Separation commonly occurs in political science, usually when a binary explanatory variable perfectly predicts a binary outcome. In these situations, methodologists often recommend penalized maximum likelihood or Bayesian estimation. But researchers might struggle to identify an appropriate penalty or prior distribution. Fortunately, I show that researchers can easily test hypotheses about the model coefficients with standard frequentist tools. While the popular Wald test produces misleading (even nonsensical) p-values under separation, I show that likelihood ratio tests and score tests behave in the usual manner. Therefore, researchers can produce meaningful p-values with standard frequentist tools under separation without the use of penalties or prior information.
{"title":"Hypothesis Tests under Separation","authors":"Carlisle Rainey","doi":"10.1017/pan.2023.28","DOIUrl":"https://doi.org/10.1017/pan.2023.28","url":null,"abstract":"\u0000 Separation commonly occurs in political science, usually when a binary explanatory variable perfectly predicts a binary outcome. In these situations, methodologists often recommend penalized maximum likelihood or Bayesian estimation. But researchers might struggle to identify an appropriate penalty or prior distribution. Fortunately, I show that researchers can easily test hypotheses about the model coefficients with standard frequentist tools. While the popular Wald test produces misleading (even nonsensical) p-values under separation, I show that likelihood ratio tests and score tests behave in the usual manner. Therefore, researchers can produce meaningful p-values with standard frequentist tools under separation without the use of penalties or prior information.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42663724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Kernel regularized least squares (KRLS) is a popular method for flexibly estimating models that may have complex relationships between variables. However, its usefulness to many researchers is limited for two reasons. First, existing approaches are inflexible and do not allow KRLS to be combined with theoretically motivated extensions such as random effects, unregularized fixed effects, or non-Gaussian outcomes. Second, estimation is extremely computationally intensive for even modestly sized datasets. Our paper addresses both concerns by introducing generalized KRLS ( gKRLS ). We note that KRLS can be re-formulated as a hierarchical model thereby allowing easy inference and modular model construction where KRLS can be used alongside random effects, splines, and unregularized fixed effects. Computationally, we also implement random sketching to dramatically accelerate estimation while incurring a limited penalty in estimation quality. We demonstrate that gKRLS can be fit on datasets with tens of thousands of observations in under 1 min. Further, state-of-the-art techniques that require fitting the model over a dozen times (e.g., meta-learners) can be estimated quickly.
{"title":"Generalized Kernel Regularized Least Squares","authors":"Qing Chang, Max Goplerud","doi":"10.1017/pan.2023.27","DOIUrl":"https://doi.org/10.1017/pan.2023.27","url":null,"abstract":"Abstract Kernel regularized least squares (KRLS) is a popular method for flexibly estimating models that may have complex relationships between variables. However, its usefulness to many researchers is limited for two reasons. First, existing approaches are inflexible and do not allow KRLS to be combined with theoretically motivated extensions such as random effects, unregularized fixed effects, or non-Gaussian outcomes. Second, estimation is extremely computationally intensive for even modestly sized datasets. Our paper addresses both concerns by introducing generalized KRLS ( gKRLS ). We note that KRLS can be re-formulated as a hierarchical model thereby allowing easy inference and modular model construction where KRLS can be used alongside random effects, splines, and unregularized fixed effects. Computationally, we also implement random sketching to dramatically accelerate estimation while incurring a limited penalty in estimation quality. We demonstrate that gKRLS can be fit on datasets with tens of thousands of observations in under 1 min. Further, state-of-the-art techniques that require fitting the model over a dozen times (e.g., meta-learners) can be estimated quickly.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136354648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Felix Hartmann, M. Humphreys, Ferdinand Geissler, H. Klüver, Johannes Giesecke
Survey experiments are an important tool to measure policy preferences. Researchers often rely on the random assignment of policy attribute levels to estimate different types of average marginal effects. Yet, researchers are often interested in how respondents trade-off different policy dimensions. We use a conjoint experiment administered to more than 10,000 respondents in Germany, to study preferences over personal freedoms and public welfare during the COVID-19 crisis. Using a pre-registered structural model, we estimate policy ideal points and indifference curves to assess the conditions under which citizens are willing to sacrifice freedoms in the interest of public well-being. We document broad willingness to accept restrictions on rights alongside sharp heterogeneity with respect to vaccination status. The majority of citizens are vaccinated and strongly support limitations on freedoms in response to extreme conditions—especially, when they vaccinated themselves are exempted from these limitations. The unvaccinated minority prefers no restrictions on freedoms regardless of the severity of the pandemic. These policy packages also matter for reported trust in government, in opposite ways for vaccinated and unvaccinated citizens.
