Ecological Inference and Entropy-Maximizing: An Alternative Estimation Procedure for Split-Ticket Voting

Publication of King's A Solution to the Ecological InferenceProblem has rekindled interest in the estimation of unknowncell values in two- and three-dimensional matrices from knowledgeof the marginal sums. This paper outlines an entropy-maximizing(EM) procedure which employs more constraints than King's EImethod and produces mathematical rather than statistical procedures:the estimates are maximum-likelihood values. The mathematicsare outlined, and the procedure's use illustrated with a studyof ticket-splitting at New Zealand's first (1996) general electionusing the mixed-member proportional representation system, forwhich official figures provide a check against the EM estimateof the number voting a straight party ticket in each constituency.     

Bayesian and Likelihood Inference for 2 x 2 Ecological Tables: An Incomplete-Data Approach

Ying Lu Department of Sociology, University of Colorado at Boulder, Boulder, CO 80309 e-mail: ying.lu{at}colorado.edu Aaron Strauss Department of Politics, Princeton University, Princeton, NJ 08544 e-mail: abstraus{at}princeton.edu e-mail: kimai{at}princeton.edu (corresponding author) Ecological inference is a statistical problem where aggregate-leveldata are used to make inferences about individual-level behavior.In this article, we conduct a theoretical and empirical studyof Bayesian and likelihood inference for 2 x 2 ecological tablesby applying the general statistical framework of incompletedata. We first show that the ecological inference problem canbe decomposed into three factors: distributional effects, whichaddress the possible misspecification of parametric modelingassumptions about the unknown distribution of missing data;contextual effects, which represent the possible correlationbetween missing data and observed variables; and aggregationeffects, which are directly related to the loss of informationcaused by data aggregation. We then examine how these threefactors affect inference and offer new statistical methods toaddress each of them. To deal with distributional effects, wepropose a nonparametric Bayesian model based on a Dirichletprocess prior, which relaxes common parametric assumptions.We also identify the statistical adjustments necessary to accountfor contextual effects. Finally, although little can be doneto cope with aggregation effects, we offer a method to quantifythe magnitude of such effects in order to formally assess itsseverity. We use simulated and real data sets to empiricallyinvestigate the consequences of these three factors and to evaluatethe performance of our proposed methods. C code, along withan easy-to-use R interface, is publicly available for implementingour proposed methods (Imai, Lu, and Strauss, forthcoming). Authors' note: This article is in the part based on two workingpapers by Imai and Lu, "Parametric and Nonparamateric BayesianModels for Ecological Inference in 2 x 2 Tables" and "QuantifyingMissing Information in Ecological Inference." Various versionsof these papers were presented at the 2004 Joint StatisticalMeetings, the Second Cape Cod Monte Carlo Workshop, the 2004Annual Political Methodology Summer Meeting, and the 2005 AnnualMeeting of the American Political Science Association. We thankanonymous referees, Larry Bartels, Wendy Tam Cho, Jianqing Fan,Gary King, Xiao-Li Meng, Kevin Quinn, Phil Shively, David vanDyk, Jon Wakefield, and seminar participants at New York University(the Northeast Political Methodology conference), at PrincetonUniversity (Economics Department and Office of Population Research),and at the University of Virginia (Statistics Department) forhelpful comments.     

Trade‐Offs,Limitations, and Promises of Big Data in Social Science Research

The promise of “big data” is a grand one. The collection and aggregation of massive datasets and the development of analytical tools by which to study these data is part of cutting‐edge efforts across scientific disciplines, with social, behavioral, and economic sciences leading the way in many of these efforts. There has been a recent rise in National Science Foundation funding for “big data” research across directorates that coincides with scholarly, public, and governmental attention to the topic. Nonetheless, there are limitations and trade‐offs to “big data” research, particularly as it corresponds to scientific modes of inquiry and the limited range of topics that fall under its umbrella, that must be recognized and incorporated into the general understanding of its long‐term promise.     

Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis

While many areas of research in political science draw inferencesfrom temporally aggregated data, rarely have researchers exploredhow temporal aggregation biases parameter estimates. With somenotable exceptions (Freeman 1989, Political Analysis 1:61–98;Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002,"Event Data Analysis and Threats from Temporal Aggregation")political science studies largely ignore how temporal aggregationaffects our inferences. This article expands upon others' workon this issue by assessing the effect of temporal aggregationdecisions on vector autoregressive (VAR) parameter estimates,significance levels, Granger causality tests, and impulse responsefunctions. While the study is relevant to all fields in politicalscience, the results directly apply to event data studies ofconflict and cooperation. The findings imply that politicalscientists should be wary of the impact that temporal aggregationhas on statistical inference.     

Beyond Polls: Using Science and Student Data to Stimulate Learning

In an effort to promote learning in classrooms, political science instructors are increasingly turning to interactive teaching strategies — experiments, simulations, etc. — that supplement traditional lecture formats. In this article, I advocate the use of student-generated data as a powerful teaching tool that can be used in a variety of ways to support learning. The “data-driven classroom” is one in which original student data are collected via survey at the beginning of the course and the results presented throughout the term. Examples and case studies across multiple courses and classroom settings are used to illustrate how the method may be applied in practice. Feedback from student assessment surveys reveals strong support for the method, even among nonmajors. The article concludes with recommendations for instructors interested in creating more data-driven classrooms.     

Creating Student-Scholar-Activists: Discourse Instruction and Social Justice in Political Science Classrooms

This article advocates the use of discourse instruction as a means of integrating issues of social justice into the classroom and transcending the debate over politicization in academia. The field of political science is at an uncomfortable juncture; it is faced with an obligation to ourselves and our communities to critically engage and push back against the more toxic components of the political moment, staying relevant and accurate and providing students with the tools they need to process the political world; while, also resisting the dual pressures to either stay apolitical/non-partisan, or to become a current events class, ceding class time to deciphering the day’s political events. We argue that discourse instruction can be used to teach the skills of social justice in political science classrooms. In addition, the infusion of diversity into the classroom through discourse instruction is both a means of enhancing student learning by engaging in high-impact practices of teaching and learning and political activism.     

残疾人社会保障:问题与对策

目前我国在残疾人社会保障方面还存在一些问题,主要表现在残疾人社会保障的供需矛盾突出、残疾人群体中贫困问题较为严重和残疾人社会保障城乡差距较大.为此,要采取以下对策:调动国家和社会力量,缓解供需矛盾;促进社会救济和社会保险相结合,改善残疾人的生活现状:统筹城乡残疾人社会保障制度,缩小城乡差距.     

Museums and the Active Citizen: Tackling the Problems of Social Exclusion

The aim of this paper is to attempt to determine the role of museums in combating social exclusion through facilitating active citizenship. It does this by presenting the results of an analysis that used a modified form of a model of citizenship created by Makela as a framework to explore the data generated by an empirical study. By focusing upon respondents in an holistic way, so understanding their experiences of museum based exhibitions and community development projects within the contexts of their lives, the study concludes that museums were able to overcome many of the barriers to active citizenship that were identified. However, it was also evident that they themselves were creating barriers as some were physically or intellectually inaccessible to a number of study participants. The most significant contribution of museums in developing active citizens was to provide a context for constructing a sense of identity and so develop greater self-confidence. The paper concludes that if the potential that museums have fostered is to be released government agencies need to work together.     

新疆籍流浪儿童社会融入障碍与对策初探——基于一项质性调查的研究

新疆籍流浪儿童在回归后,很难融入家乡的社会生活。通过文献、观察和访谈的质性方法,从经济、文化心理、社会公共关系三个方面,掌握其社会融入的状况和障碍。结合新疆籍流浪儿童的特殊性和新疆的区情,综合各种现有模式,试图提出合适的社会融入对策。