The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression
Abstract
This article briefly reviews classical suppressor variables, suppression and enhancement, opposing signs of regression coefficients and zero-order correlations, and multicollinearity. A concise and easily understood graphical structure for the study of suppressor variables and enhancementis provided. Classical suppressor variables are shown to be more valuable than other nonsuppressor variables under some conditions. Errors using ratios of correlations in the exposition of suppression are noted. Multicollinearity is shown not to affect standard errors of regression coefficientsin ways previously taught.
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
Suppressor Variables in Path Models: Definitions and Interpretations
MAASSEN, G. H., & BAKKER, A. B. (2001) · Sociological Methods & Research · doi:10.1177/0049124101030002004 · Cited by 407
Cited by
Reconsidering Baron and Kenny: myths and truths about mediation analysis
Zhao, X., Lynch, J. G., & Chen, Q. (2010) · Journal of Consumer Research · doi:10.1086/651257 · Cited by 12,583
The representation of abstract words: Why emotion matters.
Kousta, S.-T., Vigliocco, G., Vinson, D. P., Andrews, M., & Del Campo, E. (2011) · Journal of Experimental Psychology: General · doi:10.1037/a0021446 · Cited by 894
Van den Bergh, B. R. H., Van Calster, B., Smits, T., Van Huffel, S., & Lagae, L. (2008) · Neuropsychopharmacology · doi:10.1038/sj.npp.1301450 · Cited by 434
BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation
McFarland, D. J., Anderson, C. W., Muller, K.-R., Schlogl, A., & Krusienski, D. J. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875637 · Cited by 204
Multivariable Modeling Strategies
Harrell, F. E. (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7_4 · Cited by 221
Metadata
- Authors
- Friedman, L., & Wall, M.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 2005-05
- Volume
- 59
- Issue
- 2
- Pages
- 127-136
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W1973862412
- Cited by
- 248
- References
- 1
- FWCI
- 5.93
- 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: Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression. https://app.cochat.ai/papers/10.1198/000313005x41337
- The paper
- Friedman, L., & Wall, M. (2005). Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression. The American Statistician. https://doi.org/10.1198/000313005x41337
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.
