scholarly journals Two Wrongs Make a Right: Addressing Underreporting in Binary Data from Multiple Sources

2017 ◽  
Vol 25 (2) ◽  
pp. 223-240 ◽  
Author(s):  
Scott J. Cook ◽  
Betsabe Blas ◽  
Raymond J. Carroll ◽  
Samiran Sinha
Keyword(s):  
Binary Data ◽  
Science Research ◽  
Event Data ◽  
Multiple Sources ◽  
Secondary Sources ◽  
News Sources ◽  
The Social ◽  
Data Generating Process ◽  
Event Model ◽  
Conflict In Africa

Media-based event data—i.e., data comprised from reporting by media outlets—are widely used in political science research. However, events of interest (e.g., strikes, protests, conflict) are often underreported by these primary and secondary sources, producing incomplete data that risks inconsistency and bias in subsequent analysis. While general strategies exist to help ameliorate this bias, these methods do not make full use of the information often available to researchers. Specifically, much of the event data used in the social sciences is drawn from multiple, overlapping news sources (e.g., Agence France-Presse, Reuters). Therefore, we propose a novel maximum likelihood estimator that corrects for misclassification in data arising from multiple sources. In the most general formulation of our estimator, researchers can specify separate sets of predictors for the true-event model and each of the misclassification models characterizing whether a source fails to report on an event. As such, researchers are able to accurately test theories on both the causes of and reporting on an event of interest. Simulations evidence that our technique regularly outperforms current strategies that either neglect misclassification, the unique features of the data-generating process, or both. We also illustrate the utility of this method with a model of repression using the Social Conflict in Africa Database.

scholarly journals MPEDS: Automating the Generation of Protest Event Data

2017 ◽  
Author(s):  
Alex Hanna
Keyword(s):  
Machine Learning ◽  
Social Movement ◽  
Language Processing ◽  
Large Scale ◽  
Human Intervention ◽  
Event Data ◽  
Labor Costs ◽  
Specific Data ◽  
News Sources ◽  
The Social

Large-scale research of social movements has required more detailed, recent, and specific data about protest events. Analyses of these data allow for new insights into movement emergence, consequences, and tactical innovation and adaptation. One of the issues with this kind of analysis, however, is that the generation of event data is incredibly costly. Human coders must pore through news sources, looking for instances of protest and coding many variables by hand. Because of the high labor costs, projects are typically limited to one or two newspapers per country. This, in turn, exacerbates issues of selection and description biases.This article aims to address this issue with the development, validation, and application of a system for automating the generation of protest event data. This system, called the Machine-Learning Protest Event Data System (MPEDS), is the first of its kind coming from within the social movement community. MPEDS uses recent innovations from machine learning and natural language processing to generate protest event data with little to no human intervention. The system aims to have the effect of increasing the speed and reducing the labor costs associated with identifying and coding collective action events in news sources, thus increasing the timeliness of protest data and reducing biases due to excessive reliance on too few news sources. Work on MPEDS is ongoing, and to that end, the system will also be open, available for replication, and extendable by future social movement researchers, and social and computational scientists.

Brides, Bachelors and Brokers: The Marriage Market in Rural Anhui in an Era of Economic Reform

1995 ◽  
Vol 29 (4) ◽  
pp. 841-869 ◽  
Author(s):  
Han Min ◽  
J. S. Eades
Keyword(s):  
Case Studies ◽  
Rural Areas ◽  
Social Science Research ◽  
Marriage Market ◽  
Science Research ◽  
Local Variation ◽  
Secondary Sources ◽  
The Social ◽  
Changing Economy ◽  
The Impact

After a decade in which the social science research environment in China has improved dramatically, a detailed mass of information is now becoming available on the impact of the changing economy on the social structure of the rural areas. Generalizations based on secondary sources and interviews with migrants to Hong Kong have given way to detailed case studies based on longer-term fieldwork. Much of this has so far centred in Guandong Province, fanning out to Sichuan, Shandong and even as far north as Hebei and Liaoning. The picture which is emerging is one of quite considerable local variation. As Whyte and Harrell have recently suggested, the changes over large parts of the country remain unmapped, and what we need is more case studies to fill in the gaps (Whyte, 1992; Harrell, 1992).

