1. 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:

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: 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

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, 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

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
<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

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, 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

8. 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%=

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
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%

10. 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?

11. 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

12. 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

13. 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)

14. 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)

15. 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

16. 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

17. 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

18. 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 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

19. 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

20. 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.”

21. 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

22. 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

23. 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