Module Example: Influence of Race and Sex on Income 1

1. Module Example: Influence of Race and Sex on Income1 Used in Social Problems class, 100-level course • 20 students in class (all have laptops) • Takes 4-5 class days • Could be modified to be shorter or longer Substantive GOALS: • Learn about sex and race differences in income • Make national and state comparisons in terms of earnings using PUMS data (2012-2016) 1module available online at: http://serc.carleton.edu/sp/ssdan/examples/31584.html

2. Quantitative Skills Acquired: Students will: • Create and read frequency tables • Learn logic of independent and dependent variables • Create and interpret bivariate tables/cross-tabs • Learn to make data-based comparisons across states • Read and write a “story” about income inequality using data as evidence

3. Day 1: How to Read Frequencies in a Handout [First, define and examine sample and variables] Reading Frequencies: Example 1: PUMS sample of US full-time, year-round workers in 2016. What is the sex composition of the fulltime workforce? 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 2016)

4. Day 1: Start by Learning How to Read Frequencies in a Handout Test for common mistakes: Sex Composition of Full-Time, Year-Round Workers, 2016 Which of the following is true? • 57% of the workforce is male. • 57% of men are in the workforce. Answer: A is correct.

5. 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 $25,000? Table 2: Earnings for Full-Time Year-Round Workers, US, 2012-16

6. 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:

7. Day 1: Reading a Bivariate Table Earnings by Sex, PUMS 2012-16 • Must determine how to read this table – where to focus? • Teach students to focus on top and bottom portions for comparisons

8. Day 1: Learn How to Read Bivariate Table • Test for common mistakes: True or False? • 21.1% of those who make less than $25,000 are men. • False • 13.8% of men make between $25,000 and $34,000. • True • 17.8% of women earn $70,000 or more. • True • 22.9% of men and women earn more than $100,000 • False

9. Day 1: Learn How to Read Bivariate Table Earnings by Sex, ACS 2008 • Give Rules for reading table (included in module materials) • Start with 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, ….” • Most important take-home message: Emphasize “telling a story” with numbers

10. Race differences in earnings? • The PUMS data define race by the following categories: Non-Hispanic White, Black, Asian, Hispanic, Native American, NH Other, NH Multi • Given these categories, guess the racial composition of the US full-time, year-round work force:

11. Racial composition of fulltime year round workforce

12. Race differences in earnings? • Which racial/ethnic group(s) do you think earn the most, and which earn the least?

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

14. 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 • Write a story about income and sex differences in income

15. Handout with Module • http://ssdan.net/webchip/webchip4/ • Examine individual state: KY • racial and sex composition of workforce (frequencies) • Differences in earnings by sex and race (cross-tabs)

16. Handout with Module • http://ssdan.net/webchip/webchip4/ • Choose state and examine: • racial and sex composition of workforce (frequencies) • Differences in earnings by sex and race (cross-tabs)

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 (Spring 2018) Average class score on pre-test : 65% (range 11-18 of 22 points) Average class score on post-test: 90% (range 15-22 of 22 points) 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. Feedback from this spring • Asked students to provide feedback on how useful assignments were • “Data analysis was very helpful, glad you made us do it.” • “Data analysis: I loved this paper. I learned so much and it made me switch from Chem to Soc as a major.” • “Data analysis paper allowed me to effectively read and report data.” • no negative comments about data analysis [and trust me, they provided plenty of negative comments about other assignments!]

24. Overview of Module • Have been using for several years, recently updated with 2016 American Community Survey/PUMS 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

25. Main Tips • More time is always better than less • Give ungraded feedback if possible • Give lots of chances to practice (write, read aloud, present, share with peers, etc.) • Expect that you and they will make mistakes – best way to learn • If you require paper, try to break up the pieces (lit review, data sections, etc.) • Cheerlead a lot

26. Final overview: what this module includes: • Two hand-outs : • Handout 1: explanations of variables and descriptions for how to read frequencies and bivariate tables (used first day) • Handout 2: step by step instructions for students to go online and examine data for individual states; includes assignment for final paper (used second day) • Ancillary materials: • T/F test to catch common mistakes • List of “rules” for reading and writing about tables (good to create own rules as well) • Peer review guidelines • Rubric for evaluating paper • Pre- and post-test for assessing student learning