The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Model-Dependent Variance Inflation Factor Cutoff Values
Abstract
When creating designed experiments, it is not always possible to run the experiment at the exact settings required to maintain orthogonal effects. However, this is not measurement error when precise measurements of the settings can be made once the experiment begins. A comparison is made for a 15-run Box–Behnken design using both the intended design settings and the actual design settings. Variance inflation factors are used to measure the induced collinearity in the effects. Two cutoff values are suggested for use to determine when an effect's variance inflation factor is too large to keep that effect in the model.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Applied Linear Statistical Models
(1997) · Technometrics · doi:10.1080/00401706.1997.10485141 · Cited by 7,185
Some New Three Level Designs for the Study of Quantitative Variables
Box, G. E. P., & Behnken, D. W. (1960) · Technometrics · doi:10.1080/00401706.1960.10489912 · Cited by 4,177
Classical and Modern Regression With Applications.
Steinhorst, R. K., & Myers, R. H. (1988) · Journal of the American Statistical Association · doi:10.2307/2288958 · Cited by 3,690
Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
Marquardt, D. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488699 · Cited by 2,276
FISHER, R. A. (1915) · Biometrika · doi:10.1093/biomet/10.4.507 · Cited by 1,574
Graphical Interpretation of Variance Inflation Factors
Stine, R. A. (1995) · The American Statistician · doi:10.1080/00031305.1995.10476113 · Cited by 391
The general sampling distribution of the multiple correlation coefficient
Fisher, R. A. (1928) · Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character · doi:10.1098/rspa.1928.0224 · Cited by 306
Tolerance and Condition in Regression Computations
Berk, K. N. (1977) · Journal of the American Statistical Association · doi:10.1080/01621459.1977.10479972 · Cited by 72
Normalizing Transformations of Some Statistics in Multivariate Analysis
Konishi, S. (1981) · Biometrika · doi:10.1093/biomet/68.3.647 · Cited by 63
Cited by
LeBlanc, A. G., Katzmarzyk, P. T., Barreira, T. V., Broyles, S. T., Chaput, J.-P., Church, T. S., Fogelholm, M., Harrington, D. M., Hu, G., Kuriyan, R., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0129622 · Cited by 335
Danopoulos, E., Twiddy, M., West, R., & Rotchell, J. M. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2021.127861 · Cited by 230
Procentese, F., Gatti, F., & Di Napoli, I. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16245006 · Cited by 116
Political engagement and wellbeing among college students
Ballard, P. J., Ni, X., & Brocato, N. (2020) · Journal of Applied Developmental Psychology · doi:10.1016/j.appdev.2020.101209 · Cited by 61
Canale, N., Vieno, A., Doro, M., Rosa Mineo, E., Marino, C., & Billieux, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-54911-7 · Cited by 50
Metadata
- Authors
- Craney, T. A., & Surles, J. G.
- Venue
- Quality Engineering
- Publisher
- Informa UK Limited
- Published
- 2002-03-25
- Volume
- 14
- Issue
- 3
- Pages
- 391-403
- ISSN
- 0898-2112 (ISSN-L)
- 1532-4222
- OpenAlex
- W2070739968
- Cited by
- 1,205
- References
- 9
- FWCI
- 1.62
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Model-Dependent Variance Inflation Factor Cutoff Values. https://app.cochat.ai/papers/10.1081/qen-120001878
- The paper
- Craney, T. A., & Surles, J. G. (2002). Model-Dependent Variance Inflation Factor Cutoff Values. Quality Engineering. https://doi.org/10.1081/qen-120001878
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
