teachingwithdata resources for teaching quantitative literacy in the social sciences
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TeachingWithData.org Resources for Teaching
Quantitative Literacy in the Social Sciences
John Paul DeWitt & Lynette HoelterUniversity of Michigan
ASA Annual Meeting, August 15, 2010
Presentation Outline:• Introducing the project partners• Quantitative Literacy • Introducing TeachingWithData.org
– General overview (demo of Website)– Sociology-related resources– Future directions
Project Partners• ICPSR • SSDAN• Others involved:
– American Economic Association Committee on Economic Education
– American Political Science Association– American Sociological Association– Association of American Geographers– Science Education Resource Center, Carleton
College
ICPSR• World’s oldest and largest social
science data archive– Began in 1962 as ICPR
• Membership organization with 700+ members worldwide (non-members can use many resources)
• Summer Program in Quantitative Methods of Social Research
Current Snapshot of ICPSR• Currently 7,880 studies (65,200 data sets)
– Grouped into Thematic Collections– Available in multiple formats– Federal funding allows parts of the
collection to be openly available– Data sources:
• Government• Large data collection efforts• Principal Investigators• Repurposing• Other organizations
ICPSR: Undergraduate Education• Fairly recent attention
– Response to faculty– Undergrad users are fastest growing
segment• Resources
– OLC, SETUPS, ICSC, EDRL• NSF-funded projects
– TeachingWithData.org (NSDL)– Course, Curriculum, & Laboratory
Improvement project to assess the effect of using digital materials on students’ quantitative literacy skills
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SSDAN-OLC• SSDAN’s primary focus is to assist in the
dissemination of social data into the classroom with sites like DataCounts! and CensusScope
• ICPSRgreat track record in research, with a new attention on undergraduate education coming more recently with the welcomed Online Learning Center (OLC)
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SSDAN: Background• Started in 1995• University-based organization that creates
demographic media and makes U.S. census data accessible to policymakers, educators, the media, and informed citizens. – web sites– user guides – hands-on classroom materials
• Integrating Data Analysis (IDA)
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SSDAN: Classroom Products• DataCounts!
(www.ssdan.net/datacounts)– Collection of approximately 85 Data Driven Learning
Modules (DDLMs)– WebCHIP (simple contingency table software)– Datasets (repackaged decennial census and
American Community Survey)– Target audience is lower undergraduate courses
• CensusScope (www.censusscope.org)– Maps, charts, and tables – Demographic data at local, region, and national levels– Key indicators and trends back to 1960 for some
variables
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SSDAN: DataCounts!
Quickly connects users to datasets… ..or Data Driven
Learning Modules
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SSDAN: DataCounts!
Menu for choosing a dataset for analysis
Brief List of available dataset collections
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SSDAN: DataCounts!Submitting a module:• Sections are clearly laid out• Forces faculty to create modules
with specific learning goals in mind.
• Makes re-use of module much easier
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SSDAN: DataCounts!
TitleAuthor and Institution
Brief Description
Faceted browsing to refine the search• Appropriate Grade Levels• Subjects (e.g. Family, Sexuality and
Gender)• Learning Time
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SSDAN: DataCounts!Data Driven Learning Modules are clearly laid out• Easy to read• Instructors can quickly identify
whether a module would be relevant to a specific course
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SSDAN: DataCounts!• WebCHIP Commands for selecting variables,
creating tables, graphing, and recoding
Basic information about the dataset
Running the “marginals” command shows the categories for each variable and frequencies
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SSDAN: DataCounts!
Students can quickly run simple cross tabulations to see distributions and test hypotheses
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SSDAN: DataCounts!
Controlling for an additional variable allows for deeper analysis
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SSDAN• DataCounts!
