Context Analysis
An analysis of barriers and supports for female learners in the choice of
STEM careers in Kenya
January 2018
Research team:
Paul Omondi
Rosemary wanjiku
A
cknowledgments
This Research on Women in Technology was undertaken by Africa Data and Information Network with the
help of funding granted by Akirachix. This grant aimed to support research on strategies to improve the use
of research evidence in ways that benefit young women in Kenya to develop interest to pursue STEM careers
from an early age. This study consumed a lot of time, research and dedication. Even so, implementation
would not have been possible without the support of some individuals. The authors would like to take this
opportunity to thank everyone who contributed the various aspects towards the realization of this survey.
The authors wish to convey thanks to the staff of Akirachix for their enthusiastic help in conducting this
assessment. particularly Ms. Judith Owigar, for the invaluable advice in relation to the pre-survey
consultative discussionii which provided critical direction, as well as general input into the final report. And
finally, we are on the whole indebted to the respondents who consented to be part of this study. Their
enthusiasm and openness was an indication that the study was relevant.
ii
Acronyms and Abbreviations
ANCOVA
Analysis of Covariance
CATPCA
Categorical Principal Components Analysis
df
Degree of Freedom
EFA
Education for All
ICT
Information Communication Technology
LSM
Living Standard Measurement
NR
No response
SCCT
Social Cognitive Career Theory
SD
Standard Deviation
Sig.
Significance Level
SOC
Standard Occupational Classification
SPSS
Statistical Product and Service Solutions
STEM
Science, Technology, Engineering and Mathematics
VAF
Variance Accounted For
iii
Contents
Executive summary ..................................................................................................................................................................................1
Introduction ...............................................................................................................................................................................................3
1.1Study Context ........................................................................................................................................................................................3
1.1.1 Background to the study .............................................................................................................................................................3
1.1.2Purpose Statement ..........................................................................................................................................................................4
1.1.3Research questions .........................................................................................................................................................................4
1.1.4Purpose of study ..............................................................................................................................................................................4
LIterature Review......................................................................................................................................................................................5
2.1Broader Issues in Gender Disparities .........................................................................................................................................5
2.1.1Inequality issues in education ...................................................................................................................................................5
2.1.2Factors influencing educational disparities.........................................................................................................................6
2.1.3The STEM gender gap in university enrolment .................................................................................................................7
2.1.4STEM and gender gaps in employment .................................................................................................................................8
2.2Framework and hypotheses ........................................................................................................................................................ 10
2.2.1Factors that impact on STEM interest ................................................................................................................................. 11
2.3Definition of Key Terms ................................................................................................................................................................ 12
Research Methodology ......................................................................................................................................................................... 13
3.1Approach ............................................................................................................................................................................................ 13
Main Findings ........................................................................................................................................................................................... 16
4.1Participants’ Profile ......................................................................................................................................................................... 16
4.2Knowledge and preference of careers .................................................................................................................................... 18
4.3Effect of Informative, Educational and Psychological Factors on STEM Interest ...Ошибка! Закладка не
определена.
4.3.1Factors contributing to Interest ............................................................................................................................................. 20
4.3.2Factors contributing to lack of interest .............................................................................................................................. 21
4.3.3Relating career awareness and interest (Informative factor and STEM) ............................................................ 21
4.3.4Relating academic self-concept and interest (Educational factor and STEM) .................................................. 23
4.3.5Relating career self-concept and interest (Psychological factor and STEM) ..................................................... 24
4.3.6Relating socio-cultural influence and interest (Social factor and STEM) ............................................................ 25
4.4Factors Predicting STEM and Non-STEM Choices ............................................................................................................. 25
4.4.1Self efficacy ...................................................................................................................................................................................... 25
4.4.2Outcome expectations ................................................................................................................................................................ 28
4.4.3Social-contextual experiences ................................................................................................................................................ 30
4.5 Gender Issues in Career selection ............................................................................................................................................ 33
4.5.1Gender factors associated with STEM study choice ...................................................................................................... 33
4.5.2Gender factors associated with STEM career choice .................................................................................................... 36
Discussion
............................................................................................................................................................................................ 39
Conclusion
............................................................................................................................................................................................ 41
References
............................................................................................................................................................................................ 42
iv
List of Tables
Table: - 1.1: Status of gender equality in student enrolment in selected HEI in IUCEA .............................................8
Table: - 2.2: Membership Registration trends at Engineering Board in May 2012 .................................................. 10
Table: - 3.1: Sample distribution by region ................................................................................................................................. 14
Table: - 3.2: Distribution of in-depth interviews ...................................................................................................................... 14
Table: - 4.1: Demographic profile by age, education &marital status ............................................................................. 16
Table: - 4.2: Demographic profile by occupation .................................................................................................................... 17
Table: - 4.3: Living standard measurement ............................................................................................................................... 17
Table: - 4.4: Highest level of education of parent .................................................................................................................... 18
Table: - 4.5: Drivers for Career choices ....................................................................................................................................... 19
Table: - 4.6: Scale items t-test and mean for interest in STEM careers ......................................................................... 21
Table: - 4.7: Correlation between career awareness and STEM interest ..................................................................... 23
Table: - 4.8: Correlation between career self-concept and STEM interest ................................................................... 24
Table: - 4.9: Correlation between career self-concept and STEM interest .................................................................. 24
Table: - 4.10: Correlation between socio-cultural perception and STEM interest ................................................... 25
Table: - 4.11: ANCOVA results for competence belief scale ................................................................................................ 26
Table: - 4.12: ANCOVA results for interest and aptitude scale .......................................................................................... 28
Table: - 4.13: ANCOVA results for outcome expectations ................................................................................................... 29
Table: - 4.14: ANCOVA results for outcome expectations ................................................................................................... 30
Table: - 4.15: ANCOVA results for aspirations and support ............................................................................................... 31
Table: - 4.16: ANCOVA results for career guidance and information ............................................................................. 32
Table: - 4.17: ANCOVA results for barriers and obstacles ................................................................................................... 33
Table: - 4.18: CATPCA dimensions output associated with STEM study choice ....................................................... 34
Table: - 4.19: Responses categories along the dimensions associated with STEM study choice ....................... 36
Table: - 4.20: CATPCA dimensions output associated with STEM career choice ...................................................... 37
Table: - 4.21: Responses categories along the dimensions associated with STEM career choice ..................... 39
v
List of Figures
Figure: - 2.1: Females highest level of education ........................................................................................................................6
Figure: - 2.2: Percent of students meeting university eligible and STEM cut-scores benchmarks .......................7
Figure: - 2.3: Trends in female enrolment by key stem programmes at the University of Nairobi (19962005) ..................................................................................................................................................................................8
Figure: - 2. 4: Trends in female enrolment and employment in modern sector (2000-11) .....................................9
Figure: - 2.5: Social Cognitive Career Theory framework .................................................................................................... 11
Figure: - 2.6: Classification of factors involved in the selection of studies ................................................................... 11
Figure: - 4.1: Fields of preferred careers ...................................................................................................................................... 18
Figure: - 4.2: Factors contributing to lack of interest in STEM........................................................................................... 21
Figure: - 4.3: Level of knowledge of career fields ..................................................................................................................... 22
Figure: - 4.5: Contribution of dimensions to study decision-making processes ........................................................ 35
Figure: - 4.6: Contribution of dimensions to career decision-making processes ....................................................... 38
vi
E
xecutive summary
The present report discusses the results of a context analysis of barriers and supports for female learners in
the choice of Science, technology, engineering, and mathematics (STEM) careers in Kenya. The context
analysis is based on a desktop research, cross-sectional survey, focus groups with young women and girls,
and semi-structured interviews with parents, teachers and experts. The fieldwork was carried out during
the months of November and December 2018. The results are analyzed in the light of the unique challenges
and barriers facing women to enter and thrive in many of the STEM fields.
