Data Mining for the Social Sciences

2015 ◽  
Author(s):  
Paul Attewell ◽  
David Monaghan
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
The Social

Data Mining in the Social Sciences and Iterative Attribute Elimination

2008 ◽  
pp. 308-332 ◽  
Author(s):  
Anthony Scime ◽  
Gregg R. Murray ◽  
Wan Huang ◽  
Carol Brownstein-Evans
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
Living Arrangement ◽  
Well Being ◽  
Research Issues ◽  
The Social ◽  
Public Resources ◽  
Data Collections ◽  
Analytical Tools ◽  
Election Studies

Immense public resources are expended to collect large stores of social data, but often these data are under-examined thereby missing potential opportunities to shed light on some of society’s pressing problems. This chapter proposes and demonstrates data mining in general and an iterative attribute-elimination process in particular as important analytical tools to exploit more fully these important data from the social sciences. We use an iterative domain-expert and data mining process to identify attributes that are useful for addressing distinct and nontrivial research issues in social science—presidential vote choice and living arrangement outcomes for maltreated children—using the American National Election Studies (ANES) from political science and the National Survey on Child and Adolescent Well-Being (NSCAW) from social work. We conclude that data mining is useful for more fully exploiting important but under-evaluated data collections for the purpose of addressing some important questions in the social sciences.

The Job of President and the Jobs Model Forecast: Obama for '08?

2008 ◽  
Vol 41 (04) ◽  
pp. 687-690 ◽  
Author(s):  
Michael S. Lewis-Beck ◽  
Charles Tien
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
Presidential Election ◽  
Model Forecast ◽  
Special Issue ◽  
Election Forecasting ◽  
The Social ◽  
Simple Product ◽  
Forecasting Errors

The statistical modelers are back. The presidential election forecasting errors of 2000 did not repeat themselves in 2004. On the contrary, the forecasts, from at least seven different teams, were generally quite accurate (Campbell 2004; Lewis-Beck 2005). Encouragingly, their prowess is receiving attention from forecasters outside the social sciences, in fields such as engineering and commerce. Noteworthy here is the recent special issue on U.S. presidential election forecasting published in theInternational Journal of Forecasting, containing some 10 different papers (Campbell and Lewis-Beck 2008). Our contribution in that special issue explored the question of whether our Jobs Model, off by only 1 percentage point in its 2004 forecast, was a simple product of data-mining (Lewis-Beck and Tien 2008).

Digital Technologies in Social Sciences: Subject and Method of Digital Sociology

2020 ◽  
pp. 49-59
Author(s):  
E. V. Shchekotin
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
Big Data ◽  
Digital Technologies ◽  
Social Reality ◽  
Sociological Research ◽  
The Social

The article is devoted to the development of a new direction of research in the social sciences — digital sociology. This direction is connected with the use of new digital technologies for the study of social reality. The article discusses the problems of perception of digital sociology by the sociological community, discusses the use of technologies such as big data and data mining in sociological research.

Linking Knowledge

2021 ◽  
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
Semantic Web ◽  
Cultural Heritage ◽  
Open Data ◽  
Linked Open Data ◽  
Social Sciences And Humanities ◽  
The Social ◽  
Knowledge Organization Systems ◽  
Heterogeneous Knowledge

The growth and population of the Semantic Web, especially the Linked Open Data (LOD) Cloud, has brought to the fore the challenges of ordering knowledge for data mining on an unprecedented scale. The LOD Cloud is structured from billions of elements of knowledge and pointers to knowledge organization systems (KOSs) such as ontologies, taxonomies, typologies, thesauri, etc. The variant and heterogeneous knowledge areas that comprise the social sciences and humanities (SSH), including cultural heritage applications are bringing multi-dimensional richness to the LOD Cloud. Each such application arrives with its own challenges regarding KOSs in the Cloud. With contributions by Sören Auer, Gerard Coen, Kathleen Gregory, Mohamad Yaser Jaradeh, Daniel Martínez Ávila, Philipp Mayr, Allard Oelen, Cristina Pattuelli, Tobias Renwick, Andrea Scharnhorst, Ronald Siebes, Aida Slavic, Richard P Smiraglia, Markus Stocker, Rick Szostak, Marnix van Berchum, Charles van den Heuvel, J. Bradford Young, Veruska Zamborlini and Marcia Zeng.

What Can We Learn From Factorial Surveys About Human Behavior?

Methodology ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Knut Petzold ◽  
Tobias Wolbring
Keyword(s):  
Social Sciences ◽  
Behavioral Intentions ◽  
Actual Behavior ◽  
Full Time ◽  
Factorial Survey ◽  
Survey Experiment ◽  
Intended Behavior ◽  
The Social ◽  
Survey Experiments ◽  
High Level

Abstract. Factorial survey experiments are increasingly used in the social sciences to investigate behavioral intentions. The measurement of self-reported behavioral intentions with factorial survey experiments frequently assumes that the determinants of intended behavior affect actual behavior in a similar way. We critically investigate this fundamental assumption using the misdirected email technique. Student participants of a survey were randomly assigned to a field experiment or a survey experiment. The email informs the recipient about the reception of a scholarship with varying stakes (full-time vs. book) and recipient’s names (German vs. Arabic). In the survey experiment, respondents saw an image of the same email. This validation design ensured a high level of correspondence between units, settings, and treatments across both studies. Results reveal that while the frequencies of self-reported intentions and actual behavior deviate, treatments show similar relative effects. Hence, although further research on this topic is needed, this study suggests that determinants of behavior might be inferred from behavioral intentions measured with survey experiments.

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