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Advances in Political Methodology
This research collection offers a 34-article tour of recent advances and the current state of 5 important and booming areas of empirical methodology: Bayesian methods; modelling of temporal duration, dependence, and dynamics; network-analytic methodology; text, classification, and big-data analytic methods; methods for nonparametric and design-based causal inference. These prominent articles, written by leading scholars, break new ground and provide definitive statements of the current best practices in those respective areas. Together they describe the cutting-edge profile of modern empirical methodology for applied empirical analysis in political science. This is an essential resource for those studying and researching political methodology.
More Information
Critical Acclaim
Contributors
Contents
More Information
This research collection offers a 34-article tour of recent advances and the current state of 5 important and booming areas of empirical methodology: Bayesian methods; modelling of temporal duration, dependence, and dynamics; network-analytic methodology; text, classification, and big-data analytic methods; methods for nonparametric and design-based causal inference. These prominent articles, written by leading scholars, break new ground and provide definitive statements of the current best practices in those respective areas. Together they describe the cutting-edge profile of modern empirical methodology for applied empirical analysis in political science. This is an essential resource for those studying and researching political methodology.
Critical Acclaim
‘Few books have “political methodology” in their titles because the discipline is not yet well organized. This collection offers a concise picture of the field and puts landmark articles into perspective. It also covers very recent developments of statistical analysis in political science.’
– Kentaro Fukumoto, Gakushuin University, Tokyo, Japan
‘Graduate students and researchers in political (and other social) sciences will thank Professor Franzese for putting together a wide range of recent articles which deal with the most important current topics in political methodology (Bayesian methods, temporal issues, networks, text, big data and causality) in a format which makes these articles easy to use in a graduate class or for a researcher who wants to know about recent advances in these important fields. Professor Franzese has provided a new introduction, which nicely puts these articles in a wider context.’
– Nathaniel Beck, New York University, US
‘Political scientists have significantly contributed to recent advances in social science methodology. This collection of previously published articles collected by Professor Franzese gives an excellent overview of some of political science’s most important contributions to modern research methodology. This book provides the basis for an extremely useful (and demanding) course for Ph.D. students and a must-read for empirical researchers.’
– Thomas Plümper, Vienna University of Economics, Austria
‘Endogeneity, causal heterogeneity and context conditionality are ubiquitous in the empirical study of social phenomena. This collection demonstrates the remarkable progress political methodology has made over the last decade or two to solve these issues and better account for the complexity of social interactions. The empirical methods that have been developed recently and are presented in this collection reveal how much better attuned leading researchers in the field are to the data generating processes underlying the social, political and economic phenomena that we study. Rob Franzese is one of the leading methodologists and offers a selection of works in political methodology that address the fundamental methodological problems we face and this collection will become the core canon of readings in our field.’
– Vera E. Troeger, University of Warwick, UK
– Kentaro Fukumoto, Gakushuin University, Tokyo, Japan
‘Graduate students and researchers in political (and other social) sciences will thank Professor Franzese for putting together a wide range of recent articles which deal with the most important current topics in political methodology (Bayesian methods, temporal issues, networks, text, big data and causality) in a format which makes these articles easy to use in a graduate class or for a researcher who wants to know about recent advances in these important fields. Professor Franzese has provided a new introduction, which nicely puts these articles in a wider context.’
– Nathaniel Beck, New York University, US
‘Political scientists have significantly contributed to recent advances in social science methodology. This collection of previously published articles collected by Professor Franzese gives an excellent overview of some of political science’s most important contributions to modern research methodology. This book provides the basis for an extremely useful (and demanding) course for Ph.D. students and a must-read for empirical researchers.’
– Thomas Plümper, Vienna University of Economics, Austria
‘Endogeneity, causal heterogeneity and context conditionality are ubiquitous in the empirical study of social phenomena. This collection demonstrates the remarkable progress political methodology has made over the last decade or two to solve these issues and better account for the complexity of social interactions. The empirical methods that have been developed recently and are presented in this collection reveal how much better attuned leading researchers in the field are to the data generating processes underlying the social, political and economic phenomena that we study. Rob Franzese is one of the leading methodologists and offers a selection of works in political methodology that address the fundamental methodological problems we face and this collection will become the core canon of readings in our field.’