The report is organized as follows: Chapter 1, with a background to the study, purpose statement, research
questions and purpose of study. Chapter 2 provides a review of literature, leading to theoretical framework
and hypotheses. Chapter 3 delineates the study’s methodology. Chapter 4 details the main findings,
highlighting the demographic information, statistical analyses and commentaries, leading to discussion of
the key findings. The last section provides the conclusion and recommendations. The key analysis and
discussions in this report are provided according to three broad themes: self-efficacy, outcome expectations
and social-contextual experiences. The main highlights of the survey were as follows:
o STEM interest
An examination of mean differences showed that the gaps between STEM and non-STEM women in terms
of the level of interest in STEM career fields are fairly less pronounced. This was surprising because it
would be expected that interest is prerequisite to choice. For agricultural sciences and medicine/health,
where the differences were significant, the results suggest that attitudes towards these fields are generally
less positive among the non-STEM women. On the other hand, in respect of the other six career fields
examined (i.e. engineering, math/stats, computer science/ICT, architecture, medicine and construction)
where the differences were non-significant, interest levels are the same for STEM and non-STEM women.
An examination of the explanations for lack of interest in a STEM career showed that lack of interest
revolves around personal factors in the form of attitude, aptitude, awareness and assurance. By far the
most dominant factor demotivating interest was ‘difficulty of the programme’ (75%). Lack of interest are
also likely to reflect the following reasons: ‘negative attitude towards course/practioners’ (27%), ‘lack of
familiarity’ (13%), ‘gender–STEM stereotypes’ (8%), and ‘level of effort required to study’ (6%). Another
factor worthy of discussion is concerns about uncertainty over job opportunities, this emerging largely in
relation to the field of ICT (7%).
o Self-efficacy
In relation to the dimension of competence beliefs, the analysis showed that the average competence beliefs
score for STEM women in the STEM fields was significantly higher than the average competence beliefs
score for non-STEM women. On the other hand, with exception of English, where the scores were
statistically significantly higher for STEM women, the analysis did not differ significantly between the sub
groups. This pattern suggests that women who see themselves good at STEM tend to also have confidence
that they could perform well in non-STEM areas, or more generally, can be seen to have more confidence
about their ability in academic pursuit.
On the other hand, in relation to the dimension of personal effort, the results suggest two possibilities; first,
STEM women’s ability to acquire critical STEM skills shape their academic choices that lead to the STEM
careers. Alternatively, it is quite possible that their personal effort serve to strengthen their interest in
STEM. The interest differences apparent suggest STEM women have a greater tendency to persist in studies
when the material is difficult than their non-STEM counterparts, making them potentially less likely to
choose STEM study or career fields.
1
o Outcome Expectations
On the dimension of achievement and esteem, the results demonstrate a significant influence of ‘extrinsic
non-monetary’ motivations at personal level, relating to the social rewards associated with the job. The key
element surfacing in relation to this was: ‘It will/would make me to be respected by others’, which
underlines recognition as a selection driver. At familial level however, choice is driven by both intrinsic and
extrinsic motivations, as noted: ‘It will/would lift the socio-economic status of my family’. Thus, although
reflecting money goals this is not a clear-cut motive. Rather, money is only seen as beneficial under certain
circumstances, where in this case, money goals are driven by intrinsic motivation of family success and
image.
On the dimension of financial and personal goals, the results attest to the significant influence of intrinsic
rewards on aspirations to enter STEM career. Essentially, these constitute non-monetary motivations
inherent to the nature of the job. The key reasons why women choose STEM careers comprise the
combination of ‘intrinsic preferences’- linked to the satisfaction obtained from doing the job itself, and
‘intrinsic goal’- associated with long term career expectancy.
o Social-Contextual Experiences
On the dimension of aspirations and support, the results suggest that the motivators of STEM choice
comprise the combination of personal, learning/educational and familial factors. The larger portion of the
overall aspiration is attributable to personal factors, i.e. personality & character and interest. Learning
environment relates to the context of the classroom and school, and represents the external variables that
influence STEM preference. Implication for learning environment on students’ cognitive engagement
include perceptions of teaching quality, characteristics of tasks and learning activities, teachers’ behaviors
during instruction, classroom goal structures, the integration of student oriented learning, action learning,
problem-based learning, and constructivist learning, and academic fields. 1Family’s socio-economic status is
the third factor that is likely to influence STEM choices. The underlying premise here is that STEM fields are
more expensive than other disciplines, and therefore decision about whether to pursue a STEM occupation
is influenced by family’s ability to pay the programme fee.
