The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Hierarchical Partitioning
Abstract
Many users of regression methods are attracted to the notion that it would be valuable to determine the relative importance of independent variables. This article demonstrates a method based on hierarchies that builds on previous efforts to decompose R 2 through incremental partitioning. The standard method of incremental partitioning has been to follow one order among the many possible orders available. By taking a hierarchical approach in which all orders of variables are used, the average independent contribution of a variable is obtained and an exact partitioning results. Much the same logic is used to divide the joint effect of a variable. The method is general and applicable to all regression methods, including ordinary least squares, logistic, probit, and log-linear regression. A validation test demonstrates that the algorithm is sensitive to the relationships in the data rather than the proportion of variability accounted for by the statistical model used.
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Cites
Multiple Regression in Behavioral Research
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Components of a Difference Between Two Rates*
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The Analysis of Cross-Classified Categorical Data.
Holt, D., & Fienberg, S. E. (1978) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344495 · Cited by 420
Introduction to Structural Equation Models.
Beniger, J. R., & Duncan, O. D. (1976) · Contemporary Sociology · doi:10.2307/2064058 · Cited by 333
Relative Importance by Averaging over Orderings
Kruskal, W. (1987) · The American Statistician · doi:10.1080/00031305.1987.10475432 · Cited by 237
A general method of decomposing a difference between two rates into several components
Gupta, P. D. (1978) · Demography · doi:10.2307/2060493 · Cited by 225
Making It Count: The Improvement of Social Research and Theory.
Alwin, D. F., & Lieberson, S. (1988) · Journal of the American Statistical Association · doi:10.2307/2288966 · Cited by 161
Partitioning Variance in Multiple Regression Analyses as a Tool For Developing Learning Models
Mood, A. M. (1971) · American Educational Research Journal · doi:10.2307/1162174 · Cited by 155
A Development of Multiple Regression for the Analysis of Routine Data
Newton, R. G., & Spurrell, D. J. (1967) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2985237 · Cited by 137
Complex Contingency Tables Treated by the Partition of χ2
Lancaster, H. O. (1951) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1951.tb00089.x · Cited by 98
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An atlas of active enhancers across human cell types and tissues
The FANTOM Consortium, Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., Chen, Y., Zhao, X., Schmidl, C., et al. (2014) · Nature · doi:10.1038/nature12787 · Cited by 2,550
Mac Nally, R. (2000) · Biodiversity & Conservation · doi:10.1023/a:1008985925162 · Cited by 938
Climate-driven declines in arthropod abundance restructure a rainforest food web
Lister, B. C., & Garcia, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1722477115 · Cited by 730
Mac Nally, R. (2002) · Biodiversity & Conservation · doi:10.1023/a:1016250716679 · Cited by 554
Hierarchy and Scaling: Extrapolating Information along a Scaling Ladder
Wu, J. (1999) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.1999.10874736 · Cited by 462
Metadata
- Authors
- Chevan, A., & Sutherland, M.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 1991-05
- Volume
- 45
- Issue
- 2
- Pages
- 90-96
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W4247189277
- Cited by
- 1,163
- References
- 12
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
