Correcting measurement error bias in interaction models with small samplesBisbe, Josep (Avtor)
Coenders, GermÃ (Avtor)
Saris, Willem E. (Avtor)
Batista-Foguet, Joan Manuel (Avtor)
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 ofbudgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made withthe alternative methods. Methods that do not correct for measurement errorbias perform very similarly and considerably worse than disattenuated regression.20062014-07-11 14:27:08Delo ni kategorizirano22525sl