On the dimension of career guidance and information, the results demonstrate that STEM interest may be
based, at least in part, on parental messaging. Apparently, parental influence (persuasion) does not seem to
predict STEM career preferences. It is therefore possible to conclude that, it is how frequently students talk
with their parents about careers rather than persuasion that are likely to predict interest in STEM.
In relation to barriers and obstacles, significant barriers to STEM choice are academic grades and
cultural/religious sentiments. Given the distinction between STEM and non-STEM women regarding their
intent for STEM careers, it is interesting and even surprising to see that the scores are particularly
pronounced among the former. Ultimately, though, when viewed in the appropriate context, it is quite
possible that women whose explicit career goal is join a STEM career face higher levels of these obstacles
than their non-STEM counterparts on account of these two factors.
1Less,
Sunghye (2014). Educational Technology International, Vol 15, No 2
2
1
Introduction
1.1
Study context
According to the last census (2009 National Housing and Population Census), women constitute slightly
over half (50.3%) of the entire Kenyan population. This implies a near equal split between men and women.
This near equal split does not however reflect in the uptake of opportunities between the two genders as
would have been expected. In a report by Women Who Mentor and Innovate in Africa, Engineering like
most STEM fields and careers still suffer from a minimal engagement by women.
To support the view that the choice of STEM by women is a social orientation, the Gender Initiative: Gender
Equality in Education, Employment and Entrepreneurship report by the Meeting of the OECD Council at
Ministerial Level sitting in Paris, 25-26 May 2011, it is obvious that the situation is not any different in
other countries out of the developed world. Women were more likely to pursue other fields out of STEM
even if they performed well in those fields at elementary and high school levels.
Equally, women are noticeably under-represented in decision and policy making concerning technological
and socioeconomic development. Explaining the reason for this disparity, Young (1993) suggested
plausibly that development practitioners are cautious not to violate what may be strongly regarded cultural
practices and values. Mostly male- dominated government officials from the Third World often claim that
concerns about the absence of women at high levels of government and their lack of active involvement in
policy making is a Western preoccupation of no interest even to their women. The few women in positions
of power and authority are being lost through attrition as many of them experience first-hand what it
entails to be "lonely at the top." However, efforts to improve the status of women and to enlist their selfconfidence, intellectual, and decision-making capabilities for the benefit of society have culminated in
landmark conferences and policy adjustments worldwide.
Reviews of research have led to the conclusions of a link between technology and socio-economic
development of any one society. Technological innovation and information technology as a whole provides
platforms for socio-economic development. Equally, inclusion of women in the development agenda leads
to improved prospects for the next generation and development as a whole. Hence, if technology is key to
the advancement of a society, then integrating women in technology is fundamental to development as well.
1.1.1
Background to the study
Kenya has made enormous progress towards expanding access to education, with particular efforts
intensified to bridge the gender education gap. In addressing the education challenges, Kenya has
developed several policies including poverty reduction papers, National Education Master Plan (1997 2010), and more recently, the country has embarked on developing provincial Education for All (EFA)
plans, for incorporation in Kenya's national EFA plan. Significant emphasis has also been placed on
addressing minorities inclusion through measures focused on attendance issues to school facilities.
Accordingly, there have been efforts related to service provision, including bursaries, text books, school
feeding program, provision of desks and learning aids, teacher training, campaigns for girls' education,
among others.
Debate on gender equity in education currently revolves around females in mathematics and science
domains. Much of the debate about school science and mathematics, therefore, has focused around math
and science performance between girls and boys. Exam results have consistently indicated that males
perform better than girls in mathematics and sciences at secondary school. As a result, women struggle to
enter many of the science, technology, engineering, and mathematics (STEM) fields that have traditionally
been dominated by men. In part, these gaps reflect pre-college choices made by men and women, but also
3
to a large extent underpin gender disparities in various academic indicators linked to STEM, including
educational outcomes and university enrolment.
Traditionally, efforts to address the situation of gender inequalities in education have been undertaken
through such policies as quota system and catchment area factors in university admissions to address
problems of inequity of access to university education. The broader conversation taking place regarding
gender issues in STEM is seemingly, however, less concerned with gender differences even within the STEM
sector, which is particularly reflected in underrepresentation in technology and math oriented domains
such as engineering. A critical factor therefore is not just increasing representation of women generally in
STEM sectors, but also providing opportunities in targeted sectors.
1.1.2
Purpose statement
Despite the narrowing gap at the primary and secondary levels of education, a wide gender gap continues
to exist in labour participation, in respect to STEM related occupations. Career selection is one of many
important choices students will make that determines the level of disparity between men and women in
STEM fields. A range of possible factors make it difficult to generalize the conclusions for this pattern, but
one possible explanation for this pattern is that, at pre-college level, lack of confidence that they need to
continue in STEM study at higher level, is the explanation for gender disparities that has drawn most
attention. Confidence is a factor associated with students’ attitudes, and the stereotypes on gender roles
that has mainly focused on perceptions of STEM fields as male domain. The other possible explanation that
may be responsible for the observed pattern is the existence of environmental factors that interact with the
choice. Undoubtedly, secondary school stage is the most critical stage that determines a large part of that
students’ future. The risk is that, unless a very deliberate effort is made to mainstream gender at this
embryonic stage of the programmes, the system is likely to perpetuate the gender disparities that
characterize the STEM sector.
1.1.3
Research questions
The following research questions guided the conduct of this study.
1. What are the contextual factors that influence women in the choice of STEM careers?
2. What are the existing attitudes and perception of women towards STEM fields?
3. What are the existing structures and policies of the education system for strengths and weaknesses
linked to girls’ and women’s education in STEM?
4. What are key facilitators to be built on?
5. What are the best platforms to reach women as pertains STEM courses/careers?
1.1.4
Purpose of study
The purpose of this study was to understand the factors influencing women’s access and participation in
science-related programmes in Kenya. In particular, the study sought to carry out an investigation into the
influence of self-efficacy, outcome expectations and social-contextual experiences during the pre-college
education.
