Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective

TLDR

Predictive validity of measures is a major concern in marketing research. The study investigates when single items versus multi‑item scales yield better predictive validity. A comprehensive simulation study examined how inter‑item correlations, item counts, and correlation patterns affect predictive validity of single versus multi‑item measures. Results show that multi‑item scales generally outperform single items in predictive validity, with single items only matching performance under very specific conditions, so single‑item use should be limited.

Abstract

Establishing predictive validity of measures is a major concern in marketing research. This paper investigates the conditions favoring the use of single items versus multi-item scales in terms of predictive validity. A series of complementary studies reveals that the predictive validity of single items varies considerably across different (concrete) constructs and stimuli objects. In an attempt to explain the observed instability, a comprehensive simulation study is conducted aimed at identifying the influence of different factors on the predictive validity of single versus multi-item measures. These include the average inter-item correlations in the predictor and criterion constructs, the number of items measuring these constructs, as well as the correlation patterns of multiple and single items between the predictor and criterion constructs. The simulation results show that, under most conditions typically encountered in practical applications, multi-item scales clearly outperform single items in terms of predictive validity. Only under very specific conditions do single items perform equally well as multi-item scales. Therefore, the use of single-item measures in empirical research should be approached with caution, and the use of such measures should be limited to special circumstances.