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Consensus Values and Weighting Factors
Open accessAbstract
A method is presented for the statistical analysis of sets of data which are assembled from multiple experiments. The analysis recognizes the existence of both within group and between group variabilities, and calculates appropriate weighting factors based on the observed variability for each group. The weighting factors are used to calculate a "best" consensus value from the overall experiment. The technique for obtaining the consensus value is applicable to either the determination of the weighted average value, or to the parameters associated with a weighted least squares regression problem. The calculations are made by using an iterative technique with a truncated Taylor series expansion. The calculations are straightforward, and are easily programmed on a desktop computer. An examination of the observed variabilities, both within groups and between groups, leads to considerable insight into the overall experiment and greatly aids in the design of future experiments.
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The combination of estimates from different experiments
Cochran, W. G. (1954) · Biometrics · doi:10.2307/3001666 · Cited by 5,380
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Veroniki, A. A., Jackson, D., Viechtbauer, W., Bender, R., Bowden, J., Knapp, G., Kuss, O., Higgins, J. P., Langan, D., & Salanti, G. (2016) · Research Synthesis Methods · doi:10.1002/jrsm.1164 · Cited by 1,685
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Metadata
- Authors
- Paule, R. C., & Mandel, J.
- Venue
- Journal of Research of the National Bureau of Standards
- Published
- 1982-09
- Volume
- 87
- Issue
- 5
- Pages
- 377
- ISSN
- 0160-1741 (ISSN-L)
- OpenAlex
- W2128640268
- Cited by
- 572
- References
- 1
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Paule, R. C., & Mandel, J. (1982). Consensus Values and Weighting Factors. Journal of Research of the National Bureau of Standards. https://doi.org/10.6028/jres.087.022
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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.