「新刊速递」《政治分析》(PA),Vol. 30, No. 3, December, 2022 - 国政学人

期刊简介

《政治分析》（Political Analysis）是一份1948年起发行的同行评审学术期刊，由剑桥大学出版社为政治学方法学会出版。该期刊每年出版4次，主要发表讨论政治学研究方法（尤其是量化研究）的论文。据2020年发布的期刊引证报告指，《政治分析》的影响因子为8.6，在政治科学类的期刊排名第1/182位。

本期目录

1 政党有多民粹？使用监督式机器学习衡量政党宣言中的民粹主义程度

How Populist are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning

2 政治选择的结构：区分限制和多维性

The Structure of Political Choices: Distinguishing Between Constraint and Multidimensionality

3 大规模理想点估计

Large-Scale Ideal Point Estimation

4 优化衡量政治研究中的性别歧视

Optimizing the Measurement of Sexism in Political Surveys

5 实践中列表实验和直接问题间的错报权衡：来自两个国家的分区验证证据

The Misreporting Trade-Off Between List Experiments and Direct Questions in Practice: Partition Validation Evidence from Two Countries

6 党派错位：一种对代表权与选区划分在选区层面的衡量

Partisan Dislocation: A Precinct-Level Measure of Representation and Gerrymandering

7 数据质量评估：方法与运用

Assessing Data Quality: An Approach and An Application

文章摘要

政党有多民粹？使用监督式机器学习衡量政党宣言中的民粹主义程度

作者：Jessica Di Cocco，欧洲大学学院（European University Institute）政治和社会科学系马克思·韦伯研究项目博士后，意大利罗马大学（Sapienza University）经济和法律系社会经济和统计研究博士；Bernardo Monechi，索尼计算机科学实验公司研究员。

摘要：民粹主义比较研究最主要的挑战之一在于在大量政党和国家内部及其之间进行时空测量。经证明，文本分析（textual analysis）可用于这些目标，并且自动方法（automated methods）能够进一步改善这一方向上的研究。对此，本文使用监督式机器学习（supervised machine learning）对国家宣言进行文本分析，提出一种获取政党民粹主义水平分数的方法。本文验证了这一方法的优势，即能够测量大量政党和国家的民粹主义水平而无需过度占用资源的人工编码程序，并为民粹主义的时间、空间比较提供精确、及时的信息。此外，这一方法能够获取民粹主义水平的持续得分，能够在减低任意分类风险的情况下保证针对政党状况的更精细的分析。为证明这种评分的潜在贡献，本文将其作为政党民粹主义水平的指标，用于分析六个欧洲政党从本世纪初至今近20年来的平均趋势。

One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here, we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information for temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the party landscape while reducing the risk of arbitrary classifications. To illustrate the potential contribution of this score, we use it as a proxy for parties’ levels of populism, analyzing average trends in six European countries from the early 2000s for nearly two decades.

政治选择的结构：区分限制和多维性

作者：William Marble，美国宾夕法尼亚大学民意研究和选举研究项目数据科学主任，美国全国广播公司（NBC）选举分析师；Matthew Tyler，美国斯坦福大学民主与两极化实验室和美国全国选举研究（ANES）博士后。

摘要：在有关公众意见和立法行为的文献中存在两种辩论：第一，偏好有多受限；第二，偏好是否受制于单一的左右光谱或需要多元的维度。然而，由于形式化（formalization）不足，学者将缺乏限制等同于多维偏好。本文在一个正式的框架内改进了限制和多维性的概念，并描述了它们如何转化为对政治偏好的独立的可视化影响。本文利用这一讨论来激励交叉验证的估计值，它可以在典型理想点模型的背景下测量限制和维度。利用来自公众和政治家的数据，本文发现美国人的政治偏好是单一维度的，但政治家之间的政治偏好比大众更受限制。此外，本文表明，政治家和公众之间的差异不能用议程方面的差异或行为体面临的激励来解释。

In the literatures on public opinion and legislative behavior, there are debates over (1) how constrained preferences are and (2) whether they are captured by a single left–right spectrum or require multiple dimensions. But insufficient formalization has led scholars to equate a lack of constraint with multidimensional preferences. In this paper, we refine the concepts of constraint and dimensionality in a formal framework and describe how they translate into separate observable implications for political preferences. We use this discussion to motivate a cross-validation estimator that measures constraint and dimensionality in the context of canonical ideal point models. Using data from the public and politicians, we find that American political preferences are one-dimensional, but there is more constraint among politicians than among the mass public. Furthermore, we show that differences between politicians and the public are not explained by differences in agendas or the incentives faced by the actors.

