Gender and Educational and
Occupational Choices
Jacquelynne S. Eccles
University of Michigan
Paper presented at the Gender Role Conference
San Francisco April 2004
and Chinese University of Hong Kong, February, 2004
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
Why Do Women and Men Make
Such Different Choices for Their
Lives?



In most cultures, women and men are
concentrated in quite different occupations and
roles.
Why?
My goal today is to provide one perspective on
this quite complex question – a perspective
grounded in Expectancy –Value Models of
Achievement-related Choices
Overview

I began my research work in this area focused
on one specific question:

WHY ARE FEMALES LESS LIKELY TO GO
INTO MATH AND PHYSICAL SCIENCE
THAN MALES?
Overview 2

I became increasingly aware, however, that this question
is a subset of two much more general questions:

WHY DOES ANYONE DO ANYTHING?

WHAT PSYCHOLOGICAL, BIOLOGICAL, AND
SOCIAL FORCES INFLUENCE THE CRITICAL
CHOICES PEOPLE MAKE ABOUT HOW TO
SPEND THEIR TIME AND THEIR LIVES?
Goals



Provide an overview of gender differences in
occupational plans and choices
Discuss alternative explanations for these
differences – focusing on my Expectancy –
Value Model of Achievement-Related Choices
Summarize our research findings relevant to this
question and this model
Student responses to The Job Picture Story and Typical Day When I’m
Thirty Essay
NO. OF STUDENTS
K-12 CAREERS
320
280
240
200
160
120
80
40
0
Females
Males
FEMALE
A B C D E F G H I J K L M N O
CAREER
A
5%
95%
B
14%
86%
C
13%
87%
D
E
27%
32%
73%
68%
F
G
H
I
J
K
L
M
N
O
55%
48%
55%
61%
85%
98%
85%
87%
97%
42%
45%
52%
45%
39%
15%
2%
15%
13%
3%
58%
N= 1987
A TRUCKDRIVER, CARPENTER, MECHANIC
F DOCTOR, LAWYER, ARCHITECT, ACCT’NT
K NURSE
B PROFESSIONAL ATHLETE
G ARTIST, ROCK STAR,SINGER, MUSICIAN
L MODEL, DESIGNER, MOVIE STAR, DANCER
C POLICE, FIREFIGHTER, MILITARY, PILOT
H REPORTER, WRITER, TV ANNOUNCER
M SECRETAR, FLIGHT, ATT. SALES CLERK
D SCIENTIST. ENGINEER, COMPUTER SCI.
I VETERINARIAN, FOREST RANGER, FARMER
N UNPAID WORKER (HOMEMAKER, PARENT)
E EXECUTIVE, BUSINESSPERSON, BANKER
J TEACHER
0 THER
MALE
N=1962
TOTAL N= 3949
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
Bachelor’s degrees in 2000
Percents
Total M/S/E
Physical
Engineering
Math/CS
Earth
Biological
Social
Psychology
Women
28.0
0.8
1.7
2.2
0.2
6.5
8.6
8.0
Men
36.9
1.6
8.8
6.2
0.5
6.8
9.7
3.3
Source:
NSF 02-327
Differences on Academic Indicators




Females Earn Better School Marks than Males in All
Subjects Areas at All Grade Levels
Males Score Better than Females on Timed
Standardized Tests Scores on Many Subject Areas
Females are Now More Likely than Males to Pursue
Many Forms of Advanced Education
Males are More Likely than Females to be Placed in
Remedial Educational Programs, to be Expelled from
School, and to Drop Out of School Prematurely
Common Explanations

Biological Differences

Brain differences –

Hemispheric Specialization


Specialized Sensitivities for Learning and Interests



May be linked to verbal and spatial skills
Such as preferences for speech input and faces versus mechnical objects
Do not know the actual mechanisms but genetic studies suggest these
may be heritable and may be sex-liked
Disabilities



Learning particular types of materials
Social intelligence
Anxieties
Anxiety and Performance
Performance
Females
Level
Males
Anxiety
Common Explanations

