Instructional Data Sets from Q-step Launch Event (Univ of Exeter) 3-20-2014


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Presentation about using social science data in the classroom and creating (and finding) resources with which to do it. Addresses both substantive courses and research methods/statistics courses.

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Instructional Data Sets from Q-step Launch Event (Univ of Exeter) 3-20-2014

  1. 1. Instructional Data Sets Q-Step Launch Event Programme March 20, 2014 Lynette Hoelter, Ph.D. Director of Instructional Resources, ICPSR
  2. 2. Presentation Outline: • What is data? • Why use data? • When should I use data? • How can I use data? (Examples) • Where can I find data and tools?
  3. 3. Taking a step back: What do we mean by “data”? • Definitions differ by context. For example: – Newspaper articles, blogs, Twitter feeds, commercials – Transcripts of an in-depth interview or observation notes – Information from medical tests, experiments, and other scientific exercises • For this presentation, “data” refers to summary information presented numerically in graphs, charts, or tables and the underlying survey results or administrative records. – Some of the suggestions here also take advantage of “metadata” or data about the data.
  4. 4. Why use data throughout the curriculum? • Applies social science content to “real life” • Builds quantitative literacy in a non-threatening context • Active learning makes content more memorable • Repeated practice with quantitative information builds confidence and deeper learning; knowledge/skill transfer between courses • Exposes students to wider variety of data sources • Demonstrates how social scientists work
  5. 5. Quantitative Literacy • Skills learned and used within a context – Reading and interpreting tables or graphs, calculating percentages, and the like – Working within a scientific model (variables, hypotheses, etc.) – Understanding and critically evaluating numbers presented in everyday lives – Evaluating arguments based on data – Knowing what kinds of data might be useful in answering particular questions
  6. 6. Importance of QL • Availability of information requires ability to make sense of information coming from multiple sources • Use of evidence is critical in making decisions and evaluating arguments: e.g., risks related to disease or treatment, political behaviors, financial matters, costs/benefits of buying a hybrid • Understanding information is prerequisite for fully participating in a democratic society • Employers value these skills!!
  7. 7. When to Include Data ALL the time!!!!! Don’t save it for methods/stats classes…
  8. 8. No Need to “Revamp” Entire Course • One or more of the course learning objectives can relate to quantitative data: • This course will provide a context in which students can improve their writing, speaking, and critical thinking abilities. • Students will learn to create and interpret a crosstabulation table. • Students will gain an understanding of the application of the scientific method to the study of social behavior, including the use of evidence to test hypotheses.
  9. 9. Example: Begin Class with Data
  10. 10. Example: Emphasizing Content
  11. 11. Example of Activity for Intro Students
  12. 12. Example of Multi-part Exercise
  13. 13. Other ideas for including data: • Require empirical evidence to support claims in essays • Use data with online analysis tools for simple analysis assignments • Question banks and exercises allow students to work with surveys and the resulting data • Have students collect data – even in-class polls! • Engage students by having them find maps, graphs, or other data that provide examples of course content
  14. 14. Using Data without Using Data • How does religion relate to health behaviors? There’s a quiz for that! – From the Association of Religion Data Archives
  15. 15. How can I operationalize “race”?
  16. 16. Creating Instructional Datasets • Good documentation practices always apply • Depending on level, create new variables for students • With students, smaller is sometimes better – Fewer variables focuses their attention – Less likely to be overwhelming – Experience with students is that they often create their own data subsets when the original dataset is large • SPSS still most popular download format
  17. 17. Creating Activities Based on Data • Decisions: – Is the focus to be substantive or “technical”? – How much support do students need? – How much student autonomy(selection of variables, coding, etc.) is appropriate? – Which software to use? Online or Desktop? • Know when to provide explicit instructions and when that hinders learning
  18. 18. Online Analysis Packages
  19. 19. Tips • Using online analysis tools reduces barriers for students and faculty; easier/faster to implement • MANY good resources already exist – a quick search might turn up something that is easily modified to fit your purpose
  20. 20. Websites to Start Your Search • Association of Religion Data Archives Learning Center • ICPSR: Resources for Instructors – Data-driven Learning Guides • Science Education Resource Center (pedagogical materials) • Social Science Data Analysis Network (US based but good examples of exercises) • • Pew Research Center: Fact Tank, Reports, Datasets, Interactives • Consortium for Advancement of Undergraduate Statistical Education (CAUSE) • Data360 • Worldometers • Population Pyramids of the World • Gapminder • Survival Curve • Gallup Organization • UK Data Services Teaching with Data • European Social Survey EduNet • Office for National Statistics
  21. 21. Questions? Comments? Suggestions? Lynette Hoelter: