Transcript Data Analysis in the Class: Pedagogical and Curricular

Quantitative Literacy through
Local Data
•Susanne
Morgan, Department of Sociology
•Priscilla Quirk, Coordinator of Health Promotion and
Substance Abuse Prevention
•Stephen Sweet, Department of Sociology
Ithaca College
ANAC Institute 2007
Combining Good Things


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

Quantitative Literacy is Good
Integrating Data Analysis is Good
Using Local Data is Good
Analyzing Health Issues is Good
Cross-Divisional Collaboration is Good
The Two Mathematics
• “Real” Mathematics
– Geometry, Algebra,
Trigonometry,
Calculus, etc.
• Math as a purpose
unto itself.
– Principles to be
studied, dismantled,
and synthesized.
– Absolute precision is
expected
• Quantitative Literacy
– The blending of
mathematical tools with
linguistic constructs
• Application of
mathematical reasoning
to consider the workings
of the natural and social
worlds
– Math-Lite?
– Pragmatic acceptance of
imprecision
Sources: Madison, Bernard. 2004. “Two Mathematics: Ever the Twain Shall Meet? Peer Review 6:9-12.
– Reliance on “black
Sweet, Stephen and Kerry Strand. 2006. Cultivating Quantitative Literacy: The Role of Sociology. Teaching
boxes”
Sociology 34: 1-4.
The Two Mathematics
• “Real” Mathematics
– Geometry, Algebra,
Trigonometry. Calculus, etc.
• Math as a purpose unto itself.
– Principles to be studied,
dismantled, & synthesized.
– Absolute precision is expected
• Quantitative Literacy
– The blending of mathematical
tools with linguistic constructs
• Application of mathematical
reasoning to consider the
workings of the natural and
social worlds
– Math-Lite?
– Pragmatic acceptance of
imprecision
– Reliance on black boxes
• Vocational & extra vocational
applications
Sources: Madison, Bernard. 2004. “Two Mathematics: Ever the Twain Shall Meet? Peer Review 6:9-12.
Sweet, Stephen and Kerry Strand. 2006. Cultivating Quantitative Literacy: The Role of Sociology. Teaching
Sociology 34: 1-4.
Progressive Literacy

Entering (freshmen)
–

Midstream (sophomores and juniors)
–

Work with numbers, introduce software, introduce question
formation, introduce hypothesis testing
Introduce advanced methods, increase expectations on
quality of analysis, advance public presentation skills
Exiting (seniors)
–
Expected ownership of question formation, expansion of
autonomy, movement from modules to projects
Income
Distributions of
Incomes in 2000,
inserted manually
into a table by
students, pulling
information from
the CensusScope
Site
USA
Tompkins
County
NY
(Ithaca)
<$9999
9.5
12.4
$10K-$14999
6.3
$15K-$24999
Your
Home
Town’s
County
Name
Tunica
Bennett
County
County
Mississippi South
Dakota
22.8
19.9
8.0
11.9
10.8
12.8
14.6
17.7
18.7
$25K-$34999
12.8
12.1
13.7
18.5
$35K-$49999
16.5
15.6
11.8
14.1
$50K-$74999
19.5
18.5
13.8
11.1
$75K-$99999
10.2
8.0
4.7
4.9
$100K-$149999
7.7
7.0
2.3
1.8
$150K-$199999
2.2
1.7
0.2
0.0
$200,000+
2.4
2.1
1.23
0.2
28.6
35.0
52.4
49.4
Calculate the
percent of
households
with incomes
below $25,000.
?
Bennett County
Tunica County
Health Data in the Classroom Project


A collaborative initiative of the Health
Promotion Research Committee and the
Center for Faculty Excellence
Summer faculty stipends to develop modules
using campus health data
Courses Using Health Data

Health Science
–
–
–
–
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Sociology
–
–
–
–
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Computer Information Technology
Math for Decision Making with Technology*
Speech Communication
–
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Econometrics
Math
–
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Introduction to Contemporary Mental Health
Introduction to Sociology
Women & Health
Seminar: Who are We & What Do We Think?
Economics
–