Hormonal

Prenatal


Linked to developing organizational structure of brain
and other hormonal systems
Postnatal
Right after birth hormonal peaks
 Puberty
 Adulthood
 Activational systems

Psychological Differences




Ability Self Concepts for Different Skill Areas
Domain Specific Interests and Preferences
More General Differences in Values and Goals
Anxieties
Social Experiences

Family and Peers
Role Models
 Expectations
 Provision of Differential Experiences


Schools and Larger Society
Differential Treatment
 Differential Teaching Practices for Different Subject
Areas


Very Difficult to Distinguish These Hypotheses

All are Likely Influences

In Addition, People Self-Socialize into the
Culturally Approved Social Roles and Niches
One Way to Frame the Question

Do these differences exist even amongst a group
of individuals who have sufficient intelligence to
choose even the most demanding intellectual
careers? For example amongst people who are
highly gifted in both the verbal and
mathematical areas?
Highest Graduate Degrees Obtained by 1945
Degree
N
Men
Percent
Women
N
Percent
Master's (arts or
58
11%
76
18%
science)
Ph.D. (or comparable
60
11%
13
3%
doctorate)
Law
79
14%
3
<1%
M.D.
47
8%
5
1%
M.B.A.
19
3%
1
<1%
Graduate engineering
14
2%
0
degree
Graduate certificate
1
<1%
13
3%
in librarianship
Graduate diploma in
0
8
2%
social work
Other
5
1%
3
<1%
-----------------------------------------------------------------------------------------------------------Note: Percentages based on total number of college graduates.
Data derived from Terman and Oden, 1947]
Current Gifted Research

Similar differences emerge

Females now more likely to go on to college but are
still underrepresented in the physical sciences and
engineering
Another View

Look at the proportion of women at each step
along the pipeline
Figure 1
40
40
30
Percent Who are Females
Percent Who are Females
Proportion of Females in Each Group
20
10
0
30
20
10
0
Math-SAT Freshman BS Degrees
over 550 planning to
major
Engineering
PhD
Degrees
Math-SAT over 550 Freshm an planning
to m ajor
BS Degrees
Physical Sciences
PhD Degrees
Final View

Put the question into a larger perspective –

Why does anyone do anything?
Subjective Task Value
1.
Interest Value – Enjoyment one gets from
doing the activity itself

2.
Similar to Intrinsic Value
Utility Value – Relation of the activity to one’s
short and long range goals

Similar in some ways to Extrinsic Value
Subjective Task Value Continued
3. Attainment Value: Extent to which engaging in the activity confirms
an important component on one’s self-schema or increases the
likelihood of obtaining a desired future self or avoiding an
undesired future self.
a.
Individuals seek to confirm their possession of
characteristics central to their self-schema.
b.
Various tasks provide differential opportunities for such
confirmation.
c.
Individuals will place more value on those tasks that provide
the opportunities for this confirmation.
d.
Individuals will be more likely to choice those activities that
have high attainment value.
Subjective Task Value Continued
4.
Cost –
Psychological Costs
Fear of Success, Fear of Failure,
Anxiety
Financial Costs
Lost Opportunities to Fulfill Other Goals
or to do Other Activities
Key Features of Model
1.
Focuses on Choice not on Deficits
2.
Points Out Importance of Studying the
Origins of Individuals’ Perception of the
Range of Possible Options
3.
Focuses on the Fact that Choices are made
from a Wide Range of Positive Options

How Does This Relate To Gender?
Personal Identities
Personal
Experiences
Self Concepts
Personal Values
Success
Expectations
Personal Goals
Subcultural Scripts,
Beliefs, and
Stereotypes
Life
Choices
Social Identities
Salience
Content
Societal Beliefs,
Images, and
Stereotypes
Perception of Barriers
And Benefits
Due to One’s Group
Membership
Subjective
Task
Value
Gender-Roles and Ability Self
Concepts and Personal Expectations

