Quantitative Research in Political Science

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:

VOLUME ONEPart 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 WalkerPart Two: Measurement2a. Measurement & Measurement Error, Missing Data: Toward Theories of Data: The State of Political Methodology - Christopher Achen Measurement - 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 EriksonVOLUME TWOPart Three: The Foundational Multivariate-Regression Model and Models for Limited & Qualitative Dependent Variables3a. Use & Interpretation of Multivariate-Regression Models: Elementary Regression Theory and Social Science Practice - Christopher Achen3b. 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 Wittenberg3c. 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 JackmanPart Four: Heterogeneity and Heterogeneous Effects4a. 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 TroegerVOLUME THREE4b. 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 Franzese4c. 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 BafumiPart Five: Dynamic ModelsSelections5a. 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 JonesVOLUME FOUR5b. 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 Cook5c. 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 BartelsPart Six: Endogeneity and Causal InferenceSelections6a. 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 DunningVOLUME FIVE6b. 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 Reed6c. 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 Freeman6d. 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 Sekhon6f. 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 Sekhon6g. Difference-in-Difference Methods: Inference with Difference-in-Differences and Other Panel Data - Stephen Donald and Kevin Lang