Enhancing the integration and increasing the use of Quantitative Methods within Social ScienceProfessor Jane Falkingham

Teresa McGowan

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Enhancing the integration and increasing the use of QM within Social Science

• Two linked pilot projects funded by ESRC

– Enhancing the integration of QM skills in UG Social Science curricula

– Increasing the use of QM in Social Science undergraduate dissertations

• Covering all year groups

• 18 months – one RA and some senior staff time

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Study Context• Recognised centre of excellence in research methods

• All students required to take one or more quantitative methods units in their 1st and 2nd year of study

- 1st year course ‘Introduction to Quantitative Methods’

- 2nd year course ‘Research methods in the Social Sciences’

• Despite this, the number of students applying the skills and knowledge acquired in these units to their substantive work remains low outside the discipline areas of Economics and Population Sciences

• Projects aim to ↑understanding of barriers to use, inform teaching, ↑awareness of relevance and inform future

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Quantitative Methods

Enhancing the Integration of

QM in UG Curricula

Increasing the Use of QM in Dissertations

Focus Groups Staff

Focus Groups

Students

Exemplars Summer School

Drop in clinics

Winter School

Project activities

Progress (and results) so far

Part 1:

QM - the view of students and staff

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Student views on QM

1. Attitudes to QM

• A surprise

‘It seemed that a lot of people didn’t realise we were going to be doing it as part of a degree course. I heard a lot of people saying ‘what? Stats!’’

• Maths anxiety

‘When we heard we were doing QM it was kind of like everyone didn’t know whether they had to have an A’level in maths’

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Student views on QM2. QM a positive experience

• Relevance to everyday life

‘I didn’t realise before I started stats how much you can relate stats to in the real world....you know explains all the statistics you get in papers and how you can’t trust them and all that sort of stuff. I think it’s made everyone a bit more aware how not to take statistics at face value. Which is quite good’

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Student views on QM

2. QM a positive experience (cont.)

• Contrast to other work in degree course (‘right’ answers rather than argument)

• Challenge & sense of achievement

‘It’s a bit of a self-confidence boost as well. Because when you’re sat in your room and you cannot do an equation...and you’re there for three hours and at the end of it you get it, and it’s right, it’s a massive self-confidence boost. And you think if I can do this then you know, I can do it, and if you set your mind to it you can do it’

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Student views on QM3. QM a negative experience

• Need to understand data, not create/manipulate it – limited relevance

• Culture of overall course anti-stats

‘There is a big sort of, a mutual apathy, which just sort of multiplies itself and escalates’

‘People don’t feel that it’s what they are here to do’

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Student views on QM3. QM a negative experience (Cont.)

• Level & pace of course

‘As the lecture goes on, everybody starts off quite positive I would say you know, trying to be positive. And that as it, as the formulas come in and like it just sort goes over our head. You can see everybody getting frustrated’

• Endure it to pass it

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Student views on QM

4. QM and its relationship to the substantive area of study

‘It’s a very important point that it’s not actually anything to do with our degrees, all the other modules that we do relate to each other’

‘The [substantive course] lecturers never mention anything about stats in our lectures at all. They don’t relate any of it, that’s not their job to relate stats’

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Student views on QM

5. Improving QM teaching

• Relevance of examples to substantive courses important

• Importance of tutorials

• Streaming

• Pace

• Re-name

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Staff views on QM

1. Attitudes

• Important skill for students to know but not necessarily to do

• Recognise employability aspect

• Not for me

‘I’ve got a bit of a block against it. But it’s not hostility, it’s just I don’t feel all that wonderfully competent in that area myself’.

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Staff views on QM

2. QM and its relationship to the substantive area of study

• QM seen as ‘tacked on’

• Staff aware that their lack of use of quantitative examples means students don’t see its value

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Staff views on QM

3. Disincentives to integration

• Control of teaching – teaching outside of division serves as a ‘structural barrier’

• Problem of expertise

‘Because I think one of the problems with embedding it within the politics curriculum is that you can’t because we haven’t got people in our department who can do, who can actually re-enforce’

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Lessons• Staff attitude impacting on students view of QM

• Need to increase communication between disciplines

• Staff training and awareness essential to↑integration

• Promote collective responsibility for research methods teaching

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Actions - exemplars

Work with tutors from Sociology & Social Policy, Social Work and Politics an International Relations

• ‘Core’ substantive courses

• QM examples where they fit

• No increase in workload for course lecturer

Work with Social Statistics tutors

• Seminars by substantive course lecturers

• Improvement of examples

Progress (and results) so far

Part 2:

QM dissertations and the Summer and Winter Schools

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Summer School

Aim: To raise awareness, encourage and support students in the application of QM to their chosen research question

Eligible pop: 75 students 2:1 or above on 2nd yr research methods course

Recruited pop: 22 students

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Summer School Activities• Day 1

– Research questions and design

– Sources of data

• Day 2

– Sampling

– Data collection

• Day 3

– Data entry in SPSS

– Preliminary data analysis

• Day 4

– Multiple linear regression

– Logistic regression

• Day 5

– Report writing & presenting data

– Dissertation presentations

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Student evaluation• Overwhelmingly positive

• On a scale from 1 (poor) to 5 (excellent)

– 100% of attendees rated the Summer School as 4 or above for both content and organisation

– 100% of attendees rated the quality of teaching as 5 (excellent)

• Hands-on approach; small class sizes; peer review; one-to-one support; use of own dissertation; comments on practical aspects of dissertation (i.e. planning and then writing the report); relaxed atmosphere

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Student evaluation• Motivation for attending

– 88% of attendees said their main motivation was to improve their dissertation or to gain dissertation support

– 6% said it was to learn more about QM

– Only 6% said their main motivation for attending was money

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Winter School

Aim: To continue support and learning begun in Summer School

Eligible pop: All those who took part in the Summer School

Recruited pop: 19 of the 22 students attending summer school

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Winter School Activities• Day 1

– Tips on writing your literature review

– Writing about your data

– The methods chapter

– Equation editor computer workshop

• Day 2

– Independent work with supervision form staff

– Formatting your dissertation computer workshop

• Day 3

– Writing up your findings

– Writing up your discussion and conclusion

– Referencing your work

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Student evaluation• Overwhelmingly positive

• View of dissertation support overall

• 94% agreed the course had;

– Improved knowledge of QM

– Improved understanding of how to carry out QM

– Improved understanding of application to area of study

– Improved confidence in QM

– Given confidence to use it in the future

– Made QM more applicable to them

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Other activities

Drop-in Clinics/mentoring

• Little take up prior to Christmas vacation

• Spring term very hectic!

- Too much help?

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Project outcomes so far…• Successfully encouraged students to think realistically about

data use and collection

• 18/22 students went on to use an element of QM in their dissertations

• 6 students independently enrolled on optional 3rd year Multivariate Data Analysis course

• 4 students have applied for MSc in Social Statistics/Demography as a result of the project activities

• 24 students have enrolled on 2008 Summer School (not paid)

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Final thoughts• Local sustainability?

– Resources

– Intensity

• Replicable elsewhere?

- Summer/Winter Schools

- Materials/Question Bank – distance learning

- Help Desk – continued support