Presentation to 5th ACSPRI conference, Sydney, 21 July 2016

1. A vision of ‘students and workers as
comfortable with numbers as they are
with words’ (British Academy, 2015)
Dr Jackie Carter, University of Manchester, UK
@JackieCarter
www.slideshare.net/JackieCarter
Senior Lecturer and Director for Engagement with Research
Methods Training, Methods@Manchester Director and co-
Director of Q-Step

2. Acknowledgements
• Q-Step Funders – The Nuffield Foundation, Economic and
Social Research Council, Higher Education Funding Council for
England
• The University of Manchester - funded the qualitative
research
• Colleagues Dr Mark Brown and Dr Kathryn Simpson
• All the students involved
• All the employers who have participated
• Methods@Manchester and Data, Skills and Training Research
Group for co-funding this trip

3. Outline
• Quantitative ‘skills gap’ in the Social Sciences
– UK national response The Q-Step Programme
• University of Manchester approach
– Embedded and applied learning
• From the classroom to the workplace
– 3 case studies from Sociology undergraduates
• What employers say, and challenging the skills
gap
• Further research and resources

4. UK Quantitative Skills Gap
• 'critical deficit in quantitative skills within the UK‘
(Commission on the Social Sciences 2003)
• ‘’the increase in data generated by a digital society … more and more debate is
likely to turn on statistical arguments. Providing citizens with the means to
understand, analyse and criticise data becomes ever more integral to the
functioning of a democracy.’’
• “In higher education, almost all disciplines require quantitative capacity, but
students are often ill-equipped to cope with those demands. They then leave
university with skills inadequate to the needs of the workplace – be it in
business, public sector, or academia. Students are graduating with little
confidence in using what skills they do have, having had little practice in
applying them. Employers often lament the lack of quantitative skills in the
workplace.”
(‘Society Counts’ British Academy 2012)

5. British Academy 2015

6. OUR OBJECTIVES
1. To raise the level of quantitative skills attained for ALL students on social
science degree programmes at Manchester (Statistical Literacy for all)
2. To increase the number of students graduating with advanced quantitative
skills (quantitative specialists)

7. The Manchester Q-Step team
• Jackie Carter: Co-Director
• Mark Brown: Co-Director
• Marta Cantijoch Cunill: Politics
• Martin Hyde: Sociology
• Lisa Williams: Criminology
• New lecturer: Linguistics
• Patricio Troncoso: Research
Associate (internships)
• Research Associates (Helpdesk,
dissertation support)
• Nasira Asghar: Administrator

8. Our teaching approach
Embedded learning
Based on ‘Enriching Social Science
Teaching with Empirical Data’ (ESSTED)
project
Informed by real world data

9. Applied learning: Q-Step internships

10. The Q-Step internships
3 cohorts to date
• Students placed at the end
of their 2nd year into an 8
week long research project
• Must be quantitative
• Public, private and
voluntary sector
• 117 students in 3 years
– 19 (2014), 48 (2015), 50
(2016)
• Paid at living wage
Opportunity to apply learning
• From descriptive statistics
to statistical modelling
• Data-driven
– Data cleaning
– Data collection
– Data analysis
– Report writing
– Presenting
• Celebrate (not assess)
learning in the autumn

11. Internships: 2-level eligibility
Degree
• Social Sciences – BASS
(Social Sciences), Sociology,
Politics and International
Relations and PPE (not
Economics or Psychology)
• Law – Criminology and Law
with Criminology
• School of Arts, Languages
and Culture – Linguistics
and Linguistics with
Sociology
Course modules
• SOST20012 Survey Method in
Social Research
• POLI10301 Making Sense of
Politics
• POLI120901 How to Conduct
Politics Research
• LAWS20452 Accessing and
Understanding Data for
Criminologists
• LELA20071 Language Variation
and Change