Cultural Stereotypes about Which Gender is
Supposed to be Good at Which Skills

Extensive Socialization Pressures to Make Sure
These Stereotypes are Fulfilled
Gender-Roles and Subjective Task
Value
1.
2.
Different Hierarchies of Core Personal Values
a.
Concern with Social Goals versus Concern with Power or
Achievement Goals;
b.
Concern with Social Relationships versus concern with Individual
Achievement and Status.
c.
Interest in Things versus Interest in People.
d.
Interest in Cooperation versus Interest in Competition
Density of Hierarchy
a.
Single-mindedness versus Diverse Interests
Gender-Roles and Subjective Task
Value Continued
3.
Different Long Range Goals
4.
Different Definitions of Success in Various Goals and Roles.
a.
What does it take to be a successful father versus a successful
mother?
b. What does it take to be a successful professional?
c.
What does it take to be a successful human being?
Gender Differences in Values Among
Gifted Children and Youth
1.
Activity Interests
a.
Females less interested than males in physics, chemistry
b. Females more interested in English, foreign languages,
music, drama, medical-related majors, and biological
sciences
c.
Females more interested in reading, writing and
domestic activities and arts and crafts
d. Females less interested in sports, working with
machines, tools, and electronic equipment
Gender Differences in Values Among Gifted
Children and Youth
Continued
2.
Personal Values
a.
Females score higher on social and aesthetic values
b. Females score lower on theoretical, economic and
political values
3.
Density of Values
a.
Females tend to rate a broader range of activities and
future roles as important than do males.
b. Males are more likely to rate a few activities very high
and the remaining activities very low.
Michigan Study of Adolescent Life Transitions
(MSALT)
U of M Affiliated Investigators:
Waves 1-4
Jacque Eccles
Carol Midgley
Allan Wigfield
Jan Jacobs
Connie Flanagan
Harriet Feldlaufer
David Reuman
Doug MacIver
Dave Klingel
Doris Yee
Christy Miller Buchanan
Waves 5-8
Jacque Eccles
Bonnie Barber
Lisa Colarossi
Deborah Jozefowicz
Pam Frome
Sarah Lord
Mina Vida
Robert Roeser
Laurie Meschke
OVERVIEW OF DESIGN AND SAMPLE:
MICHIGAN STUDY OF ADOLESCENT LIFE
TRANSITIONS – MSALT
DESIGN:
On-going Longitudinal Study of One
Birth Cohort
Data Collected in Grades 6, 7, 10, 12;
and again at Ages 20 and 25
Data Collected from Adolescents,
Parents, and School – Most
Using Survey Forms
SAMPLE:
Nine School Districts
Approximately 1,200 Adolescents
Approximately 90% White
Approximately 51% Female
Working/Middle Class Background
Michigan Study of Adolescent/Adult Life Transitions:
MSALT
Time 1 Time 2
YEAR
Fall
1983
Spring Fall
1984
1984
GRADE
6th
6th
7th
WAVE
1
2
3
YOUTH SURVEY



PARENTS
SURVEY


TEACHER
QUESTIONNAIR
E


RECORD DATA


FACE TO FACE
INTERVIEW
SPRIN 1988
G
1985
7th
10th
1990
1992
1996
2000
12th
6
years
after
H.S.
8
9 years
after
H.S.


