Essays in political methodology
Abstract/Contents
- Abstract
- This dissertation is comprised of three chapters, which are united by their focus on measurement problems in political science and how those problems can (potentially) be resolved with applications of machine learning concepts. Chapter 1 shows how clustering techniques from machine learning can be used to reduce measurement error in human coding analyses. Chapter 2 shows when and how machine learning predictions can be used as valid proxies for missing administrative data. Finally, Chapter 3 shows how previously under-formalized concepts from political behavior can be formalized and measured with model validation ideas commonly associated with machine learning.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Tyler, Matthew |
|---|---|
| Degree supervisor | Grimmer, Justin |
| Thesis advisor | Grimmer, Justin |
| Thesis advisor | Hainmueller, Jens |
| Thesis advisor | Iyengar, Shanto |
| Degree committee member | Hainmueller, Jens |
| Degree committee member | Iyengar, Shanto |
| Associated with | Stanford University, Department of Political Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Matthew Tyler. |
|---|---|
| Note | Submitted to the Department of Political Science. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/ms358cw7802 |
Access conditions
- Copyright
- © 2021 by Matthew Tyler
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