Research Methods Festival Presentation

1. Developing data skills outside of the classroom:
a reflection on SHAPE students learning
through data fellowships
Prof Jackie Carter
Department of Social Statistics
Cathie Marsh Institute, University of Manchester
RMF 2023 9 Nov 2023

2. A Decade of Data Fellows

3. 3
Outline
1. Introduction to the Q-Step Programme and the University of
Manchester’s approach to teaching quantitative data skills to
SHAPE students
2. Embedding statistics teaching in the curriculum
3. The Data Fellows programme – taking the learning into the
workplace: alumni reflections and career pathways
4. Personal reflections – challenges and opportunities

4. Published research (1)
Méndez-Romero R.A., Carter J., Carrerá-Martínez, S., Suavita-Ramírez, M.A. & Higgins V.
(2022) Rethinking the Teaching of University Statistics: Challenges and Opportunities Learned from
the Colombia-UK Dialogue Mathematics 11(1), 52
Higgins V. and Carter J. (2022) Developing data literacy: how data services and data fellowships are
creating data skilled social researchers IASSIST Q. Print, vol. 46, no. 3
Carter J. (2021) Developing a future pipeline of applied social researchers through experiential
learning: the case of a data fellows programme. Statistical Journal of the IAOS, vol. 37, no. 3, pp.
935-950
Carter J., Méndez-Romero RA., Jones P, Higgins V & Samartini, ALS (2021) EmpoderaData: Sharing
a successful work-placement data skills training model within Latin America, to develop capacity
to deliver the SDGs. Statistical Journal of the IAOS, vol. 37, no. 3, pp. 1009-1021

5. Published research (2)
uom.link/pathways_into_ policy uom.link/pathways_into_research

6. SHAPE disciplines
COPYRIGHT ISIWSC2023
Social Sciences, Humanities and the Arts for People and the
Economy (SHAPE) is a collective name for the social sciences, humanities and
the arts
Hetan Shah, Chief Executive of the British Academy
The skills and attributes that SHAPE (social science, humanities and arts for people
and the economy) disciplines develop are highly valued by employers and open up a
wide range of options across the private, public and third sectors. Graduates who study
SHAPE disciplines are highly employable.
Work-based experiences can help students see how the skills they acquire through
studying SHAPE are relevant to the workplace. Work placements and internships
allow students to strengthen their skills by using them in a practical way, as well
as helping them find a career pathway in which they can thrive.
In Carter, J (2021) p. 262

7. 1. The Q-Step Programme
COPYRIGHT ISIWSC2023
£20m investment in developing quantitative skills in social science university
education; 2013-2021 in eighteen UK Universities
Aimed to:
I. create a step change in teaching undergraduate social science students quantitative
research skills, and
II. develop a talent pipeline for careers in applied social research.
“Design surveys and experiments and understand how to analyse the data they
generate
Analyse and interpret data (social media, government departments, longitudinal cohort
studies)
Evaluate the quality of data collection and analysis …and how you can use data to
make decisions”
Grundy, 2015, p5.

8. 8
Evaluation of Q-Step (Tazzyman, 2022)
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9. Research skills Analytical skills
R: Designing research and
collecting evidence
A: Undertaking the analysis
R1: Formulating a research question A1: Ability to manipulate, analyse and
filter information
R2: Deciding what evidence is
needed to answer the question
A2: Ability to interpret and synthesise
information using qualitative and
quantitative research methods and
appropriate technology
R3: Determining how evidence can
be collected
A3: Detecting partial or ambiguous
information
R4: Understanding the ethics of
undertaking the research
A4: Understanding the consequences of
using unreliable data and information
sources
R5: Organising the information,
selecting relevant information and
identifying gaps in the evidence
A5: Drawing conclusions based on
critically assessing the evidence and
findings
A6: Appreciating the need to be open-
minded and reflect on the evidence-
base and conclusions drawn
Top seven professional skills (PS) sought by employers
LinkedIn (2018), McKinsey (2019)
PS1 Communication
PS2 Collaboration and teamwork
PS3 Time management
PS4 Creativity
PS5 Persuasion
PS6 Adaptability
PS7 Networking
Table 7.2 Carter (2021) p. 167
Analytical, research and professional
skills framework for SHAPE graduates

