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Integrating Data Analysis at Berea College
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Integrating Data Analysis at Berea College

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  • Notes:
    For all of our elective courses (students must take 5), added data analysis exercises
    main skill, reading percentages, understanding independent and dvs, reading and creating tables
  • Transcript

    • 1. Integrating Data Analysis at Berea College • • Small, liberal arts college, 3-person department Part of NSF Integrating Data Analysis project • ADVANTAGES for adding data analysis: – Small class sizes – 10-25 – students have own laptops • DISADVANTAGES: – no TAs – heavy teaching loads • Unusual School – only low-income students – all full-scholarship, all work – often come with fairly poor prep and math skills
    • 2. Quantitative Skills being taught before and after IDA • Until 2002, very little data analysis in courses: – – – – – 1st year: GSS exercise in Intro Senior year: GSS in Methods Senior year: Collect own data in Capstone Very little in between Soc Majors – often math-phobes, failed pre-meds • Saw adding QL as way to enhance research skills and build and maintain skills across the curriculum
    • 3. Integrating Data Analysis Across our Curriculum At beginning, our department: • Outlined Quantitative Skills for all majors • Mapped skills onto Courses
    • 4. Teaching Research and Data Analysis Skills by using Modules from DataCounts1  Ready-made modules online  Students use these online data sets (so not finding own data) But, if set up properly, can include all components of research project: • pose question • review lit • propose hypotheses • analyze data – test IVs on DV • interpret tables and relationships between variables • make conclusion 1 DataCounts!: http://ssdan.net/datacounts/index.html
    • 5. Example: Influence of Race and Gender on Income1 Used in Social Problems class, 100-level course • 20 students in class • Takes four 50-minute class days • Could be modified to be shorter or longer Substantive GOALS: • Learn about race and gender inequality in income • Make national and state comparisons in terms of earnings using American Community Survey (08) module available online at: http://serc.carleton.edu/sp/ssdan/examples/31584.html 1
    • 6. Quantitative Skills Acquired: Students will: • Create and read frequency tables • Learn logic of independent and dependent variables • Create and interpret bivariate tables • Learn to make data-based comparisons across states • Read and write a “story” about income inequality using data as evidence
    • 7. Day 1: How to Read Frequencies in a Handout Reading Frequencies: Example 1: ACS sample of full-time, year-round workers in 2008. Male Female 58.7 % 56,997,160 41.3 % 40,086,536 Points to make to students about a frequency table: 1. Have both percentages and numbers 2. To make comparisons, we will usually focus on the percentages 3. Percentages should add up to 100% 4. Must understand base (all full-time year-round workers in 2008)
    • 8. Day 1: Start by Learning How to Read Frequencies in a Handout Test for common mistakes: Sex Composition of Full-Time, Year-Round Workers, 2008 Male Female 58.7 % 56,997,160 41.3 % 40,086,536 Which of the following is true? A. 58.7% of the workforce is male. B. 58.7% of men are in the workforce. Answer: A is correct.
    • 9. Day 1: Reading Frequencies Example 2: examine earnings of full-time workers Start by asking students to guess: What percent of full-time workers earn over $100,000? <15K 15-24K 25-34K 35-49K $15,000? What percent earn less than 50-69K 70-99K 100K+ 7.1Table 2: Earnings18.4 % 21.1 % 16.7 % 10.6 2008 9.3 % % 16.8 % for Full-Time Year-Round Workers, US, % 6,926,657 16,267,926 17,908,508 20,488,612 16,201,327 10,298,154 8,992,485
    • 10. After frequencies, examine bivariate tables • Now ask students to guess: Who makes more, men or women? • How might we determine that? • Show a bivariate table of sex and income, and ask them to interpret:
    • 11. Day 1: Reading a Bivariate Table Earnings by Sex, ACS 2008 Earnings Female TOTAL < 15K 15-24K 25-34K 35-49K 50-69K 70-99K 100K+ 5.7% 14.0% 16.3% 20.7% 18.2% 12.6% 12.5% 9.2% 20.6% 21.5% 21.7% 14.5% 7.7% 4.7% 7.1% 16.8% 18.4% 21.1% 16.7% 10.6% 9.3% TOTAL • • Male 100% = 56,997,160 100% = 40,086,536 Must determine how to read this table – where to focus? Teach students to focus on top and bottom portions for comparisons
