Download to read offline
Uploaded byICPSR
Instructional Data Sets from Q-step Launch Event (Univ of Exeter) 3-20-2014
The document outlines a presentation on the importance of incorporating quantitative data into educational curricula to improve students' data literacy and critical thinking skills. It discusses the definition of data, its applications in real-life contexts, and provides strategies for integrating data into various courses effectively. The presentation also emphasizes the significance of utilizing data from diverse sources to foster active learning and enhance students' analytical abilities.
More Related Content
Similar to Instructional Data Sets from Q-step Launch Event (Univ of Exeter) 3-20-2014
Instructional Data Sets from Q-step Launch Event (Univ of Exeter) 3-20-2014
- 1. Instructional Data Sets Q-StepLaunch Event Programme March 20, 2014 Lynette Hoelter, Ph.D. Director of Instructional Resources, ICPSR [email protected]
- 2. Presentation Outline: • Whatis data? • Why use data? • When should I use data? • How can I use data? (Examples) • Where can I find data and tools?
- 3. Taking a stepback: 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. Why use datathroughout 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. Quantitative Literacy • Skillslearned 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. 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. When to IncludeData ALL the time!!!!! Don’t save it for methods/stats classes…
- 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. Example: Begin Classwith Data
- 10.
- 11. Example of Activityfor Intro Students
- 12. Example of Multi-partExercise
- 13. Other ideas forincluding 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. Using Data withoutUsing Data • How does religion relate to health behaviors? There’s a quiz for that! – From the Association of Religion Data Archives
- 15. How can Ioperationalize “race”?
- 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. Creating Activities Basedon 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.
- 19. Tips • Using onlineanalysis 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
- 22. Websites to StartYour 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) • TeachingWithData.org • 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
- 23.