ISBN:
9781316636824
,
9781107185821
Language:
English
Pages:
xxvii, 298 Seiten
,
Diagramme
Series Statement:
Analytical methods for social research
Parallel Title:
Erscheint auch als Ward, Michael Don, 1948 - Maximum likelihood for social science
DDC:
300.72
Keywords:
Social sciences Research
;
Social sciences Research
Abstract:
"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"--
Abstract:
"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"--
Abstract:
Machine generated contents note: Part I. Concepts, Theory, and Implementation: 1. Introduction to maximum likelihood; 2. Theory; 3. Maximum likelihood for binary outcomes; 4. Implementing MLE; Part II. Model Evaluation and Interpretation: 5. Model evaluation and selection; Part III. The Generalized Linear Model: 6. Model evaluation and selection; Part III. The Generalized Linear Model: 7. Ordered categorical variable models; 8. Models for nominal data; 9. Strategies for analyzing count data; Part IV. Advanced Topics: 10. Duration; 11. Strategies for missing data; Part V. A Look Ahead: 13. Epilogue; Index
Note:
Literaturverzeichnis: Seite 277-292
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