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arm: Data Analysis Using Regression and Multilevel/Hierarchical Models
Open accessAbstract
Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.
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Cited by
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Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests
Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E.-J. (2011) · Perspectives on Psychological Science · doi:10.1177/1745691611406923 · Cited by 945
A Python library for probabilistic analysis of single-cell omics data
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Metadata
- Authors
- Gelman, A., & Su, Y.-S.
- Venue
- CRAN: Contributed Packages
- Publisher
- The R Foundation
- Published
- 2007-01-01
- OpenAlex
- W4399608067
- Cited by
- 955
- References
- 0
- Type
- dataset
- Language
- en
- Open access
- gold
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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.