Selecting single items to measure doubly concrete constructs: A cautionary tale
By Marko Sarstedt, Adamantios Diamantopoulos, T. Salzberger, P. Baumgartner. 2016
90 citations recorded by the source corpus.
Single-item measures have recently become more en vogue due to studies arguing in favor of their psychometric properties vis-a-vis multi-item scales. However, their effective use requires (1) expert raters to designate the focal construct as being doubly concrete and (2) researchers to find a good single item to represent the construct. This study examines whether expert raters identify the doubly concrete nature of constructs that prior research presents as exemplary in this respect. Furthermore, the study compares the efficacy of a broad range of selection mechanisms based on expert judgment and statistical criteria for identifying the best item in a scale. The results show that expert raters do not share the commonly held belief that researchers can validly measure constructs such as attitude toward the ad, or brand, with single items. Further analyses show that neither rater assessments nor statistical criteria prove valuable regarding identifying an appropriate single item from a set of candidate items.
Subfields: Applied Psychology, Marketing, Organizational Behavior and Human Resource Management
View Paper: https://www.semanticscholar.org/paper/d2e46125c5afe0527afde94f4317afd0ee9af33a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Psychology
In Marketing
- Marko Sarstedt
- Christian M. Ringle
- Joseph F. Hair
- Jörg Henseler
- Jeffrey J. Risher
- Jun-Hwa (Jacky) Cheah
- Hiram Ting
- Artur Strzelecki
In Organizational Behavior and Human Resource Management
- Marko Sarstedt
- Christian M. Ringle
- Joseph F. Hair
- Jörg Henseler
- Jeffrey J. Risher
- Jun-Hwa (Jacky) Cheah
- Hiram Ting
- Artur Strzelecki
Citations in the corpus
- When to use and how to report the results of PLS-SEM (2019, methodology): “If the size of the data set is limited, CBSEM can produce abnormal results when data are nonnormal (Reinartz et al., 2009), while PLS-SEM shows a higher robustness in these situations (Sarstedt et al., 2016b).”