The devil is in the details: Methodological nuances and challenges in evaluating construct redundancy

The focal article by Bowling et al. (2026) offers a timely and necessary critique of construct proliferation in industrial and organizational (I-O) psychology.The authors call for a temporary moratorium on introducing new constructs and provide a checklist (Table 1) intended to ensure adequate scrutiny of potential construct proliferation, which includes multiple steps for examining both theoretical and empirical distinctiveness.Although the checklist is helpful and largely well grounded, the proposed steps for gauging empirical distinctiveness (or empirical redundancy) warrant additional discussion and caution to be more informative and practically useful.In this commentary, we highlight methodological nuances and challenges involved in assessing empirical redundancy, particularly focusing on (a) the suggested construct-level correlation cutoff of .85 as a universal, uncontextualized benchmark for potential construct redundancy, (b) the information required to estimate construct-level correlations, (c) the importance of broader nomological network evidence, and (d) the role of sampling error.Unknown bases for the construct-level r of .85 as a sign of empirical redundancyStep 1 under "empirical distinctiveness" in the construct proliferation checklist (Table 1 of the focal article) asks whether a study reports correlations between a new construct and theoretically similar constructs.Regarding this step, Bowling et al., suggest that "construct-level correlations of .85 or higher may indicate significant construct redundancy."The focus on construct-level correlations in this suggestion is consistent with the focal article's broader point that construct redundancy often persists because most studies report only observed measure-level correlations that can be substantially attenuated unless corrected for all major sources of measurement error, giving a false impression that the underlying constructs may not be highly correlated and thus likely to be distinct (Banks et al., 2018;Le et al., 2009;Schmidt et al., 2003). 1 However, a universal, uncontextualized construct-level cutoff, without explicit guidance about how it should be adjusted depending on the construct in question and its reliability (the generalized coefficient of equivalence and stability [GCES]; see our discussion in the next section) risks being interpreted too mechanically and doing more harm than service to the field of I-O