Careers in Science, Engineering, Technology (SET) and Health: His and Her story Ingrid Schoon, Andy Ross, and Peter Martin City University, London 17 March.

Careers in Science, Engineering, Technology (SET) and Health:

His and Her storyIngrid Schoon, Andy Ross, and Peter Martin

City University, London

17 March 2006

ESRC Social Science Week

University of Cambridge

ESRC Gender Equality Network

GeNet

Science related careers• Increasing need for well qualified and

highly numerate individuals with a background in science related subjects

• Between 1991 and 2000: number of students with scientific and technical degrees has risen – yet fall in number of students taking science related qualifications at A-level (Sir Gareth Robert’s Review, 2002)

• Under-representation of women in science related courses and occupations (Greenfield Report, 2002)

Factors influencing career choice• Family background

– Parental education– Parental interest and expectations regarding

education– Parent’s occupation and employment– Role models and differential experiences

• Personal assets– Gender– Academic attainment and aptitude– Belief in own ability– School motivation– Domain specific interests and preferences – Differences in values

• School experiences– School type– Differential treatment by teachers – Number of science related options

Factors influencing career progression

• Socio-economic family background• Gender • School experiences/qualifications• Motivations/aspirations• Occupational stereotypes• Career opportunities• Family formation

But …

• All factors are likely influences and are difficult to distinguish

• Different factors are important at different stages in life

• Most studies are based on relatively homogenous samples, with a limited socio-economic status component

• Most studies based on cross-sectional data only

Life course perspective• Linking teenage aspirations to adult

outcomes• Focus on science-related career

development, which:– is influenced by multiple factors– takes place over time– is embedded in socio-historical context

→ How do factors combine in predicting career choice and career development?

The Data: Two British Birth Cohorts

1958 1965 1970 1975 1980 1985 1990 1995 2000

Birth

Age 5

Age 10

Age 16

Age 26

Age 30

Age7

Age 11

Age 16

Age 23

Age 33

Age 42

British Cohort Study (BCS70)

National Child Development Study (NCDS)

Birth

Socio-economic changes since 1960’s

• Increasing gender equality• Increasing participation in further

education• Increasing participation of women

in the labour market• New technologies • Changes in labour market structure• Major recession during the 1980’s

Contextual-developmental model of career development

Family Background

Personal assets

School experiences

Career choice

Adult Occupation

al Status

Birth Childhood Adolescence Adulthood

SET aspirations at age 16 and SET occupations in early 30’s

0

5

10

15

20

SET aspiration SET occupation

SET aspiration 17.2 13.5 7.4 5.4

SET occupation 11.1 9.4 3.1 2.7

BCS70 men NCDS menBCS70 women

NCDS women

Predicting SET careers by SET aspirations

• Odds Ratios:– NCDS men: 5.17– BCS70 men: 6.36– NCDS women: 17.59– BCS70 women:19.70

Predictors of career development

Focus on science-related occupations

Family background•Parental social class

•Parental education

•Mother’s interest in education

•Father’s interest in education

•Parents expectations regarding further education

•Mother’s employment

Personal Assets•Reading at 11 and 16

•Maths at 11 and 16

•Self rated math ability

•School motivation

•Educational plans

•Occupational values•Well-paid job•Helping others•Promotion•Variety

School experience•Nr of science related subjects

•School type

•Single sex school

•Teacher’s general ability rating

•Teacher rating of math ability

Predicting entry into SET Occupation: Family background

NCDS men

NCDS women

BCS70 men

BCS70 women

Parental social class (I vs IV/V)

2.19** 1.74# 3.25** 1.72#

Mother’s education ns 1.93* ns ns

Mother’s occupation•Not employed (baseline)•Semi/unskilled•Professional/skilled

nsns

nsns

0.51*ns

nsns

Mother’s interest ns ns ns ns

Father’s interest ns ns ns ns

Parental aspirations post 18- education

1.88*** 2.24* ns ns

Predicting entry into SET occupation: Personal assets

NCDS men

NCDS women

BCS70 men

BCS70 wome

n

Reading test at age 10/11 ns ns ns ns

Reading test at age 16 ns 1.48# ns ns

Math test at age 10/11 ns 1.42# ns ns

Math test at age 16 1.30** ns 1.67** 1.73*

Self rated math ability at 16 1.54** 2.41*** 1.85* ns

Educational plans post 18 ns 2.50** 2.14*** ns

School motivation 1.32*** ns ns ns

Values - well-paid job ns ns ns ns

- help others ns ns ns ns

- promotion ns ns ns 1.45*

- variety ns ns 1.25* ns

SET aspiration 2.92*** 5.90*** 4.30*** 9.62***

Predicting entry into SET occupation:

School environmentNCDS men

NCDS women

BCS70 men

BCS70 women

Number of science subjects

1.18* 1.58*** ns 1.47*

Teacher rating general ability (11)

ns ns ns ns

Teacher rating math ability (16)

1.66*** 1.95*** 1.61* 2.42*

School type ns ns ns ns

Single sex school ns ns ns ns

Predicting entry into SET occupation: Significant factors in the Full model

NCDS men

NCDS women

BCS70 men

BCS70 women

Self rated math ability (16) 1.34* 1.86* ns ns

Math test at age 16 ns ns 1.51* ns

School Motivation 1.25** ns ns ns

Educational plans ns 2.25* 1.98** ns

Occupational value: Variety ns ns 1.25* ns

SET aspiration 2.64*** 3.79*** 4.31*** 8.34***

Number of science subjects 1.18* 1.58*** ns 1.47*

Teacher rated math ability (16) 1.66*** 1.95*** 1.61* 1.76*

Predictors of

occupational choice

Predictors of occupational choice: Significant factors in the Full model

NCDS men

NCDS women

BCS70 men BCS70 women

Reading test (10/11) ns 1.35* ns 1.43#

English Exams (16) ns 0.80* ns ns

Self rated math ability (16)

ns 2.52*** ns 1.82#

Teacher rated math ability (16)

ns 1.47* ns ns

Educational plans 1.40* ns ns 2.81***

School motivation 1.45*** ns 1.30* ns

Help others ns ns ns 1.30*

Number of science subjects

1.77*** 2.43*** 1.34** 2.10***

Independent School (vs. LEA)

ns 1.99* ns ns

Summary: Who becomes a scientist,

technologist, engineer, or health professional?

• Persisting gender imbalance: both in terms of aspiration and occupation

• Increase in science-related careers is slight, and mainly driven by young men entering IT professions

Summary: Predicting entry into a science-

related career

• Interest and attachment to a science-related career are formed early in life

• Aspirations in adolescence most important

• School experiences are crucial in attracting young people to a career in science: Teacher’s maths ratings Number of science subjects entered

Conclusion• Much remains to be done to improve

intake in science-related occupation• Findings call for :

– equal opportunities in access to science related courses at school

– Recognition and encouragement of science and math related ability by teachers

– Making school experience more relevant and engaging, feeding the needs and values of young people

Thank you

For further information please contact:

[email protected]