Evaluating Evidence for Conceptually Related Constructs Using Bivariate Correlations
Abstract
The article serves as a guide for researchers in developing evidence of validity using bivariate correlations, specifically construct validity. The authors outline the steps for calculating and interpreting bivariate correlations. Additionally, they provide an illustrative example and discuss the implications.
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A common criterion for the absolute magnitude of correlations that supports the hypothesis of convergent validity is a minimum of 0.30.
“A common criterion for the absolute magnitude of correlations that supports the hypothesis of convergent validity is a minimum of 0.30 [42].”
From Dimensionality of the system usability scale among professionals using internet-based interventions for depression: a confirmatory factor analysis (2020) · cites this paper as [42]
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- Authors
- Swank, J. M., & Mullen, P. R.
- Affiliations
- University of Florida (US)
- William & Mary (US)
- Corresponding author
- Swank, Jacqueline M.
- Venue
- Measurement and Evaluation in Counseling and Development
- Publisher
- Informa UK Limited
- Published
- 2017-10-02
- Volume
- 50
- Issue
- 4
- Pages
- 270-274
- ISSN
- 0748-1756 (ISSN-L)
- 1947-6302
- OpenAlex
- W2768562455
- Cited by
- 129
- References
- 14
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 13 · 2023: 15 · 2022: 21 · 2021: 19 · 2020: 27 · 2019: 12 · 2018: 1 · 2017: 2
- FWCI
- 10.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Measurement and Evaluation in Counseling and Development (publishedVersion)
- W&M Publish (College of William & Mary) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Education › Health Education and Validation
- Social Sciences › Psychology › Applied Psychology › Psychological Testing and Assessment
- Keywords
- construct validity, bivariate correlation, psychometric assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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