2012/02/13 by Adamantios Diamantopoulos, Marko Sarstedt, Christoph Fuchs +2 · 1,667 citations
Business, Management and Accounting · Mathematics · Psychology · #Artificial intelligence #Computer science #Concurrent validity #Construct (python library) #Construct validity #Consumer Behavior in Brand Consumption and Identification #Consumer Market Behavior and Pricing #Criterion validity #Customer Service Quality and Loyalty #Developmental psychology #Econometrics #Incremental validity #Mathematics #Perspective (graphical) #Predictive validity #Psychology #Psychometrics #Scale (ratio) #Statistics
paper · pdf · doi:10.1007/s11747-011-0300-3
published in Journal of the Academy of Marketing Science 40(3), 434-449 (Springer Science+Business Media)
openalex publication_date 2012/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.