Computer Applications in Exercise Science
Biostatistics*
Tests & Measurements
Research Methods
Business & Professional Communication*
Psychology
–
–
Introduction to Psychology
Research Team
*Fall 2007
Health Surveys

Core Institute Alcohol and Drug Survey
–
Southern Illinois University

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Alcohol & drug use/abuse & consequences
National College Health Assessment
–
American College Health Association

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
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General & Preventive Health
Academic Impacts
Violence
Alcohol, Tobacco & Drug Use
Sexual Behavior
Nutrition & exercise
Depression/Mental health
Multiple Levels of Engagement

Faculty presents data for discussion
(Intro to Sociology)

Students use data for presentation or project
(Research Methods – Health Science)

Full semester work resulting in student professional
presentation
(Psychology Research Team; Econometrics Final Paper)
Why Are Ithaca College Student GPAs Related to
Substance Use?
Percent Using Substances More Than 5 Days Past 30 Days
70%
60%
50%
40%
Marijuana
Alcohol
30%
20%
10%
0%
A
B
C
Grades
Source: National College Health Assessment 2003-2005. Data Limited to Ithaca College Students
Which Causal Diagram is More
Likely to be True?
Drug Use
Academic Problems
Or
Academic Problems
Drug Use
Could you design a study to determine which
sequence is correct?
Project #1
Point-Counterpoint
Using data to support your point of view
Format:
Students will be assigned to one of 4 groups. The groups are as follows:
Group 1: Alcohol, tobacco, and drug use is a problem at Ithaca College.
Group 2: Alcohol, tobacco, and drug use is NOT a problem at Ithaca College.
Group 3: Weight, Nutrition and Exercise is a problem at Ithaca College.
Group 4: Weight, Nutrition and Exercise is NOT a problem at Ithaca College.
Groups will be comprised of 6-7 members.
Each member will serve in at least one, but not more than
2 of the following “specialized” capacities.
-Runner of the Analyses(1-2 students)
-Speaker of the House (1-2 students)
-Creator of all things Graphic (1-2 students)
-Creator of all things Tabular (1-2 students)
-Organizer of the Group (1 student)
Correlates of Aggressive Behavior in College Populations
Ashleigh Crumb, Kristen Sabat, Kathryn Cooper, Timothy Blair, Kristen Cuomo,
Brandon McLean, and Jessica Coppol, Ithaca College
Results
Introduction
• Increased use of cocaine has been correlated with
aggression; “…epidemiological and social science
investigations have validated the increased probability of
aggression with recent exposure to acute or chronic
administration of cocaine” (Cunningham, 2004).
• Kim (2004) found that diagnoses of depression in women
increases the likelihood of being involved in aggressive
behaviors.
• Men are more likely to be the aggressor, whereas women are
more likely to be the targets of aggressive acts; “…men had
significantly higher levels of expressed violence across
numerous relationship types (including overall non-partner
violence severity)…” (Chermack, 2001).
• It has been found that marijuana increases aggression.
“Greater frequency of use of marijuana was found
unexpectedly to be associated with great likelihood to commit
weapons offenses” (Friedman, 2001).
• Aggression would decrease in participants who exercised
more due to catharsis. “Nonexercisers had increased mean
aggression and hostility scores than drop-out or advanced
joggers” (Nouri, 1989).
Discussion
• Females reported significantly higher rates of being the
target of aggression (M = 3.22) than did males (M = 3.11),
t(622) = 2.93, p < .004.
Using correlations, researchers found the following
variables to be predictors of self reports of aggression:
• Strong positive relationships were found between reports
of acute alcohol consumption and aggression in females
(r = .49**) and males (r = .59**).
Gender Differences in the Target
of Aggression
Figure 2
• There was a positive relationship between chronic alcohol
consumption and aggression in females (r = .57**) and in
males (r = .62**).
• Moderate relationships between marijuana use and
aggression were found for males (r = .30**) and females
(r = .27**).
• A positive relationship between cocaine consumption and
aggression was found in males (r = .22**), but not in
females (r = .09, n.s.).
• No relationship between depression and aggression in
males (r = -.29, n.s.) or females (r = .03, n.s.).
• A weak relationship between chronic alcohol consumption
and being the target of aggression was found in females (r =
.12*), but not males (r = -.06, n.s.).
• No relationship between depression and being the target
of aggression in females (r = -.03, n.s.) or males (r = -.29,
n.s.)
3.2
3.18
3.16
3.14
3.12
3.1
3.08
3.06
In terms of predicting the target of aggression, researchers
found:
• No relationship between acute alcohol consumption and
being the target of aggression in females (r = .08, n.s.) or
males
(r = -.07, n.s).
Male
Female
3.22
Mean Target of Aggression Score
The purpose of this study was to identify correlates and
patterns of non-sexual aggressive behavior and victimization
in college students. Previous research hypothesized that
alcohol consumption, gender, cocaine use, marijuana use,
exercise, and depression would be related to aggressive
behavior.
• It has been found that alcohol consumption is positively
related to involvement in aggressive acts. “Correlational
research has found alcohol to be present in about 50% of
violent crimes” (Giancola, 2004).
Male
Female
Gender
• The amount of vigorous exercise in the last seven days
