Tackling the Problem of Construct Proliferation: A Guide to Assessing the Discriminant Validity of Conceptually Related Constructs

Shaffer, Jonathan A.; DeGeest, David; Li, Andrew

doi:10.1177/1094428115598239

Cited by 393Psychometric Methodologies and TestingBusiness Strategies and Management ResearchCognitive and psychological constructs research

Tackling the Problem of Construct Proliferation: A Guide to Assessing the Discriminant Validity of Conceptually Related Constructs

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Shaffer, Jonathan A., DeGeest, David, Li, Andrew·2016·Organizational Research Methods

DOI 10.1177/1094428115598239

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Abstract

Construct proliferation—the accumulation of ostensibly different but potentially identical constructs representing organizational phenomena—is a salient problem in contemporary research. While a number of construct validation procedures exist, relatively few validation studies conduct comprehensive assessments of the discriminant validity of theoretically distinct constructs. In this article, we outline the key considerations a researcher must take into account when attempting to establish the empirical distinctness of new or existing constructs and provide a step-by-step guide on how to assess the discriminant validity of constructs while accounting for three major sources of measurement error: random error, specific factor error, and transient error. Using a number of popular measures from the leadership literature, we provide an illustrative example of how to conduct a study of discriminant validity. We include several analytic strategies in our study and discuss the similarities and differences between the results they yield. We also discuss several additional issues related to this type of research and make recommendations for conducting discriminant validity analyses.

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Comparing the correlation against 1.0 as a test of discriminant validity has two shortcomings: the possible outcomes are whether two constructs are perfectly correlated or not, but perfect correlation is rare in the population; and with a large enough sample size, even a correlation at 0.95 is statistically significantly different from 1.0.
“However, Shaffer et al. (2016) summarized two shortcomings of comparing the correlation against 1.0 as a test of discriminant validity.First, the possible outcomes are whether two constructs are perfectly correlated or not, but it is rare for them to be perfectly correlated in the population.Second, with a large enough sample size (e.g., 1,000), even a correlation at 0.95 is statistically signiﬁc”
From Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations (2024) · cites this paper as Shaffer et al., 2016
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Experimental — accuracy not yet measured
Shaffer et al. (2016) suggested examining the correlation disattenuated for measurement errors to evaluate discriminant validity.
“Recently, Shaffer et al. (2016) suggested examining the correlation disattenuated for measurement errors to evaluate discriminant validity.”
From Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations (2024) · cites this paper as Shaffer et al. (2016)
The available passages identify the paper as a guide to assessing discriminant validity and list measurement error as a keyword, but they do not state that it recommends examining correlations disattenuated for measurement error. Searches for “disattenuated” and for correlations corrected for measurement error or unreliability found no passage establishing that recommendation. (LLM, conf=0.69)
Experimental — accuracy not yet measured
Comparing the disattenuated correlation or HTMT ratio with a threshold value of 0.85 disregards sampling error.
“Finally, comparing the disattenuated correlation or HTMT ratio with a threshold value of 0.85 disregards sampling error (Shaffer et al., 2016).”
From Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations (2024) · cites this paper as Shaffer et al., 2016
The supplied title and Abstract-labeled metadata contain no discussion of disattenuated correlations, HTMT, a 0.85 threshold, or sampling error. Searches for those terms and for confidence intervals returned no substantive passage, so our copy cannot establish whether the paper makes the attributed point. (LLM, conf=0.76)
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Authors: Shaffer, J. A., DeGeest, D., & Li, A.
Affiliations: West Texas A&M University (US)
University of Groningen (NL)
Corresponding author: Shaffer, Jonathan A.
ORCID: Shaffer, Jonathan A.
DeGeest, David
Li, Andrew
Venue: Organizational Research Methods
Publisher: SAGE Publications
Published: 2015-08-18
Volume: 19
Issue: 1
Pages: 80-110
ISSN: 1094-4281 (ISSN-L)
1552-7425
DOI: 10.1177/1094428115598239
OpenAlex: W2337454271
Cited by: 393
References: 98
Citations by year: 2026: 56 · 2025: 48 · 2024: 53 · 2023: 44 · 2022: 39 · 2021: 44 · 2020: 29 · 2019: 36 · 2018: 22 · 2017: 15 · 2016: 3 · 2015: 3 · 2014: 1
FWCI: 17.90
Type: journal article
Language: en
Open access: green
research.rug.nl
Licence: other-oa
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Organizational Research Methods (publishedVersion)
University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Social Sciences › Decision Sciences › Management Science and Operations Research › Business Strategies and Management Research
Social Sciences › Psychology › Social Psychology › Cognitive and psychological constructs research
Keywords: construct proliferation, discriminant validity, construct validation, measurement error, random error, transient faults

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Tackling the Problem of Construct Proliferation: A Guide to Assessing the Discriminant Validity of Conceptually Related Constructs

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