000			02065nam a22002297a 4500
008			191119b xxu||||| |||| 00| 0 eng d
020			_a9781107185821
037			_cPurchased _nProfessional Book Centre,Ernakulam
041			_aEnglish
082			_a300.72 _bWAR/MA
100			_aWard, Michael D.
245			_aMAXIMUM LIKELIHOOD FOR SOCIAL SCIENCE : _bStrategies for Analysis
250			_a1
260			_aNew York _bCambridge University Press _c2018/01/01
300			_g298
500			_a"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
505			_aPart 1 Concept, Theory, and Implementation 1. Introduction to Maximum Likelihood 2. Theory and Properties of maximum Likelihood Estimators 3. Maximum Likelihood for Binary Outcomes 4. Implementing MLE 5. Model Evaluation and Interpretation 6. Inference and Interpretation 7. The Generalized Liner Model 8. Ordered Categorical Variable Models 9. Models For Nominal Data 10. Strategies for Analyzing Count Data 11.Strategies for Temporal Dependence Models 12. Strategies for Missing Data
650			_aSocial sciences-Research.
700			_aAhlguist, John S.
942			_cREF
942			_2ddc
999			_c177873 _d177873