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

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

Integrating Data Analysis Across our Curriculum

At beginning, our department:

• Outlined Quantitative Skills for all majors

• Mapped skills onto Courses

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 conclusion1DataCounts!:

http://ssdan.net/datacounts/index.html

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

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

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:1. Have both percentages and numbers 2. To make comparisons, we will usually focus on the percentages3. Percentages should add up to 100%4. Must understand base (all full-time year-round workers in 2008)

Male Female

58.7 % 41.3 %56,997,160 40,086,536

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.

Male Female

58.7 % 41.3 %56,997,160 40,086,536

Day 1: Reading Frequencies

Example 2: examine earnings of full-time workersStart 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

<15K 15-24K 25-34K 35-49K 50-69K 70-99K 100K+

7.1 % 16.8 % 18.4 % 21.1 % 16.7 % 10.6 % 9.3 %

6,926,657 16,267,926 17,908,508 20,488,612 16,201,327 10,298,154 8,992,485

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 TableEarnings 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

Earnings Male Female TOTAL

< 15K 5.7% 9.2% 7.1% 15-24K 14.0% 20.6% 16.8% 25-34K 16.3% 21.5% 18.4% 35-49K 20.7% 21.7% 21.1% 50-69K 18.2% 14.5% 16.7% 70-99K 12.6% 7.7% 10.6% 100K+ 12.5% 4.7% 9.3%

TOTAL 100% =56,997,160

100% =40,086,536

Day 1: Learn How to Read Bivariate TableEarnings 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, ….”

Earnings Male Female TOTAL < 15K 5.7% 9.2% 7.1% 15-24K 14.0% 20.6% 16.8% 25-34K 16.3% 21.5% 18.4% 35-49K 20.7% 21.7% 21.1% 50-69K 18.2% 14.5% 16.7% 70-99K 12.6% 7.7% 10.6% 100K+ 12.5% 4.7% 9.3%

TOTAL 100% =56,997,160

100% =40,086,536

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

Earnings Male Female TOTAL < 15K 5.7% 9.2% 7.1% 15-24K 14.0% 20.6% 16.8% 25-34K 16.3% 21.5% 18.4% 35-49K 20.7% 21.7% 21.1% 50-69K 18.2% 14.5% 16.7% 70-99K 12.6% 7.7% 10.6% 100K+ 12.5% 4.7% 9.3%

TOTAL 100% =56,997,160

100% =40,086,536

Day 1: Learn How to Read Bivariate Table

Earnings by Sex, ACS 2008

• Most important take-home message:

– Emphasize “telling a story” with numbers

Earnings Male Female TOTAL

< 15K 5.7% 9.2% 7.1% 15-24K 14.0% 20.6% 16.8% 25-34K 16.3% 21.5% 18.4% 35-49K 20.7% 21.7% 21.1% 50-69K 18.2% 14.5% 16.7% 70-99K 12.6% 7.7% 10.6% 100K+ 12.5% 4.7% 9.3%

TOTAL 100% =56,997,160

100% =40,086,536

Homework that night: describe effect of race on income

Earnings NH-White Black Asian Hispanic Am

Indian NHOther

NHMulti TOTAL

<15K 5.5% 9.5% 6.2% 13.5% 11.6% 10.1% 7.5% 7.1% 15-24K 13.5% 21.8% 14.8% 29.4% 24.2% 20.9% 17.3% 16.8% 25-34K 17.5% 22.6% 15.2% 21.0% 21.5% 21.0% 20.0% 18.4% 35-49K 21.8% 22.1% 18.4% 17.7% 20.1% 18.8% 21.9% 21.1% 50-69K 18.5% 13.9% 16.9% 10.3% 12.6% 13.4% 16.4% 16.7% 70-99K 12.1% 6.8% 14.7% 5.0% 6.3% 9.2% 9.7% 10.6% 100K+ 11.2% 3.3% 13.8% 3.1% 3.6% 6.6% 7.1% 9.3%

TOTAL 100% =66,678,276

100% =10,610,592

100% =4,694,340

100% =13,309,425

100% =611,753

100% =216,348

100% =962,917

100% =97,083,651

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

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 assessmenta) pre/post-testb) paper, graded by rubric

B) Tried to assess both skills and confidence levels

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

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 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