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Data Librarians Represent!

Data Librarians Represent!. Integrating Data Services into the Social Science Research Process. Lynda Kellam Data Services & Government Information Librarian University of North Carolina at Greensboro lmkellam@uncg.edu. Katharin Peter Social Sciences Data Librarian

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Data Librarians Represent!

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  1. Data Librarians Represent! Integrating Data Services into the Social Science Research Process Lynda Kellam Data Services & Government Information Librarian University of North Carolina at Greensboro lmkellam@uncg.edu Katharin Peter Social Sciences Data Librarian University of Southern California kpeter@usc.edu

  2. Data Librarians Represent! Integrating Data Services into the Social Science Research Process The aim of this workshop is to begin anongoing dialogue about incorporating data services and numeric resources into the social science research process. L. Kellam, K. Peter May 2009

  3. Workshop overview • Warm-up scenarios • What does “integrating data services” mean? • Who are your users? • Teaching • Data Users • Non-data users • Other librarians • Collaborations across campus L. Kellam, K. Peter May 2009

  4. Scenario 1 You have been invited as a guest speaker to a freshman seminar on national politics and current events. The professor casually requests that you “just talk about polling data for 10-15 minutes”. How would work within these limitations and what would you cover? L. Kellam, K. Peter May 2009

  5. Scenario 2 You are providing a 50-minute library research workshop for lower-level comparative politics students. The students’ assignment is to write a paper comparing two countries and you have been asked to instruct them on locating scholarly articles and primary sources. How might you also incorporate numeric sources into the larger workshop? L. Kellam, K. Peter May 2009

  6. Scenario 3 You have been asked to give a 30-minute presentation on available data sources at the all-day, new PhD student orientation for the Sociology Department. Students are wide-ranging in knowledge, ability and research interests. How would you outline your presentation so it would be appropriate for this audience? L. Kellam, K. Peter May 2009

  7. Scenario 4 Tired of last-minute grant proposal questions, you decide to offer a “Data Resources and Tips for Faculty Writing Grant Proposals” brown bag workshop. What might you include in this 45 minute workshop/discussion? What specifically do you want the faculty to walk away knowing? L. Kellam, K. Peter May 2009

  8. Scenario 5 It is your turn to provide the monthly 1-hour lecture for reference providers at your library (including social science and non-social science librarians as well as paraprofessionals that staff the reference desk). Given the diverse computer and mathematical skill levels of your colleagues, what sources/concepts would you cover so that they could confidently field (or refer) statistical reference questions? L. Kellam, K. Peter May 2009

  9. Integrating Data Services • What is does this even mean? • Why is it important? L. Kellam, K. Peter May 2009

  10. Who are your users? • Who are the users on your campus? • What the problems/challenges do you have in working with those users? • How do you fit into their social science research process? L. Kellam, K. Peter May 2009

  11. Teaching: Data users • Incorporating data search into larger literature review • http://www.hawaii.edu/edper/pdf/Vol37Iss2/Reflections.pdf • Putting data in context L. Kellam, K. Peter May 2009

  12. Teaching: Non-data users • Finding the story in the data • Stepping stones and gateway resources • Example: Polling data and active-learning L. Kellam, K. Peter May 2009

  13. Finding the story in the data Source: World Development Indicators L. Kellam, K. Peter May 2009

  14. Stepping stones and gateway resources Source: EIU Country Reports

  15. Source: http://data.un.org

  16. Source: http://data.un.org

  17. Polling data and active-learning Example: • What is a public opinion poll? • Can results be trusted? • How do you vet a polling statistic? • How are the results displayed? • For more information L. Kellam, K. Peter May 2009

  18. Polling data and active-learning (cont.) Source: Roper Center iPoll

  19. Polling data and active-learning (cont.) L. Kellam, K. Peter May 2009

  20. Teaching: Non-data users (cont.) • Try and get students to think about the topic beforehand. • Differentiate between walk away (knowledge) vs. take away (directions/tutorials). • How can data literacy and statistical literacy fit into small assignments or limited class time? L. Kellam, K. Peter May 2009

  21. Teaching: Other librarians • Preparing non-social science librarians and staff for the reference desk. • Teaching advanced topics to interns. L. Kellam, K. Peter May 2009

  22. Source: http://www.census.gov

  23. Source: http://www.census.gov

  24. Teaching about the American Community Survey Let’s Play with Fact Sheets! • Find a partner and work on the questions below together! • Navigate to http://www.census.gov/ • On the left navigation click on American FactFinder • Look up Winston-Salem, NC in the Fact Sheets and look through the data categories. Under which category would school enrollment appear? • What is the estimate for total school enrollment for Winston-Salem, NC in the 2005-2007 ACS? What was it during the 2000 Census?

  25. Source: http://www.census.gov

  26. Teaching about the American Community Survey • Estimates • Sample is small and requires aggregation of data over time for smaller locations • Point in time versus period data • Counting on April 1 of census year versus counting continuously throughout year • Margins of error and confidence intervals • Figures are an estimate with a confidence interval of 90% What are critical concepts? L. Kellam, K. Peter May 2009

  27. Teaching about the American Community Survey Town B has 30,000 Three sets of 3 year estimates (05-07, 06-08, 07-09) & one 5 year estimate are available City C has 80,000 or more Five sets of 1 year estimates, three sets of 3 year estimates and one 5 year estimate are available Village A has 15,000 Only 5 year estimates available (2005-2009)

  28. Collaborations across campus • Course specific outreach. • Students collecting their own data for thesis. • Starting out with PhD cohorts. • How have you collaborated? L. Kellam, K. Peter May 2009

  29. What have we missed? • Undergraduate versus graduate students • Incorporating statistical literacy and data literacy • Active learning • Assessment • Other outreach ideas? L. Kellam, K. Peter May 2009

  30. Selected Bibliography Jacobs, Jim. 1991. Providing data services for machine-readable information in an academic library: Some Levels of Service. Public-Access Computer Systems Review 2(1) 144-160. Mahoe, Rochelle. 2004. Reflections on the Dissertation Process and the Use of Secondary Data. Educational Perspectives,37(2) 34-37. Reed, Eleanor J. 2007. Data services in academic libraries: Assessing needs and promoting services. Reference and User Services Quarterly, 46(3) 61-73. Stephenson, Elisabeth and Caravello, Patti Schifter. 2007.  Incorporating data literacy into undergraduate information literacy programs in the social sciences: A pilot project. Reference Services Review, 35(4) 525-540. See also: The 2004 special issue of IASSIST Quarterly 28(2/3) devoted to “Developing Statistical Literacy”. L. Kellam, K. Peter May 2009

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