4
2
2.1
Literature Review
Broader Issues in Gender Disparities
The Mainstreaming of a gender equality perspective into the economy and policy areas is a process which
involves the integration of gender concerns and perspectives into the design, implementation and
evaluation of employment and investment policies, programmes and projects. As more widely used in the
field of development, the term gender refers to the roles, responsibilities, relationships and identities
defined for, or ascribed to, men and women within a given culture, society or context (Brettel & Sargent,
1993). In 2003, UNESCO identified gender inequality as one of the challenges besetting higher education in
sub-Saharan Africa. This has led to the strategies adopted by the Millennium Project, which informs the
approach to education and gender in developing countries, and proposes short and long term goals and
objectives that should benchmark progress in the area of education and gender well into the next two
decades. Gender equity refers to the process of allocating resources, programs and decision-making fairly
to both males and females.
In the context of STEM participation, perhaps nothing is more central to the concept of gender equity than
gender equity in education. The engendering of gender into STEM fields requires, first and foremost,
documentation of the gender disparities in education and employment. Gender differences in STEM
participation are more often than not debated in relation to gender differences in educational attainment.
However, far from being a simple decision about whether or not to choose a STEM career or not, a variety
of factors throughout women’s educational experiences are likely to influence career path. Their generally
limited access to formal employment, and in this case, participation in STEM fields, is attributed to among
other factors lower level of education and lack of training and skills.
2.1.1
Inequality issues in education
The issue of gender equity and education is gaining increasing attention in the policy debate. Equity in
education is considered an important driver of participation, and mainstreaming of gender equity at all
levels of the education system is expected to lead to an increased greater access, benefits and advancement
opportunities for women. Gender equity in terms of attainment of basic education has been more or less
achieved. This is more widely attributed to eliminating user fees for primary education, which has
contributed considerably to the increase of girls’ enrolment, which may also have contributed to secondary
enrolment rates. The findings of the Economic Survey 2013 The total enrolment of girls rose by 9.4 percent
to 895,792 while that of boys grew by 7.4 percent to hit one million, closing the gender differences in
secondary schools.
It is in respect to university education where women are outdone by a wider margin. Looking at the
national and regional percentages for secondary, tertiary and university attainment for the female
population, as illustrated in Figure 2.1, the data shows that, overall, women account for just below half of
those with either secondary (47 percent) or tertiary (49 percent) education. But even so, the observed
higher levels of female enrolment can primarily be attributed to increased female enrolment in private
universities. The Ministry of Education indicate that in 2012, there were a total of 33 universities,
comprising 7 public and 26 private, and 24 university constituent colleges. Regarding enrolment, the data
indicates that 14,462, representing 41.1 percent of the total enrolled 35,179 in private universities. This
percent compared better than the figure of 45,193 of the total enrolled 124,563 in public universities that
represented 36.3 percent (Ministry of Education, 2012).
5
Figure: - 2.1: Females highest level of education
Secondary
Tertiary
University
100
80
% 60
49 51
47 49
38
40
49 48
49 49
39
33
48
36
46
45
51
42
45
49
37
31
42
46
35
31 32
21
20
0
Total
Central
Western
Eastern
Nyanza
Nairobi
Rift Valley
Coast
North
Eastern
Source: Compilation from census data
2.1.2
Factors influencing educational disparities
Existing data show that women continue to experience constraints in varying degrees at different levels of
education. Gender differences surface at two spheres: vertically and horizontally. Vertical inequality
consists in inequality among individuals, while horizontal inequality is defined as inequality among groups,
in this case, among males and female, typically defined on the basis of socio-cultural and economic factors.
Gender inequalities in relation to career paths in STEM are more pronounced in secondary than in primary
or tertiary/university education. According to Ndoye (2003), secondary education in Africa is the
foundation of the scientific and technological advancement that Africa needs to develop industrialized
economies; it is also the gateway to higher education and to employment. This particular review advances
two factors to explain why girls or women have lower participation in STEM, namely educational outcomes,
and career choice. A discussion on the relevance of these factors is provided below.
Vertical inequality
Explanations for vertical inequality are more broadly linked to educational outcomes. Educational
outcomes have important influences on both school to university transition and career options available to
women. The system of education in Kenya consists of eight years of primary, four years of secondary and
four or more years of university education, depending on the programme. Within the ‘STEM’ cluster,
students are expected to take Mathematics as a compulsory subject and two other science subjects drawn
from the group of Chemistry, Biology and Physics. Secondary school culminates with the KCSE examination,
and is the single most important determinant of what career a student chooses when the student’s school
life comes to end. Official exam statistics for the past five years indicate that boys have consistently
outperformed girls, and more readily mirrors the trends observed at the university level. As shown in
Figure 2.2, gender difference in terms of the percentage of female students with minimum qualification of
C+ ranged from a low of 6 percent in 2008 to a high of 10 percent in 2006.
Considering the ‘cut-scores’ for enrolment into STEM programmes based on the analysis of the same
dataset, the differences are particularly striking when the results are considered by the thresholds for
competitive selection to STEM programmes. The proportion of females attaining a score of B and above
ranged between 12 and 15 percent. Dismal performance, particularly in Math, which anchors in the STEM
programmes naturally mean that girls have limited opportunities in STEM programmes. For example, a
study conducted in 40 secondary schools in Western Province to establish the disparities in performance in
chemistry and biology showed that boys’ schools had an upper hand in both subjects for the period 2005 to
2009. Average scores computed for the five years showed that boys schools had the average mean of 6.282
6
(C) and 7.715 (B-) in Chemistry and Biology respectively as compared with the corresponding scores
among girls’ schools of 6.240 (C) and 5.180 (C-) (Amunga, Amadalo & Musera, ud). In view of the fact that
most of the science oriented programmes at degree level require a minimum grade of C+ in the science
subjects, naturally, this limits the opportunity of more female students getting into STEM fields.
Figure: - 2.2: Percent of students meeting university eligible and STEM cut-scores benchmarks
100
Male
80
%
Female
60
40
32
30
22
22
27
21
31
27
20
20
% with STEM
cut-score
23
0
2006
2007
2008
2009
2010
Male
20
21
18
18
21
Female
13
12
13
12
15
Source: Kenya National Examinations Council, performance statistics for various years
Horizontal inequality
The next step is to examine horizontal inequality, which is reflected in and further strengthened by the
choices made at the higher levels of education and training. Stereotyping is a big factor militating against
the girls’ preference for STEM fields. A major factor affecting girls’ attitudes is bias or negative attitude
towards women’s educational attainment or schooling (Masanja, 2010). It is noted that the stereotyping of
knowledge and skills girls and boys are subjected to at the introduction of formal schooling combined with
marginalization and discrimination against women continues to influence the gendered nature of education
even today and hence determines the occupation of men and women (Ibid).
