An in-depth description of the various Political Boundaries GIS data layers outlining terms of use, update frequency, attribute explanations, and more. District data layers include: Lake County Boundary, County Board, Judicial Circuit Court Subcircuits, Political Townships, State Representative Districts, State Senate, Congressional Districts, and Voting Precincts.

NYU Libraries has licensed access to the L2 Political Academic Voter File. The file is a continuously updated dataset consisting of public information for every registered voter in the United States and includes basic socio-demographic indicators (some of which are modeled), consumer preferences, political party affiliation, voting history, and more.

The data consists of .tab files organized into individual state folders (all states and DC). Each state folder contains two files: demographics data and voter history data, with a data dictionary for each dataset. The size of the folders vary by state and data for all states adds up to approximately 40 GB. The data is organized into releases, generally two per year (spring and fall), which represent a snapshot of the country's voters at the time of the dataset creation.

NYU has also licensed access to L2 Political historical backlog of data. This backlog includes versions of the L2 Processed voter file going back to 2008 (for most U.S. states) and unprocessed "raw" state voter rolls, also going back to 2008 for most U.S. states.

This collection is available to NYU faculty and students only, and requires user to first submit a data management plan to account for how access and storage of the data will be handled. Information on how to submit a request to use this data and create a data management plan is available at https://guides.nyu.edu/l2political.

This data set contains a summary of information about candidate campaigns and political committees by election year. For candidate campaigns and single-year/election committees, a single record is provided that covers all activity of the campaign for the given election year. Information for continuing political committees is summarized by calendar/reporting year. The data set covers that prior 16 years plus the current election year. The data are compiled from the campaign reports deposit (C3), campaign summary reports (C4), campaign registrations (C1/C1pc) and candidate declarations and elections data provided to the PDC by the Washington Secretary of State. Records are updated in near real-time, typically less than 2 minutes from the time the campaign submits new data.

This dataset is a best-effort by the PDC to provide a complete set of records as described herewith. The PDC provides access to the original reports for the purpose of record verification.

Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements.

CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

Event data provide high-resolution and high-volume information about political events and have supported a variety of research efforts across fields within and beyond political science. While these datasets are machine coded from vast amounts of raw text input, the necessary dictionaries require substantial prior knowledge and human effort to produce and update, effectively limiting the application of automated event-coding solutions to those domains for which dictionaries already exist. I introduce a novel method for generating dictionaries appropriate for event coding given only a small sample dictionary. This technique leverages recent advances in natural language processing and machine learning to reduce the prior knowledge and researcher-hours required to go from defining a new domain-of-interest to producing structured event data that describe that domain. I evaluate the method via actor-country classification and demonstrate the method’s ability to generalize to new domains with the production of a novel event dataset on cybersecurity.

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This table contains data on the percent of adults (18 years or older) who are registered voters and the percent of adults who voted in general elections, for California, its regions, counties, cities/towns, and census tracts. Data is from the Statewide Database, University of California Berkeley Law, and the California Secretary of State, Elections Division. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Political participation can be associated with the health of a community through two possible mechanisms: through the implementation of social policies or as an indirect measure of social capital. Disparities in political participation across socioeconomic groups can influence political outcomes and the resulting policies could have an impact on the opportunities available to the poor to live a healthy life. Lower representation of poorer voters could result in reductions of social programs aimed toward supporting disadvantaged groups. Although there is no direct evidentiary connection between voter registration or participation and health, there is evidence that populations with higher levels of political participation also have greater social capital. Social capital is defined as resources accessed by individuals or groups through social networks that provide a mutual benefit. Several studies have shown a positive association between social capital and lower mortality rates, and higher self- assessed health ratings. There is also evidence of a cycle where lower levels of political participation are associated with poor self-reported health, and poor self-reported health hinders political participation. More information about the data table and a data dictionary can be found in the About/Attachments section.

