MEASUREMENT ERROR IN THE ANALYSIS OF INTERACTION EFFECTS BETWEEN CONTINUOUS PREDICTORS USING MULTIPLE-REGRESSION - MULTIPLE INDICATOR AND STRUCTURAL EQUATION APPROACHES
MEASUREMENT ERROR IN THE ANALYSIS OF INTERACTION EFFECTS BETWEEN CONTINUOUS PREDICTORS USING MULTIPLE-REGRESSION - MULTIPLE INDICATOR AND STRUCTURAL EQUATION APPROACHES
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DOI：
10.1037/0033-2909.117.2.348
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发表时间：
1995-03-01
影响因子：
22.4
通讯作者：
WAN, CK
中科院分区：
文献类型：
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作者：
JACCARD, J;WAN, CK
测量的不可靠性会在回归系数中产生偏差。在多元回归中使用产品术语时，这种测量误差尤其成问题，因为产品术语的可靠性相对于其组成部分通常相当低。验证性因子分析作为处理不可靠性问题的一种手段，在模拟研究中进行了探索。该设计比较了传统回归分析(忽略测量误差)和基于潜在变量结构方程模型的方法，后者使用最大似然和加权最小二乘估计标准。结果表明，潜在变量法与极大似然估计法相结合，在存在测量误差的情况下，对存在I型和II型误差的交互作用进行了较好的分析。
Unreliability of measures produces bias in regression coefficients. Such measurement error is particularly problematic with the use of product terms in multiple regression because the reliability of the product terms is generally quite low relative to its component parts. The use of confirmatory factor analysis as a means of dealing with the problem of unreliability was explored in a simulation study. The design compared traditional regression analysis (which ignores measurement error) with approaches based on latent variable structural equation models that used maximum-likelihood and weighted least squares estimation criteria. The results showed that the latent variable approach coupled with maximum-likelihood estimation methods did a satisfactory job of interaction analysis in the presence of measurement error in terms of Type I and Type II errors.