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.

1. Instructional Data Sets
Q-Step Launch Event Programme
March 20, 2014
Lynette Hoelter, Ph.D.
Director of Instructional Resources, ICPSR
lhoelter@umich.edu

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. 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. 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. 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. 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 Include Data
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 Class with Data

10. Example: Emphasizing Content

11. Example of Activity for Intro Students

12. Example of Multi-part Exercise

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. 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. How can I operationalize “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 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. Online Analysis Packages

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

22. 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)
• 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. Questions? Comments? Suggestions?
Lynette Hoelter: lhoelter@umich.edu