Scale modification: alternative approaches and their consequences

This work reviews approaches to scale modification, selects an influential marketing scale and use context, and examines the effects of the different scale modification approaches on which items are included in the refined scale, and cross-validates the psychometric performance of the resulting scales.

Abstract

Important marketing scales such as SERVQUAL are often adapted for use in a particular applied/theory-testing context and/or refined using some statistical method, assuming the resultant scale will have improved psychometric properties for a particular application. However, little attention has been paid to the consequences of how scale modifications are made, the criteria that are used to assess how well a modified scale performs, and whether scale modification is in fact worthwhile. To investigate these issues, we review approaches to scale modification, select an influential marketing scale and use context, and then examine the effects of the different scale modification approaches on which items are included in the refined scale. We then cross-validate the psychometric performance of the resulting scales using criteria that reflect the multiple purposes to which a scale can be applied. The results show approaches that leads to a more reliable scale for one purpose (e.g., segmentation) can be far less adequate for another purpose (e.g., benchmarking).