4
5
6
2
years
after
H.S.
7














Time 3
9

+
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
BELIEFS AND
GENDERED
STEREOTYPES ABOUT
MATH-RELATED
PROFESSIONS
PERCEPTIONS OF
SOCIALIZERS
ATTITUDES AND
EXPECTATIONS
GOALS AND GENERAL
SELF-SCHEMATA
1.
Personal Identity
2.
Gender Role Identity
3.
Career and Other Life
Goals/Values
4.
Minimum Standards for
Achievement
PERCEPTION OF
TASK VALUE
1.
Liking of math
2.
Perceived
usefulness of
math
3.
4.
GENDERED
STEREOTYPES
ABOUT MATH SKILLS
APPROPRIATENESS
SELF-CONCEPT
OF MATH ABILITY
Importance of
doing well in
math
1.
Worth of the
amount of effort
needed to do
well
Enroll in
advanced
courses
2.
Aspire to
mathrelated
careers
EXPECTANCIES
INTERPRETATION OF
PAST MATH EVENTS
PERCEPTIONS OF
THE DIFFICULTY OF
MATH
ACHIEVEMENT
BEHVAIOR
1.
Current
2.
Future
Two Basic Questions
ARE THERE GENDER DIFFERENCES ON
THESE SELF-RELATED BELIEFS?
DO THE GENDER DIFFERENCES IN
THESE SELF-RELATED BELIEFS
MEDIATE THE GENDER DIFFERENCES
IN INVOVLEMENT?
BUT FIRST, ARE THERE GENDER
DIFFERENCES IN LONG TERM
OCCUPATIONAL PLANS?
Gender Differences in Ability Self
Concepts – 7th Grade
6
5.5
5
Girls
Boys
4.5
4
3.5
3
Math
English
Sports
Gender Differences in Subjective
Task Value – 7th Grade
6.5
6
5.5
5
Girls
Boys
4.5
4
3.5
3
Math
English
Sports
How Young Do These Differences
Emerge

Childhood and Beyond Study
Similar Measures
 Similar Population in Southeastern Michigan
 4 Middle Class School Districts
 Primarily White
 3 Cohorts Beginning in 1st, 2nd, and 4th grades
 Followed Longitudinally until age 22

Gender Differences in Ability Self-Concepts:
1st, 2nd, & 4th Graders
Mean Ratings
7
Girls
Boys
6
5
4
General Throw
Sports
Tumble
Music
Ability Self-Concepts
Read
Math
WORRY ABOUT PERFOMANCE
ACROSS DOMAINS
Mean Rating for Worry
5.5
5.0
4.5
4.0
Girls
Boys
3.5
3.0
2.5
Math
Reading
Sports
Domain
Not be
liked
Hurt oth.
Feel.
Mean Rating of Importance
IMPORTANCE OF ABILITY IN
DIFFERENT DOMAINS
6.5
6.0
5.5
Girls
Boys
5.0
4.5
4.0
Math
Reading Sports
Domain
Music
Social
Enjoyment of Different Domains
Mean Rating for Liking
6.5
6
5.5
Girls
Boys
5
4.5
4
Math
Reading
Sports
Domain
Music
Conclusion

Gender Differences Occur across Several
Domains for Both Ability Self Concepts and
Subjective Task Values

Gender Differences Emerge Quite Young

Do These Differences Mediate Gender
Differences in Course Taking and Activity
Involvement?
Predicting Number of Honors Math Classes (sex, DAT)
N = 223 (honors students)
Gender
.15
Number of
Honors Math
Courses
(R² = .08)
.22
Math
Aptitude
Predicting Number of Honors Math Classes
N = 223 (honors students)
Self-Concept
of Ability in
Math
.15
Gender
(R² = .06)
.12
.14
.18
Number of
Honors Math
Courses
Interest in
Math
(R² = .19)
(R² = .02)
.13
.25
Math
Aptitude
.14
Utility of
Math
(R² = .04)
Predicting # of Physical Science Classes (sex,
DAT)
Gender
.16
Number of
Physical
Science
Courses
.34
Math
Aptitude
(R2 = .15)
Predicting # of Physics Classes
Gender
Self-Concept of
Ability in P.S.
(R2=.06)
.16
.09
.13
Number
of
Physical
Sciences
Courses
(R2=.34)
.09
Linking P.S.
(R2=.03)
.17
.09
.48
.20
Math
Aptitude
.19
Utility
Of P.S.
(R2=.05)
Predicting Team Sports
Self-Concept of
Ability in Sports
(R² = .09)
Team Sports
10th grade
(R² = .29)
.15
.31
.04
Gender
.24
.13
.29
Utility of Sports
(R² = .08)
.23
.27
.18
Liking Sports
(R² = .05)
Team Sports
12th grade
(R² = .21)
Ability Self-Concept
R² = 8%
.11 (.46)
.28
Sex
.21
Utility Value
.36 (.53)
Free Time Spent
R² = 32%
R² = 5%
.19
.17 (.46)
Importance Value
R² = 4%
Correlation: Sex – Time Spent = .14
Partial Correlation: Sex – Time Spent = .002
(controlling mediating variables)
Conclusion

In this sample, the gender differences in Utility Value
were the strongest mediators of gender differences in
math and physical science course enrollments.

