Integrating Data Analysis at Berea College

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

5. 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 1DataCounts!: http://ssdan.net/datacounts/index.html

6. 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) 1module available online at: http://serc.carleton.edu/sp/ssdan/examples/31584.html

7. 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

8. Day 1: How to Read Frequencies in a Handout Reading Frequencies: Example 1: ACS sample of full-time, year-round workers in 2008. Points to make to students about a frequency table: • Have both percentages and numbers • To make comparisons, we will usually focus on the percentages • Percentages should add up to 100% • Must understand base (all full-time year-round workers in 2008)

9. 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 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.

10. 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? What percent earn less than $15,000? Table 2: Earnings for Full-Time Year-Round Workers, US, 2008

11. 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:

12. Day 1: Reading a Bivariate Table Earnings by Sex, ACS 2008 • Must determine how to read this table – where to focus? • Teach students to focus on top and bottom portions for comparisons

13. Day 1: Learn How to Read Bivariate Table Earnings by Sex, ACS 2008 • 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, ….”

14. Day 1: Learn How to Read Bivariate Table Earnings by Sex, ACS 2008 • 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

15. Day 1: Learn How to Read Bivariate Table Earnings by Sex, ACS 2008 • Most important take-home message: • Emphasize “telling a story” with numbers

16. Homework that night: describe effect of race on income

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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.”

24. 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