大规模理想点估计

作者：Michael Peress，纽约州立大学石溪分校政治系和经济系副教授，研究兴趣是投票行为、立法机构、选举系统、方法论和形式理论。

摘要：近期在投票行为研究和立法机关研究上取得的进步都依赖于通过理想点估计来衡量政治行为体的偏好。而且，这些研究也越来越设计大型数据矩阵的应用。这对目前为止大范围应用的方法来说都颇具挑战。现有方法的不足包括对大型数据库的大量运算时间和大量内存需求，不能很好的应对系数数据矩阵，低效标准差运算，以及无效生成初始值方法。作者开发了一种在大型应用中估计多维理想值的方法来克服这些不足。作者通过将其应用于数个挑战性难题来证明其有效性。作者开发的方法通过一个R使用包来实施。

Recent advances in the study of voting behavior and the study of legislatures have relied on ideal point estimation for measuring the preferences of political actors, and increasingly, these applications have involved very large data matrices. This has proved challenging for the widely available approaches. Limitations of existing methods include excessive computation time and excessive memory requirements on large datasets, the inability to efficiently deal with sparse data matrices, inefficient computation of standard errors, and ineffective methods for generating starting values. I develop an approach for estimating multidimensional ideal points in large-scale applications, which overcomes these limitations. I demonstrate my approach by applying it to a number of challenging problems. The methods I develop are implemented in an r package (ipe).

优化衡量政治研究中的性别歧视

作者：Brian F. Schaffner，美国塔夫茨大学（Tufts University）艺术与科学学院政治科学系教授。

摘要：政治科学家越来越多地关注理解针对预测投票选择和议题意见的性别歧视态度。然而，此类研究采用多种不同的条目和量表来测量性别歧视态度。本文评估了其中最重要的当代性别歧视测量方法，并基于聚合效度、预测效度和政治距离提出了确定最佳条目的方法。本文发现来自敌意性别歧视量表的一个条目子集表现出最令人满意的测量特性。最后，本文推荐一个简单的减少敌意性别歧视序列的2-5条目，这将利于学者更高效、合理并持续地测量性别歧视。

Political scientists are paying increasing attention to understanding the role of sexist attitudes on predicting vote choices and opinions on issues. However, the research in this area measures sexist attitudes with a variety of different items and scales. In this paper, I evaluate some of the most prominent contemporary measures of sexism and develop an approach for identifying optimal items based on (1) convergent validity, (2) predictive validity, and (3) distance from politics. I find that a subset of items from the hostile sexism scale exhibit the most desirable measurement properties and I conclude by recommending a simple two- to five-item reduced hostile sexism battery that will allow scholars to efficiently, validly, and consistently measure sexism.

实践中列表实验和直接问题间的错报权衡：来自两个国家的分区验证证据

作者：Patrick M. Kuhn，杜伦大学比较政治学副教授，研究兴趣是发展与冲突的比较政治经济学；Nick Vivyan，杜伦大学政治学教授，研究兴趣是用定量方法研究英国的政治行为和问责与代表。

摘要：为降低在敏感问题上的战略性错报，调查研究者更多地使用列表实验而非直接问题。然而，列表实验地复杂性可能会增加非战略性误报。作者做出了战略错报和非战略错报权衡的实证检验。作者在两个国家就投票率进行列表实验，收集受访者地真实投票率。作者详细介绍并应用了一种分区验证方法，该方法使用真实分数来区分列表实验的真假积极和消极因素，从而检测非战略性报告错误。在两组列表实验中，分区验证都显示出非战略性误报，且具有以下属性：未被标准诊断或验证发现；比现有模拟研究设想的要大；足够严重，使得受战略性错报困扰的直接问题法都对此表现出更小的误差。作者讨论了其结果如何能够为其他主题和调查背景的列表实验和直接问题提供参考。

To reduce strategic misreporting on sensitive topics, survey researchers increasingly use list experiments rather than direct questions. However, the complexity of list experiments may increase nonstrategic misreporting. We provide the first empirical assessment of this trade-off between strategic and nonstrategic misreporting. We field list experiments on election turnout in two different countries, collecting measures of respondents’ true turnout. We detail and apply a partition validation method which uses true scores to distinguish true and false positives and negatives for list experiments, thus allowing detection of nonstrategic reporting errors. For both list experiments, partition validation reveals nonstrategic misreporting that is: undetected by standard diagnostics or validation; greater than assumed in extant simulation studies; and severe enough that direct turnout questions subject to strategic misreporting exhibit lower overall reporting error. We discuss how our results can inform the choice between list experiment and direct question for other topics and survey contexts.

