1. Integrating Data Analysis at Berea College
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•
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
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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:
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–
–
–
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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
1
DataCounts!:
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)
module available online at: http://serc.carleton.edu/sp/ssdan/examples/31584.html
1

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.
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)

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
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.

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?
<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

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
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
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•
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

13. 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, ….”

14. 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%

15. 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

16. 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

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:
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•
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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