rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Maximum likelihood for social science
    sub title: strategies for analysis
  name:
    name part: Ward, Michael Don
    name part: 1948-
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Ahlquist, John S.
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: nyu
    date issued: 2018
    edition: 1 Edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xxvii, 298 pages : illustrations ; 24 cm.
  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"--
  table of contents: 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: Michael D. Ward, John S. Ahlquist.
  note: Includes bibliographical references and index.
  subject:
    topic: Social sciences
    topic: Research
  subject:
    topic: POLITICAL SCIENCE / General
  subject:
    topic: Social sciences
    topic: Research
  classification: H62 .W277 2018
  classification: 300.72
  classification: POL000000
  related item:
    title info:
      title: Analytical methods for social research
  identifier: 9781107185821
  identifier: 1107185823
  identifier: 9781316636824
  identifier: 1316636828
  identifier: 9781316952887 (PDF ebook)
  identifier: 2018010101
  record info:
    record content source: DLC
    record creation date: 180323
    record change date: 20190122092015.0
    record identifier: in00004023568
    language of cataloging:
      language term: eng