2.1.3
The STEM gender gap in university enrolment
Existing data indicates that inequality in respect of STEM has affected women more than men. Table 2.1
provides a comparison of the status of gender equality in student enrolment in selected Higher education
institutions (HEI) affiliated to the Inter-University Council for East Africa (IUCEA). Making country
comparisons, it is seen that gender disparities in Kenya remain very stark, with the proportion of female
students in Science and technology emerging lowest at slightly below one-fifth -17% (Ibid).
7
Table: - 1.1: Status of gender equality in student enrolment in selected HEI in IUCEA
HEI members of IUCEA
Total
students
10 Universities and colleges in Kenya
11 Universities and colleges in Tanzania
7 Universities and colleges in Uganda
National University in Rwanda (NUR)
77,921
38,683
21,467
12,796
Female proportion
of Total student
Female proportion of Science
& Technology students
41
39
51
29
17
24
18
27
Despite the fact that women are fairly well represented in STEM fields; the inequality can still be
decomposed into within-sub group components. These inequalities more often than not reflect female
overrepresentation in such programmes as nursing and social work programmes, even up to 95% while
physics, mathematics and engineering programmes have low proportions of women, below 10% (Ibid).
Women are especially underrepresented in most STEM fields.
Figure 2.3 indicates the female composition as a total of the students admitted to the University of Nairobi
in key STEM programmes between 1996 and 2005. The University of Nairobi mirrors the broader situation
found in Kenya. As seen, the total female enrollment averaged below half, varying from 12 in respect to
architecture & engineering to 32% in respect to health sciences.
Figure: - 2.3: Trends in female enrolment by key stem programmes at the University of Nairobi (1996-2005)
80
60
%
40
32
23
23
20
18
14
12
0
Health Sciences
Biological/
Physical Sciences
Veterinary
Medicine
Agric/ Food
Technology
Computer
Science
Architecture &
Engineering
2004/5
35
27
26
21
15
14
2002/3
33
26
24
19
14
8
2000/1
36
23
24
19
13
11
1998/9
31
20
22
14
15
13
1996/9
26
20
17
18
12
16
Academic yr
Source: Griffin, 2007
2.1.4
STEM and gender gaps in employment
Gender differences in educational opportunities are an important factor that contributes to gender
differences in access to employment opportunities. Of course, education may in this context be simply
acting as a proxy for gender disparities, but when leveraged on labour market participation, women’s
restricted access to educational and training opportunities not only reduces their chances for employment
in any modern sector, but also restricts the range of occupational options that can be available to them.
8
Educational access appears to be a significant barrier to women’s access to employment opportunities. As
presented in Figure 2.4 below, the university education gap between males and females in Kenya has been
closing over the years. However, even as the gender disparities in university education have become
smaller over the years, in contrast, gender differences in employment opportunities in the modern sector
have narrowed less, showing the corresponding changes of 17 and 1 percent.
Figure: - 2. 4: Trends in female enrolment and employment in modern sector (2000-11)
80
University enrolment
Wage employment in modern sector
60
%
40
34.7
29.5
32.4
33.6
29.6
29.6
34.7
29.6
36.7
36.3
39.1
40.1
40.1
37.9
39.4
29.6
29.4
30.3
30.1
30.2
29.8
28.7
40.6
30.5
20
0
2000
2002
2004
2006
2008
2010
Source: Compilation from KNBS, Economic Survey
Looking further into these differences, it can be argued that educational differences account for gender
difference in access to formal labour market. The basic argument here is that, education being a significant
factor in labour market participation has contributed to lower female participation rates. However, at
another level, women’s limited access to employment opportunities may also be closely linked to the type
of course studied. Still, from a different angle, it is noted that, although a large number of women have
entered the labour force over the last two decades they are mainly concentrated in low-status, low paying
occupations such as teaching, nursing, secretarial work and domestic services (Suda, 2002). Generally,
female students have women have lower representative in in-demand graduate programmes, particularly
those relating to the STEM programmes, which are widely dubbed as ‘a male domain’. Accordingly, young
women are much less likely than young men to choose STEM programmes as field of study at graduate
level.
Drawing from the findings of a study undertaken by Riechi (2008), the research demonstrated that from
the total job advertisements in 2006, the Kenyan labour market has a high preference for business,
economics, medical, engineering and information and communication technology (ICT) graduates. This
study also revealed that graduates of sciences - mathematics, biological, chemistry and physics, legal
courses, education and languages, agricultural courses, water, environmental and energy courses are
moderately demanded in Kenya’s labour market. The lower concentration of women in the science oriented
courses would then explain why women have a weaker presence in the formal sector, and in particular
within STEM. The gender figures by the Engineering Registration Board (of Kenya) indicate wide gaps
between males and females in terms of engineers and technicians.
9
Table: - 2.2: Membership Registration trends at Engineering Board in May 2012
Category
Registered Consulting Engineers
Registered Engineers
Reg. Graduate Engineers
Graduate Technicians
Male
Female
Aggregate
272 (98.2%)
1,298 (96.8%)
4,974 (92.3%)
1,128 (98.5%)
5 (1.8%)
43 (3.2%)
413 (7.7%)
17 (1.5%)
277
1,341
5,387
1,145
Engaging in and pursuing careers in science, technology, and mathematics (STM) are attributable to
traditional and cultural norms, attitudes and prejudices, religion, poverty, and ignorance (Nzewi, 1996).
According to Nzewi, these are normally inherent in the socialization process of societies and are a
particularly damaging depiction of gender roles as biological rather than social constructs. The power of the
socialization process in inhibiting women's education in science, engineering, mathematics, and technology
education is often underestimated and has not received the attention it deserves among professionals in
the field.
2.2
Framework and hypotheses
The theoretical model for this research was based on the Social Cognitive Career Theory (SCCT). SCCT,
which is grounded on Bandura’s (1986) social cognitive theory, and explores how career and academic
interests mature, how career choices are developed, and how these choices are turned into action. SCCT
focuses on several cognitive-person variables (self-efficacy, outcome expectations and, goals), and how
these variables interact with other aspects of the person and his or her environment (gender, ethnicity,
social supports, and barriers) to help shape the course of career development (Lent, Brown & Hackett,
2000).