A slightly different pattern is emerging for math in the
CAB study: Math Ability Self Concept is having a
stronger effect.

In this sample, the gender differences in all three
expectancy – value beliefs mediated the gender
differences in involvement in sports.
What about College Course Choices?
MSALT DESIGN
Wave
1,2
3,4
5
6
7
8
9
Grade
6
7
10
12
12+2
12+6
12+9
Age
12
13
16
18
20
24
27
Year
83-'84
84-'85
88
90
92
96
99
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
Analyses: Within Sex
Discriminant 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
Analyses 2: Between Sex

Logistic regression to test for mediators of sex
differences in college
Math/Engineering/Physical Science majors
Time 1 Measures:







th
12
Grade
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
Sex Differences in Domain Specific
Self Concepts and Values
Self Concept and Value at Age 18 by Sex
5.5
5
Mean Value
4.5
4
Female
3.5
Male
3
2.5
2
t
ep
c
n
e
M
/
ath
lu
Va
i
Sc
M
/
ath
i
Sc
lf
Se
Co
gy
o
ol
Bi
l
Se
pt
ce
n
o
fC
o
Bi
e
alu
V
gy
lo
gli
n
E
sh
lf
Se
nc
Co
t
ep
E
li
ng
sh
lue
Va
Fi
A
GP
l
na
Time 2 Measures: Age 20
Ability-Related

Math/Science General Ability Self Concept


Intellectual Ability Self Concept


Efficacy for jobs requiring math/science
Relative ability in logical and analytical thinking
High School Grade Point Average
Time 2 Measures: Occupational Values

Job Flexibility


Mental Challenge


Opportunity to be creative and learn new things
Working with People


Does not require being away from family
Working with others
Autonomy

Own Boss
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
Sex Differences in General Self
Concepts and Values
6
5.5
Mean Value
5
4.5
4
3.5
3
2.5
A
y
y
t
t
pt
GP
t ed
ilit
ge
ted
en
om
ple
e
l
n
ep
b
n
n
n
d
i
c
o
a
e
c
e
x
ie
e
n
n
to
ri
ll
en
Fin
Or
F le
ha
Co
Au
hP
Co
ep
eO
t
s
f
C
f
l
e
d
i
l
e
l
s
l
p
u
n
w
l
e
I
o
ta
Se
Se
alu
Va
sin
Pe
al
en
in g
lu e
*V
ce
u
u
k
a
M
t
r
n
B
V
o
e
ie
le c
alu
/Sc
eW
t el
V
h
u
n
*
t
l
I
Va
Ma
Female
Male
Time 3 Measures: Age 25

