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.

‘Who Wants to be a Social Engineer? A commentary on David Blunkett's Speech to the ESRC’

2000 ◽  
Vol 5 (1) ◽  
pp. 74-84 ◽  
Author(s):  
Peter Hodgkinson
Keyword(s):  
Social Science ◽  
Policy Process ◽  
Social Research ◽  
Social Scientists ◽  
Politics And Government ◽  
The Government ◽  
Education And Employment ◽  
Social Scientific ◽  
The Uk

This article is a response to a speech addressed to the Economic and Social Research Council which was made, in February this year, by the UK Secretary of State for Education and Employment, David Blunkett. The speech was entitled ‘Influence or Irrelevance: can social science improve government?’ . Blunkett's programme for engaging social science in the policy process is far from unique and many of the arguments have been heard before. However, the curiosity of the speech lies in the fact that the conception of social science which Blunkett advocates mirrors the approach New Labour itself has to politics and government. This raises some rather interesting difficulties for social scientists. How do we engage in a debate about the role of social scientific research in the policy process when our own conception of the discipline may be radically at odds with that of the government? Furthermore, New Labour's particular conception of the relationship between social and policy-making means that we not only have to contest their notion of what it is we do, but also challenge their conception of the policy process. We cannot ignore this engagement, even if we wanted to. The challenge is to address it and to do so, moreover, in terms which Blunkett might understand. This article is an attempt to start this process.

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 Towards victim-oriented crime modelling in a social science e-infrastructure

2011 ◽  
Vol 369 (1949) ◽  
pp. 3353-3371 ◽  
Author(s):  
Nick Malleson ◽  
Mark Birkin
Keyword(s):  
Social Science ◽  
Social Research ◽  
Research Programme ◽  
Prevention Strategies ◽  
Social Scientists ◽  
Agent Based ◽  
Individual Level ◽  
Realistic Representation ◽  
The Uk ◽  
Burglary Risk

The National e-Infrastructure for Social Simulation (NeISS) is a multi-disciplinary collaboration between computation and social science within the UK Digital Social Research programme. The project aims to develop new tools and services for social scientists and planners to assist in performing ‘what-if’ scenario predictions in a variety of policy contexts. A key part of the NeISS remit is to explore real-world scenarios and evaluate real policy applications. Research into the processes and drivers behind crime is an important application area that has major implications for both improving crime-related policy and developing effective crime prevention strategies. This paper will discuss how the current e-infrastructure and available microsimulation tools can be used to improve an existing agent-based burglary simulation (BurgdSIM) by including a more realistic representation of the victims of crime. Results show that the model produces different spatial patterns when individual-level victim data are used and a risk profile of the synthetic victims suggests which types of people have the largest burglary risk.

The e-Social Science research agenda

2010 ◽  
Vol 368 (1925) ◽  
pp. 3761-3778 ◽  
Author(s):  
Peter Halfpenny ◽  
Rob Procter
Keyword(s):  
Social Science ◽  
Future Development ◽  
Research Agenda ◽  
Social Research ◽  
Social Science Research ◽  
Science Research ◽  
National Centre ◽  
The Future ◽  
The Uk

In this paper, we use the experience of the first 5 years of the UK Economic and Social Research Council’s National Centre for e-Social Science as a basis for reflecting upon the future development of the e-Social Science research agenda.

Where are we now?

2013 ◽  
Vol 16 (2) ◽  
pp. 7-19
Author(s):  
E. Sharland
Keyword(s):  
Social Work ◽  
Social Care ◽  
Social Research ◽  
Research Field ◽  
Step Change ◽  
Care Research ◽  
The Core ◽  
Economic Austerity ◽  
The Uk

In 2008, the UK Economic and Social Research Council called for ‘a fundamental step change’ in breadth, depth and quality of UK social work and social care research. This paper reports some of the findings from the ESRC Strategic Adviser for Social Work and Social Care initiative, focusing on the appraisal of the existing strengths and deficits of the research field. Discussion begins with highlighting some of the challenges of identifying and characterising both social work and social care research, explaining how these were addressed. It then outlines thematically the core substantive and methodological strengths and limitations of the field identified by key informants from social work and cognate disciplines, drawing attention to disciplinary and interdisciplinary distinctiveness and synergies. Discussion concludes with pointers to the way forward for research growth and excellence, with the argument that a commitment to developing social work and social care research is all the more crucial in times of economic austerity and challenges to social welfare and wellbeing.

