Unleashing the Power of Quantitative Literacy

1. Quantitative Literacy = Don’t Be Afraid of Data! Quantitative literacy is competence in the skills involved in the effective production, utilization and comprehension of quantitative information for the purposes of description, analysis, discovery, evaluation, reasoning, decision making, problem solving, and persuasion. Furthermore, quantitative literacy involves recognizing when such skills are needed and using them appropriately and ethically. The cultivation of quantitative literacy is essential for effective living skills, for informed citizenship and for personal enrichment.

2. 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

3. Four Elements of Numerical Literacy • Measurement, Data Collection and Interpretation • This category of skills involves quantitative description of phenomena, creation of a testable hypothesis, collection of accurate, valid quantitative data to address the hypothesis, and the analysis and interpretation of the data into usable information to test the hypothesis. • Quantitative Expression • This category of skills involves representing data in a graphical, tabular or abstracted form for the purposes of summarizing the data, revealing underlying trends and structure, and communicating the key meanings of the data.

4. Four Elements of Numerical Literacy • Evaluating Evidence and Assertions • The ability to evaluate evidence and assertions based on quantitative information and reasoning. Involves appropriately utilizing the precision of quantitative information and the rigor of quantitative reasoning for the purposes of prediction, decision making, and problem solving, as well as evaluating risk. • Quantitative Intuition • Amenability to working with quantitative data and conducting quantitative analyses; having a feel for orders of magnitude, and an implicit apprehension of whether numerical answers seem right or wrong without the conscious use of calculations or reasoning.

5. What do we mean by “data”? • Definitions differ by context. Data can be: • Analysis of newspaper articles, blogs, Twitter feeds, commercials, etc. looking for themes • Sensor Measurements • Information from medical tests, experiments, and other “scientific” exercises • Census and other social Data • Field data as represented by maps • Overall “data” refers to summary information presented numerically in graphs, charts, or tables.

6. 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” – This is Clearly Not Believed by the Millennial Generation • Quantitative literacy largely determines an individual’s capacity to control his or her quality of life and to participate effectively in social decision-making”

7. Importance (Con’t) Job skills

8. 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”  QL is not just “doing math”

9. 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.

10. 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

11. More Arguments/Evidence • Provides students with marketable skills • ASA survey – statistical knowledge most widely represented on resumes • Enhances writing and critical thinking • Learn to build a resume or e-portfolio that highlights your quantitative skills.