Quantitative Research in Political Science

• VOLUME ONEPart One: Introduction: The Challenges of and an Approach to Empirical Analysis in Social ScienceMulticausality, Context-Conditionality, and Endogeneity - Robert FranzesePuzzles, Proverbs, and Omega Matrices: The Scientific and Social Significance of Empirical Implications of Theoretical Models (EITM) - Jim Granato and Frank ScioliStatistical 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 AchenMeasurement - Simon JackmanMeasurement Error across Disciplines - Robert GrovesEnhancing 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 RosenthalDemocracy as a Latent Variable - Simon Jackman and Shawn TreierDynamic 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 KingExtracts from Generalized Linear Models - Jeff GillMaking 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 GillPooling Disparate Observations - Larry BartelsEstimation 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 YoonThrowing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon - Nathaniel Beck and Jonathan KatzProblematic Choices: Testing for Correlated Unit Specific Effects in Panel Data - Vera TroegerVOLUME THREE4b. Interaction & Nonlinear Models:Theory to Practice - Cindy Kam and Robert FranzeseMultiple 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 WesternModeling Multilevel Data Structures - Marco Steenbergen and Bradford JonesBayesian 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 BeckTaking Time Seriously - Suzanna De Boef and Luke KeeleTaking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable - Nathaniel Beck, Jonathan Katz and Richard TuckerBack to the Future: Modeling Time Dependence in Binary Data - David Carter and Curtis SignorinoTime 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 HaysNetwork Analysis and Political Science - Michael Ward, Katherine Stovel and Audrey SacksInferential Network Analysis with Exponential Random Graph Models - Skyler Cranmer and Bruce DesmaraisSpatial- 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 StimsonEstimating Dynamic Panel Data Models in Political Science - Gregory WawroModeling Dynamics in Time-Series-Cross-Section Political Economy Data - Nathaniel Beck and Jonathan KatzBeyond 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 BartelsInstrumental Variables Estimation in Political Science: A Readers’ Guide - Allison Sovey and Donald GreenModel Specification in Instrumental-Variables Regression - Thad DunningVOLUME FIVE6b. Full-Information Maximum-Likelihood (FIML) Methods:Endogeneity and Structural Equation Estimation in Political Science - John JacksonInterdependent Duration Models in Political Science - Jude Hays and Aya KachiA Unified Statistical Model of Conflict Onset and Escalation - William Reed6c. Temporal Ordering and Vector-Autoregressive Methods:Temporal Order and Causal Inference - Warren MillerVector Autoregression and the Study of Politics - John Freeman, John Williams and Tse-min LinDemocratic Accountability in Open Economies - Thomas Sattler, Patrick Brandt and John Freeman6d. Experimental Methods:Experimental Methods in Political Science - Rose McDermottGrowth 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 ButlerElections 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

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