Final College Major

Occupation at Age 25: Coded into Global
Categories based on Census Classification
Criteria
Sex Differences in College Majors
120
100
Frequency
80
Female
60
Male
40
20
0
Math/Science
Biology
Business
Social Science
Sex Differences in Occupations
Occupation at Age 25 by Sex
160
140
Frequency
120
100
Female
80
Male
60
40
20
0
Math/Science
Biology
Business
Domain Specific
Attractors: + Self
Concepts and Values
+
Domain Specific
Detractors:
Anxieties
Non-Domain
Detractors: +
Values and
Self Concepts
Academic Choice
-
+
Non-Domain
Attractors:
General
Achievement
Predicting Women’s Math/Engineering/Physical Science
(M/E/PS) and Biological Science College Major from
Domain Specific SCs
and Values at 18
Predicting Science vs. Other College Major
Final GPA
Math/sci value
Math/sei self
concept
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
English value
Math/Sci Value
Biology self
concept
Value Biology
-0.4
-0.2
0
0.2
0.4
0.6
Discriminant Function Coefficient
0.8
1
Predicting Women’s M/E/PS and
Biological Science College Major from
General Self-Concepts and Values at 20
Predicting Math /Science vs. Other College Major
Working with
people
Final GPA
Intellectual Self
Concept
Pridicting Biology vs. Other College Major
Math/Sci Self
Concept
-0.4
-0.2
0
0.2
0.4
0.6
Discriminant Function Coefficient
0.8
1
Value working
with people
People Oriented
Math/sci Self
Concept
0
0.1
0.2
0.3
0.4
0.5
0.6
Discriminant Function Coefficient
0.7
0.8
Predicting Men’s M/E/PS and Biological
Science College Major from Domain
Specific SCs and Values at 18
Predicting Science vs. Other College Major
Final GPA
Math self concept
Math/sci 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
Final Gpa
Biology self
concept
Biology Value
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Discriminant Function Coefficient
0.8
0.9
Predicting Men’s M/E/PS and Biological
Science College Major from General SelfConcepts and Values at 20
Predicting Math/Science vs Other College Major
People oriented
Value Working with
People
Predicting Biology vs. Other College Major
Final GPA
Math/Sci
-0.4
Value flexibility
-0.2
0
0.2
0.4
Discriminant Function Coefficients
0.6
0.8
Math/Sci Self Concept
Value working with people
Value mental challenge
Final GPA
People Oriented
Business Oriented
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
Discriminant Function Coefficient
0.4
0.5
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
Time 1 Predictors of
Science College Major
l
Fi na
G PA
Ma th
Math
SC
Valu
e
er 2
Gend
er 1
Gend
0
0.1
0.2
0.3
0.4
Coefficient B
0.5
0.6
0.7
Time 2 Predictors of
Science College Major
Final GPA
M ath/SC
Gender
0
0.1
0.2
0.3
Coe fficie nt B
0.4
0.5
0.6
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
Predicting M/E/PS vs. Biology Major
From General Self-Concepts and Values at
20
Business Oriented
Final Gpa
Intellectual Self
Concept
People Oriented
Math/Sci self concept
Value working with
People
Intellectual Self Concept
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
Discriminant Function Coefficient for Females
0.6
0.8
Math/Science Self -Concept
Final GPA
Value Flexibility
Business Oriented
People Oriented
Value Work With People
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
Discriminant Function Coefficient for Males
0.3
Predicting M/E/PS vs. Social Science Major
From Self-Concepts and Values at 18
Math/Sci self
concept
Math/Sci Value
English Self
Concept
English Value
Final GPA
-0.6
-0.4
-0.2
0
0.2
0.4
Discriminant Function Coefficient for Females
0.6
0.8
Final GPA
English Value
English Self
Concept
Math/Sci Value
Math/Sci self
concept
-0.6
-0.4
-0.2
0
0.2
0.4
Discriminant Function Coefficient for Males
0.6
0.8
Predicting M/E/PS vs. Social Science Major
From General Self-Concepts and Values at 20
Final GPA
Intellectual Self
Concept
Math/Sci Value
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 Value
Intellectual SelfConcept
0
0.1
0.2
0.3
0.4
0.5
0.6
Discriminant Function Coefficient for Males
0.7
0.8
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
Analyses 3