12. What quantitative research methods
have they learned?
• Identification and use of secondary data sources
(typically national surveys)
• Critical analysis of academic paper
• Lab practice in software (R, SPSS, Excel)
• Descriptive statistics and up to Chi-square tests,
univariate and bivariate analyses, regression
• Assessment – development of a research question
and report of research methods

13. Survey Method in Social Research:
Learning Outcomes (1)
• Develop fit for purpose research questions and
hypotheses for survey research
• Identify and access a range of secondary sources of
survey data (using UK Data Service)
• Critically evaluate the suitability of secondary data
sources for a given research question
• Demonstrate understanding of the process and
elements of research design in survey research
• Design survey questions that operationalise
sociological concepts

14. Survey Method in Social Research:
Learning Outcomes (2)
• Demonstrate understanding of the principles of sampling and
have knowledge of the different types of sample design and
their strengths and weaknesses
• Understand and be able to apply a range of techniques for the
exploratory analysis of survey data using specialist data
analysis software (SPSS) Techniques including basic univariate
statistics, cross-tabulation with use of control variables, Chi
square tests, recoding of variables, simple weights.
• Accurately and critically interpret the output from secondary
data analysis, including simple tests for statistical significance
• Evaluate the relative strengths and weaknesses of secondary
analysis of survey data to address social research questions
• Write a dissertation research proposal based on the survey
method

15. Three years of Q-Step internships
0
2
4
6
8
10
12
14
2016
2015
2014
(47)
(48)
(19)

16. Where are they placed?
• National government
departments
• Local government
• Polling companies
• Market research
• Social research
consultancies
• Public libraries
(research)
• Charities
• Banks
• International Statistical
Organisations (The
World Bank)
• Think tanks
• Digital agencies
• Social enterprises
• Universities

17. Sociology student posters

18. 3 case studies
• All studied Sociology
• All took the Survey Method in Social Research
module
• All did internships in 2014
• Varying degrees of maths/stats background
• Interned in different types of organisations

19. • Placed in Manchester City Council’s Age
Friendly research team
“I liked that it was a vocational way of using social
sciences and at that point I was thinking about what
I was going to do with my degree”

20. Selected quotes
• Had avoided studying statistics as he’d found it ‘a bit
scary’
• “We didn’t do a lot of analysis…it was a good
opportunity to learn about some of the difficulties in
working with secondary data. It led to quite a few
problems about the comparability of data”
• “There’s a lot of sociologists but not a lot with good
quants skills. I feel more discerning now when it comes
to statistics and numbers.”
• Described data analysis as “how to use methods to
solve social puzzles.”

21. • Placed in Integrity Research Consultancy
(London) undertaking research in fragile, post-
conflict countries
“I thought it would be very well suited to what I can actually
do and what would be important for me to learn, unlike some
big companies who put you into a predetermined role that I
may not necessarily know anything about.”

22. Selected quotes
• “I really liked that I would be using statistics and data analysis
as I’m really interested in that”
• “I worked on a big body of qualitative interviews … and I
became the go-to person for sampling techniques….the things
I worked on were more related to methodology and design
than actual analysis”
• “doing these things, the sampling, the methodology, that’s
what I want to be doing for my career, ..…. and knowing I have
a sector I am passionate about and doing something I’m good
at and something I can get better at is good to know ”
• “I’ve always thought it’s good to be good at maths, it helps
you, not necessarily applying the maths but the thinking
involved”

23. • Placed in academic unit in University College
London: Centre for Sexual Health and HIV
Research
“I thought it might be fun to do an internship, have a job; it
wasn’t the statistics or quants that drew me in, it was more
going somewhere to work as I’d never done that before. ”

24. Selected quotes
• Confident with maths but had not studied it since she was 16
• “This internship was my dream. I saw it and thought ‘yes this is the perfect
internship’ as I love studying sex and sexuality”
• “I did simple crosstabs, regression, measured change over time, so I had
to combine the two datasets which was incredibly difficult. It took me
weeks. And I never want to do it again but it was satisfying.”
• “It’s not something to be scared of and it’s good practice” [On a
presentation to the research team]
• “which seems scary but actually it’s not. And it took me a couple of days to
get my head around it … but I wasn’t frightened [On learning to use Stata
and the command line]
• “You don’t have to be brilliant at maths. If you’re fairly logical in the way
you think it will make sense to you. Even if you have to read it a few
times.”

