{"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}
{"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}
{"title":"<scp>Relatio</scp>: Text Semantics Capture Political and Economic Narratives – ERRATUM","authors":"Elliott Ash, Germain Gauthier, Philine Widmer","doi":"10.1017/pan.2023.15","DOIUrl":"https://doi.org/10.1017/pan.2023.15","url":null,"abstract":"An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135626082","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}
{"title":"The Role of Majority Status in Close Election Studies","authors":"Matteo Alpino, Marta Crispino","doi":"10.1017/pan.2023.14","DOIUrl":"https://doi.org/10.1017/pan.2023.14","url":null,"abstract":"Abstract Many studies exploit close elections in a regression discontinuity framework to identify partisan effects, that is, the effect of having a given party in office on some outcome. We argue that, when conducted on single-member districts, such design may identify a compound effect: the partisan effect, plus the majority status effect, that is, the effect of being represented by a member of the legislative majority. We provide a simple strategy to disentangle the two, and test it with simulations. Finally, we show the empirical relevance of this issue using real data.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135927278","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}
{"title":"Simulating Party Shares","authors":"D. Cohen, Chris Hanretty","doi":"10.1017/pan.2023.13","DOIUrl":"https://doi.org/10.1017/pan.2023.13","url":null,"abstract":"\u0000 We tackle the problem of simulating seat- and vote-shares for a party system of a given size. We show how these shares can be generated using unordered and ordered Dirichlet distributions. We show that a distribution with a mean vector given by the rule described in Taagepera and Allik (2006, Electoral Studies 25, 696–713) fits real-world data almost as well as a saturated model where there is a parameter for each rank/system size combination.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44901798","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}
{"title":"It’s All in the Name: A Character-Based Approach to Infer Religion","authors":"Rochana Chaturvedi, Sugat Chaturvedi","doi":"10.1017/pan.2023.6","DOIUrl":"https://doi.org/10.1017/pan.2023.6","url":null,"abstract":"Abstract Large-scale microdata on group identity are critical for studies on identity politics and violence but remain largely unavailable for developing countries. We use personal names to infer religion in South Asia—where religion is a salient social division, and yet, disaggregated data on it are scarce. Existing work predicts religion using a dictionary-based method and, therefore, cannot classify unseen names. We provide character-based machine-learning models that can classify unseen names too with high accuracy. Our models are also much faster and, hence, scalable to large datasets. We explain the classification decisions of one of our models using the layer-wise relevance propagation technique. The character patterns learned by the classifier are rooted in the linguistic origins of names. We apply these to infer the religion of electoral candidates using historical data on Indian elections and observe a trend of declining Muslim representation. Our approach can be used to detect identity groups across the world for whom the underlying names might have different linguistic roots.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136151852","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}
Y. Zhukov, Jason S. Byers, Marty Davidson, Ken Kollman
{"title":"Integrating Data Across Misaligned Spatial Units","authors":"Y. Zhukov, Jason S. Byers, Marty Davidson, Ken Kollman","doi":"10.1017/pan.2023.5","DOIUrl":"https://doi.org/10.1017/pan.2023.5","url":null,"abstract":"\u0000 Theoretical units of interest often do not align with the spatial units at which data are available. This problem is pervasive in political science, particularly in subnational empirical research that requires integrating data across incompatible geographic units (e.g., administrative areas, electoral constituencies, and grid cells). Overcoming this challenge requires researchers not only to align the scale of empirical and theoretical units, but also to understand the consequences of this change of support for measurement error and statistical inference. We show how the accuracy of transformed values and the estimation of regression coefficients depend on the degree of nesting (i.e., whether units fall completely and neatly inside each other) and on the relative scale of source and destination units (i.e., aggregation, disaggregation, and hybrid). We introduce simple, nonparametric measures of relative nesting and scale, as ex ante indicators of spatial transformation complexity and error susceptibility. Using election data and Monte Carlo simulations, we show that these measures are strongly predictive of transformation quality across multiple change-of-support methods. We propose several validation procedures and provide open-source software to make transformation options more accessible, customizable, and intuitive.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41679125","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}
Sonja Häffner, Martin Hofer, Maximilian Nagl, Julian Walterskirchen
{"title":"Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation: A Use Case for Conflict Prediction","authors":"Sonja Häffner, Martin Hofer, Maximilian Nagl, Julian Walterskirchen","doi":"10.1017/pan.2023.7","DOIUrl":"https://doi.org/10.1017/pan.2023.7","url":null,"abstract":"Abstract Recent advancements in natural language processing (NLP) methods have significantly improved their performance. However, more complex NLP models are more difficult to interpret and computationally expensive. Therefore, we propose an approach to dictionary creation that carefully balances the trade-off between complexity and interpretability. This approach combines a deep neural network architecture with techniques to improve model explainability to automatically build a domain-specific dictionary. As an illustrative use case of our approach, we create an objective dictionary that can infer conflict intensity from text data. We train the neural networks on a corpus of conflict reports and match them with conflict event data. This corpus consists of over 14,000 expert-written International Crisis Group (ICG) CrisisWatch reports between 2003 and 2021. Sensitivity analysis is used to extract the weighted words from the neural network to build the dictionary. In order to evaluate our approach, we compare our results to state-of-the-art deep learning language models, text-scaling methods, as well as standard, nonspecialized, and conflict event dictionary approaches. We are able to show that our approach outperforms other approaches while retaining interpretability.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"31 1","pages":"481 - 499"},"PeriodicalIF":5.4,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44604107","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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