Self-efficacy refers to the beliefs people have about their ability to successfully complete the steps required
for a give task. Individuals develop their sense of self-efficacy from personal performance, learning by
example, social interactions, and how they feel in a situation. Outcome expectations are the beliefs related
to the consequences of performing a specific behavior or taking a specific action. Typically, outcome
expectations are formed thorough past experiences, either direct or vicarious, and the perceived results of
these experiences. Goals are defined as the decisions to begin a particular activity or future plan, and are
seen as playing a primary role in behavior or action. Thus, behavior is organized or sustained based on the
previously set goals. The contextual variables include factors, such as supports and barriers, and person
inputs, such as predispositions.
SCCT posits that career choice process can be explained as a result of the influence of a variety of person,
environmental and behavioural variables. Thus, self-efficacy promotes favourable outcome expectations,
and both, individually and in concert, reinforce and foster career interests (liking) and career goals. In turn,
these social-cognitive variables stimulate career choice actions, or career behaviours, such as career
planning and career exploration, which are necessary for the individual to make progress towards
identified career goals. The SCCT framework is shown in Figure 2.5. In the figure, self-efficacy and outcome
expectations comprise the first level independent variables, and contextual influences as the second level
independent variables. On the other hand, the combination of interests, goals and actions comprise the
dependent variables.
Bandura (in Lent, Brown. & Hackett, 2000) distinguished several classes of outcome expectations, such as
the anticipation that certain physical (e.g. monetary), social (e.g., approval of significant others), or selfevaluative (e.g., self-satisfaction) outcomes will follow particular actions. These expected positive outcomes
operate as potent motivators that, along with other variables (e.g. self-efficacy), help to determine whether
people will undertake certain actions.
10
Figure: - 2.5: Social Cognitive Career Theory framework
Contextual influences
Proximal to behavior choice
Person inputs
Predispositions
Gender
Disability
Self-efficacy
Expectations
Learning
experiences
Background
Contextual
Affordances
2.2.1
Interests
Goals
Actions
Outcome
Expectations
Factors that impact on STEM interest
There are various views among researchers about factors that are considered the most important in the
study choice process. One of the detailed analyses of study choice process was developed by the Research
Centre for Science and Mathematics Education at the Universitat Autònoma de Barcelona. The factors
identified as influencing the choice of study generally fall into four categories: i) ‘educational factor’, which
is concerned with students' level of skill and interest in STEM subjects; ii) ‘psychological factor’, which is
concerned with perception of the match between personal characteristics (including aptitudes, personal
interests) and STEM training and professional requirements; iii) ‘informative factor’, which is concerned
with knowledge of the job prospects in the scientific and technical sectors; and iv) ‘social factor’, which is
concerned with social perception of scientific and technical professions. Figure 2.6 presents the framework
diagram for the classification of factors.
Figure: - 2.6: Classification of factors involved in the selection of studies
Psychological Factor:
Educational Factor:
Level of skill and interest shown by
student in STEM subjects
Perception of alignment between
personal characteristics (skills, personal
interests etc) and the requirements of
training and professions in the STEM
field
Factors involved in the
selection of studies
Informative Factor:
Knowledge of job
opportunities in scientifictechnical field
Social Factor:
Social percepetion of scientific and
technical professions
Source: CRECIM, Research Centre for Science and Mathematics Education at the
Universitat Autònoma de Barcelona (UAB)
11
To carry out this investigation, this study assumes the four factors constitute contextual supports, which
determine what might gravitate students toward STEM fields. The question here is, how do these factors
influence STEM interests? And how can such influence be determined? The independent variable –‘Interest,’
was operationalized on a 10-point Likert-scale reflecting how much participants liked selected STEM fields.
For the purpose of this study, the dependent variable was operationalized by indicators of Informative,
Educational, Psychological and Social factors. Accordingly, the corresponding hypotheses were
conceptualized as follows: i.
Informative factor:
Providing academic and professional advice about job prospects in the scientific and technical
sectors, not only through teachers in schools but also through professionals employed in STEM
sectors. This indicator is assessed based on the level of knowledge of the STEM careers. The first
hypothesis was that pre-college levels of awareness of opportunities influence the rates of interest
to pursue a STEM field
ii.
Educational factor:
This has to do with improving the acquisition of STEM skills, i.e. knowledge, abilities and attitudes.
This indicator is assessed based on self-perceptions of academic self-concept. The second
hypothesis was that the levels of self-concept of personal capability to perform well academically
in determine the rates of interest to pursue a STEM field
iii.
Psychological factor
Encouraging pupils to actively examine their skills and interests and how they match STEM
requirements. This indicator is assessed based on self-perceptions of career self-concept. The third
hypothesis was that the levels of self-concept of personal capability to perform competently in a
job determine the rates of interest to pursue a STEM field
iv.
Social factor:
This has to do with improving the social image of STEM degrees among students and the general
public, with a particular focus on families. This indicator is assessed based on the perceptions of
cultural and religious factors that constrain career choice. The fourth hypothesis was that the
levels of perceived societal encouragement determine the rates of interest to pursue a STEM field.
2.3
Definition of Key Terms
To facilitate discussions of the analysis and the results of this study, the following provides the definitions
of the key terms used.
Career choice
Career choice is defined as the selection of a particular path or vocation taken as the basis for occupation
and personal goals. These include decisions made on the basis of both personal preference as well as
external influence by parental guidance, vocational counseling, and training opportunities.
Career /study field
The term career/study field refers to academic or technical career-focused coursework or programme,
intended to lead to a specialty and employment in a specific field.
Gender
Gender is defined in terms of men and women are socially positioned. The focus here is less on biological
differential conceptualization of man and woman, but rather on the prescribed, socialization, and assigned
roles of men and women in the society.
STEM
The definition of STEM used in this study includes the cluster of occupations under the 2010 Standard
Occupational Classification (SOC) system. These include the four broad fields of life and physical science,
engineering, mathematics, and information technology occupations, architecture occupations, and health
occupations STEM occupations require a postsecondary degree.
