JSM 2005 - Toronto

Abstract #302995

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Activity Number: 147
Type: Contributed
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #302995
Title: Estimating and Testing Parameters in the Presence of Measurement Error: A Comparison of Approaches
Author(s): Todd Bodner*+
Companies: Portland State University
Address: Department of Psychology, Hillsboro, OR, 97123,
Keywords: Measurement Error ; Reliability ; Correlation ; Regression Slopes ; Hypothesis Testing
Abstract:

Measurement error attenuates correlations and regression slopes when predictors are measured with error. If a researcher can estimate the reliability of measures, several techniques are available to estimate what the parameters would be in the absence of measurement error. The present paper uses a hypothetical dataset with two variables to illustrate and evaluate four approaches to estimate and conduct hypothesis tests for adjusted parameters: the classic correction for attenuated correlations approach, a latent correlation approach, a latent predictor approach, and an adjusted predictor variance regression approach. The degree of measurement error was manipulated by varying the reliability of one of the variables. Each approach yielded the same adjusted (standardized) point estimates at each level of reliability. However, in significance testing for the adjusted parameters, several notable differences were found. In short, when testing the statistical significance of these adjusted parameter estimates, researchers should use likelihood ratio tests (rather than Wald tests) and the latent correlation or regression approaches, as these approaches have greater statistical power.

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