The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Applying Bifactor Statistical Indices in the Evaluation of Psychological Measures
Abstract
The purpose of this study was to apply a set of rarely reported psychometric indices that, nevertheless, are important to consider when evaluating psychological measures. All can be derived from a standardized loading matrix in a confirmatory bifactor model: omega reliability coefficients, factor determinacy, construct replicability, explained common variance, and percentage of uncontaminated correlations. We calculated these indices and extended the findings of 50 recent bifactor model estimation studies published in psychopathology, personality, and assessment journals. These bifactor derived indices (most not presented in the articles) provided a clearer and more complete picture of the psychometric properties of the assessment instruments. We reached 2 firm conclusions. First, although all measures had been tagged "multidimensional," unit-weighted total scores overwhelmingly reflected variance due to a single latent variable. Second, unit-weighted subscale scores often have ambiguous interpretations because their variance mostly reflects the general, not the specific, trait. Finally, we review the implications of our evaluations and consider the limits of inferences drawn from a bifactor modeling approach.
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“Omega is a reliability estimate that does not depend on the assumption of tau equivalence unlike its classic counterpart Cronbach’s alpha [39].”
From Dimensionality of the system usability scale among professionals using internet-based interventions for depression: a confirmatory factor analysis (2020) · cites this paper as [39]
In the citing paper
“When PUC is greater than .70 and ECV greater than .70 relative bias will be slight and the common variance can be regarded as essentially unidimensional [39].”
From Dimensionality of the system usability scale among professionals using internet-based interventions for depression: a confirmatory factor analysis (2020) · cites this paper as [39]
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“…on this equation, it is clear that, as the proportion of variance explained by the general factor increases, ECV will increase, and, as noted by Reise (2012), higher ECV values indicate a strong general factor, which could guide researchers in deciding whether to fit a unidimensional model…”
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Metadata
- Authors
- Rodriguez, A., Reise, S. P., & Haviland, M. G.
- Affiliations
- University of California, Los Angeles (US)
- Loma Linda University (US)
- Corresponding author
- Reise, Steven P.
- Venue
- Journal of Personality Assessment
- Publisher
- Informa UK Limited
- Published
- 2015-10-29
- Volume
- 98
- Issue
- 3
- Pages
- 223-237
- ISSN
- 0022-3891 (ISSN-L)
- 1532-7752
- OpenAlex
- W1881923603
- Cited by
- 990
- References
- 77
- Citations by year
- 2026: 105 · 2025: 97 · 2024: 104 · 2023: 134 · 2022: 127 · 2021: 124 · 2020: 102 · 2019: 75 · 2018: 59 · 2017: 49 · 2016: 13
- FWCI
- 50.26
- Type
- journal article
- Language
- en
- Update notices
- correction (2015-12-23)
- Open access
- closed
- Locations
- Journal of Personality Assessment (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Psychological Testing and Assessment
- Keywords
- bifactor model, multidimensional measure, personality assessment, psychopathology assessment
- MeSH terms
- Factor Analysis, Statistical, Humans, Models, Psychological, Psychometrics, Models, Statistical
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