was associated with the likelihood of reporting having
injured someone unintentionally while drunk in the
previous year, t(240) = -2.22, p < .027. Interestingly, the
effect was in the opposite direction of the hypothesized
result. Males who reported having injured someone
exercised more days
(M = 4.60) than males who
didn’t report injuring someone
(M = 3.55).
References
Gender differences were found in reports of aggression.
Exercise and Self-Reported Aggression in Males
Figure 3
No
Yes
5
4.8
Gender Differences in Self-Reported Aggression
Figure 1
4.6
Male
Female
5.5
5
4.4
Exercise Score
Data from the National College Health Assessment
survey (NCHA) was analyzed. A sample of 633 students
(285 male, 348 female) from a small college in upstate New
York was considered in the analyses. Acute alcohol
consumption was operationalized using item 13 from
NCHA data asking, “The last time you partied/socialized,
how many drinks did you have?” Cocaine, marijuana, and
chronic alcohol use were operationalized by reports of how
many days in the last 30 the substance was used.
Aggression was also operationalized using combined
scores of 3 different items relating to self reports of
aggression. The target of aggression was operationalized
using the combined score of 3 separate items related to
being assaulted (non-sexually) and involvement in
emotionally or physically abusive relationships. Exercise
was operationalized by the number of days in the past
week that one reported engaging in vigorous exercise.
• There was a significant difference between males and
females in self-reported levels of aggression. Males
reported significantly higher rates of aggression (M=5.06)
than did females (M=4.88), t(622) = -2.433, p < .015.
Mean Aggression Score
Method
4.2
4
3.8
3.6
4.5
3.4
3.2
The present data is mostly consistent with the existing
literature. As hypothesized, aggression was associated with a
number of variables including alcohol consumption, gender,
cocaine use, marijuana use, and exercise. Men reported
higher levels of aggression than women, and the associations
with the aforementioned variables were stronger for men. The
lower correlations with women may be partly a function of the
low incidence of self-reported aggression as a consequence
of restricted range.
Although chronic and acute alcohol consumption were
both predictors of aggression for both men and women,
chronic consumption had stronger associations than acute
consumption with reports of aggressive acts. Being the target
of aggression was more rarely predicted, and only for women,
by chronic alcohol consumption (see Figure 2).
Although previous research has suggested that high
levels of vigorous exercise is predictive of lower levels of
aggression, the present research indicated the opposite effect
for males (see Figure 3). Perhaps different operationalizations
in different studies were measuring different levels or
constructs of aggression and exercise. Extreme exercise may
reflect aggressive tendencies rather than “releasing them.”
The hypothesis that men were more likely to be the
aggressor and that the women were more likely to be the
target of aggression was supported (see Figure 1).
Consistent with previous literature, increased marijuana
use was associated with greater levels of self-reported
aggression.
The relatively weak associations between cocaine and
aggression may have been due to a reluctance to admit
cocaine use.
Beer, J., & Nouri, S.; (1989). Relations of moderate physical exercise to
scores on hostility, aggression, and trait-anxiety. Perceptual and
Motor Scores, 68(3, Pt 2), 1191-1194.
Chermack, S. T., Walton, M. A., Fuller, B. E., & Blow, F. C. (2001).
Correlates of expressed and received violence across relationship
types among men and women substance abusers. Psychology of
Addictive Behaviors, 15, 140-151.
Cunningham, K. A. (2004). Aggression upon adolescent cocaine
exposure linked to serotonin anomalies: theoretical comment on Ricci
et al. Behavioral Neuroscience, 118, 1143-1144.
Friedman, A.S., Glassman, K., & Terras, A. (2001). Violent behavior as
related to use of marijuana and other drugs. Journal of
Addictive Diseases, 20(1), 49-72.
Giancola, P. R. (2004). Executive Functioning and Alcohol-Related
Aggression. Journal of Abnormal Psychology, 113, 541-555.
Kim, H. K., & Capaldi, D. M. (2004). The association of antisocial
behavior and depressive symptoms between partners and risk for
aggression in romantic relationships. Journal of Family Psychology,
18, 82-96.
4
3
No
3.5
Yes
Injured Someone Unintentionally
in the Last Year?
3
Legend
2.5
Male
Female
Gender
* = p < .05; ** = p < .01.
Research Team 12 is an Ithaca College Psychology Department
Research Team. Members of the team are Ashleigh Crumb, Kristen
Sabat, Kathryn Cooper, Timothy Blair, Kristen Cuomo, Brandon McLean,
Jessica Coppol, and Dr. Hugh Stephenson.
Table 7 Comparative logistic regression results
In(binge/1binge)= β0 + β1X1i + β2X2i +…+ βkXki + εi
Variable
Coefficient
Coefficient
Coefficient
Coefficient
Constant
term
-2.478816**
-3.322515**
-2.458542**
-2.679203**
MALE
0.698083**
0.700001**
0.718836**
0.706650**
CIG
0.907406**
0.926558**
0.903585**
0.899662**
POT
1.611103**
1.603674**
1.595446**
1.599584**
INTRA
0.607585**
0.608450**
0.606731**
0.602097**
CLASS
0.309515**
0.305944**
0.312627**
0.294173**
WHITE
0.663876**
0.687868**
0.660575**
0.664063**
PERCEP
0.123537
PCAMPUS
-0.228518
CONCERN
McFadden
R-Squared
0.298683
0.226815
0.228396
0.227424
0.228503
n = 695** significant at the 1 percent critical level * significant at the 5 percent critical level
Health Data in the Classroom Goals