– Vera E. Troeger, University of Warwick, UK
Contributors
34 articles, dating from 1997 to 2016
Contributors include: F.J. Boehmke, J.M. Box–Steffensmeier, J. Freeman, A. Gelman, J. Gill, J. Hays, S. Jackman, L. Keele, J. Sekhon, R. Titiunik
Contributors include: F.J. Boehmke, J.M. Box–Steffensmeier, J. Freeman, A. Gelman, J. Gill, J. Hays, S. Jackman, L. Keele, J. Sekhon, R. Titiunik
Contents
Contents:
Research Review Robert J. Franzese Jr.
PART I ADVANCES IN BAYESIAN METHODS
1. Simon Jackman (2000), ‘Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo’, American Journal of Political Science, 44 (2), April, 375–404
2. Joshua Clinton, Simon Jackman and Douglas Rivers (2004), ‘The Statistical Analysis of Roll Call Data’, American Political Science Review, 98 (2), May, 355–70
3. Richard Traunmüller, Andreas Murr and Jeff Gill (2015), ‘Modeling Latent Information in Voting Data with Dirichlet Process Priors’, Political Analysis, 23 (1), Winter, 1–20
4. Yair Ghitza and Andrew Gelman (2013), ‘Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups’, American Journal of Political Science, 57 (3), July, 762–76
5. Devin Caughey and Christopher Warshaw (2015), ‘Dynamic Estimation of Latent Opinion Using a Hierarchical Group-Level IRT Model’, Political Analysis, 23 (2), Spring, 197–211
PART II ADVANCES IN TIME-SERIES, TIME-SERIES-CROSS-SECTION/PANEL, AND EVENT-HISTORY/DURATION MODELLING
6. Janet M. Box–Steffensmeier and Bradford S. Jones (1997), ‘Time Is of the Essence: Event History Models in Political Science’, American Journal of Political Science, 41 (4), October, 1414–61
7. Frederick J. Boehmke, Daniel S. Morey and Megan Shannon (2006), ‘Selection Bias and Continuous-Time Duration Models: Consequences and a Proposed Solution’, American Journal of Political Science, 50 (1), January, 192–207
8. Jude C. Hays, Emily U. Schilling and Frederick J. Boehmke (2015), ‘Accounting for Right Censoring in Interdependent Duration Analysis’, Political Analysis, 23 (3) Summer, 400–14
9. Jude C. Hays and Robert J. Franzese, Jr. (2009), ‘A Comparison of the Small-Sample Properties of Several Estimators for Spatial-Lag Count Models’, paper submitted at the 2009 Summer Meeting of The Society of Political Methodology, New Haven, CT, USA, July 23–5, i, 1–27
10. Patrick T. Brandt, Michael Colaresi and John R. Freeman (2008), ‘The Dynamics of Reciprocity, Accountability, and Credibility’, Journal of Conflict Resolution, 52 (3), June, 343–74
11. Patrick T. Brandt, John R. Freeman and Philip A. Schrodt (2011), ‘Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict’, Conflict Management and Peace Science, 28 (1), February, 41–64
12. Daniel Stegmueller (2013), ‘Modeling Dynamic Preferences: A Bayesian Robust Dynamic Latent Ordered Probit Model’, Political Analysis, 21 (3), Summer, 314–33
13. Xun Pang (2014), ‘Varying Responses to Common Shocks and Complex Cross-Sectional Dependence: Dynamic Multilevel Modeling with Multifactor Error Structures for Time–Series Cross–Sectional Data’, Political Analysis, 22 (4), Autumn, 464–96
14. Robert J. Franzese, Jr. and Jude C. Hays (2008), ‘Empirical Models of Spatial Interdependence’ in Janet M. Box-Steffensmeier, Henry E. Brady and David Collier (eds), Oxford Handbook of Political Methodology, Oxford, UK: Oxford University Press, Part VII, Chapter 25, 570–604
15. Robert J. Franzese, Jr., Jude C. Hays and Scott J. Cook (2016), ‘Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes’, Political Science Research and Methods, 4 (1), January, 151–73
PART III ADVANCES IN NETWORK ANALYSIS
16. B. A. Desmarais and S. J. Cranmer (2012), ‘Statistical Mechanics of Networks: Estimation and Uncertainty’, Physica A: Statistical Mechanics and it’s Applications, 391 (4), February, 1865–76
