AIMSS: An Architecture for Data Driven Simulations in the Social Sciences

RESUMO A ciência contemporânea e seus fundamentos metodológicos têm sido impactados pelo fenômeno do big data, que proclama que na era dos dados medidos em petabytes, de supercomputadores e sofisticados algoritmos, o método científico está obsoleto e que as hipóteses e modelos estão superados. As estratégias do big data científico confia em estratégias de análises computacionais de massivas quantidades de dados para revelar correlações, padrões e regras que vão gerar novos conhecimentos, que vão das ciências exatas até as ciências sociais, humanidade e cultura, delineando um arquétipo de ciência orientada por dados. O presente ensaio coloca em pauta as controvérsias em torno da ciência orientada por dados em contraposição à ciência orientada por hipóteses, e analisa alguns dos desdobramentos desse embate epistemológico. Para tal, tomo como metodologia os escritos de alguns autores mais proximamente envolvidos nessa questão.Palavras-chave: Big Data; Método Cientifico; Ciência Orientada por Dados; Ciência Orientada por Hipóteses.ABSTRACT Contemporary science and its methodological foundations have been impacted by the big data phenomenon that proclaims that in the age of data measured in petabytes, supercomputers and sophisticated algorithms the scientific method is obsolete and that the hypotheses and models are outdated.The strategies of the scientific big data rely on computational analysis strategies of massive amounts of data to reveal correlations, patterns and rules that will generate new knowledge, ranging from the exact sciences to the social sciences, humanity and culture, outlining an archetype of data-driven science. The present essay addresses the debates around data-driven science as opposed to hypothesis-oriented science and analyzes some of the ramifications of this epistemological confrontation. For this, the writings of some authors who are more closely involved in this question are taken as methodology.Keywords: Big Data; Scientific Method; Data-Driven Science; Hypothesis-Driven Science.

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Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities

Future Internet ◽

10.3390/fi13030059 ◽

2021 ◽

Vol 13 (3) ◽

pp. 59

Author(s):

Albert Weichselbraun ◽

Philipp Kuntschik ◽

Vincenzo Francolino ◽

Mirco Saner ◽

Urs Dahinden ◽

...

Keyword(s):

Social Sciences ◽

Domain Knowledge ◽

Data Driven ◽

Media Criticism ◽

Domain Experts ◽

Social Sciences And Humanities ◽

The Social ◽

Recent Developments ◽

Selection Of

Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline.

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Teaching for a Data-Driven Future: Intentionally Building Foundational Computing Skills

Teaching Sociology ◽

10.1177/0092055x211033632 ◽

2021 ◽

pp. 0092055X2110336

Author(s):

Amy L. Johnson ◽

Rebecca D. Gleit

Keyword(s):

Social Sciences ◽

Data Analysis ◽

Graduate Students ◽

Data Driven ◽

Statistical Software ◽

The Social ◽

Computing Skills ◽

Software Skills ◽

Sociology Departments

Despite the centrality of data analysis to the discipline, sociology departments are currently falling short of teaching both undergraduate and graduate students crucial computing and statistical software skills. We argue that sociology instructors must intentionally and explicitly teach computing skills alongside statistical concepts to prepare their students for participation in a data-driven world. We illuminate foundational concepts for computing in the social sciences and provide easy-to-integrate recommendations for building competency with these concepts in the form of a workshop designed to introduce sociology undergraduate and graduate students to the logic of statistical software. We use our workshop to show that students appreciate and gain confidence from being taught how to think about computing.

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Data Driven Simulation to Support Model Building in the Social Sciences

Journal of Algorithms & Computational Technology ◽

10.1260/1748-3018.5.4.561 ◽

2011 ◽

Vol 5 (4) ◽

pp. 561-581 ◽

Cited By ~ 7

Author(s):

Catriona Kennedy ◽

Georgios Theodoropoulos ◽

Volker Sorge ◽

Edward Ferrari ◽

Peter Lee ◽

...

Keyword(s):

Social Sciences ◽

Model Building ◽

Data Driven ◽

The Social ◽

Support Model

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Approaches and Methodologies in the Social Sciences

10.1017/cbo9780511801938 ◽

2008 ◽

Cited By ~ 88

Keyword(s):

Social Sciences ◽

The Social

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What Can We Learn From Factorial Surveys About Human Behavior?

Methodology ◽

10.1027/1614-2241/a000161 ◽

2019 ◽

Vol 15 (1) ◽

pp. 19-30 ◽

Cited By ~ 10

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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