A Tale of Two Cultures

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Social Sciences ◽  
Quantitative Research ◽  
Social Science Research ◽  
Measurement Data ◽  
Science Research ◽  
Quantitative Research Methods ◽  
Qualitative And Quantitative ◽  
The Social ◽  
Scientific Worldview ◽  
Different Cultures

Some in the social sciences argue that the same logic applies to both qualitative and quantitative research methods. This book demonstrates that these two paradigms constitute different cultures, each internally coherent yet marked by contrasting norms, practices, and toolkits. The book identifies and discusses major differences between these two traditions that touch nearly every aspect of social science research, including design, goals, causal effects and models, concepts and measurement, data analysis, and case selection. Although focused on the differences between qualitative and quantitative research, the book also seeks to promote toleration, exchange, and learning by enabling scholars to think beyond their own culture and see an alternative scientific worldview. The book is written in an easily accessible style and features a host of real-world examples to illustrate methodological points.

Designing and Evaluating Survey Instruments for Research in Human Issues in Horticulture

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 554c-554
Author(s):  
Sonja M. Skelly ◽  
Jennifer Campbell Bradley
Keyword(s):  
Survey Research ◽  
Survey Methodology ◽  
Human Subjects ◽  
Social Science Research ◽  
Reliability And Validity ◽  
Science Research ◽  
Survey Instrument ◽  
Measurement Tool ◽  
The Social ◽  
Survey Instruments

Survey research has a long precedence of use in the social sciences. With a growing interest in the area of social science research in horticulture, survey methodology needs to be explored. In order to conduct proper and accurate survey research, a valid and reliable instrument must be used. In many cases, however, an existing measurement tool that is designed for specific research variables is unavailable thus, an understanding of how to design and evaluate a survey instrument is necessary. Currently, there are no guidelines in horticulture research for developing survey instruments for use with human subjects. This presents a problem when attempting to compare and reference similar research. This workshop will explore the methodology involved in preparing a survey instrument; topics covered will include defining objectives for the survey, constructing questions, pilot testing the survey, and obtaining reliability and validity information. In addition to these topics some examples will be provided which will illustrate how to complete these steps. At the conclusion of this session a discussion will be initiated for others to share information and experiences dealing with creating survey instruments.

Collaboration supports successful outcomes

Impact ◽  
2019 ◽  
Vol 2019 (9) ◽  
pp. 4-5
Author(s):  
Antonio Loprieno
Keyword(s):  
Full Range ◽  
Science Research ◽  
European Federation ◽  
Council Of Europe ◽  
Political Factors ◽  
Eu Member States ◽  
Research And Innovation ◽  
The Social ◽  
Learned Societies ◽  
Framework Conditions

ALLEA (All European Academies) is the European Federation of Academies of Sciences and Humanities. It was founded in 1994 and brings together almost 60 Academies of Sciences and Learned Societies from over 40 countries in the Council of Europe region. ALLEA is financed by annual dues from its member academies and remains fully independent from political, religious, commercial or ideological interests.<br/> Member Academies operate as learned societies, think tanks, or research performing organisations. They are self-governing communities of leaders of scholarly enquiry across all fields of the natural sciences, the social sciences and the humanities. ALLEA therefore provides access to an unparalleled human resource of intellectual excellence, experience and expertise. Furthermore, its integrative membership structure comprises Academies from both EU and non-EU member states in Europe.<br/> ALLEA seeks to contribute to improving the framework conditions under which science and scholarship can excel. Jointly with its Member Academies, ALLEA is in a position to address the full range of structural and policy issues facing Europe in science, research and innovation. In doing so, it is guided by a common understanding of Europe, bound together by historical, social and political factors as well as for scientific and economic reasons.

An Introduction to Statistical Models for Networks

2020 ◽  
pp. 218-233
Author(s):  
Valentina Kuskova ◽  
Stanley Wasserman
Keyword(s):  
Social System ◽  
Exponential Family ◽  
Behavioral Science ◽  
Social Science Research ◽  
Science Research ◽  
Uniform Distributions ◽  
The Social ◽  
Special Cases ◽  
Social And Behavioral Science ◽  
Network Methods

Network theoretical and analytic approaches have reached a new level of sophistication in this decade, accompanied by a rapid growth of interest in adopting these approaches in social science research generally. Of course, much social and behavioral science focuses on individuals, but there are often situations where the social environment—the social system—affects individual responses. In these circumstances, to treat individuals as isolated social atoms, a necessary assumption for the application of standard statistical analysis is simply incorrect. Network methods should be part of the theoretical and analytic arsenal available to sociologists. Our focus here will be on the exponential family of random graph distributions, p*, because of its inclusiveness. It includes conditional uniform distributions as special cases.

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