The Cross-National Time-Series Data Archive provides more than 200 years of annual data for nations and empires of the world including those that no longer exist. It covers demographic, social, political, and economic topics. Select data goes back to 1815. Not all indicators are available for all countries or in all years. Fore data definitions, list of variables and countries covered, consult the accompanying codebook and user manuals. More information on topics, list of variables and countries covered is also available on CNTS website. DATA AVAILABLE FOR YEARS: 1815-2024

This dataset contains cash and in-kind contributions, (including unpaid loans) made to Washington State Candidates and Political Committees for the last 10 years as reported to the PDC on forms C3, C4, Schedule C and their electronic filing equivalents. It does not include loans which have been paid or forgiven, pledges or any expenditures.

For candidates, the number of years is determined by the year of the election, not necessarily the year the contribution was reported. For political committees, the number of years is determined by the calendar year of the reporting period.

Candidates and political committees choosing to file under "mini reporting" are not included in this dataset. See WAC 390-16-105 for information regarding eligibility.

This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification.

Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information political finance disclosure requirements.

CONDITION OF RELEASE: This publication constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

This dataset contains a list of all campaign finance reports (C3 and C4) for the last 10 years including attached schedules. It includes reports that have been superseded by an amendment. The primary purpose of this dataset is for data consumers to track report amendments and to examine the reporting history for a filer. Refer to other datasets to get actual values for any of the reports referenced herewith. For candidates, the number of years is determined by the year of the election, not necessarily the year the report was filed. For political committees, the number of years is determined by the calendar year of the reporting period. This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

The dataset provides a sentiment dictionary for German political language as well as the replication material for exemplary applications in speech, manifesto and media text corpora. A detailed guide to the resource and the different validation exercises is provided in: Rauh, Christian (2018) 'Validating a sentiment dictionary for German political language - a workbench note', Journal of Information Technology & Politics, forthcoming. Please make reference to this article when using the resources provided here. All analyses have been conducted in R 3.4.4 with additional packages specified at the beginning of the respective scripts.