12
3
Research Methodology
3.1
Approach
Research design
The explanatory design was adopted in view of the designated objective of this research: factors influencing
STEM selection as well as opinions on gender issues on STEM study and career selection. The term
explanatory research implies that the research in question is intended to explain, rather than simply to
describe, the phenomena studied. The main objective of this design was to enable an investigation of
potential causal relationships between variables. Causal inferences were made by comparing two subgroups of the participants, classified according to their inclination to choose either the STEM or non-STEM
disciplines in higher education.
Research procedure
This study employed a mixed methods research design in which different but complementary quantitative
and qualitative data were collected. For this particular study, the research design was based on two data
collection techniques used concurrently, and structured under the same themes. The main objective of this
approach was to triangulate results to gain a broader understanding about the themes under investigation.
This approach also allowed both confirmatory and exploratory analyses to be undertaken, which was
suited to hypothesis testing and estimating causal associations using a mix of statistical data and qualitative
causal assumptions. The main purpose of exploratory data analysis is to isolate patterns and features of the
data which in turn are useful for identifying suitable models for confirmatory analysis.
i)
Household survey
The household survey was used to produce quantitative data, and was intended to allow for valid and
reliable measurement of the survey indicators. The population of this study consisted of women aged 15+
years who were pro-academic and/or pro-career progression. This age was targeted because it marks the
beginning of exploration stage during which an individual engages in an active process of exploring oneself
and the world of work, leading to a tentative decision regarding the choice of an occupation (Super, 1990).
It is also the age at which majority of Kenyans have joined secondary school. 2 The survey was conducted
with a national random or probability sample of 1,666 respondents. Random sample was considered
appropriate because it is an important principle of external validity, or the extent to which the results of the
study can be generalized to the larger population. External validity refers to the generalizability of the
research, that is, the ability of its conclusions to be validly extended from the specific environment in which
the research study is conducted to similar “real world” situations (Watt, J. & van den Berg, S, 2002).
Generalizing results requires that the people selected to be in a study are very representative of the
population to which the results are to be generalized Altermatt, B. (2009).
The sample was stratified by geographical location and urban/rural residence, proportionally to the female
population, meaning these data can be applied to the general women population. For the sampling, regions
were adopted for the first stage of sample selection. Note that under the 2010 Constitution; Kenya is now
divided into 47 counties first-level administrative divisions. Thus, to constitute the regions, the selected
counties were grouped according to the former province of which they were part. Accordingly, the second
stage of sample selection corresponded to counties, while the third stage corresponded to the sublocations. All respondents were approached in their homes and asked to participate in face-to-face
interviews. One person per household was randomly selected to participate. The interviewers determined
the household composition and the respondents were then selected from amongst eligible female members
2
Secondary school starting age (years) in Kenya was 12.00 as of 2012
13
using a Kish grid. For data collection, a standardized questionnaire was developed in electronic format for
use on android devices. The set-up of the questions was based mostly on Likert-type scales with a few
open-ended.
Table 3.1 presents the sample distribution by region, representing former eight provinces.
Table: - 3.1: Sample distribution by region
Rift Valley
Eastern
Nyanza
Central
Western
Nairobi
Coast
North Eastern
N
%
389
252
227
209
176
160
140
113
23
15
14
13
11
10
8
7
ii)
Key informant interviews
A key informant interview is a loosely structured conversation with a person who has informed opinion
about the topic under investigation. The key informant interviews were used to gather qualitative
information that were used to complement and cross-validate the information obtained from household
survey. The objective was to obtain an in-depth look at the factors influencing career decisions and to
understand how to address the under-representation of women in STEM.
Participants were selected purposively, in particular targeting those with experiential knowledge about
STEM. A total of 60 key-informant interviews were conducted. For this survey, four unique groups formed
this segment, namely:
 Parents (Important choice influencers to children in their tentative years):
 Departmental heads of Tertiary Institutions/Institutions in the technological sector
 Women in the technological sector
 Students in STEM and non-STEM sectors
The distribution of key informants is presented in Table 3.2 below.
Table: - 3.2: Distribution of in-depth interviews
Parents
Students in STEM courses
Students in Non-STEM courses
Departmental/Institutional Heads
(of higher learning)
Women in Tech Careers
TOTAL
Total
Nairobi
Nyeri
(Rural)
Machakos
(Rural)
Mombasa
Kilifi
(Rural)
27
8
7
10
8
7
5
0
0
5
0
0
5
0
0
2
0
0
8
10
60
8
7
40
0
0
5
0
0
5
0
3
8
0
0
2
14
Data analysis method
Qualitative analysis: - The chosen method for the qualitative data was ‘Thematic analysis.” The principle
purpose of thematic analysis is to explore the understanding of an issue, rather than to reconcile conflicting
definitions of a problem. The analysis was undertaken through processes that involved coding the dataset
and identifying important features of the data that might be relevant to answering the research question,
examining the codes and collated data to identify significant broader patterns of meaning, and developing a
detailed analysis of each theme, working out the scope and focus of each theme, determining the ‘story’ of
each.
Quantitative analysis: - Quantitative data was analyzed using the Statistical Product and Service Solutions
(SPSS). In order to determine whether the responses varied for STEM and non-STEM sub groups, a series
of analyses were performed using both descriptive and inferential statistics. An independent t-test was
conducted to determine if there was any significant difference between STEM and non-STEM women in
terms of awareness and interest in STEM. Pearson correlations were conducted to assess STEM interest in
relation to academic self-concept and career self-concept. Analysis of covariance (ANCOVA) was used to
explore which factors (independent variables) best predicted STEM choice. To reduce error variance, the
demographic variables which have significant relationships with the dependent variable of study were
controlled; these included parents’ education (both father and mother), geographical location and living
standard measurement (LSM). Lastly, categorical principle component analysis (CATPCA) was performed
to bring out the dimensional patterns and quantify the categorical variables (study fields) and their
correspondence to gender-related constructs. For this analysis, Cronbach’s alpha was used to measure the
internal consistency reliability of the scores (α>0.7 was considered reliable).
