You are here

Quantitative Research in Political Science

Quantitative Research in Political Science

Five Volume Set
Edited by:

July 2015 | 1 736 pages | SAGE Publications Ltd

This four volume Major Work brings together the key articles that laid the foundations, extended and deepened the techniques, and demonstrated the application of the empirical-methodological toolbox of modern positive political science. The fundamental challenges of positive, empirical political science are many, and this collection helps to untangle and delineate the various issues by structuring the contents into four thematic sections:


·         Multicausality

·         Heterogeneity & Context Conditionality

·         Temporal & Unit (Inter)Dependence

·         Ubiquitous Endogeneity


The rationale behind the collection’s structure and selection of contents is carefully laid out and explained in an illuminating introductory chapter, written by esteemed editor Robert J. Franzese.



Part One: Introduction: The Challenges of and an Approach to Empirical Analysis in Social Science
Multicausality, Context-Conditionality, and Endogeneity

Robert Franzese
Puzzles, Proverbs, and Omega Matrices: The Scientific and Social Significance of Empirical Implications of Theoretical Models (EITM)

Jim Granato and Frank Scioli
Statistical Backwards Induction: A Simple Method for Estimating Recursive Strategic Models

Muhammet Ali Bas, Curtis Signorino and Robert Walker
Part Two: Measurement
2a. Measurement & Measurement Error, Missing Data:
Toward Theories of Data: The State of Political Methodology

Christopher Achen

Simon Jackman
Measurement Error across Disciplines

Robert Groves
Enhancing the Validity and Cross-Cultural Comparability of Measurement in Survey Research

Gary King et al.
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation

Gary King et al.
2b. Measurement Applications:
Extract from Congress: A Political-Economic History of Roll Call Voting

Keith Poole and Howard Rosenthal
Democracy as a Latent Variable

Simon Jackman and Shawn Treier
Dynamic Representation

James Stimson, Michael MacKuen and Robert Erikson
Part Three: The Foundational Multivariate-Regression Model and Models for Limited & Qualitative Dependent Variables
3a. Use & Interpretation of Multivariate-Regression Models:
Elementary Regression Theory and Social Science Practice

Christopher Achen
3b. Use & Interpretation of Limited & Qualitative Dependent-Variable Models:
Extracts from Unifying Political Methodology

Gary King
Extracts from Generalized Linear Models

Jeff Gill
Making the Most of Statistical Analyses: Improving Interpretation and Presentation

Gary King, Michael Tomz and Jason Wittenberg
3c. Estimation and Inference in the Bayesian Paradigm:
Single-Parameter Models

Jeff Gill
Pooling Disparate Observations

Larry Bartels
Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation

Simon Jackman
Part Four: Heterogeneity and Heterogeneous Effects
4a. Unit & Period “Fixed Effects”:
Dirty Pool

Donald Green, Soo Yeon Kim and David Yoon
Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon

Nathaniel Beck and Jonathan Katz
Problematic Choices: Testing for Correlated Unit Specific Effects in Panel Data

Vera Troeger
4b. Interaction & Nonlinear Models:
Theory to Practice

Cindy Kam and Robert Franzese
Multiple Hands on the Wheel: Empirically Modeling Partial Delegation and Shared Control of Monetary Policy in the Open and Institutionalized Economy

Robert Franzese
4c. Random-Coefficient/Hierarchical/Multilevel Models:
Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modelling Approach

Bruce Western
Modeling Multilevel Data Structures

Marco Steenbergen and Bradford Jones
Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls

David Park, Andrew Gelman and Joseph Bafumi
Part Five: Dynamic Models
5a. Models for Temporal Dependence:
Comparing Dynamic Specifications: The Case of Presidential Approval

Nathaniel Beck
Taking Time Seriously

Suzanna De Boef and Luke Keele
Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable

Nathaniel Beck, Jonathan Katz and Richard Tucker
Back to the Future: Modeling Time Dependence in Binary Data

David Carter and Curtis Signorino
Time Is of the Essence: Event History Models in Political Science

Janet Box-Steffensmeier and Bradford Jones
5b. Models for Cross-UnitInterdependence:
Empirical Models of Spatial Interdependence

Robert Franzese and Jude Hays
Network Analysis and Political Science

Michael Ward, Katherine Stovel and Audrey Sacks
Inferential Network Analysis with Exponential Random Graph Models

Skyler Cranmer and Bruce Desmarais
Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes

Robert Franzese, Jude Hays and Scott Cook
5c. Models for Time-Series-Cross-Section and Panel Data:
Regression in Space and Time: A Statistical Essay

James Stimson
Estimating Dynamic Panel Data Models in Political Science

Gregory Wawro
Modeling Dynamics in Time-Series-Cross-Section Political Economy Data

Nathaniel Beck and Jonathan Katz
Beyond Fixed versus Random Effects: A Framework for Improving Substantive and Statistical Analysis of Panel, Time-Series Cross-Sectional, and Multilevel Data

Brandon Bartels
Part Six: Endogeneity and Causal Inference
6a. Instrumental-Variables Methods:
Instrumental and ‘Quasi-Instrumental’ Variables

Larry Bartels
Instrumental Variables Estimation in Political Science: A Readers’ Guide

Allison Sovey and Donald Green
Model Specification in Instrumental-Variables Regression

Thad Dunning
6b. Full-Information Maximum-Likelihood (FIML) Methods:
Endogeneity and Structural Equation Estimation in Political Science

John Jackson
Interdependent Duration Models in Political Science

Jude Hays and Aya Kachi
A Unified Statistical Model of Conflict Onset and Escalation

William Reed
6c. Temporal Ordering and Vector-Autoregressive Methods:
Temporal Order and Causal Inference

Warren Miller
Vector Autoregression and the Study of Politics

John Freeman, John Williams and Tse-min Lin
Democratic Accountability in Open Economies

Thomas Sattler, Patrick Brandt and John Freeman
6d. Experimental Methods:
Experimental Methods in Political Science

Rose McDermott
Growth and Development of Experimental Research in Political Science

James Druckman et al.
6e. Matching Methods:
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

Daniel Ho et al.
Opiates for the Matches: Matching Methods for Causal Inference

Jasjeet Sekhon
6f. Discontinuity-Design Methods:
Regression Discontinuity Design Analysis of the Incumbency Advantage and Tenure in the US House

Daniel Mark Butler
Elections and the Regression Discontinuity Design: Lessons from Close US House Races, 1942–2008

Devin Caughey and Jasjeet Sekhon
6g. Difference-in-Difference Methods:
Inference with Difference-in-Differences and Other Panel Data

Stephen Donald and Kevin Lang

Select a Purchasing Option

ISBN: 9781473902176