17. Bruce A. Desmarais and Skyler J. Cranmer (2012), ‘Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks’, Policy Studies Journal, 40 (3), August, 402–34
18. Bruce A. Desmarais, Jeffrey J. Harden and Frederick J. Boehmke (2015), ‘Persistent Policy Pathways: Inferring Diffusion Networks in the American States’, American Political Science Review, 109 (2), May, 392–406
19. Jeff Gill and John R. Freeman (2013), ‘Dynamic Elicited Priors for Updating Covert Networks’, Network Science, 1 (1), April, 68–94
20. Jude C. Hays, Aya Kachi and Robert J. Franzese, Jr. (2010), ‘A Spatial Model Incorporating Dynamic, Endogenous Network Interdependence: A Political Science Application’, Statistical Methodology, 7 (3), May, 406–28
21. Robert J Franzese, Jr., Jude C. Hays and Aya Kachi (2012), ‘Modeling History Dependence in Network-Behavior Coevolution’, Political Analysis, 20 (2), Spring, 175–90
PART IV ADVANCES IN TEXT-ANALYTIC, CLASSIFICATION AND BIG-DATA METHODS
22. Phillip A Schrodt and David Van Brackle (2013) ‘Automated Coding of Political Event Data’ in V.S. Subrahmanian (ed.), Handbook of Computational Approaches to Counterterrorism, Chapter 2, New York, USA: Springer, 23–49
23. Justin Grimmer and Gary King (2011), ‘General Purpose Computer-Assisted Clustering and Conceptualization’, Proceedings of the National Academy of Sciences, 108 (7), February, 2643–50
24. Vito D’Orazio, Steven T. Landis, Glenn Palmer and Philip Schrodt (2014), ‘Separating the Wheat from the Chaff: Applications of Automated Document Classification Using Support Vector Machines’, Political Analysis, 22 (2), Spring, 224–42
25. Justin Grimmer and Brandon M. Stewart (2013), ‘Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts’, Political Analysis, 21 (3), Summer, 267–97
26. Martin Elff (2013), ‘A Dynamic State-Space Model of Coded Political Texts’, Political Analysis, 21 (2), Spring, 217–32
27. Christopher Lucas, Richard A. Nielson, Margaret E. Roberts, Brandon M. Stewart, Alex Storer and Dustin Tingley (2015), ‘Computer Assisted Text Analysis for Comparative Politics’, Political Analysis, 23 (2), Spring, 254–77
PART V ADVANCES IN NONPARAMETRIC & DESIGN-BASED INFERENCE METHODS
28. Jasjeet S. Sekhon (2008), ‘The Neyman-Rubin Model of Causal Inference and Estimation via Matching Methods’, in Janet M. Box-Steffensmeier, Henry E. Brady and David Collier (eds), Oxford Handbook of Political Methodology, Part VI, Chapter 11, Oxford, UK: Oxford University Press, 271–99
29. Jasjeet Sekhon and Rocío Titiunik (2012), ‘When Natural Experiments Are Neither Natural Nor Experiments’, American Political Science Review, 106 (1), February, 35–57
30. Peter M. Aronow and Allison Carnegie (2013), ‘Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable’, Political Analysis, 21 (4), Autumn, 492–506
31. Kosuke Imai, Luke Keele, Dustin Tingley and Teppai Yamamoto (2011), ‘Unpacking the Black Box of Causality: Learning about Casual Mechanisms from Experimental and Observational Studies’, American Political Science Review, 105 (4), November, 765–89
32. Kosuke Imai and Marc Ratkovic (2013), ‘Estimating Treatment Effect Heterogeneity in Randomized Program Evaluation’, Annals of Applied Statistics, 7 (1), 443–70
33. Luke Keele and Rocío Titiunik (2016), ‘Natural Experiments Based on Geography’, Political Science Research and Methods, 4 (1), January, 65–95
34. Luke Keele, Rocío Titiunik and Jose Zubizarreta (2015), ‘Enhancing a Geographic Regression Discontinuity Design Through Matching To Estimate the Effect of Ballot Initiatives on Voter Turnout’, Journal of the Royal Statistical Society: Statistics in Society, Series A, 178 (1), 223–39 [17]
Index
Research Review Robert J. Franzese Jr.
