Data analysis using regression and multilevel/hierarchical models /
"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 instructi...
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| Format: | Book |
| Language: | English |
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Cambridge ; New York :
Cambridge University Press,
2007.
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| Series: | Analytical methods for social research.
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| Online Access: | Publisher description Table of contents only Contributor biographical information Publisher description Table of contents only |
Table of Contents:
- Why?
- Concepts and methods from basic probability and statistics
- Linear regression: the basics
- Linear regression: before and after fitting the model
- Logistic regression
- Generalized linear models
- Simulation for checking statistical procedures and model fits
- Causal inference using regression on the treatment variable
- Causal inference using more advanced models
- Multilevel structures
- Multilevel linear models: the basics
- Multilevel linear models: varying slopes, non-nested models, and other complexities.
- Multilevel logistic regression
- Multilevel generalized linear models
- Multilevel modeling Bugs and R: the basics
- Fitting multilevel linear and generalized linear models in Bugs and R
- Likelihood and Bayesian inference and computation
- Debugging and speeding convergence
- Sample size and power calculations
- Understanding and summarizing the fitted models
- Analysis of variance
- Causal inference using multilevel models
- Model checking and comparison
- Missing-data imputation
- Six quick tips to improve your regression modeling
- Statistical graphics for research and presentation
- Software.