– Collection of approximately 85 Data Driven Learning Modules (DDLMs)
– WebCHIP (simple contingency table software)– Datasets (repackaged decennial census and
American Community Survey)– Target is lower undergraduate courses
• CensusScope– Maps, charts, and tables – Demographic data at local, region, and national levels– Key indicators and trends back to 1960 for some
variables
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SSDAN: CensusScope
New ACS data with improved look & feel coming Fall 2010
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SSDAN: CensusScope• Charts, Trends,
and Tables• All available for
states, counties, and metropolitan areas
Thinking about Quantitative Literacy (QL)
• CCLI project to measure effectiveness of using online modules to teach QL
• First need to agree on skill set representing QL in the social sciences– Most use data-based exercises to teach
content– QL/QR has gotten much recent attention
in institutional assessment, many schools requiring a QL component
What is QL?• “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
Similar to Critical Thinking:• Students as participants in a
democratic society• Skills include:
– Questioning the source of evidence in a stated point
– Identifying gaps in information– Evaluating whether an argument is based
on data or opinion/inference/pure speculation
– Using data to draw logical conclusions
Quantitative Literacy• Necessary for informed citizenry• 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)
Translating to Learning Outcomes• Began with AAC&U rubric for quantitative reasoning• QL in social sciences:
– Calculation– Interpretation– Representation– Analysis– Method selection– Estimation/Reasonableness checks– Communication– Find/Identify/Generate data– Research design– Confidence
Learning Outcome Dimensions• Calculation: Ability to perform mathematical
operations• Interpretation: Ability to explain information
presented in a mathematical form (e.g., tables, equations, graphs, or diagrams)
• Representation: Ability to convert relevant information from one mathematical form to another (e.g., tables, equations, graphs or diagrams)
• Analysis: Ability to make judgments based on quantitative analysis
Learning Outcomes (con’t)• Method selection: Ability to choose the
mathematical operations required to answer a research question
• Estimation/Reasonableness Checks: Ability to recognize the limits of a method and to form reasonable predictions of unknown quantities
• Communication: Ability to use appropriate levels and types of quantitative information (data, reasoning, tools) to support a conclusion or explain a situation in a way that takes the audience into account.
Learning Outcomes (con’t)• Find/Identify/Generate Data:
Ability to identify or generate appropriate information to answer a question
• Research design: Understand the links between theory and data
• Confidence: Level of comfort in performing and interpreting a method of quantitative analysis
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Assessment Tools and Results
QL Skills Are Marketable• Often cited by students as
something “tangible” that they have learned
• Definable skill set useful in many career paths
• Easy to tie to everyday life
Including Data Builds QL and:
• Engages students with disciplines more fully – Active learning– Better picture of how social scientists work– Prevents some of the feelings of
“disconnect” between substantive and technical courses
• Piques student interest• Opens the door to the world of data
TeachingWithData.org• National Science Digital Library – only social science
pathway• Goal: Make it easier for faculty to use real data in
classes– Undergraduate (esp. “non-methods”)– K(9)-12 efforts
• Includes survey of ~3600 social science faculty • Repository of data-related materials
– Exercises, including games and simulations– Static and dynamic maps, charts, tables– Data – Publications
• Tagged with metadata for easy searching
Major Changes since Oct. 2009• Redesign of the interface on the main page
– Guided Search from home page– Resources categorized by more general ‘resource type’ controlled vocabulary
• Data focused on tables and figures vs. data sets• Reference Shelf Data Sources, events, pedagogy• Classroom Resources Grouped like resources,
– Search box with grade level • Spring Cleaning – removed hundreds of resources• Identified items at lower levels (higher granularity)• User log-in (OpenID) and submission• Local content• Data in the News blog• Data for Online Analysis• Reading list: ability to create, save, and share
– Favorites– List of resources for course, project, or textbook– TwD and external resources
New Account Setup (OpenID)
New Account Setup
TeachingWithData.org
TeachingWithData.org
TeachingWithData.org
TeachingWithData.org
Future Changes• Professional Association editors
– Submit, edit metadata, review resources• “Report” button for review and edit
– Cleaner metadata, outdated links, etc• Comments• OpenStudy partnership?
– Ratings– Recommendations– User Collaborations (Instructor-Instructor, Instructor-
Student)– Instant feedback and help
– TRAILS indexing
OpenStudy.com
Sociology Resources
Example Resources• “Data in the News” feature – good
way to bring in current events• Lesson plans/lectures• Data-driven exercises• Data sources• Tools
Lesson Plans (Example)
International Data & Information for Comparison (Example)
Example: Short Video on Family Change in Canada
Data-Based Exercises: “Low-Tech” (Example)
Data-Based Exercises: Online (Example)
Data-Based Exercises: No Stat Software Needed (Example)
Data for Online Analysis: No Software Needed (Example)
Educational Data Extracts for Statistics Packages (Example)
Future Directions:• Include resources for high school teachers• Ability to link data to analysis and/or
visualization tools• Ability for faculty to rate and comment on
resources• Peer-reviewed materials and capability for
faculty to upload their own resources• Community building through professional
associations and networks of users
Your Turn!• What have you tried? • What has worked best? • Favorites we should include in TwD?
Acknowledgements• PI: George C. Alter, ICPSR• Co-PI: William H. Frey, SSDAN
• Funded by National Science Foundation grant DUE-0840642
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