Now lets shift to the second set of analyses:
those linking self concepts and values from ages
18 and 20 to occupational plans at age 20 and
actual occupations at age 25
Predicting M/E/PS vs Biology Occupations
at 25 from Self Concepts and Values at 18
Value Biology
Final GPA
Math/Sci self
concept
-0.4
-0.2
0
0.2
0.4
0.6
Discriminant Function Coefficient for
Females
0.8
Final GPA
Math/sci self
concept
Math/sci value
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Discriminant Function Coefficient for Males
0.8
Predicting M/E/PS vs Biology
Occupation at 25 from General Self
Concepts and Values at 20
Final GPA
Value Flexibility
Value Math/Sci
Value Working with People
People Oriented
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Value Autonomy
Discriminant Function Coefficient for
Females
Value Working with
People
-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1
Discriminant Function
Coefficient for Males
0
Predicting M/E/PS vs Business Occupations
at 25 From Self Concepts and Values at 18
Math/sci
Value
Math/Sci Self
Concept
Final GPA
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Discriminant Function Coefficient for Females
Biology Self Concept
Final GPA
Value Biology
Math/Sci Self Concept
Math/Sci Value
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Discriminant Function Coefficient for Males
0.8
Predicting M/E/PS vs Business
Occupation at 25 from General Self
Concepts and Values at 20
Value Flexibility
Value Mental Challenge
Value Working with People
Intellectual Self Concept
Math/Sci Value
-0.4
People Oriented
-0.2
0
0.2
0.4
0.6
0.8
1
Intellectual Self Concept
Discriminant Function Coefficient for Females
Value Working People
Value flexibility
Math/Sci Self Concept
Final GPA
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Discriminant Function Coefficient for Males
0.8
Conclusions

Expectancy Value Model provides a good
explanatory framework for understanding both
individual differences and sex differences in
educational and occupational choices
 What
about Gender Roles?
 Role
of Traditionality in Terms of Family
 Role of Gender Role Stereotypes of
Achievement Domain
The Impact of Girls’ Gender-Role Beliefs on their Educational and Occupational
Decisions.
Self-Concept of
Abilities
Gender-Role Beliefs
Achievement-Related Decisions
Content
•High school courses taken
•Gender stereotypes of ability and
value
•Belief that women’s domain is in the
home
Values
•Occupational aspirations
•College major
•Occupation at age 20
Expectations
for Adult
Responsibilities
Status and Expected Labor Force Participation
•Aspire to a family flexible job
•Status of educational and occupational
aspirations
Figure 7. Traditionality, Values, Expectations of Adult Responsibilities, and Aspirations – Theoretical Model.
Importance of
Children
GPA
Importance of
career
Traditionality
Status/Family Flexible Achievement
Choices
•Value of family flexible occupation
•Status of occupational aspiration
•Status of age 20 occupation
Degree of
Responsibility
for Income
SES
Degree of
Responsibility for
Childcare and
Household
•Status of age 20 salary
What About Gender Role
Stereotypes?
Figure 3.Gender Stereotypes of Math, Self-Concept, Values & Math/Physical Science Outcomes – Theoretical
Model.
10th Grade Math
Self-Concept of
Ability
Math Ability
Stereotype
7th Grade Math
Self-Concept of
Ability
10th Grade
Physical Science
Self-Concept of
Ability
Math and Physical
Science Achievement
Choices
•Number of high school
courses taken
•Occupational aspirations
•College major
Math Value
Stereotype
7th Grade Math
Value
10th Grade
Math Value
10th Grade
Physical
Science Value
Note: The paths between the stereotype variables and the outcomes are free.
•Current occupation
CONCLUSIONS





General psychological model works very well across
domains
Values are key and yet they are often neglected in
studies of gender differences while efficacy/ability selfconcepts and over emphasized
Gender-role ideology is central to acquisition of
gendered values
Gendered values help predict both sex differences and
individual differences within sex in activity choice
Anticipated costs may be critical in long term choices
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.
Characteristics of Effective Classrooms







Frequent Use of Cooperative Learning Opportunities
Frequent Use of Individualized Learning Opportunities
Infrequent Use of Competitive Motivational Strategies
Frequent Use of Hands-On Learning Opportunities
Frequent Use of Practical Problems as Assignments
Active Career and Educational Guidance Aimed at Broadening
Students’ View of Math and Physical Sciences
Frequent Use of Strategies Designed to Create Full Class
Participation
The End
Thank You
More details and copies can be found at
www.rcgd.isr.umich.edu/garp/
Descargar

Slide 1