scholarly journals Social prediction: a new research paradigm based on machine learning

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yunsong Chen ◽  
Xiaogang Wu ◽  
Anning Hu ◽  
Guangye He ◽  
Guodong Ju
Keyword(s):  
Machine Learning ◽  
Social Science ◽  
Social Action ◽  
Quantitative Research ◽  
Social Research ◽  
Causal Explanation ◽  
Statistical Tests ◽  
Historical Context ◽  
Research Paradigm ◽  
Social Prediction

AbstractSociology is a science concerned with both the interpretive understanding of social action and the corresponding causal explanation, process, and result. A causal explanation should be the foundation of prediction. For many years, due to data and computing power constraints, quantitative research in social science has primarily focused on statistical tests to analyze correlations and causality, leaving predictions largely ignored. By sorting out the historical context of "social prediction," this article redefines this concept by introducing why and how machine learning can help prediction in a scientific way. Furthermore, this article summarizes the academic value and governance value of social prediction and suggests that it is a potential breakthrough in the contemporary social research paradigm. We believe that through machine learning, we can witness the advent of an era of a paradigm shift from correlation and causality to social prediction. This shift will provide a rare opportunity for sociology in China to become the international frontier of computational social sciences and accelerate the construction of philosophy and social science with Chinese characteristics.

Organising: creating research worlds

2020 ◽  
pp. 79-110
Author(s):  
Paul Thompson ◽  
Ken Plummer ◽  
Neli Demireva
Keyword(s):  
Social Science ◽  
Quantitative Research ◽  
Research Council ◽  
Social Research ◽  
Social Science Research ◽  
Science Research ◽  
Academic Disciplines ◽  
Community Studies ◽  
Central Statistical ◽  
Major Section

This chapter looks at how social research gradually became organized through the work of our pioneers. It starts by looking at the growth of both universities and academic disciplines (like anthropology and sociology) as key backgrounds for understanding the growth of organized research. A major section discusses a range of early research agencies — the Colonial Research Council, Political and Economic Planning (PEP), the Institute of Community Studies, the CSO (Central Statistical Office), the SSRC, Social Science Research Council, and the UK Data Archive. Some new university-based centres are also considered: medical social science at Aberdeen, methods at Surrey and the BCCS (Birmingham Centre for Contemporary Cultural Studies). There are brief discussions of the Banbury Study with Meg Stacey and Colin Bell; and the Affluent Worker study. The chapter closes with some pioneering work on quantitative research, longitudinal studies and the rise of computing.

scholarly journals Pengaruh Kepemimpinan, Diklat dan Budaya Organisasi terhadap Kinerja Pegawai Direktorat Jenderal Pembinaan Penempatan Tenaga Kerja Kemenaketrans RI

Sosio e-kons ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 86
Author(s):  
Olos Wasahua
Keyword(s):  
Organizational Culture ◽  
Social Science ◽  
Leadership Training ◽  
Research Method ◽  
Quantitative Research ◽  
Employee Performance ◽  
Statistical Tests ◽  
Coefficient Of Determination ◽  
Multiple Regression Equation ◽  
Validity And Reliability

<p align="center"><strong><em>ABSTRACT</em></strong></p><p><em>This study to determine the effect simultaneously and partially between Leadership, Training and Organizational Culture on Employee Performance At Directorate General of Manpower Placement Development Kemenaketrans RI. This research method is a quantitative research method. The population in this study is 394 employees and the sample used is 80 samples with 10% error sampling .. The test used is a test of validity and reliability, correlation test, simple linear regression, t-test and F test where the significance of trust 95% (? = 0.05). All statistical tests were performed using SPSS (Statistical Package for Social Science) version 19. The results showed that leadership, training and organizational culture together have a positive influence on employee performance shown by coefficient of determination R2 = 0.107 and multiple regression equation = 28.383 + 0.010X1 + 0.276X2 - 0.002X3 so that the contribution and influence of leadership, training and organizational culture together on employee performance is significant.</em></p><p><strong><em>Keyword: leadership, training, organizational culture, employee performance</em></strong></p><p align="center"><strong>ABSTRAK</strong></p><p>Penelitian ini untuk mengetahui pengaruh secara simultan dan parsial antara Kepemimpinan, Diklat dan Budaya Organisasi Terhadap Kinerja Pegawai  Pada Direktorat Jenderal Pembinaan Penempatan Tenaga Kerja Kemenaketrans RI. Metode penelitian ini adalah metode  penelitian kuantitatif.  Populasi dalam penelitian ini adalah Pegawai berjumlah 394 orangdan  sampel yang digunakan adalah sebanyak 80 sampel dengan sampling eror 10%.. Uji satistik yang digunakan yaitu :  uji validitas dan reliabilitas, uji korelasi , regresi linear sederhana, uji t dan uji F dimana signifikansi kepercayaan 95%     (? = 0,05 ). Semua uji statistik dilakukan dengan menggunakan  program SPSS (<em>Statistical Package for Social Science</em>) versi 19.Hasil penelitian menunjukkan Kepemimpinan, diklat dan budaya organisasi secara bersama-sama memiliki pengaruh positif terhadap kinerja pegawai yang ditunjukkan oleh koefisien determinasi <strong>R<sup>2</sup> = 0.107</strong> dan persamaan regresi ganda <strong>? = 28.383 + 0.010X1+ 0.276X<sub>2</sub>- 0.002X<sub>3</sub></strong> sehingga kontribusi dan pengaruh kepemimpinan, diklat dan budaya organisasi secara bersama-sama terhadap kinerja pegawai adalah signifikan.</p><p><strong>Keyword : kepemimpinan, diklat, budaya organisasi, kinerja pegawai</strong></p>