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.

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 Engaging Social Science Students with Statistics: Opportunities, Challenges and Barriers

Numeracy ◽  
2021 ◽  
Vol 14 (2) ◽  
Author(s):  
Charlotte Brookfield ◽  
Malcolm Williams ◽  
Luke Sloan ◽  
Emily Maule
Keyword(s):  
Social Science ◽  
Research Methods ◽  
Quantitative Research ◽  
Quantitative Methods ◽  
Science Students ◽  
Quantitative Research Methods ◽  
The Social ◽  
The Uk ◽  
The Impact ◽  
Quantitative Approaches

In 2012, in a bid to improve the quantitative methods training of social science students in the UK, the £19.5 million Q-Step project was launched. This investment demonstrated a significant commitment to changing how we train social science students in quantitative research methods in the UK. The project has involved eighteen higher education institutions exploring and trialling potential ways of engaging social science students with quantitative approaches. This paper reflects on the activities of one Q-Step centre based in the School of Social Sciences at Cardiff University. As well as describing some of the pedagogic changes that have been implemented, the paper draws on data to begin to evaluate the success of new approaches. Specifically, data showing the proportion of students undertaking a quantitative final-year dissertation project is used to measure the impact of these activities. The data presented in this paper suggest that resistance to learning quantitative research methods and engaging with such techniques has decreased. The data also indicates that students see this learning as beneficial for their own employability. Despite this, closer analysis reveals that several students change their mind about employing quantitative methods in their own research part way through their dissertation journey. We argue that while social science students are comfortable learning about quantitative approaches, they are less confident at applying these techniques. Thus, the paper argues that there is a wider challenge of demonstrating the relevance and appropriateness of such approaches to understanding the social world.

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.

Has the SARS-CoV-2 outbreak had an impact on uptake of digital cessation support in UK smokers? An interrupted time series analysis (Preprint)

2020 ◽  
Author(s):  
Olga Perski ◽  
Aleksandra Herbec ◽  
Lion Shahab ◽  
Jamie Brown
Keyword(s):  
Time Series ◽  
Smoking Cessation ◽  
Secondary Analysis ◽  
Interrupted Time Series ◽  
Step Change ◽  
Outcome Variable ◽  
Primary Analysis ◽  
Increasing Trend ◽  
The Uk ◽  
Temporary Closure

BACKGROUND The SARS-CoV-2 outbreak may motivate smokers to attempt to stop in greater numbers. However, given the temporary closure of UK stop smoking services and vape shops, smokers attempting to quit may instead seek out digital support, such as websites and smartphone apps. OBJECTIVE We examined, using an interrupted time series approach, whether the SARS-CoV-2 outbreak has been associated with a step change or increasing trend in UK downloads of an otherwise popular smoking cessation app, Smoke Free. METHODS Data were from daily and non-daily adult smokers in the UK who had downloaded the Smoke Free app between 1 January 2020 and 31 March 2020 (primary analysis) and 1 January 2019 and 31 March 2020 (secondary analysis). The outcome variable was the number of downloads aggregated at the 12-hourly (primary analysis) or daily level (secondary analysis). The explanatory variable was the start of the SARS-CoV-2 outbreak, operationalised as 1 March 2020 (primary analysis) and 15 January 2020 (secondary analysis). Generalised Additive Mixed Models adjusted for relevant covariates were fitted. RESULTS Data were collected on 45,105 (primary analysis) and 119,881 (secondary analysis) users. In both analyses, there was no evidence for a step change or increasing trend in downloads attributable to the start of the SARS-CoV-2 outbreak. CONCLUSIONS In the UK, between 1 January 2020 and 31 March 2020, and between 1 January 2019 and 31 March 2020, there was no evidence that the SARS-CoV-2 outbreak has been associated with a surge in downloads of a popular smoking cessation app. CLINICALTRIAL osf.io/zan2s

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