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Essays in Political Methodology.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essays in Political Methodology./
Author:	Tyler, Matthew.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	183 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
Subject:	Election results. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28671130
ISBN:	9798544200093

Essays in Political Methodology.
Tyler, Matthew.

Essays in Political Methodology. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 183 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

This dissertation is comprised of three chapters, each of which is a complete essay on a separate topic in political methodology. Each chapter is accompanied by an appendix containing supplementary results and information.The three chapters 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. Applications of machine learning in political science are traditionally associated with prediction and clustering tasks, but these tasks are not actually that common in a discipline so focused on descriptive and causal research. Instead, this dissertation shows how in some cases machine learning outputs can be used as measurements in substantive analyses, provided there is a rigorously developed rational for doing so. 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.This dissertation would not have been possible without my partial co-authors: Christian Fong (Chapter 2) and William Marble (Chapter 3). I am also particularly indebted to Erik Peterson and Adam Bonica for sharing important datasets. A number of people contributed helpful comments on various components of this dissertation, in particular Justin Grimmer, Shanto Iyengar, and Jens Hainmueller. This list also includes Aala Abdelgadir, Alejandra Aldridge, Ala' Alrababa'h, Jason Anastasopoulos, Pablo Barbera, Vincent Bauer, Adam Bonica, David Broockman, Sharad Goel, Carl Gustafson, Andy Hall, D. Sunshine Hillygus, Kosuke Imai, Haemin Jee, Hans Lueders, Walter Mebane, Rachel Myrick, Michael Robinson, Jonathan Rodden, Brandon Stewart, Dan Thompson, Elisabeth van Lieshout, Jesse Yoder, and Yiqing Xu. All errors are, of course, my own.

ISBN: 9798544200093Subjects--Topical Terms:

3556048
Election results.

Essays in Political Methodology.
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