Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis
Open accessAbstract
Suppression effect in multiple regression analysis may be more common in research than what is currently recognized. We have reviewed several literatures of interest which treats the concept and types of suppressor variables. Also, we have highlighted systematic ways to identify suppression effect in multiple regressions using statistics such as: R2, sum of squares, regression weight and comparing zero-order correlations with Variance Inflation Factor (VIF) respectively. We also establish that suppression effect is a function of multicollinearity; however, a suppressor variable should only be allowed in a regression analysis if its VIF is less than five (5).
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Collinearity diagnostics showed no VIF values > 3 and no tolerance values < 0.3, indicating low concern for multi-collinearity.
“Collinearity diagnostics showed that none of the VIF values of the study variables were > 3, and no tolerance values were < 0.3, indicating low concern for multi-collinearity issues [56].”
From Social media addiction and academic engagement as serial mediators between social anxiety and academic performance among college students (2024) · cites this paper as [56]
Section 1.4, Handling Multicollinearity (page unavailable), says VIF near or above 5 may indicate a problem, supporting low concern when the citing study’s VIFs are below 3. The cited paper does not give a tolerance threshold of 0.3; the reported diagnostic values are the citing study’s own results, not results attributed to this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredVariance inflation factors in the regression remain below the commonly accepted threshold of five.
“We verify that variance inflation factors (see SI, Tab. S3), used as indicators of multicollinearity, remain below the commonly accepted threshold of five (Akinwande et al. 2015).”
From Characterizing AI-Generated Misinformation on Social Media (2025) · cites this paper as Akinwande et al. 2015
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Characterizing AI-Generated Misinformation on Social Media
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- Authors
- Akinwande, M. O., Dikko, H. G., & Samson, A.
- Affiliations
- Ahmadu Bello University (NG)
- Venue
- Open Journal of Statistics
- Publisher
- Scientific Research Publishing, Inc.
- Published
- 2015
- Volume
- 05
- Issue
- 07
- Pages
- 754-767
- ISSN
- 2161-718X (ISSN-L)
- 2161-7198
- OpenAlex
- W2271643717
- Cited by
- 1,534
- References
- 19
- Citations by year
- 2026: 187 · 2025: 333 · 2024: 228 · 2023: 220 · 2022: 216 · 2021: 160 · 2020: 90 · 2019: 57 · 2018: 35 · 2017: 7 · 2013: 1
- FWCI
- 12.46
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Open Journal of Statistics (publishedVersion, cc-by)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- variance inflation factor, suppressor variables, suppression effect, multicollinearity, multiple linear regression, regression coefficients
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