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![Page 1: Predicting Mathematics-Related Educational and Career Choices Mina Vida and Jacquelynne Eccles University of Michigan Presentation at SRCD, Tampa, FL April.](https://reader030.fdocuments.us/reader030/viewer/2022032703/56649d265503460f949fce78/html5/thumbnails/1.jpg)
Predicting Mathematics-Related Educational and Career Choices
Mina Vida and Jacquelynne EcclesUniversity of Michigan
Presentation at SRCD, Tampa, FLApril 2003
Acknowledgements: This research was funded by grants from NIMH, NSF, and NICHD to Eccles and by grants from NSF, Spencer Foundation and W.T. Grant to Eccles and Barber. The authors want to thank Bonnie Barber, Margaret Stone, Laurie Meschke, Lisa Colarossi, Deborah Jozefowicz, and Andrew Fuligni for their role in study design, data collection, and data processing.
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Participation in M/S/E careers
In 1997, women represented* 23% of all scientists and engineers* 63% of psychologists* 42% of biologists* 10% of physicists/astronomers* 9% of engineers
Source: National Science Foundation, 2000
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Bachelor’s degrees in 2000
Percents Women MenTotal M/S/E 28.0 36.9Physical 0.8 1.6Engineering 1.7 8.8Math/CS 2.2 6.2Earth 0.2 0.5Biological 6.5 6.8Social 8.6 9.7Psychology 8.0 3.3
Source:NSF 02-327
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Figure 1. General Expectancy Value Model of Achievement Choices
A. Cultural Milieu
1. Gender role stereotypes2. Cultural stereotypes of subject matter and occupational characteristics3. Family Demographics
E. Child's Perception of…
1. Socializer's beliefs, expectations, attitudes, and behaviors2. Gender roles3. Activity stereotypes and task demands
G. Child's Goals and General Self-Schemata
1. Personal and social identities2. Possible and future selves3. Self-concept of one's general/other abilities4. Short-term goals5. Long-term goals
I. Activity Specific Ability Self Concept and Expectations for Success
B. Socializer's Beliefs and Behaviors
C. Stable Child Characteristics 1. Aptitudes of child and sibs 2. Child gender3. Birth order
D. Previous Achievement- Related Experiences
F. Child's Interpretations of Experience
H. Child's Affective Reactions and Memories
J. Subjective Task Value
1. Interest -enjoyment value2. Attainment value3. Utility value4. Relative cost
K. Achievement-Related Choices, Engagement and Persistence
Across Time
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Basic Expectancy Value Model
Occupational/Educational
Choice
Occupational/Educational
Choice
Domain-RelatedAbility Self Concepts/
Expectations for Success
Domain-Related Perceived Task Values
Domain-Related Perceived Task Values
Non-Domain-RelatedPerceived Task Values
Non-Domain-RelatedPerceived Task Values
Non-Domain-RelatedAbility Self Concepts/
Expectations for Success
Non-Domain-RelatedAbility Self Concepts/
Expectations for Success
+
_
+
_
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Michigan Study of Adolescent/Adult Life Transitions: MSALT
Time 1 Time 2 Time 3
YEAR Fall 1983
Spring 1984
Fall 1984
SPRING 1985
1988 1990 1992 1996 2000
GRADE 6th 6th 7th 7th 10th 12th 2 years after H.S.
6 years after H.S.
9 years after H.S.
WAVE
1 2 3 4 5 6 7 8 9
YOUTH SURVEY
PARENTS SURVEY
TEACHER QUESTIONNAIRE
RECORD DATA
FACE TO FACE INTERVIEW
+
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MSALT Sample General Characteristics
School based sample drawn from 10 school districts in the small city communities surrounding Detroit.
Predominantly White, working and middle class families
Approximately 50% of sample of youth went on to some form of tertiary education
Downsizing of automobile industry caused major economic problems while the youth were in secondary school
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Specific Sample Characteristics for Analyses Reported Today
Those who participated at Wave 8 (age 25) Female N = 791 Male N = 575
Those who completed a college degree by Wave 8 Female N = 515 Male N = 377
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Predicting # of Honors Math Classes
.15
.14
.14
.12
.13
.25
.18
GenderGender
Self-ConceptOf AbilityIn Math(R2 = .06)
Self-ConceptOf AbilityIn Math(R2 = .06)
InterestIn Math(R2 = .02)
InterestIn Math(R2 = .02)
Number ofHonorsMath
Courses(R2 = .19)
Number ofHonorsMath
Courses(R2 = .19)
MathAptitude
MathAptitude
Utility of Math(R2 = .04)
Utility of Math(R2 = .04)
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Predicting # of Physical Science Classes (sex, DAT)
Number ofPhysicalScienceCourses
(R2 = .15)
Number ofPhysicalScienceCourses
(R2 = .15)
GenderGender
MathAptitude
MathAptitude
.34
.16
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Predicting # of Physics Classes
Gender
Math Aptitude
Utility Of P.S. (R2=.05)
Linking P.S.
(R2=.03)
Self-Concept of Ability in P.S.