党派错位：一种对代表权与选区划分在选区层面的衡量

作者：Daryl R. Deford, 美国华盛顿大学数学学院数据分析专业助理教授；Nicholas Eubank, 杜克社会科学研究所助理研究员；Jonathan Rodden ，斯坦福大学政治科学系教授，胡佛研究所和斯坦福经济政策研究所高级研究员。

摘要：作者引入了一种微观的衡量标准以衡量选区以一种不自然的方式合并和分裂当地同党社区的程度。作者所采用的指标——其将之称为“党派错位”的指标是用以衡量选民在地理上最近的邻居的党派构成与她所在选区的党派构成之间的差异。作者证明，其提供的测量方法是一种很好的并能够实现对地区操作化的地方与全球指标。这种方法可以很容易的识别地区划分同党集团（分裂）或以不自然的方式组合（组合）的情况。作者证明了他们所提出的测量方法和其他测量不公正选区划分相关但又有所不同，并更具有一些优势，尤其是这种方法是对基于模拟的方法的补充并作为识别受不公正选区划分影响最大的特定社区的一种方法。它也可以被一些希望创建反映选民地理分布的选区划分者前瞻性的使用，但根据作者的分析，该目标有时会与追求党派公正的目标相冲突。

We introduce a fine-grained measure of the extent to which electoral districts combine and split local communities of co-partisans in unnatural ways. Our indicator—which we term Partisan Dislocation—is a measure of the difference between the partisan composition of a voter’s geographic nearest neighbors and that of her assigned district. We show that our measure is a good local and global indicator of district manipulation, easily identifying instances in which districts carve up clusters of co-partisans (cracking) or combine them in unnatural ways (packing). We demonstrate that our measure is related to but distinct from other approaches to the measurement of gerrymandering, and has some clear advantages, above all as a complement to simulation-based approaches, and as a way to identify the specific neighborhoods most affected by gerrymandering. It can also be used prospectively by district-drawers who wish to create maps that reflect voter geography, but according to our analysis, that goal will sometimes be in conflict with the goal of partisan fairness.

数据质量评估：方法与运用

作者：Kelly McMann ，凯斯西储大学政治科学系教授，研究方向为民主化、腐败、政治经济学以及后共产主义政治；Daniel Pemstein， 北达科他州立大学政治科学与公共政策系教授；Brigitte Seim， 北卡罗莱纳大学公共政策系副教授，研究方向为公民与政府官员关系，发展中国家问责制；Jan Teorell ，斯德哥尔摩大学政治科学系教授；Staffan Lindberg ，哥德堡大学政治科学系教授，研究兴趣为比较政治、民主与民主化。

摘要：政治学家经常面临在实质性研究中使用的测量方式的质量（即效度与信度）方面的质疑。虽然存在许多优秀的评估工具，但研究者很少将他们综合起来进行运用。此外，尽管存在大量文献为数据生产者提供了信息，但数据使用者在如何评估用于实际研究的现有方式上缺乏相应的指导。作者描述了数据质量评估的实用方法，这种方法由三部分组成并集成了互补的多元方法工具来进行评估：（1）内容的有效性；（2）数据生成过程的有效性和可靠性；以及（3）同证效度。作者将他们提出的质量评估方法应用于各种民主（V-Dem）项目中对腐败的测量，既阐明了本文的标题，作者又发现与其他现有的腐败测量方法相比，V-Dem措施所具有的几个在质量方面的优势和劣势。

Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multimethod tools to assess: (1) content validity; (2) the validity and reliability of the data generation process; and (3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties of Democracy (V-Dem) project, both illustrating our rubric and unearthing several quality advantages and disadvantages of the V-Dem measures, compared to other existing measures of corruption.

编译 | 林怡娉 徐一凡 谭政

审校 | 卫艺璇

排版 | 杨文杰

文章来源于《政治分析》第3期。文章评译内容为公益分享，服务于学术科研教学工作，不代表国政学人观点。