10. 10
Social science at The University of Manchester
Single or joint honours
Bachelor’s degrees
• Sociology
• Criminology
• Social Anthropology
• Politics and International Relations
• Philosophy
• Economics
• + Data Analytics
• Politics, Philosophy and Economics
• Law

11. 11
Q-Step at The University of Manchester
• Embedded statistics
teaching
• Data Fellowships
https://doi.org/10.1080/13645579.2015.1062624

12. 12
2. Embedding statistics teaching in the
curriculum
Learning Outcomes and Course Schedule
• Making Sense of Politics
• Making Sense of Criminological Data
• The Survey Method in Social Research
Carter J. (2021) Developing a future pipeline of applied social researchers through experiential
learning: the case of a data fellows programme. Statistical Journal of the IAOS, vol. 37, no. 3, pp.
935-950

13. 13
Making Sense of Politics
Students will be able to demonstrate the following skills: –
• An ability to analyse some of the central questions in politics
research empirically
• Knowledge about how politics researchers develop strategies
to analyse relevant and contemporary questions
• Knowledge of widely used data analysis techniques and
software (SPSS and Excel)
• Knowledge of some of the most widely used data resources,
such as election studies, comparative surveys or databases of
democracy
• A critical awareness of the strengths and weaknesses of
different methods of gathering data and applying them to
political research questions
• A critical awareness of the use of data in political and media
debate
• Knowledge of how data resources can be found and used
to inform research on central political and social issues
• An ability to communicate ideas in writing and verbally
Course schedule (lecture followed by tutorial)
1. The process of measurement / Finding quantitative
data online
2. Surveys and sampling / Introduction to SPSS
3. Analysing quantitative data (I) / Descriptive
analysis in SPSS
4. Analysing quantitative data (II) / From SPSS to
Excel
5. Analysing quantitative data (III) / Q&A session
about first assessed report
6. Analysing quantitative data (IV) / Crosstabs in
SPSS
7. Introduction to qualitative methods / Developing a
topic guide
8. Analysing qualitative data / Qualitative analysis
9. Other quantitative approaches in politics research /
Quantitative content analysis
10. Wrap-up and final assessment support

14. 14
Making Sense of Criminological Data
After this course the students should be able to: –
• Identify the principal data sources for a number of
key areas in criminology and other cognate areas of
social policy
• Demonstrate a critical awareness of key data
quality issues and how they are linked to research
design decisions
• Produce, read, and interpret quantitative
information in the form of tables and graphs
• Understand the basic tenets and concepts of
exploratory data analysis (e.g. measures of central
tendency and spread, various types of charts), as well
as principles of good data visualisation
• Understand the different levels at which social and
personal characteristics (variables) are measured
and how resulting data are distributed
• Become aware of the range of existing qualitative
data and basic approaches to their analysis
Course schedule (lab session followed by lecture)
1. Data sets and variables
2. Describing and visualising single variables
3. Making comparisons I: the basics
4. Concepts, operationalization, measurement
5. Making comparisons II: the relevance of research
design
6. Data visualization
7. Looking at trends
8. Qualitative methods 1
9. Qualitative methods 2
10. Wrap up and project support
Note: Uses Nvivo and Excel, and prepares them to
conduct secondary data analysis in Modelling
Criminological Data in the following semester, using R

15. 15
The Survey Method in Social Research
On completion of this unit students will be able to: –
• Understand the ways social surveys can be used to better
understand the social world in a range of research and policy
settings
• Understand the characteristics of a social survey dataset
and the process by which survey data is derived
• Understand and be able to apply a range of techniques for
the analysis of survey data using specialist data analysis
software (SPSS)
• Accurately and critically interpret the output from secondary
data analysis and use it to write a research report
• Identify and evaluate a range of secondary sources of
survey data
• Be able to design a survey with consideration of
questionnaire, sampling and fieldwork
• Evaluate the relative strengths and weaknesses of survey
methods in social research
• Write a dissertation research proposal based on the survey
method.
Course schedule (weekly briefing session (whole class),
practical clinics (small groups))
Part 1 Introduction: About Social Surveys
1. Why a course about surveys?
2. About Survey data
3. Surveys in research
Part 2 How to analyse survey data
4. How to analyse survey data 1
5. How to analyse survey data 2
6. How to analyse survey data 3
7. Part 2 review
Part 3 Using Surveys in your own research
8. A dissertation using survey data?
9. Secondary analysis of an existing survey . . .
10. . . . or doing your own survey
11. Part 3 review