{"title":"Trading Liberties: Estimating COVID-19 Policy Preferences from Conjoint Data","authors":"Felix Hartmann, M. Humphreys, Ferdinand Geissler, H. Klüver, Johannes Giesecke","doi":"10.1017/pan.2023.25","DOIUrl":"https://doi.org/10.1017/pan.2023.25","url":null,"abstract":"\u0000 Survey experiments are an important tool to measure policy preferences. Researchers often rely on the random assignment of policy attribute levels to estimate different types of average marginal effects. Yet, researchers are often interested in how respondents trade-off different policy dimensions. We use a conjoint experiment administered to more than 10,000 respondents in Germany, to study preferences over personal freedoms and public welfare during the COVID-19 crisis. Using a pre-registered structural model, we estimate policy ideal points and indifference curves to assess the conditions under which citizens are willing to sacrifice freedoms in the interest of public well-being. We document broad willingness to accept restrictions on rights alongside sharp heterogeneity with respect to vaccination status. The majority of citizens are vaccinated and strongly support limitations on freedoms in response to extreme conditions—especially, when they vaccinated themselves are exempted from these limitations. The unvaccinated minority prefers no restrictions on freedoms regardless of the severity of the pandemic. These policy packages also matter for reported trust in government, in opposite ways for vaccinated and unvaccinated citizens.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41612451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elliott Ash, Sergio Galletta, Dominik Hangartner, Yotam Margalit, Matteo Pinna
Abstract In the early weeks of the 2020 coronavirus (COVID-19) pandemic, the Fox News Channel advanced a skeptical narrative that downplayed the risks posed by the virus. We find that this narrative had significant consequences: in localities with higher Fox News viewership—exogenous due to random variation in channel positioning—people were less likely to adopt behaviors geared toward social distancing (e.g., staying at home) and consumed fewer goods in preparation (e.g., cleaning products, hand sanitizers, and masks). Using original survey data, we find that the effect of Fox News came not merely from its long-standing distrustful stance toward science, but also due to program-specific content that minimized the COVID-19 threat. Taken together, our results demonstrate the significant impact that misinformation in media coverage can exert on viewers’ beliefs and behavior, even in high-stakes situations.
{"title":"The Effect of Fox News on Health Behavior during COVID-19","authors":"Elliott Ash, Sergio Galletta, Dominik Hangartner, Yotam Margalit, Matteo Pinna","doi":"10.1017/pan.2023.21","DOIUrl":"https://doi.org/10.1017/pan.2023.21","url":null,"abstract":"Abstract In the early weeks of the 2020 coronavirus (COVID-19) pandemic, the Fox News Channel advanced a skeptical narrative that downplayed the risks posed by the virus. We find that this narrative had significant consequences: in localities with higher Fox News viewership—exogenous due to random variation in channel positioning—people were less likely to adopt behaviors geared toward social distancing (e.g., staying at home) and consumed fewer goods in preparation (e.g., cleaning products, hand sanitizers, and masks). Using original survey data, we find that the effect of Fox News came not merely from its long-standing distrustful stance toward science, but also due to program-specific content that minimized the COVID-19 threat. Taken together, our results demonstrate the significant impact that misinformation in media coverage can exert on viewers’ beliefs and behavior, even in high-stakes situations.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135099276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We provide closed-form solutions for measuring electoral closeness of candidates in proportional representation (PR) systems. In contrast to plurality systems, closeness in PR systems cannot be directly inferred from votes. Our measure captures electoral closeness for both open- and closed-list systems and for both main families of seat allocation mechanisms. This unified measure quantifies the vote surplus (shortfall) for elected (nonelected) candidates. It can serve as an assignment variable in regression discontinuity designs or as a measure of electoral competitiveness. For illustration, we estimate the incumbency advantage for the parliaments in Switzerland, Honduras, and Norway.
