The document discusses the importance of quantitative literacy (QL) in education, emphasizing its necessity for informed citizenship and participation in democratic society. It outlines various methods for incorporating data into teaching across disciplines, suggesting tools and examples that enhance student engagement and critical thinking. The presentation highlights that teaching QL is essential for students to effectively analyze and interpret numerical information in everyday life.

1. Quantitative Literacy
Through Social Science:
Don’t Be Afraid of Data!
International Conference on College
Teaching and Learning
April 11, 2012
Linda Detterman
Lynette Hoelter
ICPSR, Univ. of Michigan

2. Session Outline
• Defining “quantitative literacy (QL)” and
“data”
• Why the emphasis on quantitative
literacy?
• “But, I teach English…
– …. I don‟t „do‟ data”
– …. my students don‟t „do‟ data”
– …. what does quantitative literacy mean
for me?”
• Tools for incorporating data in the
classroom
• Evidence of effectiveness from
social sciences

3. Defining Quantitative
Literacy/Reasoning, Numeracy
“Statistical literacy, quantitative literacy, numeracy --Under
the hood, it is what do we want people to be able to do: Read
tables and graphs and understand English statements that
have numbers in them. That‟s a good start,” said Milo
Schield, a professor of statistics at Augsburg College and a
vice president of the National Numeracy Network.
Shield was dismayed to find that, in a survey of his new
students, 44 percent could not read a simple 100 percent row
table and about a quarter could not accurately interpret a
scatter plot of adult heights and weights.
Chandler, Michael Alison. What is Quantitative
Literacy?, Washington Post, Feb. 5, 2009

4. • Skills learned & used within a context
• Skills:
– Reading and interpreting tables or graphs
and to 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
• For a straightforward definition/skill
list, see Samford University‟s (not social
science specific)

5. What do we mean by “data”?
• Definitions differ by context. Data can be:
– Citing another author who supports your point
– Analysis of newspaper articles, blogs, Twitter
feeds, commercials, etc. looking for themes
– The result of an in-depth interview or observation
– 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.

6. From Where Do Data Come?
• Administrative records (e.g., human
resource files, police records)
• Census and other government data
collections
• Individuals responding to a survey
– Highly standardized
– Recorded (“coded”) as numbers and
these numbers can be used in
combination to say something about the
group of people who responded

7. Why is QL Important?
• Critical for a democratic society (Steen
2001)
– Informed citizenry – must be able to make
sense of information coming from multiple
sources.
– “The wall of ignorance between those who
are quantitatively literate and those who
are not can threaten democratic culture.
– Quantitative literacy largely determines an
individual‟s capacity to control his or her
quality of life and to participate effectively
in social decision-making” (MAA 2004: xii)

8. Importance (Con’t)
• Job skills

9. Why QL Across the Curriculum?
• “Quantitative literacy largely determines an individual‟s
capacity to control his or her quality of life and to
participate effectively in social decision-making.
• Educational policy and practice have fallen behind the
rapidly changing data-oriented needs of modern
society, and undergraduate education is the appropriate
locus of leadership for making the necessary changes
• QL is not about „basic skills‟ but rather, like reading and
writing, is a demanding college-level learning
expectation that cuts across the entire undergraduate
curriculum
• The current calculus-driven high school curriculum is
unlikely to produce a quantitatively literate student
population” (MAA 2004:xii)

10. QL Outside of Math/Statistics
• Other disciplines provide context for
numbers, giving them meaning
• More repetition of skills, better
learning
• Inclusion in multiple settings reduces
student anxiety
• Teacher anxiety can be reduced with
tools (pre-made
exercises, interpretations given)

11. How to Include Data
• Start class with a data-based news article
• Have students interpret charts/graphs from
popular media and critique news articles
• Require empirical evidence to support claims
in essays
• Question banks and exercises allow students
to work with surveys and the resulting data
• Have students collect data
• Engage students by having them find
maps, graphs, or other data that provide
examples of course content.

12. Tools for Faculty
• Data archives
– Public opinion
– Topic-specific archives
• Quantitative news blogs
• Pre-made exercises, pedagogical
examples
• Collections of resources

13. Public Opinion Data
• Roper Center for Public Opinion
Research
http://www.ropercenter.uconn.edu
• Gallup: http://www.gallup.com
• NORC reports & data:
www.norc.org/Research/DataFindings
• Pew Social & Demographic Trends:
http://www.pewsocialtrends.org/

14. Topic-specific Archives
• Association of Religion Data Archives
(www.thearda.com)
• Sociometrics (family, AIDS, maternal
drug abuse, etc.)

15. News Blogs & Quick Facts
• TeachingWithData.org – Data in the
News
• U.S. Census Newsroom
• Other government sources;
organizations – beware of credibility

16. Collections of Resources
• Science Education Resource Center
(Carleton College – pedagogical
materials)
• TeachingWithData.org
• ICPSR
– Online Learning Center
– Modules
– Tools (SSVD, Bibliography, SDA)

17. Arguments and Evidence from
Social Sciences
• Increased learning
– Makes course content relevant to students
– Emphasizes substantive points
– Higher student engagement (typically)
• Better sense of field
– Less disconnect between substantive and
technical courses
– Learn how social scientists actually work

18. More Arguments/Evidence
• Provides students with marketable
skills
– ASA survey – statistical knowledge
most widely represented on resumes
– Enhances writing and critical thinking

19. How might you use
survey or other data in
YOUR course? Other
ideas? Challenges?

20. Questions???
• For more information:
– Lynette Hoelter (lhoelter@umich.edu)
– Linda Detterman
(lindamd@umich.edu)