Disseminate health data collected at Ithaca College

Enhance activity-based education in the classroom

Increase student competence in basic data analysis

Provide accurate health data to the college
community, and

Further the goals of the health promotion program.
Additional National Surveys

NSSE: Nat'l Survey of Student Engagement
–

George Kuh, Indiana University
CIRP Freshman Survey
–
–
Astin: Higher Education Research Institute
Cooperative Institutional Research Program
Additional Quant. Literacy Resources


Discipline-based projects, like IDA
(Integrating Data Analysis)
National sources of modules, like SSDAN
(Social Science Data Analysis Network)
Integrating Data Analysis Project



Jointly sponsored by American Sociological
Association and National Science Foundation
To address the “scientific literacy” gap for
undergraduate students in sociology through
departmental initiatives aimed at creating enduring
curricular change
Produced modules in non-research courses
–
Brief, active, data analysis activities related to course
content
SSDAN Resources
• Social Science
Data Analysis
Network
(SSDAN) at the
University of
Michigan
• Most popular
resources
– Census Scope
– General Social
Surveys
– WebCHIP
Challenges





Preparing modules takes time
Institutional permission can be tricky
Faculty reluctant to teach “math”
Faculty reluctant to teach “health”
Faculty don’t know the content 
misinformation is conveyed
Rewards





Students love using “real” data
Faculty get energized about new exercises
Grants provide positive incentives
Institutional goal of quantitative literacy is
supported
Institutional goal of health promotion is
enhanced
For More Information



Priscilla Quirk, [email protected]
Steve Sweet, [email protected]
Susanne Morgan, [email protected]