Assessing Distinctiveness in Multidimensional Instruments Without Access to Raw Data – A Manifest Fornell-Larcker Criterion

摘要

The assessment of an instrument’s conceptual framework as prerequisite for conducting further analyses has been advocated for decades. Multidimensional instruments posit several components that are each expected to be homogeneous but distinct from each other. However, validity evidence supporting the proposed internal structure is often missing. This leaves researchers and practitioners who are interested in a certain instrument in a precarious situation: Before starting their own data collection, they do not know whether dimensions adequately discriminate from each other and thus whether they can have confidence in any interpretation of these dimensions. Adapting the Fornell–Larcker criterion, we propose estimating distinctiveness between dimensions by using nothing but the most commonly reported statistics: Cronbach’s alpha and the correlation matrix between the manifest composite scores of the dimensions in question. A simulation study demonstrates the usefulness of this “manifest Fornell–Larcker criterion” in providing an easily assessable method for vetting existing instruments, whereas a systematic literature review shows the necessity to do so even for instruments published in well-received journals.