International data: developing QM social science capacity John MacInnes 1

Training/teaching QM: some challenges Low confidence in maths or statistics ability Low motivation: doubts about worth of QM Low expectation of achievement or experience Low reinforcement elsewhere in curriculum Little curriculum space Real, relevant data are most convincing, but rarely yield simple, clear patterns 2

Training/teaching QM: some resources More, better, easier to access data Better GUIs, range of software and IT infrastructure Better visualisation resources e.g gapminder 3

Training/teaching QM: special relevance of international data All social sciences consider ‘globalisation’. Study of host society in isolation increasingly seen as parochial Cosmopolitan student body e.g. of Edinburgh CQDA course majority non-UK based students Comparison is core of social science and QM Country level data is typically at interval level It addresses engaging cross-disciplinary issues It is suitable for both transversal and time series approaches 4

The CQDA ‘blended learning’ course 5

Using World Bank and UNHDI data 6 New challenges Old model: pay for a data set and analyse with SPSS, SAS etc New model: data transparency / ‘open data’ New skills in data location, manipulation and retrieval which complicate core task of learning e.g. OLS regression analysis Temporary solution ‘Teaching’ datasets

The WDI/HDI dataset Data from latest available year to minimise missing cases Only countries with > 3m pop 100 variables: manageable for new learners Online access to meta data, but sufficient var label description to facilitate simple analyses Deliberate inclusion of non-interval variables 7

The WDIHDI teaching dataset 8

The CQDA ‘blended learning’ course 9 The strong association between GDP and fertility

The CQDA ‘blended learning’ course 10 The spurious correlation between Mobile phone subscriptions and Infant mortality

Data checking procedures 3000 tractors per 100 sq. km = 30 tractors per sq km = 1 tractor per 3 hectares? 11

Conclusions Advantages: Very useful teaching tool Combines relevance with clarity, but also complexity for more advanced learners Drawbacks Resource intensive to produce Less flexible that original data sources What facilitates QM T&L may not teach students data complexity management skills 12