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Asa integrating data 2 19-2014 with cites

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Information about Asa integrating data 2 19-2014 with cites
Education

Published on February 19, 2014

Author: ICPSR

Source: slideshare.net

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Presentation for the American Sociological Association's Department Affiliates Webinar Series. Discussion of using quantitative data in courses throughout the undergraduate curriculum, including why it's a good practice, how it can be done, and where one can find resources that make it easier.
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Integrating Data Analysis into the Undergraduate Curriculum (aka Making Sociology Real) American Sociological Association Webinar January 19, 2014 Lynette Hoelter lhoelter@umich.edu

Presentation Outline: • • • • • • Gathering a bit of data What is data? Why use data? When should I use data? How can I use data? Where can I find data and tools?

A Quick Poll What is the average class size for your department? A. B. C. D. Small – under 25 students Medium – 25 to 50 students Large – 51 to 100 students Massive – over 100 students

Which statement best describes your relationship to quantitative data? A. I try to keep my distance from data as much as possible (not very comfortable). B. We have a civil relationship, but you wouldn’t likely catch us hanging out at the coffee shop (somewhat comfortable). C. Data and I are the best of friends (very comfortable). D. I wake up in the morning excited about data and all the cool ways I can manipulate, I mean use, it that day (extremely comfortable).

Thinking about the students in your classes, would you say they… A. B. C. D. E. panic at the sight of a number across the street (not at all comfortable) can tolerate numbers about the same way they tolerate Brussels sprouts (not very comfortable) are willing to put their toes in the data pool and maybe even go into the water (somewhat comfortable) have caught the “data bug” and spend your class period dreaming about new questions to answer with data (very comfortable) will be the next Pearson or Tukey developing new statistical tests (extremely comfortable)

Taking a step back: What do we mean by “data”? • Definitions differ by context, all are valuable for sociology. 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.

Why use data throughout the curriculum? • Applies sociology 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 • Demonstrates how social scientists work

Quantitative Literacy • Skills learned and used within a context – 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

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!!

“…practices are clearly seen by employers as having potential for improving the quality of college learning. . . . The top practice they endorse is research. Employers believe that students who are challenged to ‘develop research questions in their fields’ and who can conduct “evidence-based analysis’ will be well positioned to succeed in the workplace.” (AAC&U 2013:10) Skills most highly valued include: critical thinking, communicate clearly, and complex problem solving..

When to Include Data ALL the time!!!!! Don’t save it for methods/stats classes…

No Need to “Revamp” Entire Course • Make course/learning objectives clear to students – One or more of these objectives can relate to quantitative data: • 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. • Cover the same substantive content, drop in data-based experiences as appropriate

Example: Begin Class with Data • Rather than jumping directly into lecture, provide a “daily fact.” – Present a statistic, graph, or chart from recent news media and ask students to interpret what it says and whether it is accurately portrayed in the media. – All can be accomplished in about 5 minutes and serves to get students’ focus shifted from whatever happened just before class. – Students will often begin bringing in items of their own.

• Does the chart/graph/map accurately describe the data? • From where do the data come? • What point does the author make? • Is it valid? Source: www.nbcolympics.com/medals

Example: Emphasizing Content

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 Any others?? Any questions so far??

Using Data without Using Data • How does religion relate to health behaviors? There’s a quiz for that!

How can I operationalize “life satisfaction”? How satisfied are people overall? (Depends whom you ask!)

Finding the Data • • • • • Visualizations Interesting statistics Public opinion Quantitative news blogs Pre-made exercises, pedagogical examples • Collections of resources

Visualization Examples • • • • Social Explorer CensusScope Visualizing Economics Storytellingwithdata

Visualizing Data Using Animations • Gapminder • Survival Curve • $1 Trillion Video

Relevant Statistics • Worldometers (www.worldometers.info/) • USA Right Now (www.usarightnow.com) • Population Pyramids of the World (populationpyramid.net/) • US Census (factfinder2.census.gov)

Public Opinion Data • Roper Center for Public Opinion Research www.ropercenter.uconn.edu • Gallup: www.gallup.com • NORC reports & data: www.norc.org/Research/DataFindings • Pew Research Center: www.pewresearch.org – Fact Tank, Reports, Datasets, Interactives

Quantitative News Blogs • TeachingWithData.org – Data in the News • U.S. Census Newsroom • Data360 • The Economist: Graphic Detail Blog • Pew Research Center: Fact Tank • USA Today Snapshots • FiveThirtyEight (Nate Silver) • FloatingSheep.org From Data360

Collections of Resources • ASA TRAILS • Association of Religion Data Archives Learning Center • ICPSR: Resources for Instructors – Data-driven Learning Guides (Short Exercises) • Science Education Resource Center (Carleton College – pedagogical materials) • Social Science Data Analysis Network • TeachingWithData.org

Data can be FUN! Detecting funky data displays can be even more fun!

Sites for “Brushing Up” on Statistics • Consortium for Advancement of Undergraduate Statistical Education (CAUSE) • Khan Academy Probability and Statistics • Statistics Learning Centre • UCLA Institute for Digital Research and Education: Data Analysis Examples • UK Data Services Support/How to Guides • Understanding Statistics through Dance found on the British Psychological Society’s YouTube Channel

Some helpful citations… • Ganter, S. L. 2006. Issues, Politics, and Activities in the Movement for Quantitative Literacy. Pp. 11-15 in Current Practices in Quantitative Literacy, R. Gillman (ed). Washington, DC: Math Assoc of America. • Grawe, Nathan D. and Rutz, Carol A. (2009). Integration with Writing Programs: A Strategy for Quantitative Reasoning Program Development. Numeracy: Vol. 2: Iss. 2, Article 2. DOI: http://dx.doi.org/10.5038/1936-4660.2.2.2 • Schield, Milo. (2010) Assessing Statistical Literacy: Take CARE. Ch 11 in Assessment Methods in Statistical Education, pp. 133-152. Wiley. • Steen, Lynn Arthur. 2004. Everything I Needed to Know about Averages I Learned in College. Peer Review 6(4): 4-8. • Wiest, Lynda R., Heidi J. Higgins, and Janet Hart Frost. 2007. Quantitative Literacy for Social Justice. Equity & Excellence in Education 40(1): 47-55.

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

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