Do We Really Need Multiple-Item Measures in Service Research?

Abstract

Increasingly, marketing academics advocate the use of multiple-item measures. However, use of multiple-item measures is costly, especially for service researchers. This article investigates the incremental information of each additional item in a multiple-item scale. By applying a framework derived from the forecasting literature on correlated experts, the authors show that, even with very modest error term correlations between items, the incremental information from each additional item is extremely small. This study’s “information” (as opposed to “reliability”) approach indicates that even the second or third item contributes little to the information obtained from the first item. Furthermore, the authors present evidence that added items actually aggravate respondent behavior, inflating across-item error term correlation and undermining respondent reliability. Researchers may want to consider the issue of item information in addition to reliability. This article discusses ways in which researchers can construct scales that maximize the amount of information scale items offer without compromising measurement reliability.