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- Author: Michael D Ward
- Publisher: Cambridge University Press
- Year: 2018
- Pages: 324
- ISBN-10: 1316636828
- ISBN-13: 9781316636824
- Format: 23.1 x 15.5 x 1.8 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques.
EXTRA 10 % discount with code: EXTRA
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- Author: Michael D Ward
- Publisher: Cambridge University Press
- Year: 2018
- Pages: 324
- ISBN-10: 1316636828
- ISBN-13: 9781316636824
- Format: 23.1 x 15.5 x 1.8 cm, minkšti viršeliai
- Language: English English
This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques.
Reviews