Teachingwithdata org resources for teaching quantitative literacy in the social sciences
1 / 62

TeachingWithData Resources for Teaching Quantitative Literacy in the Social Sciences - PowerPoint PPT Presentation

  • Uploaded on

TeachingWithData.org Resources for Teaching Quantitative Literacy in the Social Sciences. John Paul DeWitt & Lynette Hoelter University of Michigan ASA Annual Meeting, August 15, 2010. Presentation Outline:. Introducing the project partners Quantitative Literacy

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' TeachingWithData Resources for Teaching Quantitative Literacy in the Social Sciences' - denim

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Teachingwithdata org resources for teaching quantitative literacy in the social sciences

TeachingWithData.orgResources for Teaching Quantitative Literacy in the Social Sciences

John Paul DeWitt & Lynette Hoelter

University of Michigan

ASA Annual Meeting, August 15, 2010

Presentation outline
Presentation Outline:

  • Introducing the project partners

  • Quantitative Literacy

  • Introducing TeachingWithData.org

    • General overview (demo of Website)

    • Sociology-related resources

    • Future directions

Project partners
Project Partners



  • 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


  • 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
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
ICPSR: Undergraduate Education

  • Fairly recent attention

    • Response to faculty

    • Undergrad users are fastest growing segment

  • Resources


  • NSF-funded projects

    • TeachingWithData.org (NSDL)

    • Course, Curriculum, & Laboratory Improvement project to assess the effect of using digital materials on students’ quantitative literacy skills

Ssdan olc

  • SSDAN’s primary focus is to assist in the dissemination of social data into the classroom with sites like DataCounts! and CensusScope

  • ICPSRgreat track record in research, with a new attention on undergraduate education coming more recently with the welcomed Online Learning Center (OLC)

Ssdan background
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)

Ssdan classroom products
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

Ssdan datacounts

Quickly connects users to datasets…

..or Data Driven Learning Modules

SSDAN: DataCounts!

Ssdan datacounts1

Brief List of available dataset collections

Menu for choosing a dataset for analysis

SSDAN: DataCounts!

Ssdan datacounts2
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

Ssdan datacounts3

SSDAN: DataCounts!


Author and Institution

Brief Description

Ssdan datacounts4
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

Ssdan datacounts5

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

SSDAN: DataCounts!

  • WebCHIP

Ssdan datacounts6
SSDAN: DataCounts! and recoding

Students can quickly run simple cross tabulations to see distributions and test hypotheses

Ssdan datacounts7
SSDAN: and recodingDataCounts!

Controlling for an additional variable allows for deeper analysis

SSDAN and recoding

  • 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

Ssdan censusscope
SSDAN: CensusScope and recoding

New ACS data with improved look & feel coming Fall 2010

Ssdan censusscope1
SSDAN: CensusScope and recoding

  • Charts, Trends, and Tables

  • All available for states, counties, and metropolitan areas

Thinking about quantitative literacy ql
Thinking about Quantitative Literacy (QL) and recoding

  • 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
What is QL? and recoding

  • “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
Similar to Critical Thinking: and recoding

  • 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
Quantitative Literacy and recoding

  • 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
Translating to Learning Outcomes and recoding

  • 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
Learning Outcome Dimensions and recoding

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
Learning Outcomes ( and recodingcon’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 t1
Learning Outcomes ( and recodingcon’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

Ql skills are marketable
QL Skills Are Marketable and recoding

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
Including Data Builds QL and recodingand:

  • 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
TeachingWithData.org and recoding

  • 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
Major Changes since Oct. 2009 and recoding

  • 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 ( and recodingOpenID)

New account setup
New Account Setup and recoding

Teachingwithdata org1
TeachingWithData.org and recoding

Teachingwithdata org2
TeachingWithData.org and recoding

Teachingwithdata org3
TeachingWithData.org and recoding

Teachingwithdata org4
TeachingWithData.org and recoding

Future changes
Future Changes and recoding

  • 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
OpenStudy.com and recoding

Sociology resources
Sociology Resources and recoding

Example resources
Example Resources and recoding

“Data in the News” feature – good way to bring in current events

Lesson plans/lectures

Data-driven exercises

Data sources


Lesson plans example
Lesson Plans ( and recodingExample)

More extensive lesson plans example
More Extensive Lesson Plans ( and recodingExample)

Example short video on family change in canada
Example: Short Video on and recodingFamily Change in Canada

Static tables example
Static Tables ( and recodingExample)

Graphs maps example
Graphs & Maps ( and recodingExample)

Interactive maps example
Interactive Maps ( and recodingExample)

Data based exercises online example
Data-Based Exercises: Online ( and recodingExample)

Simulations example
Simulations ( and recodingExample)

Tools for data visualization example
Tools for Data Visualization ( and recodingExample)

Future directions
Future Directions: and recoding

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
Your Turn! and recoding

What have you tried?

What has worked best?

Favorites we should include in TwD?

Acknowledgements and recoding

PI: George C. Alter, ICPSR

Co-PI: William H. Frey, SSDAN

Funded by National Science Foundation grant DUE-0840642