This study is about political inequality of voice in terms of political participation. Political inequality research is diverse in disciplinary input, methods and topics (Dubrow 2015). We define political inequality as structured differences in influence over government decisions, and the outcomes of those decisions. Inequality is a gap. Political inequality can be interpreted as an influence and outcomes gap between social groups, and thus a measurable gap between social groups in terms of political voice.Political voice can be defined minimally or maximally. Minimally, political voice is the expression of interests within the political system (e.g. Schlozman et al 2012). Scholars tend to think of voice as participation. Participation can be defined, to use one of Teorell et al’s (2007) dimensions, as influence attempts (see also van Deth 2014). A maximalist definition presents the contours of voice and adds “representation” as a dimension. Maximally, political voice is (a) participation – verbal, physical, symbolic, monetary, or otherwise – in the political sphere by individuals, organizations, social groups, interest groups, or entire populations in electoral and non-electoral situations. In this maximalist sense, voice is also (b) representation by movements, organizations, or political leaders and other figures. From a voice perspective, representation is someone or something engaged in the expression of interests in the political sphere on behalf of others or to promote an idea. This study presents a minimalist interpretation of political voice as it focuses on political participation. Verba et al (1978: 1) defined participation as ‘legal acts by private citizens that are more or less directly aimed at influencing the selection of government personnel and/or the actions they take’; van Deth (2014) pithily defined it as ‘citizens’ activities affecting politics’ (351); and Teorell (2006) expands on different types, as does van Deth (2014). As identifiable participation modes proliferate over time, the scope of what is considered political participation widens. At root, they, and we, define participation as an attempt to influence the decisions of decision-makers who operate in a political sphere.This study is about the political inequality between social groups in terms of a specific form of political voice – political participation. The purpose of the data is to measure political voice inequality on the basis of political participation for social groups. Our main measure of political participation is “participation potential.” In using the World Values Survey and European Social Survey, we consider “have done” and “might do” as expressions of this potential, and thus combine them. “Would never do” is a strong statement about not participating, or zero potential.We use the combined World Values Survey/European Values Survey data (see below). We aggregate these survey data to construct proportions and ratios for the specific social groups on the basis of three political participation items. For each social group we produced a ratio of political participation, e.g. Women to Men in Attending a Demonstration, Young to Old in Signing a Petition, and so on. The entire dataset is age standardized for those 18-75 years old.The result is a country year dataset of 44 countries, 248 country-years, from 1981 to 2021.Participation MeasureThe political participation items are (I) Non-electoral participation, defined as (a) Attending demonstrations and (b) Signing petitions; NEP is Non-Electoral Participation Potential and it is the combination of either Attending or Signing, Have done it or Might do it. (I) Electoral Participation is measured as (c) Voted in last election. The inequality measure is ratio and thus the variables are ratios that compare social groups.Social GroupsThe social groups are:Gender (women to men). For issues in how gender is defined by surveys, see Dubrow, Joshua K. and Corina Ilinca. 2019. “Quantitative Approaches to Intersectionality: New Methodological Directions and Implications for Policy Analysis,” pp. 195 – 214 in The Palgrave Handbook of Intersectionality in Public Policy edited by Olena Hankivsky and Julia S. Jordan-Zachery. London: Palgrave Macmillan.We have two versions of age. One is defined as follows: Proportion of the young (18 – 29) to the mid-aged (30 – 55) and Proportion of the young (18 – 29) to old (56 to 75).The other is defined by the PaReSoGo dataset: Zelinska, Olga; Dubrow, Joshua K.: Party Representation of Social Groups (PaReSoGo) [data]. Institute of Philosophy and Sociology of the Polish Academy of Sciences [producer], Warsaw, 2021. PADS21317. Polish Social Data Archive [distributor], Repozytorium Danych Społecznych [publisher], 2021. https://doi.org/10.18150/NPXPAT, V1PaReSoGo age groups are as follows: young adults (18-29 y.o.), the middle-aged (40-65 y.o.) Old (66-75 y.o.)Education: Proportion of higher education (post-secondary) to those with lower education (secondary and below).We defined “Low education:” inadequately completed elementary education; completed (compulsory) elementary education; incomplete secondary school: technical course; incomplete secondary: university-preparatory course.We defined “High education:” complete secondary school: technical/vocational; complete secondary: university-preparatory course; some university without degree/higher education; university with degree/higher educationRural to not-rural: Proportion of Rural to not-rural, i.e. urban and peripheral urban.

This dataset contains expenditures made by Washington State Candidates and Political Committees for the last 10 years as reported to the PDC on forms C3, C4, Schedule C and their electronic filing equivalents.

In-kind contributions are included in this data set as they are considered as both a contribution and expenditure. In-kind contributions are also included in the data set "Contributions to Candidates and Political Committees"

For candidates, the number of years is determined by the year of the election, not necessarily the year the expenditure was reported. For political committees, the number of years is determined by the calendar year of the reporting period.

Candidates and political committees choosing to file under "mini reporting" are not included in this dataset. See WAC 390-16-105 for information regarding eligibility.

This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification.

Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements.

CONDITION OF RELEASE: This publication constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

In latent scaling applications, such as the positioning of political parties, differential item functioning (DIF) may occur because of measurement issues or because of substantive differences in the association between latent and manifest variables. While the first source of DIF has received considerable attention, the second has not, although it is of potential interest to comparative scholars. In this research note, we introduce a novel hierarchical Bayesian item response model that allows us to disentangle different sources of DIF. Drawing on the 2019 Chapel Hill Expert Survey (CHES), we highlight how the same issues are unequally politicized across Western Europe, and how some issues are less ideologically determined than others. Our model can be adapted to alternate settings, allowing researchers to shine a light on variation in, e.g., ideology, issue politicization, or party competition.