    • 12. Day 1: Learn How to Read Bivariate Table Earnings by Sex, ACS 2008 Earnings < 15K 15-24K 25-34K 35-49K 50-69K 70-99K 100K+ TOTAL • • Male 5.7% 14.0% 16.3% 20.7% 18.2% 12.6% 12.5% 100% = 56,997,160 Female 9.2% 20.6% 21.5% 21.7% 14.5% 7.7% 4.7% 100% = 40,086,536 TOTAL 7.1% 16.8% 18.4% 21.1% 16.7% 10.6% 9.3% Give Rules for reading table (included in module materials) – Start with a general statement; use percentages as evidence; end with summary Teach students useful phrases: – e.g. “A disproportionately high percentage of women fall into the low-income categories. For example, ….”
    • 13. Day 1: Learn How to Read Bivariate Table Earnings < 15K 15-24K 25-34K 35-49K 50-69K 70-99K 100K+ TOTAL • Male Female 5.7% 9.2% 14.0% Earnings by 20.6% Sex, ACS 2008 16.3% 21.5% 20.7% 21.7% 18.2% 14.5% 12.6% 7.7% 12.5% 4.7% 100% = 100% = 56,997,160 40,086,536 Test for common mistakes: True or False?  14% of those who make between $15,000 and $24,000 are men. • False  14% of men make between $15,000 and $24,000. • True  25.1% of men earn more than $70,000 • True  17.2% of men and women earn more than $100,000 • False TOTAL 7.1% 16.8% 18.4% 21.1% 16.7% 10.6% 9.3%
    • 14. Day 1: Learn How to Read Bivariate Table Earnings Male Female < 15K 15-24K 25-34K 35-49K 50-69K 70-99K 100K+ TOTAL • 5.7% Earnings9.2% Sex, by 14.0% 20.6% 16.3% 21.5% 20.7% 21.7% 18.2% 14.5% 12.6% 7.7% 12.5% 4.7% 100% = 56,997,160 TOTAL 7.1% ACS 2008 16.8% 18.4% 21.1% 16.7% 10.6% 9.3% 100% = 40,086,536 Most important take-home message: – Emphasize “telling a story” with numbers
    • 15. Homework that night: describe effect of race on income <15K 15-24K 25-34K 35-49K 50-69K 70-99K 100K+ NHWhite 5.5% 13.5% 17.5% 21.8% 18.5% 12.1% 11.2% TOTAL 100% = 100% = 100% = 66,678,276 10,610,592 4,694,340 Earnings 13.5% 29.4% 21.0% 17.7% 10.3% 5.0% 3.1% Am NH Indian Other 11.6% 10.1% 24.2% 20.9% 21.5% 21.0% 20.1% 18.8% 12.6% 13.4% 6.3% 9.2% 3.6% 6.6% NH Multi 7.5% 17.3% 20.0% 21.9% 16.4% 9.7% 7.1% 100% = 13,309,425 100% = 100% = 611,753 216,348 100% = 962,917 Black Asian Hispanic 9.5% 21.8% 22.6% 22.1% 13.9% 6.8% 3.3% 6.2% 14.8% 15.2% 18.4% 16.9% 14.7% 13.8% TOTAL 7.1% 16.8% 18.4% 21.1% 16.7% 10.6% 9.3% 100% =97,083,651
    • 16. Day 2: Students Run Module in class (or could do as homework) • Module will walk students through an exercise, step by step, for a state of their own choosing to examine  sex  earnings  race  earnings • Learn independent and dependent variables • Make hypotheses about relationship between variables • Learn how to run frequencies and set up simple bivariate tables • Learn how to create properly labeled tables from the data generated
    • 17. Day 3: Learn How to Present Data • Students work in pairs on state of own choosing • 5-minute presentation of findings to class: – Give hypothesis (and let others guess) – Show table of results – Describe findings with proper language
    • 18. Day 4: Peer Review of Paper • Students come to class with completed draft of data analysis paper • In pairs, review and edit one another’s papers, following guided prompts • Main goal: students learn to write “story” using data as evidence
    • 19. Assessment A) Used 2 forms of assessment a) pre/post-test b) paper, graded by rubric B) Tried to assess both skills and confidence levels
    • 20. Comparison of Pre-test to Post-test (past four years) Overall score on pre-test : 55 - 60% Overall score on post-test: 80 - 94% Assessment of Pre and Post-test: • Great improvement in basic skills at reading and interpreting exactly this kind of table • Improved confidence in working with data and numbers
    • 21. Assessment of Paper: • • • • • Demands higher-order skills: difficult paper Skills vary quite a bit Peer review helpful Allow re-writes for students with most trouble Students report that paper is difficult, but worth it
    • 22. Comments on Student Evals • “I worked a lot in this class, and was always taken to the brink of overwhelmed but not crossing over. I think this is a sign of an excellent class. The data analysis we did was a particular challenge. I came away from the exercise knowing I learned something completely out of my comfort zone.” • “Keep on trying with the Data Analysis.... we (students) need it... no matter how badly we do not like it at first.”
    • 23. Overview of Module • Have been using for several years, recently updated with 2008 American Community Survey data • Cheerleading helps – keep telling them they’re learning useful skills • Fun to teach– hands-on activity; improves own engagement in teaching these content areas • Students generally enjoy (positive evals) • Pre/post test shows students learn skills • Exams and papers show modules reinforces content [truly see race and gender inequality] • See evidence of skills in later courses

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