PART I ADVANCES IN BAYESIAN METHODS
1. Simon Jackman (2000), ‘Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo’, American Journal of Political Science, 44 (2), April, 375–404
2. Joshua Clinton, Simon Jackman and Douglas Rivers (2004), ‘The Statistical Analysis of Roll Call Data’, American Political Science Review, 98 (2), May, 355–70
3. Richard Traunmüller, Andreas Murr and Jeff Gill (2015), ‘Modeling Latent Information in Voting Data with Dirichlet Process Priors’, Political Analysis, 23 (1), Winter, 1–20
4. Yair Ghitza and Andrew Gelman (2013), ‘Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups’, American Journal of Political Science, 57 (3), July, 762–76
5. Devin Caughey and Christopher Warshaw (2015), ‘Dynamic Estimation of Latent Opinion Using a Hierarchical Group-Level IRT Model’, Political Analysis, 23 (2), Spring, 197–211
PART II ADVANCES IN TIME-SERIES, TIME-SERIES-CROSS-SECTION/PANEL, AND EVENT-HISTORY/DURATION MODELLING
6. Janet M. Box–Steffensmeier and Bradford S. Jones (1997), ‘Time Is of the Essence: Event History Models in Political Science’, American Journal of Political Science, 41 (4), October, 1414–61
7. Frederick J. Boehmke, Daniel S. Morey and Megan Shannon (2006), ‘Selection Bias and Continuous-Time Duration Models: Consequences and a Proposed Solution’, American Journal of Political Science, 50 (1), January, 192–207
8. Jude C. Hays, Emily U. Schilling and Frederick J. Boehmke (2015), ‘Accounting for Right Censoring in Interdependent Duration Analysis’, Political Analysis, 23 (3) Summer, 400–14
9. Jude C. Hays and Robert J. Franzese, Jr. (2009), ‘A Comparison of the Small-Sample Properties of Several Estimators for Spatial-Lag Count Models’, paper submitted at the 2009 Summer Meeting of The Society of Political Methodology, New Haven, CT, USA, July 23–5, i, 1–27
10. Patrick T. Brandt, Michael Colaresi and John R. Freeman (2008), ‘The Dynamics of Reciprocity, Accountability, and Credibility’, Journal of Conflict Resolution, 52 (3), June, 343–74
11. Patrick T. Brandt, John R. Freeman and Philip A. Schrodt (2011), ‘Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict’, Conflict Management and Peace Science, 28 (1), February, 41–64
12. Daniel Stegmueller (2013), ‘Modeling Dynamic Preferences: A Bayesian Robust Dynamic Latent Ordered Probit Model’, Political Analysis, 21 (3), Summer, 314–33
13. Xun Pang (2014), ‘Varying Responses to Common Shocks and Complex Cross-Sectional Dependence: Dynamic Multilevel Modeling with Multifactor Error Structures for Time–Series Cross–Sectional Data’, Political Analysis, 22 (4), Autumn, 464–96
14. Robert J. Franzese, Jr. and Jude C. Hays (2008), ‘Empirical Models of Spatial Interdependence’ in Janet M. Box-Steffensmeier, Henry E. Brady and David Collier (eds), Oxford Handbook of Political Methodology, Oxford, UK: Oxford University Press, Part VII, Chapter 25, 570–604
15. Robert J. Franzese, Jr., Jude C. Hays and Scott J. Cook (2016), ‘Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes’, Political Science Research and Methods, 4 (1), January, 151–73
PART III ADVANCES IN NETWORK ANALYSIS
16. B. A. Desmarais and S. J. Cranmer (2012), ‘Statistical Mechanics of Networks: Estimation and Uncertainty’, Physica A: Statistical Mechanics and it’s Applications, 391 (4), February, 1865–76
17. Bruce A. Desmarais and Skyler J. Cranmer (2012), ‘Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks’, Policy Studies Journal, 40 (3), August, 402–34
