Your SlideShare is downloading. ×
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Using socioeconomic data in teaching and research
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Using socioeconomic data in teaching and research

935

Published on

Teaching with Data - ESDS International and World Bank project

Teaching with Data - ESDS International and World Bank project

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
935
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • The map shows the global pattern of downloads for the World Bank World Development Indicators accessed through ESDS International. A country was counted each time data for that country was downloaded as part of a multi-national download between September 2007 and August 2009. India was the country with the highest number of downloads during the period, followed by the United Kingdom, Mexico, Brazil and South Africa. The data comes from the ESDS International web server logs. Looked at total number of selections China came top which suggested people were looking for data on China but not finding what thye wanted.It wa during a period of a great crisis over sharp increases in the price of basic foodstuff you may remember the riots We wrote a paper about this which has been published by the OECD
  • GDP can be measured in different ways – producing very different pictures of global outputs. So using GDP by exchange rate data – high income countries accounted for 76% of global output, whereas using the GDP by PPP reduces their share to 57%, whilst that of the lower middle income group increases from 12 to 24%. So this illustrates how perception of the world, and policy preferences, change according to data selection. In this case – i.e. GDP – GDP by PPP is not appropriate for evaluating financial resources, although PPPs are necessary for making meaningful international comparisons of welfare or standards of living.
  • Transcript