{"title":"Measuring Closeness in Proportional Representation Systems","authors":"Simon Luechinger, Mark Schelker, L. Schmid","doi":"10.1017/pan.2023.22","DOIUrl":"https://doi.org/10.1017/pan.2023.22","url":null,"abstract":"\u0000 We provide closed-form solutions for measuring electoral closeness of candidates in proportional representation (PR) systems. In contrast to plurality systems, closeness in PR systems cannot be directly inferred from votes. Our measure captures electoral closeness for both open- and closed-list systems and for both main families of seat allocation mechanisms. This unified measure quantifies the vote surplus (shortfall) for elected (nonelected) candidates. It can serve as an assignment variable in regression discontinuity designs or as a measure of electoral competitiveness. For illustration, we estimate the incumbency advantage for the parliaments in Switzerland, Honduras, and Norway.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45491443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
v a a n d W . C o x, D. A, t F l c ó - G i m r d i M u ñ o z, E. N. O. A. D. A. L. B. E. R. T. F. A. L. C. Ó. -. G. I. M. E. N. o, . o b o l t a J . L ee p e r, N. T, E. M. I. L. Y. M. .. F. A. R. I. S. A. J. A. N. E. L. A. W. R. E. N. C. E. S. u m n e r, N. S, T. R. A. Y. M. O. N. D. D. U. C. H. D e n i s e L a o z e, h o m a s R o b i n s D e n i s e L a r o z e, N. N., P. A. B. L. O. B. J. A. M. E. S. T. I. L. E. y, I. P. A. B. L. O. B. E. R. A. M. E. N. D. i, A. O. L. u, D. H., H. E. R. N. Á. N. D. E. z, K. K., M. N, Jeff Gill, R. M. Alvarez, Jonathan N. Katz, L. Atkeson, D. S. Hillygus, Dan Hopkins, Nathaniel N. Beck, A. Gelman, Vera E. Troeger, Marisa A. Abrajano, Antoine Banks, Facebook Usa Pablo Barberá, F. Boehmke, John O. Brehm, Lisa Bryant, K. Cowles, Andrew C. Eggers, R. Franzese, K. Fukumoto, Elisabeth Gerber, Jay Goodliff, Michael Herron, S. Hix, Gary King, T. Koenig, Jen Larson, Jan E. Leighley, J. Lewis, Drew A. Linzer, Cherie D. Maestas, G. Marfleet, Sara Mitchell, Pablo Montagnes, S. Mut
V o lu m e 28• N u m b er • A p rl20 20 V o l u m e 2 8•Number2•April2020 V o lu m e 28• N u m b er • A p rl20 20 ARICLES Dcrete Coice Data w ith U noerved H etereneity:A CondnalBinary Q untile M odel Xiao Lu M eauring he om petienessof Eltions Gary W .Cx,Jon H .iva nd DnielM Sm ith U neected Eveduring Srvey Dign: Prom se nd PitfallsfusalInference JrdiM uñoz,rtFalcó-Gim no nd Erique H erndez M eauring Sugroup Preencesin ConjtExperim ents Thom asJ.per,Sara .H obltand am esilley
•28 V或lu m和m b帮助•p rl20 20号V或m和l 2•8 Number2••28 April2020 V或lu m和N u m b帮助•p rl20 20 ARICLES Dcrete noerved H etereneity Coice w ith日期:CondnalBinary Q untile米肖或lu m eauring he om petienessof Eltions Gary w . ex乔恩·H .增值税nd DnielM Sm ith u neected Eveduring Srvey Dign: Prom如果nd PitfallsfusalInference JrdiM uñ奥芝、rtFalcó-Gim不nd Erique H erndez m eauring Sugroup Preencesin ConjtExperim ents汤姆·asJ。谢谢你，莎拉
{"title":"PAN volume 31 issue 3 Cover and Front matter","authors":"v a a n d W . C o x, D. A, t F l c ó - G i m r d i M u ñ o z, E. N. O. A. D. A. L. B. E. R. T. F. A. L. C. Ó. -. G. I. M. E. N. o, . o b o l t a J . L ee p e r, N. T, E. M. I. L. Y. M. .. F. A. R. I. S. A. J. A. N. E. L. A. W. R. E. N. C. E. S. u m n e r, N. S, T. R. A. Y. M. O. N. D. D. U. C. H. D e n i s e L a o z e, h o m a s R o b i n s D e n i s e L a r o z e, N. N., P. A. B. L. O. B. J. A. M. E. S. T. I. L. E. y, I. P. A. B. L. O. B. E. R. A. M. E. N. D. i, A. O. L. u, D. H., H. E. R. N. Á. N. D. E. z, K. K., M. N, Jeff Gill, R. M. Alvarez, Jonathan N. Katz, L. Atkeson, D. S. Hillygus, Dan Hopkins, Nathaniel N. Beck, A. Gelman, Vera E. Troeger, Marisa A. Abrajano, Antoine Banks, Facebook Usa Pablo Barberá, F. Boehmke, John O. Brehm, Lisa Bryant, K. Cowles, Andrew C. Eggers, R. Franzese, K. Fukumoto, Elisabeth Gerber, Jay Goodliff, Michael Herron, S. Hix, Gary King, T. Koenig, Jen Larson, Jan E. Leighley, J. Lewis, Drew A. Linzer, Cherie D. Maestas, G. Marfleet, Sara Mitchell, Pablo Montagnes, S. Mut","doi":"10.1017/pan.2023.17","DOIUrl":"https://doi.org/10.1017/pan.2023.17","url":null,"abstract":"V o lu m e 28• N u m b er • A p rl20 20 V o l u m e 2 8•Number2•April2020 V o lu m e 28• N u m b er • A p rl20 20 ARICLES Dcrete Coice Data w ith U noerved H etereneity:A CondnalBinary Q untile M odel Xiao Lu M eauring he om petienessof Eltions Gary W .Cx,Jon H .iva nd DnielM Sm ith U neected Eveduring Srvey Dign: Prom se nd PitfallsfusalInference JrdiM uñoz,rtFalcó-Gim no nd Erique H erndez M eauring Sugroup Preencesin ConjtExperim ents Thom asJ.per,Sara .H obltand am esilley","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"31 1","pages":"f1 - f4"},"PeriodicalIF":5.4,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47782753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Moritz Laurer, Wouter van Atteveldt, Andreu Casas, Kasper Welbers