25. And what do the employers say?
“So I don’t think there were any analysis tasks that we assigned her
that she wasn’t able to perform to a high level of quality. The concepts
of doing the academic theory and then trying to out it into practice in
a company like ours is exactly what the balance should be to prepare
somebody for the workplace and apply it commercially”
– They went on the take on more interns and pay them living wage
“ from their applications we were not expecting them to be as strong
as they actually were. I was told don’t expect them to come and do
much complicated analysis and then they came and they really could.
So all of this is analysing Natsal, and it’s complex. And they whizzed
through [what we’d planned]. They were really keen, they were really
interested in adjusting for confounders and thinking about them ..
They picked that up really quickly”
– Both students went on the be co-authors on academic papers

26. What did they all do next?
• Pete – completing Master’s in Social Research
Methods and Statistics and just awaiting outcome
of PhD proposal in social statistics and survey
methods
• Bella – working for a commercial company in Big
Data analysis and starting Master’s in Social
Research Methods and Statistics in September
• Natassia - completing Master’s in Social Research
Methods and Statistics and not sure what next
but includes social research (possibly in the US)

27. Key themes
• Attitude to maths
• Motivation for applying
• Eager to be stretched and challenged
• Confidence develops
• Appreciation of quantitative methods in the
workplace as a practical skill
• Provided a stepping stone for all 3 students

28. Challenging the skills deficit narrative
• Our students demonstrate creativity, passion
for their subject, willingness to learn,
eagerness to apply knowledge
• The ‘skills gap’ mantra is not always helpful
• We need to show how they can do data
analysis starting often from a low base
• Embedded and applied learning is fast
becoming a successful route to securing good
students at graduate level

29. 3 years on …

30. 4 emerging lessons for teaching and
curriculum development
1. Data skills need to be taught and learned in the context of
meaningful applications
2. Beware the sanitised teaching data set and a formulaic
approach to teaching stats - data skills in the real world
include problem solving and messy data… (with implications
for assessment)
3. (1) + (2) makes data analysis exciting – we need to replicate
that in the classroom
4. The data skillset is ever changing – we need to reflect this in
the curriculum

31. Publications
• Carter, J. and Nicholson. J. Teaching
Statistical Literacy by Getting
Students to Use Real World Data: 40
Years’ Worth of Experience in 40
Minutes. Long paper accepted for
presentation at International
Association for Statistical Education
(IASE) Conference, Berlin, July 2016
• Carter, J., Brown, M., and Simpson,
K. (Under submission) From the
classroom to the workplace: how
social science students are learning
to do data analysis for real Special
Edition of Statistics Education
Research Journal
• Carter, J. (Under submission). Work
Placements, Internships and Applied
Research. SAGE (publication date of
2017).
• Special Issue; the Teaching and
Learning of Social Research Methods
in International Journal of Social
Research Methodology
http://www.tandfonline.com/toc/tsr
m20/18/5#.V42ETYt33ww
• Buckley, J., Brown, M., Thomson, S.,
Olsen W. and Carter, J. (2015)
Embedding quantitative skills into the
Social Science Curriculum: case
studies from Manchester.
International Journal of Social
Research Methodology, pp. 495-510,
DOI:
10.1080/13645579.2015.1062624
• Can’t Count or Won’t Count?
Embedding Quantitative Methods in
Substantive Sociology Curricula: A
Quasi-Experiment Sloan et al,
Sociology June 29, 2015

32. Further research and resources
• Maths Anxiety Project
at mathsisok.com
• Short videos at
tinyurl.com/UoMQStep

33. Questions?