(R2=.06)
Number of
Physical Sciences Courses (R2=.34)
.20
.19
.48
.09
.09
.17
.13
.16
.09
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New Analyses: Within SexDiscriminant Function Analyses
Use 12th grade Domain Specific Ability SCs and Values to predict College Major at age 25
Use age 20 General Ability SCs and Occupational Values to predict College Major at age 25
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New Analyses 2:Between Sex
Logistic regression to test for mediators of sex differences in college Math/Engineering/Physical Science majors
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New Within-Sex Discriminant Function Analyses: Part 3
Use 12th grade Domain Specific Ability SCs and Values to predict Occupations at age 25
Use age 20 General Ability SCs and Occupational Values to predict Occupations at age 25
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Time 1 Measures
Math/Physical Science Self-Concept of Ability
Math/PS Value and Usefulness Biology Self-Concept of Ability Biology Value and Usefulness English Self-Concept of Ability English Value and Usefulness High School Grade Point Average
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Sex Differences in Domain Specific Self Concepts and Values
2
2.5
3
3.5
4
4.5
5
5.5M
ean
Val
ue
Self Concept and Value at Age 18 by Sex
Female
Male
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Time 2 Measures: Ability-Related
Math/Science General Ability Self Concept Efficacy for jobs requiring
math/science Intellectual Ability Self Concept
Relative ability in logical and analytical thinking
High School Grade Point Average
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Time 2 Measures: Occupational Values
Job Flexibility Does not require being away from family
Mental Challenge Opportunity to be creative and learn
new things Working with People
Working with others Autonomy
Own Boss
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Time 2 Measures: Comfort with Job Characteristics
Business Orientation: Comfort with tasks associated with being a supervisor
People Orientation: Comfort working with people and children
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Sex Differences in General Self Concepts and Values
2.5
3
3.5
4
4.5
5
5.5
6
Me
an
Va
lue
Female
Male
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Time 3 Measures
Final College Major
Occupation at Age 25: Coded into Global Categories based on Census Classification Criteria
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Sex Differences in College Majors
0
20
40
60
80
100
120F
req
uen
cy
Math/Science Biology Business Social Science
Female
Male
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Sex Proportions in College Majors
10
20
30
40
50
60
70
80
90
Per
cen
tag
e
College Major by Sex
Female
Male
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Sex Differences in Occupations
0
20
40
60
80
100
120
140
160
Fre
qu
ency
Math/Science Biology Business
Occupation at Age 25 by Sex
Female
Male
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Sex Proportions in Occupations at 25
0
10
20
30
40
50
60
70
80
90
Pe
rce
nta
ge
Math/Science Biology Business
Participant s' Occupation at Age 25 by Sex
Female
Male
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Predicting Women’s Math/Engineering/Physical Science (M/E/PS) and Biological Science College Major from Domain Specific SCs and Values at 18
-0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Discriminant Function Coefficient
Value Biology
Biology selfconcept
Math/Sci Value
English value
Predicting Biology vs. Other College Major 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Discriminant Function Coefficient
Math/sei selfconcept
Math/sci value
Final GPA
Predicting Science vs. Other College Major
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Predicting Women’s M/E/PS and Biological Science College Major from General Self-Concepts and Values at 20
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Discriminant Function Coefficient
Math/sci SelfConcept
People Oriented
Value workingwith people
Pridicting Biology vs. Other College Major
-0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Discriminant Function Coefficient
Math/Sci SelfConcept
Intellectual SelfConcept
Final GPA
Working withpeople
Predicting Math /Science vs. Other College Major
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Predicting Men’s M/E/PS and Biological Science College Major from Domain Specific SCs and Values at 18
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Discriminant Function Coefficient
Biology Value
Biology selfconcept
Final Gpa
Predicting Biology vs. Other College Major 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Discriminant Function Coefficient
Math/sci value
Math self concept
Final GPA
Predicting Science vs. Other College Major
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Predicting Men’s M/E/PS and Biological Science College Major from General Self-Concepts and Values at 20
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5
Discriminant Function Coefficient
Business Oriented
People Oriented
Final GPA
Value mental challenge
Value working with people
Math/Sci Self Concept
Value flexibility
Predicting Biology vs. Other College Major
-0.4 -0.2 0 0.2 0.4 0.6 0.8
Discriminant Function Coefficients
Math/Sci
Final GPA
Value Working withPeople
People oriented
Predicting Math/Science vs Other College Major
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Mediation of Sex Differences
Used logistic regression to assess the extent to which the Time 1 and Time 2 predictors explained the sex difference in majoring in Math/Engineering/Physical Science
Step 1: Sex only Step 2: Sex plus all of Time 1 or
Time predictors
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Time 1 Predictors of Science College Major
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Coefficient B
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Time 2 Predictors of Science College Major
0 0.1 0.2 0.3 0.4 0.5 0.6
Coefficient B
Gender
Math/SC
Final GPA
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Conclusions 1:
Strong support for the predictive power of constructs linked to the Expectancy Value Model. Domain Specific SCs and Values push both
