Errors Measurement and their Presence in Group Slope Differences - Evaluation of OLS, SEM and EIV Estimators

doi:10.7176/jep/11-22-06

Abstract

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. Keywords : EIV, Estimators, OLS, SEM, Type I Error DOI: 10.7176/JEP/11-22-06 Publication date: August 31 st 2020

Highlights

The mismeasured variables are contaminated within many economic data sets
The results have concluded that instead of using ordinary least squares (OLS) and structural equation model (SEM) estimators, EIV estimator should be used for the estimation of group slope differences in the presence of measurement error
The relevance of SEM estimators has not been diminished in the modelling of economic phenomena

Summary

Introduction

The mismeasured variables are contaminated within many economic data sets. The issue of measurement errors is one of the essential issues. The occurrence of measurement errors results in inconsistent and biased parameter measures and links to erroneous estimations to different degrees in economic analysis [1]. There are two different dimensions in which measurement error problems can be addressed such as linear errorsin-variables (EIV) models and nonlinear EIV models [2]. The problem of measurement error is identified by social scientists in terms of data collection, but usually ignored it during their corresponding statistical analyses. The bias induced by measurement error might be ignored if it is estimated to be smaller as compared to the effects being measured in the most optimistic scenario [3]. Appropriate application-particular methods to handle measurement error are present, but they are complicated to integrate, which need difficult-to-satisfy assumptions, or drive to high model dependence levels [4]

Methods

Results

Discussion

Conclusion