The DOI matches its Crossref record.
One Degree of Freedom for Non-Additivity
Abstract
TN DISCUSSING the possible shortcomings of the analysis of variance, much attention has been paid to non-constancy and non-normality of the error contribution. (The recent papers in Biometrics by Eisenhart [4], Cochran [3] and Bartlett [1] discuss these matters and give references.) The present writer is usually much more concerned with and worried about non-additivity, and until recently has suffered from the lack of a systematic way to seek it out, and then to measure it. (Conversations with Frederick F. Stephan have contributed greatly to this development and presentation.) The purpose of the present paper is to indicate such a way, when the data is in the form of a row-by-column table. (The professional practitioner of the analysis of variance will have no difficulty in extending the process to more complex designs.) We shall show how to isolate one degree of freedom from the residue, error, 'interaction' or 'discrepance, call it what you will. There are two known situations to which this single degree of freedom is expected to react by swelling:
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
Cited by
Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method
WEDDERBURN, R. W. M. (1974) · Biometrika · doi:10.1093/biomet/61.3.439 · Cited by 2,122
Glass, G. V., Peckham, P. D., & Sanders, J. R. (1972) · Review of Educational Research · doi:10.3102/00346543042003237 · Cited by 1,826
Group representations in probability and statistics
Diaconis, P. (1988) · doi:10.1214/lnms/1215467407 · Cited by 1,381
Environmental Impact Assessment: "Pseudoreplication" in Time?
Stewart-Oaten, A., Murdoch, W. W., & Parker, K. R. (1986) · Ecology · doi:10.2307/1939815 · Cited by 1,331
Functional measurement and psychophysical judgment.
Anderson, N. H. (1970) · Psychological Review · doi:10.1037/h0029064 · Cited by 576
Separate modifiability, mental modules, and the use of pure and composite measures to reveal them
Sternberg, S. (2001) · Acta Psychologica · doi:10.1016/s0001-6918(00)00045-7 · Cited by 266
Multiplicative models for interaction in three-way ANOVA, with applications to plant breeding
van Eeuwijk, F. A., & Kroonenberg, P. M. (1998) · Biometrics · doi:10.2307/2533660 · Cited by 51
Metadata
- Authors
- Tukey, J. W.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1949-09
- Volume
- 5
- Issue
- 3
- Pages
- 232
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2316025483
- Cited by
- 910
- References
- 0
- FWCI
- 2.18
- 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
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.