This is the data set that includes rankings of competing definitions of open government by a sample of Canadian journalists, parliamentarians and bloggers.

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. A Black supplement of 263 respondents, who were asked the same questions that were administered to the national cross-section sample, is included with the national cross-section of 1,571 respondents. In addition to the usual content, the study contains data on opinions about the Supreme Court, political knowledge, and further information concerning racial issues. Voter validation data have been included as an integral part of the election study, providing objective information from registration and voting records or from respondents' past voting behavior. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. United States citizens of voting age living in private households in the continental United States. A representative cross-section sample, consisting of 1,571 respondents, plus a Black supplement sample of 263 respondents. 2015-11-10 The study metadata was updated.1999-12-14 The data for this study are now available in SAS transport and SPSS export formats, in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. In addition, SAS and SPSS data definition statements have been created for this collection, and the data collection instruments are now available as a PDF file. face-to-face interview, telephone interviewThe SAS transport file was created using the SAS CPORT procedure.

Does the public know much more about politics than conventionally thought? A number of studies have recently argued, on various grounds, that the “don’t know” (DK) and incorrect responses to traditionally designed and scored survey knowledge items conceal a good deal of knowledge. This paper examines these claims, focusing on the prominent and influential argument that discouraging DKs would reveal a substantially more knowledgeable public. Using two experimental surveys with national random samples, we show that discouraging DKs does little to affect our picture of how much the public knows about politics. For closed-ended items, the increase in correct responses is large but mainly illusory. For open-ended items, it is genuine but minor. We close by examining the other recent evidence for a substantially more knowledgeable public, showing that it too holds little water.

American politics scholarship has relied extensively on self-reported measures of ideology. We evaluate these widely used measures through an original national survey. Descriptively, we show that Americans’ understandings of “liberal” and “conservative” are weakly aligned with conventional definitions of these terms and that such understandings are heterogeneous across social groups, casting doubt on the construct validity and measurement equivalence of ideological self-placements. Experimentally, we randomly assign one of three measures of ideology to each respondent: (1) the standard ANES question, (2) a version that adds definitions of “liberal” and “conservative,” and (3) a version that keeps these definitions but removes ideological labels from the question. We find that the third measure, which helps to isolate symbolic ideology from operational ideology, shifts self-reported ideology in important ways: Democrats become more conservative, and Republicans more liberal. These findings offer first-cut experimental evidence on the limitations of self-reported ideology as a measure of operational ideology, and contribute to ongoing debates about the use of ideological self-placements in American politics.

This data set contains candidate and political committee loan information for the last 17 years.

Data includes loans received, loan repayments, interest payments, and loans forgiven.

Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements.

CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

The terms 'left' and 'right' are widely used to organize party competition and to shape connections between citizens and political parties. Recent and dramatic changes in the world, however, raise important questions about the meaning and importance of left-right ideology. Most notably, the collapse of communism has led to the development of a host of new democracies. And in advanced industrial societies, conflict has emerged over issues like the environment and immigration. This paper draws on a survey of political experts in 42 societies to address three questions raised by these changes. First, is the language of left and right still widely used, even in recently democratized countries? Second, do there exist secondary dimensions of political conflict that are orthogonal to the left-right dimension? Third, and most importantly, what substantive issues define the meaning of left-right ideology? In addition to addressing these questions, we present data on the left-right locations of political parties in each of the 42 countries

Full edition for scientific use. This dataset contains a German-language sentiment dictionary of 5001 negative words and their associated sentiment strength on a five-point-scale from 0 (not negative) to 4 (very strongly negative). The procedure for building the dictionary contains the following steps: (1) Sampling 12713 sentences from Austrian parliamentary debates (1995-2013), party press releases (2006-2013), and political news reports (2013), (2) Crowdcoding the sentiment strength of sentences, (3) Estimating a sentence tonality score (4), Estimating a word tonality score, (5) Discriminating between important and unimportant words.