Coverart for item
The Resource Maximum likelihood for social science : strategies for analysis, Michael D. Ward, John S. Ahlquist, (electronic resource)

Maximum likelihood for social science : strategies for analysis, Michael D. Ward, John S. Ahlquist, (electronic resource)

Label
Maximum likelihood for social science : strategies for analysis
Title
Maximum likelihood for social science
Title remainder
strategies for analysis
Statement of responsibility
Michael D. Ward, John S. Ahlquist
Creator
Contributor
Author
Subject
Language
eng
Summary
"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"--
Member of
Assigning source
Provided by publisher
http://library.link/vocab/creatorDate
1948-
http://library.link/vocab/creatorName
Ward, Michael Don
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
Ahlquist, John S.
Series statement
Analytical methods for social research
http://library.link/vocab/subjectName
Social sciences
Label
Maximum likelihood for social science : strategies for analysis, Michael D. Ward, John S. Ahlquist, (electronic resource)
Link
https://eui.idm.oclc.org/login?url=https://doi.org/10.1017/9781316888544
Instantiates
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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
Control code
CR9781316888544
Dimensions
unknown
Extent
1 online resource (xxvii, 298 pages)
Form of item
online
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9781316888544
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
digital, PDF file(s).
Specific material designation
remote
System control number
(OCoLC)1085517876
Label
Maximum likelihood for social science : strategies for analysis, Michael D. Ward, John S. Ahlquist, (electronic resource)
Link
https://eui.idm.oclc.org/login?url=https://doi.org/10.1017/9781316888544
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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
Control code
CR9781316888544
Dimensions
unknown
Extent
1 online resource (xxvii, 298 pages)
Form of item
online
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9781316888544
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
digital, PDF file(s).
Specific material designation
remote
System control number
(OCoLC)1085517876

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