Comparing four contemporary statistical software tools for introductory data science and statistics in the social sciences

2021 ◽  
Vol 43 (S1) ◽  
Author(s):  
Sedigheh Abbasnasab Sardareh ◽  
Gavin T. L. Brown ◽  
Paul Denny
Keyword(s):  
Social Sciences ◽  
Data Science ◽  
Software Tools ◽  
Statistical Software ◽  
The Social

SAGE Campus

2021 ◽  
Vol 23 (1) ◽  
pp. 49-52
Author(s):  
Joseph R. Kraus
Keyword(s):  
Social Sciences ◽  
Online Learning ◽  
College Student ◽  
Graduate Students ◽  
Information Literacy ◽  
Data Science ◽  
Education Students ◽  
Learning Modules ◽  
The Social ◽  
Teaching Faculty

The SAGE Campus platform provides 18 different courses with roughly 220 hours of online learning modules. The author reviewed the service from the perspective of a college student to see if it was an appropriate learning environment. The primary audience for the courses are graduate students in the social sciences, but undergraduate and graduate students of all disciplines may find courses that are worthwhile to investigate. At the time of the review, the course topics covered content such as information literacy, data management and other data science skills, research design, and how to get published. Many librarians and teaching faculty may recommend students take these courses to supplement their education. Students can learn through these courses in a self-paced manner, and there are no scores or grades associated with completion of a course. Overall, the SAGE Campus platform provides a low-stress way for students to enhance their understanding of many topics relevant to research in the social sciences.

scholarly journals Computational Social Science of Disasters: Opportunities and Challenges

2019 ◽  
Vol 11 (5) ◽  
pp. 103 ◽  
Author(s):  
Burger ◽  
Oz ◽  
Kennedy ◽  
Crooks
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Data Science ◽  
Open Data ◽  
Computational Social Science ◽  
Crisis Informatics ◽  
Disaster Research ◽  
Social Responses ◽  
Digital Revolution ◽  
The Social

Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

scholarly journals A Human-Computer Interaction Approach for Integrity in Economics

2017 ◽  
Author(s):  
Kweku A. Opoku-Agyemang
Keyword(s):  
Social Sciences ◽  
Language Processing ◽  
User Interfaces ◽  
Data Science ◽  
Research Integrity ◽  
Social Scientists ◽  
Subjective Data ◽  
The Social ◽  
Interaction Approach ◽  
Data Visualizations

Emerging data science platforms using simplified and automated user interfaces can help research become significantly more transparent and ethical. By depending on standard human-generated code, many statistical software programs commonly used in economics and the social sciences inadvertently rely on the human willpower of scientists, and inspite of an assumed invincibility, such individuals are nearly necessarily prone to errors and research integrity compromises, as is increasingly clear. Removing the vast majority of arbitrary and subjective data judgments, including the generation of code, from researcher control would free behavioural and social scientists from human limitations. Automating the text annotations that accompany data visualizations in figures and diagrams using emerging natural language processing tools can also free scientists from overconfidence or the temptation to embellish findings. Scientific communities across economics as well as other social science fields should embrace such systems to enhance the integrity and transparency of the next-generation of research.

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

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.

PERCEPTIONAL DIFFERENCES IN ARCHITECTURAL FACADE PERCEPTION DUE TO ARCHITECTURAL EDUCATION

2021 ◽  
Vol 0 (23) ◽  
pp. 0-0
Author(s):  
Emine YILDIZ KUYRUKÇU ◽  
Tuğba ÖZDEMİR ERDOĞAN
Keyword(s):  
Social Sciences ◽  
Software Package ◽  
Architectural Education ◽  
Data Sets ◽  
Behavioral Performance ◽  
Statistical Software ◽  
The Social ◽  
Package Program ◽  
Semantic Differentiation ◽  
Differentiation Scale

Aim: In this study, the façades of tourism buildings designed with different currents (universalism, regionalism, syncreticism, contextualism, neovernakularism) on the Antal-ya coastline, in terms of 'taste', 'chaos', 'affection', 'familiarity', by groups of architects and non-architects. It is aimed to examine how it is perceived. Method: For this purpo-se, the fronts of 20 tourism buildings from 5 different styles, designed with a modern and postmodern approach, were analyzed by 60 people through a questionnaire. In the survey, adjective pairs such as impressive / ordinary, original / imitation, coarse / elegant, modest / flamboyant, complex / plain, familiar / unfamiliar, modern / outdated, questio-ning the façade features for tourism buildings were evaluated with a five-digit semantic differentiation scale. The analysis of the data sets obtained through the questionnaire was performed with the IBM Statistical Package For The Social Sciences (SPSS) 23 For Windows statistical software package program. At the beginning of the study, it was thought that the subjects would have perception-behavioral performance differences depending on the architectural education. Results: Unlike the non-architect profession, the architect group liked the universalism, neo-natalism and regionalism movements, found it impressive and original; It was determined that he did not like the synchterism and contextualism movements as complex, rude and outdated. Conclusion: In the analysis, it was seen that the differences between architects and non-architects are statis-tically significant. As a result of the study, it was determined that there are significant differences between architects and non-architects in evaluating the adjectives of expres-siveness, complexity, familiarity and originality. İndividuals who do not have an archi-tectural education are familiar with the traditional inspired structures, and they find the-se structures impressive It is for the individuals who study architecture to find modern and regionalist structures impressive and original.

scholarly journals PSYCHOMETRIC PROPERTIES OF TEN ITEM PERSONALITY IVENTORY (TIPI)

2016 ◽  
Vol 1 (2) ◽  
pp. 115 ◽  
Author(s):  
Sonia Brito-Costa ◽  
Ana Moisão ◽  
Hugo De Almeida ◽  
Florencio Vicente Castro
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Psychometric Properties ◽  
Software Package ◽  
Factor Model ◽  
Elite Athletes ◽  
Five Factor Model ◽  
Statistical Software ◽  
The Social

This study based on the Five Factor Model of Costa and McCrae (1987) sought to determine theninternal consistency and the psychometric properties of the Ten Item Personality Inventory (TIPI) of Gosling, Rentfrow, & Swann (2003), Lima and Castro (2009) Portuguese version. The sample consisted of 170 male soccer athletes whose average age stood at 18.50 years, with a minimum of 13 and a maximum of 33 years. Statistical analysis was performed by the Statistical Software Package for the Social Sciences (SPSS) in its 19 version for Windows. The TIPI showed low internal consistency (=0.462) and factor analysis that meets the criteria postulated by the instrument authors, so considered it valid to evaluate the personality rapidly in samples with little time available, for example elite athletes.

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