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Parameter estimates of interaction effects: Moderated multiple regression versus errors-in-variables regression

机译：相互作用效应的参数估计：缓和多元回归与变量误差回归

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摘要

Past research has shown that the results of moderated multiple regression (MMR) are highly affected by the unreliability of the predictor variables. Some researchers (Bohrnstedt & Marwell, 1978; Busemeyer & Jones, 1983) have suggested that errors-in-variables regression (EIVR) (Warren, White, & Fuller, 1974) may be useful for dealing with the problems of measurement error in the search for moderators. The method suggested by these researchers entails obtaining an estimate of the measurement error present in the cross-product term of the regression through a formula developed by Bohrnstedt and Marwell (1978). This estimated error covariance matrix can then be used in the EIVR calculations. This procedure has been advocated by several researchers, and implemented in a number of cases. Yet, little is known about the properties of the EIVR estimators in the context of moderator variable detection. The present study is a simulation designed to compare the parameter estimates of interaction effects under each of these techniques. The dependent variables were the bias and the mean squared error (MSE) of the parameter estimates. MMR and EIVR were compared using these criteria under varying conditions of sample size, reliability of the predictor variables, mean to standard deviation ratios of the predictor variables, and intercorrelations among the predictor variables. Results indicated that the EIVR parameter estimates were consistently less biased than the MMR estimates, however, the MSE of the EIVR estimates was greater than that for the MMR estimates in many cases. Increases in the reliability of the predictor variables led to improved estimates under both techniques. However, the reliability of the predictors had a greater effect on EIVR estimates. Increases in sample size also improved the parameter estimates of both techniques, however, the EIVR estimates were affected to a much greater degree than the MMR estimates. In general, the findings showed that EIVR estimates are superior to MMR estimates when sample size is high (i.e., at least 250) and the reliabilities of the predictors are high (i.e., r $geq$.65). However, MMR appears to be the superior strategy under more typical research circumstances.
机译：过去的研究表明，适度多元回归（MMR）的结果受预测变量的不可靠性影响很大。一些研究者（Bohrnstedt＆Marwell，1978； Busmeeyer＆Jones，1983）提出，变量误差回归（EIVR）（Warren，White和Fuller，1974）可能有助于解决测量中的测量误差问题。搜索版主。这些研究人员提出的方法需要通过Bohrnstedt和Marwell（1978）开发的公式获得对回归的叉积项中存在的测量误差的估计。然后，可以在EIVR计算中使用此估计的误差协方差矩阵。一些研究人员提倡此程序，并在许多情况下实施。然而，关于主持人变量检测的背景下，EIVR估计器的性质知之甚少。本研究是一种模拟，旨在比较每种技术下的相互作用效应的参数估计。因变量是参数估计值的偏差和均方误差（MSE）。使用这些标准，在不同的样本量，预测变量的可靠性，预测变量的均值与标准差之比以及预测变量之间的相互关系的条件下，对MMR和EIVR进行了比较。结果表明，EIVR参数估计的偏差始终小于MMR估计，但是，在许多情况下，EIVR估计的MSE大于MMR估计的MSE。在两种技术下，预测变量的可靠性提高导致估计值提高。但是，预测变量的可靠性对EIVR估计影响更大。样本量的增加也改善了这两种技术的参数估计，但是，EIVR估计的影响程度比MMR估计大得多。总的来说，研究结果表明，当样本量较大（即至少250个）且预测变量的可靠性较高（即r $ geq $ .65）时，EIVR估计要优于MMR估计。但是，MMR在更典型的研究情况下似乎是一种更好的策略。

著录项

  • 作者

    Anderson, Lance E.;

  • 作者单位

    Bowling Green State University.;

  • 授予单位 Bowling Green State University.;
  • 学科 Quantitative psychology.
  • 学位 Ph.D.
  • 年度 1989
  • 页码 125 p.
  • 总页数 125
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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