18. Bruce A. Desmarais, Jeffrey J. Harden and Frederick J. Boehmke (2015), ‘Persistent Policy Pathways: Inferring Diffusion Networks in the American States’, American Political Science Review, 109 (2), May, 392–406
19. Jeff Gill and John R. Freeman (2013), ‘Dynamic Elicited Priors for Updating Covert Networks’, Network Science, 1 (1), April, 68–94
20. Jude C. Hays, Aya Kachi and Robert J. Franzese, Jr. (2010), ‘A Spatial Model Incorporating Dynamic, Endogenous Network Interdependence: A Political Science Application’, Statistical Methodology, 7 (3), May, 406–28
21. Robert J Franzese, Jr., Jude C. Hays and Aya Kachi (2012), ‘Modeling History Dependence in Network-Behavior Coevolution’, Political Analysis, 20 (2), Spring, 175–90
PART IV ADVANCES IN TEXT-ANALYTIC, CLASSIFICATION AND BIG-DATA METHODS
22. Phillip A Schrodt and David Van Brackle (2013) ‘Automated Coding of Political Event Data’ in V.S. Subrahmanian (ed.), Handbook of Computational Approaches to Counterterrorism, Chapter 2, New York, USA: Springer, 23–49
23. Justin Grimmer and Gary King (2011), ‘General Purpose Computer-Assisted Clustering and Conceptualization’, Proceedings of the National Academy of Sciences, 108 (7), February, 2643–50
24. Vito D’Orazio, Steven T. Landis, Glenn Palmer and Philip Schrodt (2014), ‘Separating the Wheat from the Chaff: Applications of Automated Document Classification Using Support Vector Machines’, Political Analysis, 22 (2), Spring, 224–42
25. Justin Grimmer and Brandon M. Stewart (2013), ‘Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts’, Political Analysis, 21 (3), Summer, 267–97
26. Martin Elff (2013), ‘A Dynamic State-Space Model of Coded Political Texts’, Political Analysis, 21 (2), Spring, 217–32
27. Christopher Lucas, Richard A. Nielson, Margaret E. Roberts, Brandon M. Stewart, Alex Storer and Dustin Tingley (2015), ‘Computer Assisted Text Analysis for Comparative Politics’, Political Analysis, 23 (2), Spring, 254–77
PART V ADVANCES IN NONPARAMETRIC & DESIGN-BASED INFERENCE METHODS
28. Jasjeet S. Sekhon (2008), ‘The Neyman-Rubin Model of Causal Inference and Estimation via Matching Methods’, in Janet M. Box-Steffensmeier, Henry E. Brady and David Collier (eds), Oxford Handbook of Political Methodology, Part VI, Chapter 11, Oxford, UK: Oxford University Press, 271–99
29. Jasjeet Sekhon and Rocío Titiunik (2012), ‘When Natural Experiments Are Neither Natural Nor Experiments’, American Political Science Review, 106 (1), February, 35–57
30. Peter M. Aronow and Allison Carnegie (2013), ‘Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable’, Political Analysis, 21 (4), Autumn, 492–506
31. Kosuke Imai, Luke Keele, Dustin Tingley and Teppai Yamamoto (2011), ‘Unpacking the Black Box of Causality: Learning about Casual Mechanisms from Experimental and Observational Studies’, American Political Science Review, 105 (4), November, 765–89
32. Kosuke Imai and Marc Ratkovic (2013), ‘Estimating Treatment Effect Heterogeneity in Randomized Program Evaluation’, Annals of Applied Statistics, 7 (1), 443–70
33. Luke Keele and Rocío Titiunik (2016), ‘Natural Experiments Based on Geography’, Political Science Research and Methods, 4 (1), January, 65–95
34. Luke Keele, Rocío Titiunik and Jose Zubizarreta (2015), ‘Enhancing a Geographic Regression Discontinuity Design Through Matching To Estimate the Effect of Ballot Initiatives on Voter Turnout’, Journal of the Royal Statistical Society: Statistics in Society, Series A, 178 (1), 223–39 [17]
Index