scholarly journals FROM THE CLASSROOM TO THE WORKPLACE: HOW SOCIAL SCIENCE STUDENTS ARE LEARNING TO DO DATA ANALYSIS FOR REAL

2017 ◽  
Vol 16 (1) ◽  
pp. 80-101
Author(s):  
JACKIE CARTER ◽  
MARK BROWN ◽  
KATHRYN SIMPSON
Keyword(s):  
Data Analysis ◽  
Social Science ◽  
Quantitative Research ◽  
Social Research ◽  
Statistics Education ◽  
Skills Training ◽  
Science Students ◽  
Quantitative Research Methods ◽  
Research Journal ◽  
Research Organisations

In British social science degree programmes, methods courses have a bad press, and statistics courses in particular are not well-liked by most students. A nationally-coordinated, strategic investment in quantitative skills training, Q-Step, is an attempt to address the issues affecting the shortage of quantitatively trained humanities and social science graduates. Pedagogic approaches to teaching statistics and data analysis to social science students are starting to indicate positive outcomes. This paper contributes to these debates by focusing on the perspective of the student experience in different learning environments: first, we explain the approach taken at the University of Manchester to teaching a core quantitative research methods module for second-year sociology students; and second, we introduce case studies of three undergraduates who took that training and went on to work as interns with social research organisations, as part of a Manchester Q-Step Centre initiative to take learning from the classroom into the workplace. First published May 2017 at Statistics Education Research Journal Archives

scholarly journals Developing a future pipeline of applied social researchers through experiential learning: The case of a data fellows programme

2021 ◽  
pp. 1-16
Author(s):  
Jackie Carter
Keyword(s):  
Experiential Learning ◽  
Social Science ◽  
Quantitative Research ◽  
Social Research ◽  
Undergraduate Curriculum ◽  
Step Change ◽  
Statistical Literacy ◽  
Research Skills ◽  
And Mathematics ◽  
The Uk

This paper presents an innovative model for developing data and statistical literacy in the undergraduate population through an experiential learning model developed in the UK. The national Q-Step (Quantitative Step change) programme (2013–2021) aimed to (i) create a step change in teaching undergraduate social science students quantitative research skills, and (ii) develop a talent pipeline for future careers in applied social research. We focus on a model developed at the University of Manchester, which has created paid work placement projects in industry, for students to practise their data and statistical skills in the workplace. We call these students data fellows. Our findings have informed the development of the undergraduate curriculum and enabled reflection on the skills and software that we teach. Data fellows are graduating into careers in fields that would previously have been difficult to enter without a STEM (Science, Technology, Engineering and Mathematics) degree. 70% of data fellows to date are female, with 25% from disadvantaged backgrounds or under-represented groups. Hence the programme also addresses equality and diversity. The paper documents some of the successes and challenges of the programme and shares insight into non-STEM pipelines into social research careers that require data and statistical literacy, A major advantage of our approach is the development of hybrid data analysts, who are able to bring social science subject expertise to their research as well as data and statistical skills. Focusing on the value of experiential learning to develop quantitative research skills in professional environments, we provoke a discussion about how this activity could not only be sustained but also scaled up.