{"title":"Less Annotating, More Classifying: Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT-NLI","authors":"Moritz Laurer, Wouter van Atteveldt, Andreu Casas, Kasper Welbers","doi":"10.1017/pan.2023.20","DOIUrl":"https://doi.org/10.1017/pan.2023.20","url":null,"abstract":"","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47961074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Estimating the ideological positions of political actors is an important step toward answering a number of substantive questions in political science. Survey scales provide useful data for such estimation, but also present a challenge, as respondents tend to interpret the scales differently. The Aldrich–McKelvey model addresses this challenge, but the existing implementations of the model still have notable shortcomings. Focusing on the Bayesian version of the model (BAM), the analyses in this article demonstrate that the model is prone to overfitting and yields poor results for a considerable share of respondents. The article addresses these shortcomings by developing a hierarchical Bayesian version of the model (HBAM). The new version treats self-placements as data to be included in the likelihood function while also modifying the likelihood to allow for scale flipping. The resulting model outperforms the existing Bayesian version both on real data and in a Monte Carlo study. An R package implementing the models in Stan is provided to facilitate future use.
{"title":"Hierarchical Bayesian Aldrich–McKelvey Scaling","authors":"Jørgen Bølstad","doi":"10.1017/pan.2023.18","DOIUrl":"https://doi.org/10.1017/pan.2023.18","url":null,"abstract":"Abstract Estimating the ideological positions of political actors is an important step toward answering a number of substantive questions in political science. Survey scales provide useful data for such estimation, but also present a challenge, as respondents tend to interpret the scales differently. The Aldrich–McKelvey model addresses this challenge, but the existing implementations of the model still have notable shortcomings. Focusing on the Bayesian version of the model (BAM), the analyses in this article demonstrate that the model is prone to overfitting and yields poor results for a considerable share of respondents. The article addresses these shortcomings by developing a hierarchical Bayesian version of the model (HBAM). The new version treats self-placements as data to be included in the likelihood function while also modifying the likelihood to allow for scale flipping. The resulting model outperforms the existing Bayesian version both on real data and in a Monte Carlo study. An R package implementing the models in Stan is provided to facilitate future use.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135269421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract A standard text-as-data workflow in the social sciences involves identifying a set of documents to be labeled, selecting a random sample of them to label using research assistants, training a supervised learner to label the remaining documents, and validating that model’s performance using standard accuracy metrics. The most resource-intensive component of this is the hand-labeling: carefully reading documents, training research assistants, and paying human coders to label documents in duplicate or more. We show that hand-coding an algorithmically selected rather than a simple-random sample can improve model performance above baseline by as much as 50%, or reduce hand-coding costs by up to two-thirds, in applications predicting (1) U.S. executive-order significance and (2) financial sentiment on social media. We accompany this manuscript with open-source software to implement these tools, which we hope can make supervised learning cheaper and more accessible to researchers.
{"title":"Selecting More Informative Training Sets with Fewer Observations","authors":"Aaron R. Kaufman","doi":"10.1017/pan.2023.19","DOIUrl":"https://doi.org/10.1017/pan.2023.19","url":null,"abstract":"Abstract A standard text-as-data workflow in the social sciences involves identifying a set of documents to be labeled, selecting a random sample of them to label using research assistants, training a supervised learner to label the remaining documents, and validating that model’s performance using standard accuracy metrics. The most resource-intensive component of this is the hand-labeling: carefully reading documents, training research assistants, and paying human coders to label documents in duplicate or more. We show that hand-coding an algorithmically selected rather than a simple-random sample can improve model performance above baseline by as much as 50%, or reduce hand-coding costs by up to two-thirds, in applications predicting (1) U.S. executive-order significance and (2) financial sentiment on social media. We accompany this manuscript with open-source software to implement these tools, which we hope can make supervised learning cheaper and more accessible to researchers.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135269847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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