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Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Faculty of Social Sciences University of Ljubljana

Author: Bisbe, Josep
Coenders, Germà
Saris, Willem E.
Batista Foguet, Joan Manuel
Abstract: Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression
Document access: http://hdl.handle.net/2072/300355
Language: eng
Publisher: Faculty of Social Sciences University of Ljubljana
Rights: Tots els drets reservats
Subject: Anàlisi d’error (Matemàtica)
Anàlisi de regressió
Mostreig (Estadística)
Error analysis (Mathematics)
Sampling (Statistics)
Regression analysis
Title: Correcting Measurement Error Bias in Interaction Models with Small Samples
Type: info:eu-repo/semantics/article
Repository: Recercat

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