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Quantitative Literacy through Local Data

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Quantitative Literacy through Local Data Susanne Morgan, Department of Sociology Priscilla Quirk, Coordinator of Health Promotion and Substance Abuse Prevention – PowerPoint PPT presentation

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Title: Quantitative Literacy through Local Data


1
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

2
Combining Good Things
  • Quantitative Literacy is Good
  • Integrating Data Analysis is Good
  • Using Local Data is Good
  • Analyzing Health Issues is Good
  • Cross-Divisional Collaboration is Good

3
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
  • Reliance on black boxes
  • Vocational and extra-vocational applications

Sources Madison, Bernard. 2004. Two
Mathematics Ever the Twain Shall Meet? Peer
Review 69-12. Sweet, Stephen and Kerry Strand.
2006. Cultivating Quantitative Literacy The Role
of Sociology. Teaching Sociology 34 1-4.
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 69-12. Sweet, Stephen and Kerry Strand.
2006. Cultivating Quantitative Literacy The Role
of Sociology. Teaching Sociology 34 1-4.
5
Progressive Literacy
  • Entering (freshmen)
  • Work with numbers, introduce software, introduce
    question formation, introduce hypothesis testing
  • Midstream (sophomores and juniors)
  • 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

6
(No Transcript)
7
Distributions of Incomes in 2000, inserted
manually into a table by students, pulling
information from the CensusScope Site
8
Bennett County
9
Tunica County
10
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

11
Courses Using Health Data
  • Health Science
  • Computer Applications in Exercise Science
  • Biostatistics
  • Tests Measurements
  • Research Methods
  • Sociology
  • Introduction to Contemporary Mental Health
  • Introduction to Sociology
  • Women Health
  • Seminar Who are We What Do We Think?
  • Economics
  • Econometrics
  • Math
  • Computer Information Technology
  • Math for Decision Making with Technology
  • Speech Communication
  • Business Professional Communication
  • Psychology
  • Introduction to Psychology

12
Health Surveys
  • Core Institute Alcohol and Drug Survey
  • Southern Illinois University
  • Alcohol drug use/abuse consequences
  • National College Health Assessment
  • American College Health Association
  • General Preventive Health
  • Academic Impacts
  • Violence
  • Alcohol, Tobacco Drug Use
  • Sexual Behavior
  • Nutrition exercise
  • Depression/Mental health

13
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)

14
Why Are Ithaca College Student GPAs Related to
Substance Use?
Percent Using Substances More Than 5 Days Past 30
Days
Grades
Source National College Health Assessment
2003-2005. Data Limited to Ithaca College
Students
15
Which Causal Diagram is More Likely to be True?
Drug Use
Academic Problems
Or
Drug Use
Academic Problems
Could you design a study to determine which
sequence is correct?
16
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.
17
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)

18
Correlates of Aggressive Behavior in College
Populations Ashleigh Crumb, Kristen Sabat,
Kathryn Cooper, Timothy Blair, Kristen Cuomo,
Brandon McLean, and Jessica Coppol, Ithaca
College
  • Introduction
  • 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.
  • Results
  • 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).
  • 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.).

Discussion 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.
  • 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 lt .004.
  • 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).
  • 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).
  • No relationship between depression and
    aggression in males (r -.29, n.s.) or females
    (r .03, n.s.).
  • 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).
  • 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.)
  • 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 lt .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 didnt report injuring someone (M
    3.55).
  • 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).

References 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. 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.
  • Gender differences were found in reports of
    aggression.
  • There was a significant difference between males
    and females in self-reported levels of
    aggression. Males reported significantly higher
    rates of aggression (M5.06) than did females
    (M4.88), t(622) -2.433, p lt .015.

Method 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.
Legend p lt .05 p lt .01.
19
Table 7 Comparative logistic regression results
In(binge/1binge) ß0 ß1X1i ß2X2i ßkXki
ei
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 0.298683
McFadden R-Squared 0.226815 0.228396 0.227424 0.228503
n 695 significant at the 1 percent critical
level significant at the 5 percent critical
level
20
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.

21
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

22
Additional Quant. Literacy Resources
  • Discipline-based projects, like IDA (Integrating
    Data Analysis)
  • National sources of modules, like SSDAN (Social
    Science Data Analysis Network)

23
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

24
SSDAN Resources
  • Social Science Data Analysis Network (SSDAN) at
    the University of Michigan
  • Most popular resources
  • Census Scope
  • General Social Surveys
  • WebCHIP

25
Challenges
  • Preparing modules takes time
  • Institutional permission can be tricky
  • Faculty reluctant to teach math
  • Faculty reluctant to teach health
  • Faculty dont know the content ? misinformation
    is conveyed

26
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

27
For More Information
  • Priscilla Quirk, pquirk_at_ithaca.edu
  • Steve Sweet, ssweet_at_ithaca.edu
  • Susanne Morgan, morgan_at_ithaca.edu
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