The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Measures of Association for Cross Classifications*
Abstract
When populations are cross-classified with respect to two or more classifications or polytomies, questions often arise about the degree of association existing between the several polytomies. Most of the traditional measures or indices of association are based upon the standard chi-square statistic or on an assumption of underlying joint normality. In this paper a number of alternative measures are considered, almost all based upon a probabilistic model for activity to which the cross-classification may typically lead. Only the case in which the population is completely known is considered, so no question of sampling or measurement error appears. We hope, however, to publish before long some approximate distributions for sample estimators of the measures we propose, and approximate tests of hypotheses. Our major theme is that the measures of association used by an empirical investigator should not be blindly chosen because of tradition and convention only, although these factors may properly be given some weight, but should be constructed in a manner having operational meaning within the context of the particular problem.
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Cites
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Cited by
Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment
Pontius, R. G., & Millones, M. (2011) · International Journal of Remote Sensing · doi:10.1080/01431161.2011.552923 · Cited by 2,046
High agreement but low kappa: II. Resolving the paradoxes
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Fleming, S. M., & Lau, H. C. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00443 · Cited by 1,408
Beyond kappa: a review of interrater agreement measures
Banerjee, M., Capozzoli, M., McSweeney, L., & Sinha, D. (1999) · Canadian Journal of Statistics · doi:10.2307/3315487 · Cited by 976
Clustering ensembles: models of consensus and weak partitions
Topchy, A., Jain, A. K., & Punch, W. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.237 · Cited by 675
Metadata
- Authors
- Goodman, L. A., & Kruskal, W. H.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1954-12
- Volume
- 49
- Issue
- 268
- Pages
- 732-764
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W4293257135
- Cited by
- 678
- References
- 11
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