16. 16
Embedding data skills in social sciences
at The University of Manchester
Methods, skills, software
• R, SPSS, Excel, NVivo
• From descriptive statistics to linear
regression
• More advanced courses in year 3
(inferential statistics) and specialist data
analytics pathways
• Data literacy
• Focus on the application of skills
Data, substantive
• Surveys, Official statistics, Polling data
• Data usually pre-prepared
• Administrative and population data in
year 3
• All data skills taught through a theory-to-
application approach embedded in the
subject domain studied
• Research questions (and design) skills
taught

17. 17
3. The Data Fellows programme
• Paid 8-week long data-driven research projects in host organisations
• Eligibility criteria apply
• Competitive and rigorous selection process
• 350 students have undertaken data fellowships since 2014
– 70+% female
– 25% from historically under-represented background

18. 18
Host organisations
Government
departments
Polling companies
Data consultancies
Charities
Media companies
Market research
organisations
University research
departments

19. 19
Benefits to the data fellows
• Enable the practice of applied social research in a safe, supportive
environment
• Observe and conduct research in the workplace
• Acquire confidence in applying knowledge to real research questions
• Develop and hone existing and acquire new analytical and research
skills
• Collaborate with professionals to co-create new knowledge
• Networking opportunities
• Learn about new careers
Carter (2021) Work placements, internships and applied social research p. 32

20. 20
Class of 2023

21. 21
Case studies: reflections and career
pathways
Carter J. (2021) Developing a future pipeline of applied social researchers through experiential learning: the
case of a data fellows programme. Statistical Journal of the IAOS, vol. 37, no. 3, pp. 935-950
“Combining my understanding of social science and quantitative skills was something I wanted to do in
my career. An online retailer took a chance on me, took me on as a data analyst in the customer insights
division – recognised that I had done quite a lot of quantitative research in my internship. This helped me grow
and … become a bit more technical.”
“…(my internship and part-time work for them afterwards) was such a good signal to potential employers
because I already had experience. You can absolutely evidence the skills you have developed . . . so when
asked competency-based interview questions … I had real hands-on experience of working with quantitative
research.”
“.. the shining light for the recruitment manager, . . . she was really, really interested in what I could bring to the
role and how we could get some deeper insights, using the statistical techniques I’d learned. And [I went]
from the most at-sea person – the social scientist – to being one of the technical leaders. I found myself
excelling and my trajectory was rapid. I ended up being promoted to team lead.

22. 22
Case studies – data fellows’ reflections
and career pathways
“Whilst a lot of businesses do data analytics with a transactional or financial focus,
….that’s something that the social science side really helped with because we’re
trained in social research methods – like what are people’s attitudes, and how to
measure people’s behaviour – how do we look at the big picture, the context? No
one was really thinking through that relationship, which I think people – especially
from a social science background like sociology – are well-equipped to think
about. So I brought that strength. And from statistics I brought knowledge of
things like regression analysis and dimensional reduction, so you know, how do
we find the underlying factors in people’s behaviours – things like that, plus the
technical skills that came from knowing these statistical techniques helped me
pick up predictive analytics really quickly. In my current job I’m now back to
applying sampling theory – so sort of back to my roots.

23. 23
Alumni of the data fellows programme
uom.link/pathways_into_policy, pp 22-23

24. 24
Alumni of the data fellows programme
uom.link/pathways_into_research, pp 26-27

25. 25
Alumni of the data fellows programme
uom.link/pathways_into_research, pp 34-35

26. 26
4. Personal reflections: challenges and
opportunities
The structure - look on both
sides of the bridge
The skills - challenge the
‘deficit narrative’
The talent – look for
capability not just competency
Experiential learning –
develop data fellowships
Costs - share the costs of
crossing the bridge

27. Acknowledgements
• The Nuffield Foundation and ESRC for funding the Q-Step Programme
• All data fellows, past present and future
• The University of Manchester Employability funds
• Social science alumni for contributing their stories
Keep in touch:
E: jackie.carter@manchester.ac.uk
LI: www.linkedin.com/in/drjackiecarter/
COPYRIGHT ISIWSC2023
T: @JackieCarter