出版社：The International Institute for Science, Technology and Education (IISTE)
摘要：The biases interaction, considered as measurement error, is responsible for affecting and distorting various inferences about the interactive hypotheses. The study aims focus on a single-indicator and depicted the accuracy of estimate group slope differences by disattenuation of interactive effects, together with error-in-variables (EIV) regression. The simulation results and analytic findings were used for the comparison between relative bias, Type I error of EIV, power, sparse multi-group structural equation model (SEM), and ordinary least squares (OLS). The results have shown that EIV estimators were less biased as compared to the OLS and SEM estimators. In a situation, where groups differ in the prediction of reliability, the OLS and SEM estimators are unable to control the rate of type I error. However, the impact of additional derivations using Cronbach’s alpha depicted decreased reliability with EIV estimator. While using alpha, the bias in EIV estimators was not increased as compared to the SEM and OLS estimators. The results suggested that EIV estimator should be used instead of using OLS and SEM estimators, for the estimation of group slope differences in the presence of measurement error.