    • 1. Using socioeconomic data in teaching: a tale from the UK Data and statistical skills for UK social science students
      Dr Jackie Carter
      Learning and Teaching &
      Social Science Data Services
    • 2. The UK context
      Data services and infrastructure
      Quantitative skills deficit in UK Social Sciences
      A project with the World Bank
      What we did and what we found out
      What’s next
      How data services can help address the ‘quantitative skills deficit’ in UK Social Sciences
      Overview
    • 3. Economic and Social Data Service
      UK National Data Infrastructure
      Census Programme
      Funded at national level by Economic and Social Research Council (ESRC) and Joint Information Systems Committee (JISC)
    • 4. Socioeconomic Data Services at Mimas
      Census Dissemination Unit
      ESDS International
      Time series data from Intergovernmental Organisations
      Census Aggregate data since 1971
    • 5. Quantitative Skills deficit (1)
      'critical deficit in quantitative skills within the UK’
      Commission on the Social Sciences (2003)
      quantitative social science a ‘strategically important and vulnerable subject’
      HEFCE (2005)
    • 6. Quantitative Skills deficit (2)
      www.esrc.ac.uk/funding-and-guidance/funding-opportunities/15407/latest-opportunity-13.aspx
      http://www.hefce.ac.uk/news/hefce/2011/sivs.htm
    • 7. Royal Statistical Society: getstats
      www.getstats.org.uk
    • 8. ESDS International
      Evidence of use from researchers
      CDU
    • 9. Point One
      Point One One
      Point One OneOne
      Point One OneOne
      Point One One
      Second Point
      Third of the points
      Another point, the fourth one
      The student experience
      Higher skills and employability
      Gathering evidence
      "The cutting edge of social science is reliant on use of real-world datasets, and there is a great desire to improve research-led teaching in this area. The UK provides access to a rich set of social science data resources with ESDS regarded as a jewel in the crown for the UK's social science data community.
      "The challenge for educators in addressing quantitative literacies lies in promoting students' use of data, but the benefits in doing so can improve both academic performance and job prospects for students. "
      Real World, Real Data, Real Stories
    • 10. Conferences and papers (including IASSIST 2010, E1)
      Case study www.esds.ac.uk/international/casestudies/real-data/real-data.asp
      ESDS International’s Teaching Tools (IASSIST 2011, PK)
      Project with the World Bank
      Open University Teaching Fellowship
      Real World, Real Data, Real Stories
    • 11. Simon Industrial Fellowship funded
      Promotion of research and teaching in social sciences
      Dr Eric Swanson World Bank Group
      Series of talks
      Semi-structured interviews with top users of ESDS International WB data
      The Project
    • 12. Paper accepted for International Journal of Research and Method in Education
      Special Edition on Using Secondary Sources in Educational and Social research
      The aim is to contribute to the methodological debates regarding the use of secondary data sources in Educational and Social research, as well as to present examples of empirical research which use this approach innovatively as the main method or in combination with other approaches
      Carter, J., Noble, S., Russell, A. and Swanson, E. ‘Developing Statistical Literacy Using Real World Data: Investigating Socioeconomic Data Resources used in Research and Teaching
      Further information
    • 13. Mutual interest to explore use of data.
      To strengthen links between the service, the data providers and the data users.
      Opening up of World Bank’s data made it timely to focus on academic users of the data in order to better understand the benefits afforded through ESDS-International, and any barriers to use.
      To better understand what the academic community do with the Bank’s data, why they do/or don’t use it and what would help them to use it more.
      To find out if users of one dataset tend to use other datasets too and if so what obstacles (if any) they face when accessing data from multiple domains.
      To contribute to improving statistical literacies and capacity building; trying to learn from good practice where this exists
      To take good practice from one discipline and extend into other social science disciplines if possible; to avoid ‘re-inventing the wheel ‘.
      Identify users who could help to build narratives around dataset use through either case studies or contribution to the Teaching Tools area of the website through future activity.
      Communicate the benefits of using international data in teaching and research across the social science community.
      Project aims and objectives
    • 14. Building an International Data Community
      Source: www.esds.ac.uk/international/news/news.asp
    • 15. World Bank Data
      Table source: ESRC Annual Report 2009-10 www.esrc.ac.uk/_images/Annual%20Report%2009-10_tcm8-13375.pdf
    • 16. The WDI database brings together a wide variety of statistical indicators relevant for monitoring social and economic characteristics of developing and developed countries
      Over 1200 indicators
      Social, economic, financial, natural resources, environmental
      213 countries
      Back to 1960
      User guides http://esds.ac.uk/international/support/user_guides/wb/wbwdiwe.asp
      World Development Indicators
    • 17. Celia Russell , Paul Murphy and Islay Gemmell, What do academics want? Research requirements for cross-national data, OECD Data Forum Background Paper, Available from: http://www.oecd.org/document/45/0,3343,en_40033426_40033828_44115565_1_1_1_1,00.html(30 March 2010)
      Future Directions for International Data IASSIST 2010 presentation (http://www.iassistdata.org/downloads/2010/2010_d2_wiseman_etal.pdf)
      What academics do with data
      Foreign direct investmentLand useFood exportsFinal consumption expenditureAdjusted net savings
      India United Kingdom
      Mexico Brazil South Africa Ghana Nigeria Kenya China United States
    • 18. Talk held at University of Manchester
      http://tinyurl.com/3z8u9rf
      Open Data and its consequences
      Data sources and coverage
      Improving statistical capacity
      Standardizing data
      The International Comparison Program
      Tools and applications
      Talks
    • 19. GDP can be measured in different ways, producing very different pictures of global output
      Using GDP by exchange rates data, high income countries account for 76% of global output, whereas using GDP by PPP reduces their share to 57%. That of lower middle income countries doubles from 12 to 24%
      Illustrates how perception of the world, and policy preferences, change according to data selection. GDP by PPP is not appropriate for evaluating financial resources, although PPPs are necessary for making meaningful international comparisons of welfare or standards of living
      Example: Using GDP
    • 20. Top 40 users emailed and Quants Methods list used to identify data users
      Resulted in 12 interviews
      7 were PhD or Masters level students; 1 researcher (post-doc); 1 senior lecturer; 2 profs; 1 research librarian
      Economics and Development Economics (7); Health and Pop Studies (2); Statistics (1); Finance (1); Library and Management (1).
      Highly diverse range of topics using international data in teaching and/or research
      Trade poverty
      Energy poverty
      Financial development
      Concept of household in developing country surveys
      Impact of family planning and reproductive health on social outcomes
      Evasion and corruption
      Financial regulation and credit availability
      Labour markets in developing countries
      Interviews with data users
    • 21. World Bank’s World Development Indicators (WDI)
      used in Master’s programmes
      no time to collect primary data sources
      useful as contains long time series for a wide variety of internationally comparable indicators
      enables students to apply statistical methods with emphasis on cross-country and panel data analysis
      Other data used:
      UNIDO’s Industrial Statistics
      IMF’s International Financial Statistics and Global Financial Statistics
      International Energy Agency datasets
      UN Common Database
      Some used microdata as well as macrodata e.g. Demographic and Health Surveys and Living Standards Measurement Studies (not in ESDS International)
      Data used
    • 22. Requests
      more data e.g World Bank’s Worldwide Governance Indicators (WGI)
      more tools – e.g. World Integrated Trade Solution for UN COMTRADE
      Liked a single access point and tool for macrodata – ‘good to have it all in one place’
      Although data available on open web – would continueto use ESDS International as it adds value
      Feedback on data
      University of Essex, Wyvern http://www.essex.ac.uk/wyvern/documents/March_11.pdf
      Feedback from Case Studies and training courses supports this – especially for teaching
    • 23. Access interface ‘looking a little outdated’
      We know!
      Evaluating other tools
      Data download format not suitable for panel analysis
      Training requirement
      Feedback on usability
    • 24. Most downloaded data and imported to other software
      Excel, e-views, SPSS, Stataand MatLab
      Often processed in Excel first
      Research/teaching spilt of tools and methods
      Multiple/simple regression
      ‘think like a social scientist’ course – real world data
      WDI particularly useful for descriptive statistics
      Used in undergrad dissertations, and to explore substantive issues around policy formation
      IMF IFS data also well used
      More complex analysis at postgrad level
      Tools and methods used
    • 25. Only started to uncover ‘tip of the iceberg’ use in teaching
      Top down and bottom up approach helps get data used
      Teacher/student and research as teacher role important
      Data access – how much analysis functionality is required?
      Librarians help support data use
      More teaching resources and datasets
      Single point of access – and search - value added
      More examples outside of economics needed and at undergrad level
      Conclusions
    • 26. SCORE (Support Centre for Open Resources in Education) fellowship
      Future research
    • 27. Sharing Teaching Resources and Practice
      Engaging with open educational resources (OER) and open data agenda
      Learning from others (ICPSR’s Data Driven Guides)
      Sharing pedagogy as well as resources
      Exploiting (funding) opportunities
    • 28. Thank you
      Jackie.Carter@manchester.ac.uk
      Twitter: JackieCarter
      Slides available on Slideshare

    ×