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Cited by 601

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

ISSUES AND STRATEGIES FOR REDUCING THE LENGTH OF SELF‐REPORT SCALES

STANTON, JEFFREY M., SINAR, EVAN F., BALZER, WILLIAM K., SMITH, PATRICIA C.·2002·Personnel Psychology
DOI 10.1111/j.1744-6570.2002.tb00108.x
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Abstract

Greater understanding of the complex interrelationships among work‐relevant constructs has increased the number of constructs on organizational surveys. Good psychometric practice also dictates the use of multiple items per construct. The net result has been longer surveys. Longer surveys take more time to complete, tend to have more missing data, and have higher refusal rates than short surveys. Arguably, then, techniques for reducing the length of scales while maintaining psychometric quality are worthwhile. Little guidance exists on how to reduce the length of a multi‐item scale and we argue that the most common technique, maximizing internal consistency, is problematic and should be avoided. We present a set of item “quality indices” to help conceptualize the competing issues that influence item retention decisions. Statistical analysis of an example case using these indices suggested that there are 3 key aspects of item quality to consider when reducing a scale. We describe strategies that can assist scale developers in using these 3 aspects of item quality when making scale reduction decisions.

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Crossref · 10.1111/j.1744-6570.2002.tb00108.x

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Metadata

Authors
STANTON, J. M., SINAR, E. F., BALZER, W. K., & SMITH, P. C.
Venue
Personnel Psychology
Publisher
Wiley
Published
2002-03
Volume
55
Issue
1
Pages
167-194
ISSN
  • 0031-5826 (ISSN-L)
  • 1744-6570
DOI
10.1111/j.1744-6570.2002.tb00108.x
OpenAlex
W1994975184
Cited by
601
References
11
FWCI
8.12
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.