1. IASSIST May 26-29, 2009 Tampere, Finland John Paul DeWitt (SSDAN) and Lynette Hoelter (ICPSR) The University of Michigan Bringing Data to Undergraduate Classrooms The Social Science Data Analysis Network (SSDAN) and ICPSR's Online Learning Center (OLC)

2. 2 Outline Quantitative Literacy – Why is it important? Resources from the Social Science Data Analysis Network (SSDAN) – DataCounts! – CensusScope Resources from ICPSR’s Online Learning Center (OLC) – Learning Modules and Data Coming soon! (ICPSR and SSDAN Partnerships) – Assessment Materials – Digital Library (TeachingWithData.org)

3. 3 Quantitative Literacy Importance – Students are participants in a democratic society – Skills include: Questioning the source of evidence in a stated point Identifying gaps in information Evaluating whether an argument is based on data or opinion/inference/pure speculation Using data to draw logical conclusions Student Comfort and Aptitude – Over 50% of early undergraduate students report substantial “statistics anxiety” (DeCesare, 2008) – Using only one or two learning modules has yielded significant increases in students’ comfort Solution: Introduce students to “real world” data early and often

4. 4 Quantitative Literacy

5. 5 SSDAN: Background Started in 1995 University-based organization that creates demographic media and makes U.S. census data accessible to policymakers, educators, the media, and informed citizens. – web sites – user guides – hands-on classroom computer materials

6. 6 SSDAN DataCounts! (www.ssdan.net/datacounts) – Collection of approximately 85 Data Driven Learning Modules (DDLMs) – WebCHIP (simple contingency table software) – Datasets (repackaged decennial census and American Community Survey) – Target is lower undergraduate courses CensusScope (www.censusscope.org) – Maps, charts, and tables – Demographic data at local, region, and national levels – Key indicators and trends back to 1960 for some variables

7. 7 SSDAN: DataCounts! Quickly connects users to datasets…..or Data Driven Learning Modules

8. 8 SSDAN: DataCounts! Menu for choosing a dataset for analysis Brief List of available dataset collections

9. 9 SSDAN: DataCounts! Submitting a module: Sections are clearly laid out Forces faculty to create modules with specific learning goals in mind. Makes re-use of module much easier

10. 10 SSDAN: DataCounts! Title Author and Institution Brief Description Faceted browsing to refine the search Appropriate Grade Levels Subjects (e.g. Family, Sexuality and Gender) Learning Time

11. 11 SSDAN: DataCounts! Data Driven Learning Modules are clearly laid out Easy to read Instructors can quickly identify whether a module would be relevant to a specific course

12. 12 SSDAN: DataCounts! WebCHIP Commands for selecting variables, creating tables, graphing, and recoding Basic information about the dataset Running the “marginals” command shows the categories for each variable and frequencies

13. 13 SSDAN: DataCounts! Students can quickly run simple cross tabulations to see distributions and test hypotheses

14. 14 SSDAN: DataCounts! Controlling for an additional variable allows for deeper analysis

15. 15 SSDAN DataCounts! – Collection of approximately 85 Data Driven Learning Modules (DDLMs) – WebCHIP (simple contingency table software) – Datasets (repackaged decennial census and American Community Survey) – Target is lower undergraduate courses CensusScope – Maps, charts, and tables – Demographic data at local, region, and national levels – Key indicators and trends back to 1960 for some variables

16. 16 SSDAN: CensusScope New ACS data with improved look & feel coming Fall 2009

17. 17 SSDAN: CensusScope Charts, Trends, and Tables All available for states, counties, and metropolitan areas

18. 18 SSDAN-OLC SSDAN’s primary focus is to assist in the dissemination of social data into the classroom with sites like DataCounts! and CensusScope Until recently, ICPSR primarily targeted researchers, the OLC now provides a welcomed Instructors’ portal to ICPSR resources and is continuing its outreach to educators

19. 19 Founded in 1962 at the University of Michigan, is the largest non- governmental social science data archive in the world. ICPSR has special expertise in on-line dissemination of data, the development of metadata standards, and training in quantitative methods to facilitate effective data use. ICPSR now has nearly 650 diverse members around the world The ICPSR archive currently holds – 7,121 studies consisting of – 60,979 datasets with – 519,934 files The collection grows by 1200-1400 files per year Data are available instantaneously ICPSR: Background

20. 20 ICPSR: OLC OLC: www.icpsr.umich.edu/OLCwww.icpsr.umich.edu/OLC Over 40 DDLMs (also called DDLGs or Data Driven Learning Guides) Format of DDLGs allows instructors to make decisions on how to incorporate into class – Based on lesson plan format A tool to help develop classroom lectures and exercises that integrate data early into the learning process. Intended for use in introductory-level substantive classes. Requires no additional software.

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22. 22 How to Use the OLC Faculty use of charts in class to introduce topic Sending students to the website to work through a DDLG in class or as homework Using DDLG as part of larger project

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26. 26 Links go directly to the processed SDA functions so there is nothing to break (no additional manipulation needed—no functions to run)

27. 27 Sample SDA Output Frequency Distribution Cells contain: -Column percent -Weighted N V150 1 MALE:( 1) 2 FEMALE:(2) ROW TOTAL RELIMP ORT 1: not important 21.1 1,086 14.3 820 17.5 1,906 2: little important 25.7 1,320 24.2 1,386 24.9 2,707 3: pretty important 27.2 1,401 28.4 1,628 27.9 3,029 4: very important 26.0 1,336 33.0 1,891 29.7 3,227 COL TOTAL 100.0 5,143 100.0 5,725 100.0 10,868

28. 28 Important pitfalls and limitations that the instructor should address with students Summary of results

29. 29 Includes not only bibliography, but access to related electronic articles via the local institutions library

30. 30 Student Benefits: Critical thinking skills Increases students’ comfort with quantitative reasoning Many schools have focus on quantitative literacy and related skills – ASA charge for exposure “early and often” Engages students with the discipline more fully – Better picture of how social scientists work – Prevents some of the feelings of “disconnect” between substantive and technical courses Piques student interest Opens the door to the world of data

31. 31 Looking Ahead (ICPSR-SSDAN Partnerships) Assessment Tools and Results

32. 32 Looking Ahead TeachingWithData.org (October 2009) – Social Science Pathway to the National Science Digital Library – Virtual repository of resources to support the teaching of quantitative social sciences Data-Driven Learning Modules Data Sources Pedagogical Resources Analysis and Visualization Tools Other Resources useful to instructors – Collaboration tools – Web 2.0 technologies

33. 33 Acknowledgements National Science Foundation Inter-university Consortium for Political and Social Research Population Studies Center Institute for Social Research University of Michigan