Moderating Effects of Management Control Systems and Innovation on Performance. Simple Methods for Correcting the Effects of Measurement Error for Interaction Effects in Small Samples

Germà Coenders (), Josep Bisbe, Willem E. Saris and Joan M. Batista-Foguet

No 7, Working Papers of the Department of Economics, University of Girona from Department of Economics, University of Girona

Abstract: In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression.

Keywords: measurement error; interaction effects; disattenuation; small samples; moderated regression; reliability; Chronbach’s alpha (search for similar items in EconPapers)
JEL-codes: C43 C51 C30 M41 O31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2003-06
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