Module Example: Influence of Race and Gender 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 race and gender inequality in income Make national and state comparisons in terms of earnings using American Community Survey (12) 1module available online at: http://serc.carleton.edu/sp/ssdan/examples/31584.html

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

Day 1: How to Read Frequencies in a Handout [First, define and examine sample and variables] Reading Frequencies: Example 1: ACS sample of US full-time, year-round workers in 2012. 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 2012) Male Female 57.01 % 42.99 56,777,424 42,816,495

Day 1: Start by Learning How to Read Frequencies in a Handout Test for common mistakes: Sex Composition of Full-Time, Year-Round Workers, 2012 Which of the following is true? 57% of the workforce is male. 57% of men are in the workforce. Answer: A is correct. Male Female 57.01 % 42.99 56,777,424 42,816,495

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 <25K 25-34K 35-49K 50-69K 70-99K 100K+ 21.25% 16.72% 20.34% 17.74% 12.32% 11.62% 21,164,805 16,656,906 20,252,549 17,670,738 12,273,126 11,575,795

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:

Day 1: Reading a Bivariate Table Earnings by Sex, ACS 2012 Must determine how to read this table – where to focus? Teach students to focus on top and bottom portions for comparisons Earnings Male Female TOTAL <25K 18% 25.5% 21.3% 25-34K 14.7% 19.4% 16.7% 35-49K 19.2% 21.8% 20.3% 50-69K 18.6% 16.6% 17.7% 70-99K 14.1% 9.9% 12.3% 100K+ 15.4% 6.7% 11.6% Total 100%= 56,777,424 42,816,495 99,593,919

Day 1: Learn How to Read Bivariate Table Test for common mistakes: True or False? 18% of those who make less than $25,000 are men. False 14.7% of men make between $25,000 and $34,000. True 16.6% of women earn more than $70,000 22.1% of men and women earn more than $100,000 Earnings Male Female TOTAL <25K 18% 25.5% 21.3% 25-34K 14.7% 19.4% 16.7% 35-49K 19.2% 21.8% 20.3% 50-69K 18.6% 16.6% 17.7% 70-99K 14.1% 9.9% 12.3% 100K+ 15.4% 6.7% 11.6% Total 100%=

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 Earnings Male Female TOTAL <25K 18% 25.5% 21.3% 25-34K 14.7% 19.4% 16.7% 35-49K 19.2% 21.8% 20.3% 50-69K 18.6% 16.6% 17.7% 70-99K 14.1% 9.9% 12.3% 100K+ 15.4% 6.7% 11.6% Total 100%

Race differences in earnings? The ACS data define race by the following categories: Non-Latino White, Black, Asian, Latino, Native American, NL Other, NL Multi Which group(s) do you think earn the most, and which earn the least?

Homework that night: describe effect of race on income NL-white Black Asian Latino Am Indian NL- Other Multi Total <25K 16.5% 27.6% 19.3% 37.9% 33.4% 25.8% 23.3% 21.3% 25-34K 15.5% 20.9% 12.7% 20.8% 19.4% 16.8% 17.2% 16.7% 35-49K 21.8% 16.3% 18.4% 22% 20.3% 50-69K 15.7% 16.9% 12.1% 13.4% 15.3% 17.7% 70-99K 13.9% 9.2% 16.1% 6.4% 8.3% 11.3% 11.8% 12.3% 100K+ 13.8% 4.8% 18.7% 4.3% 4.2% 8.8% 10.3% 11.6% 100%   66,373,488 10,941,577 5,634,626 14,520,086 651,281 181,575 1,291,286 99,593,919

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

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)

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)

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

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

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

Comparison of Pre-test to Post-test (Fall 2016) Average class score on pre-test : 60% (range 6-15 of 22 points) Average class score on post-test: 91% (range 17-22 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

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

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.”

Overview of Module Have been using for several years, recently updated with 2012 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

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

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