Ascertaining the Role of Leadership on Employee Satisfaction in Market and Social Research Industries in Gauteng Province of South Africa Using Qualitative Research

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Kingstone Mutsonziwa ◽  
Philip Serumaga-Zake
Keyword(s):  
South Africa ◽  
Employee Satisfaction ◽  
Quantitative Research ◽  
Social Research ◽  
Labour Productivity ◽  
Working Environment ◽  
Gauteng Province ◽  
Career Growth ◽  
Quantitative Results ◽  
Mixed Approach

This paper is based on the study a Doctor of Business Leadership (DBL) thesis titled A Statistical Model for Employee Satisfaction in the Market and Social Research Industries in Gauteng Province. The purpose of this study was to identify the attributes that affect employee satisfaction in the Market and Social Research Industries in Gauteng Province, South Africa. In order to address the overall objective of this study, the researcher used a two-tiered (mixed) approach in which both qualitative and quantitative research methodologies were used to complement and enrich the results. This paper is only based on the qualitative component of the study on leadership aspects based on six leaders (two from Social research and four from Market research) that were interviewed. The leaders were selected based on their knowledge of the industry and the expertise they have. Participation in the survey was voluntary. This paper illustrates the power of the qualitative techniques to uncover or unmask the leadership aspects in the Market and Social Research Industries and also gives the human touch to the quantitative results. It was found that leadership and management within the Market and Social Research Industries in Gauteng Province must ensure that they are accommodative in terms of mentoring their subordinates. The industry is driven by quality driven processes and strong leadership. More importantly, issues of a good working environment, remuneration, career growth, and recognition must always be addressed in order to increase employee satisfaction, reduce staff turnover, and attempt to optimize labour productivity. The qualitative findings also help a deeper understanding of leadership within the industry.

Millennials as Emerging Adults

2018 ◽  
Author(s):  
Barbara J. Risman
Keyword(s):  
Social Science ◽  
Emerging Adulthood ◽  
Human Development ◽  
Emerging Adults ◽  
21St Century ◽  
Millennial Generation ◽  
Historical Context ◽  
Growing Up ◽  
Traditional Beliefs ◽  
Gender Revolution

This chapter begins by providing a historical context for the Millennial generation. Growing up is different in the 21st century than before; it takes much longer. Given how many years youth take to explore their identities before they emerge into adulthood with stable jobs and committed partners, the chapter reviews what we now about “emerging adulthood” as a stage of human development. The chapter also highlights a debate in social science as to whether Millennials are entitled narcissists or a new civically engaged generation that will re-energize America. The chapter concludes with an overview of another debate, whether Millennials are pushing the gender revolution forward or returning to more traditional beliefs.

Why they shared: recovering early arguments for sharing social scientific data

2020 ◽  
Vol 33 (2) ◽  
pp. 101-119
Author(s):  
Emily Hauptmann
Keyword(s):  
Social Science ◽  
Data Sharing ◽  
Financial Stability ◽  
Social Research ◽  
Scientific Data ◽  
Social Scientists ◽  
Ethical Imperative ◽  
Social Scientific ◽  
The University ◽  
University Consortium

ArgumentMost social scientists today think of data sharing as an ethical imperative essential to making social science more transparent, verifiable, and replicable. But what moved the architects of some of the U.S.’s first university-based social scientific research institutions, the University of Michigan’s Institute for Social Research (ISR), and its spin-off, the Inter-university Consortium for Political and Social Research (ICPSR), to share their data? Relying primarily on archived records, unpublished personal papers, and oral histories, I show that Angus Campbell, Warren Miller, Philip Converse, and others understood sharing data not as an ethical imperative intrinsic to social science but as a useful means to the diverse ends of financial stability, scholarly and institutional autonomy, and epistemological reproduction. I conclude that data sharing must be evaluated not only on the basis of the scientific ideals its supporters affirm, but also on the professional objectives it serves.

Social Science and Social Action

1935 ◽  
Vol 46 (1) ◽  
pp. 1-33 ◽  
Author(s):  
Frank H. Knight
Keyword(s):  
Social Science ◽  
Social Action

Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach

2021 ◽  
Author(s):  
Siwei Song ◽  
Fang Chen ◽  
Yi Wang ◽  
Kangcai Wang ◽  
Mi Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
High Throughput ◽  
Experimental Approach ◽  
Chemical Data ◽  
Research Paradigm ◽  
New Materials ◽  
High Throughput Virtual Screening

With the growth of chemical data, computation power and algorithms, machine learning-assisted high-throughput virtual screening (ML-assisted HTVS) is revolutionizing the research paradigm of new materials. Herein, a combined ML-assisted HTVS...

scholarly journals Application of Bayesian networks to generate synthetic health data

2020 ◽  
Author(s):  
Dhamanpreet Kaur ◽  
Matthew Sobiesk ◽  
Shubham Patil ◽  
Jin Liu ◽  
Puran Bhagat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Data Privacy ◽  
Statistical Tests ◽  
Synthetic Data ◽  
Original Data ◽  
Health Data ◽  
Minimal Risk ◽  
Data Types ◽  
Automated Method

Abstract Objective This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. Materials and Methods We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data. Results Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. Discussion Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. Conclusion We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

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