women and men towards the related majors Some evidence that more general values
can also push people away from M/S/PS majors and towards Biology-Related majors
Sex differences in selection of M/E/PS college major are accounted for by Expectancy Value Model
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Next Step
Do Within Sex Discriminant Function Analysis comparing Choice of Math/Science Major with Specific Alternative Major
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Predicting M/E/PS vs. Biology Major From Domain Specific SCs and Values at 18
-0.6 -0.4 -0.2 0 0.2 0.4 0.6
Discriminant Function Coefficient for Males
Math/Sci SelfConcept
Math/Sci Value
Value Biology
Biology SelfConcept
-0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Discriminant Function Coefficient for Females
Math/Sci SelfConcept
Math/Sci Value
Value Biology
Biology SelfConcept
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Predicting M/E/PS vs. Biology Major From General Self-Concepts and Values at 20
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
Discriminant Function Coefficient for Females
Value working withPeople
Math/Sci self concept
People Oriented
Intellectual SelfConcept
Final Gpa
Business Oriented
-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
Discriminant Function Coefficient for Males
Value Work With People
People Oriented
Business Oriented
Value Flexibility
Final GPA
Math/Science Self -Concept
Intellectual Self Concept
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Predicting M/E/PS vs. Social Science Major From Self-Concepts and Values at 18
-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
Discriminant Function Coefficient for Males
Math/Sci selfconcept
Math/Sci Value
English SelfConcept
English Value
Final GPA-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
Math/Sci selfconcept
Math/Sci Value
English SelfConcept
English Value
Final GPA
Discriminant Function Coefficient for Females
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Predicting M/E/PS vs. Social Science Major From
General Self-Concepts and Values at 20
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Discriminant Function Coefficient for Females
Math/Sci Value
Intellectual SelfConcept
Final GPA
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Discriminant Function Coefficient for Males
Intellectual Self-Concept
Math/Sci Value
Final Gpa
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Conclusions 2
Even stronger support for both the push and pull aspects of the Eccles et al. Expectancy Value Model
Strong evidence that valuing having a job that allows one to work with and for people pushes individuals away from M/E/PS majors and pulls them toward the Biological Sciences
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New Analyses 3
Now lets shift to the second set of analyses: those linking self concepts and values from ages 18 and 20 to actual occupations at age 25
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Predicting M/E/PS vs Biology Occupations at 25 from Self Concepts and Values at 18
-0.4 -0.2 0 0.2 0.4 0.6 0.8
Discriminant Function Coefficient for Females
Math/Sci selfconcept
Final GPA
Value Biology
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Discriminant Function Coefficient for Males
Math/sci value
Math/sci selfconcept
Final GPA
![Page 42: Predicting Mathematics-Related Educational and Career Choices Mina Vida and Jacquelynne Eccles University of Michigan Presentation at SRCD, Tampa, FL April.](https://reader030.fdocuments.us/reader030/viewer/2022032703/56649d265503460f949fce78/html5/thumbnails/42.jpg)
Predicting M/E/PS vs Biology Occupation at 25 from General Self Concepts and Values at 20
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6
Discriminant Function Coefficient for Females
People Oriented
Value Working with People
Value Math/Sci
Value Flexibility
Final GPA
-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0
Discriminant Function Coefficient for Males
Value Working withPeople
Value Autonomy
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Predicting M/E/PS vs Business Occupations at 25 From Self Concepts and Values at 18
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Discriminant Function Coefficient for Females
Final GPA
Math/Sci SelfConcept
Math/sciValue
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Discriminant Function Coefficient for Males
Math/Sci Value
Math/Sci Self Concept
Value Biology
Final GPA
Biology Self Concept
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Predicting M/E/PS vs Business Occupation at 25 from General Self Concepts and Values at 20
-0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Discriminant Function Coefficient for Females
Math/Sci Value
Intellectual Self Concept
Value Working with People
Value Mental Challenge
Value Flexibility
-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
Discriminant Function Coefficient for Males
Final GPA
Math/Sci Self Concept
Value flexibility
Value Working People
Intellectual Self Concept
People Oriented
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Conclusions 3
Expectancy Value Model provides a good explanatory framework for understanding both individual differences and sex differences in educational and occupational choices
![Page 46: Predicting Mathematics-Related Educational and Career Choices Mina Vida and Jacquelynne Eccles University of Michigan Presentation at SRCD, Tampa, FL April.](https://reader030.fdocuments.us/reader030/viewer/2022032703/56649d265503460f949fce78/html5/thumbnails/46.jpg)
Applications
Interventions to increase the participation of females in M/E/PS need to focus on increasing women’s understanding that M/E/PS and Informational Technology jobs can help people and do involve working with people as well as increasing their confidence in their ability to succeed in these fields.
![Page 47: Predicting Mathematics-Related Educational and Career Choices Mina Vida and Jacquelynne Eccles University of Michigan Presentation at SRCD, Tampa, FL April.](https://reader030.fdocuments.us/reader030/viewer/2022032703/56649d265503460f949fce78/html5/thumbnails/47.jpg)
Thank You
More details and copies can be found at
www.rcgd.isr.umich.edu/garp/
The End