estimation, and then test the mediated effect based on the robust estimates. Analytic details of both local influence and robust methods particular for mediation models were provided and one real data example was given. We first used local influence and robust methods to identify influential cases. Then, for the original data and the data with the identified influential cases removed, the mediated effect was tested using two estimation methods: normal theory ML and the robust method, crossing two tests of mediation: the Sobel (1982) Sobel, M. E. 1982. “Asymptotic confidence intervals for indirect effects in structural equation models”. In Sociological methodology, Edited by: Leinhardt, S. 290–312. Washington, DC: American Sociological Association. [Crossref] , [Google Scholar] test using information-based standard error (z _I ) and sandwich-type standard error (z _SW ). Results show that local influence and robust methods rank the influence of cases similarly, while the robust method is more objective. The widely used z _I statistic is inflated when the distribution is heavy-tailed. Compared to normal theory ML, the robust method provides estimates with smaller standard errors and more reliable test.     

Bias and Efficiency in Structural Equation Modeling: Maximum Likelihood Versus Robust Methods

In the structural equation modeling literature, the normal-distribution-based maximum likelihood (ML) method is most widely used, partly because the resulting estimator is claimed to be asymptotically unbiased and most efficient. However, this may not hold when data deviate from normal distribution. Outlying cases or nonnormally distributed data, in practice, can make the ML estimator (MLE) biased and inefficient. In addition to ML, robust methods have also been developed, which are designed to minimize the effects of outlying cases. But the properties of robust estimates and their standard errors (SEs) have never been systematically studied. This article studies two robust methods and compares them against the ML method with respect to bias and efficiency using a confirmatory factor model. Simulation results show that robust methods lead to results comparable with ML when data are normally distributed. When data have heavy tails or outlying cases, robust methods lead to less biased and more efficient estimators than MLEs. A formula to obtain consistent SEs for one of the robust methods is also developed. The formula-based SEs for both robust estimators match the empirical SEs very well with medium-size samples. A sample of the Cross Racial Identity Scale with a 6-factor model is used for illustration. Results also confirm conclusions of the simulation study.     

A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given sample size, also provides more accurate results than those based on standard asymptotics. But the procedure needs a matrix to play the role of the population covariance matrix. The closer the matrix is to the true population covariance matrix, the more valid the bootstrap inference is. The current paper proposes a class of covariance matrices by combining theory and data. Thus, a proper matrix from this class is closer to the true population covariance matrix than those constructed by any existing methods. Each of the covariance matrices is easy to generate and also satisfies several desired properties. An example with nine cognitive variables and a confirmatory factor model illustrates the details for creating population covariance matrices with different misspecifications. When evaluating the substantive model, bootstrap or simulation procedures based on these matrices will lead to more accurate conclusion than that based on artificial covariance matrices.     

干扰任务对说谎影响的研究

摘要:本文将干扰任务对记忆的影响同测谎结合起来,试图运用这一方法来对求职者的真实学历和虚假学历进行区分。实验采用模拟面试的方式,对被试在有或无干扰任务条件下进行两次提问,结果发现,四种任务条件对被试真实经历和虚假经历都具有区分度,其中双双条件的区分度是最高的。并且,被试在双双任务条件下虚假经历的得分显著低于其他三种任务条件。     

幼儿教师职业承诺与情绪耗竭:情绪劳动的中介作用

以218名幼儿教师为被试,采用问卷调查法,探讨了职业承诺对情绪耗竭的影响,以及情绪劳动在职业承诺对情绪耗竭影响中的中介效应。结果表明:(1)职业承诺与自然行为、深层行为呈显著正相关,而与表层行为、情绪耗竭呈显著负相关。(2)自然行为、深层行为与情绪耗竭均呈显著负相关,而表层行为与情绪耗竭呈显著正相关。(3)深层行为和表层行为在职业承诺与情绪耗竭关系中起部分中介作用,自然行为在职业承诺与情绪耗竭关系间中介效应不显著。     

Ridge structural equation modelling with correlation matrices for ordinal and continuous data

This paper develops a ridge procedure for structural equation modelling (SEM) with ordinal and continuous data by modelling the polychoric/polyserial/product‐moment correlation matrix R . Rather than directly fitting R , the procedure fits a structural model to R _a= R +a I by minimizing the normal distribution‐based discrepancy function, where a > 0. Statistical properties of the parameter estimates are obtained. Four statistics for overall model evaluation are proposed. Empirical results indicate that the ridge procedure for SEM with ordinal data has better convergence rate, smaller bias, smaller mean square error, and better overall model evaluation than the widely used maximum likelihood procedure.     

俄罗斯神秘主义流行现状研究(上)

神秘主义,从灵学史的角度来考察,是一个世界现象。所以考察俄罗斯神秘主义的流行,就必须放在一个大环境下来思考。俄罗斯神秘主义流行与前苏联的演变有密不可分的关系,与美苏的争霸及世界格局的变迁也息息相关。本文即是从与前苏联的比较研究中,寻找可以借鉴的历史教训。     

《内景图》与《修真图》初探

近几十年,道教中的两幅修炼图《内景图》(又名《内经图》)和《修真图》被高度评价,更有人考证其为丘处机及张三丰所画,图中藏有秘而不传的功法。然而,透过图中注文来源、选用诗句及所绘内容,即可考其创制年代及修炼含义。     

广州医学院校中医学生与西医学生对中西医结合认识的调查报告

建国以来,我国大力致力于培养中西医结合型人材,然而到目前为止,关于中西医结合的概念在医学层面上仍达不到统一的共识,对于当今的医学生们,他们对中西医结合了解有多少,他们如何看待中西结合,中西医结合课程所起的作用有多大等等,这些都非常值得我们关注。为此,2005年6月份期间对广州市五所医学院校的学生进行了有关问卷调查。