Limitations
As with any research, in designing analyzing the data for this survey, three factors represented a limitation
of one sort or another. Readers therefore need to consider the presented results within the context of these
limitations.
i. Research design: One limitation of this study was related to the research design, i.e. pure probability
national sample. The bulk of the sample was from the rural setting, which resulted in
overrepresentation of “out of school and job market” women in the sample. The over representation
may reflect selection bias, on the basis that this segment (housewives) was more likely to be at home,
and therefore more likely to be selected. However, whether inclusion of more in-school and working
women may have produced different results can only be determined by further investigation.
ii. Still on research design, this study has addressed the research questions within somewhat differing
‘time-frames’. The data was collected from target groups across the various career preparation and
career practice stages. Targeting of women in differing time-frames raise questions about the degree
to their prospective and retrospectively responses were able to jointly, accurately reflect on the
factors that lead women to choose a career field.
iii. Sample size: In this connection, It should be noted that, even with relatively large samples (N=1,666)
set for this survey, a random household survey was unable to obtain a sufficient number of STEM
women, particularly according to the SOC classification system. Because of this, the STEM sub-group
was considered broadly to those with STEM-related career intentions and those with science-related
university qualifications.
15
4
Main Findings
4.1
Participants’ Profile
Demographic profile
Table 4.1 shows the distribution of the participants by age, education and marital status. In terms of age,
just above one-tenth (15%) were in the age band 15-19 years. The highest portions were in the age bands
20-25 (33%) and 26-35 years (36%). Nearly one-tenth (12%) were in the age band 35-49 years, while just
4% were 50 year and above.
In terms of education, 8% and a paltry 0.4% had university and postgraduate education respectively, while
corresponding portions of 6 and 14% had vocation training and tertiary education. These results suggest a
high progression between primary and secondary school, but a much lower one between secondary school
and post-secondary education. Efforts aimed at promoting access to post-secondary school will perhaps
increase the likelihood of women pursuing STEM related subjects/courses and hence careers. When an
assessment is made by STEM and Non-STEM occupations, findings show that women in STEM related
occupations hold at least a diploma and above, while majority of those in non-STEM occupations fall
between primary school and secondary school. Just under a quarter (24%) had primary education, while
just below half had secondary qualification. A minimal portion (1%) had no formal education.
Regarding marital status, majority was either married or had previous married status, being those who are
currently married (54%), and the widowed and separated constituting in total just 3%. Those never
married were 42%.
Table: - 4.1: Demographic profile by age, education &marital status
N
%
Age band
15 – 19
20 – 25
26 – 35
35 – 49
50 +
249
550
599
207
61
15
33
36
12
4
Education
University
Tertiary ( Diploma)
Vocational Training
Secondary
Primary
No formal Education
138
233
92
770
403
23
9
14
6
46
24
1
Marital status
Single
Married
Widowed
Separated/Divorced
708
904
30
24
42
54
2
1
16
Occupation
Table 4.2 shows the distribution of the participants by occupation. Here, it is seen that of the total 55% who
were in any kind of employment, STEM women were significantly more likely to be in engaged than their
non-STEM counterparts, suggesting fairly better career prospects for women (94% vs. 54%).
Table: - 4.2: Demographic profile by occupation
N
Total-%
STEM-%
Non-STEM-%
Employed
Permanently employed (formal sector)
Temporary Employed
Self employed
Permanently employed (non-formal sector)
112
181
569
55
7
11
34
3
40
24
16
14
6
10
35
3
Unemployed
Not working
Student
Housewife
Incapacitated
248
299
197
5
248
15
18
12
5
15
6
0
0
0
6
15
19
12
0
15
Living standard measurement
Table 4.3 shows the distribution of the participants by Living Standard Measurement (LSM). For the
analysis, simple weighted sum of the durable household assets owned was first calculated, following which
each respondent was categorized on six bands- LSM 1 to 6. The upper socio-economic categories (LSM 6
and 5) comprised 5% and 12% respectively. The middle categories (LSM 4 and 3) comprised the highest
portions, 24% and 32% respectively, while the lower categories (LSM 2 and 1) comprised the highest
portions, 20% and 7% respectively.
Table: - 4.3: Living standard measurement
LSM 6 (Upper high SE)
LSM 5 (Lower high SE)
LSM 4 (Upper middle SE)
LSM 3 (Lower middle SE)
LSM 2 (Upper low SE)
LSM 1 (Lower low SE)
N
%
86
208
392
531
331
118
5
12
24
32
20
7
Parental education
Table 4.4 shows the distribution of the participants’ parental education. Those with university level
education comprised 7% (father) and 4% (mother), while corresponding portions of 12% (father) and 11%
(mother), had tertiary education. Those with secondary education comprised 26% (father) and 20%
(mother), while 23% (father) and 29% (mother) had primary education. Corresponding portions of 19%
and 26% had not formal education.
17
Table: - 4.4: Highest level of education of parent
Mother/Female guardian
N
%
Father/Male guardian
N
%
University
Tertiary
Secondary
Primary
No formal education
Deceased
NR/DK
4.2
117
204
437
378
321
75
134
7
12
26
23
19
5
8
68
175
341
489
432
43
118
4
11
20
29
26
3
7
Knowledge and preference of careers
Career Preference
An assessment of the career preferences for those participants still at school or anticipating to continue
with school, on the whole indicate higher preferences for non-STEM career fields (63%) compared with the
STEM career fields (29%). In respect to the specific career fields, the result suggests that education (18%)
and health (14%), although as noted, around one-tenth (noted by ‘no response of 9%) has not yet made a
decision on their career, suggesting that their choices could go one way or another.
Figure: - 4.1: Fields of preferred careers
Health
14
Agricultural sciences
6
Computer science/ICT
Engineering/Construction mgt
Higher mathematics
STEM, 29%
4
4
1
Education
18
Marketing/Business mgt
12
Fashion design/Art
9
Tourism/Hospitality
Accounting
5
Journalism
3
Law
Security sector
Others
NR
Non-STEM, 63%
7
2
1
6
9
10
20
%
30
40
18
Drivers for preference
Previous estimates from the Kenya Integrated Household Budget Survey of 2005/6 placed overall
unemployment at 12.7% of the total labor force; with the figures emerging higher among women than it
was among men (11.2%) from the same group. In relation to this particular inquiry (Table 4.5), it is
probably not surprising that when the participants were asked the reasons for choosing their current
careers, the issue of availability/ease of finding emerged the most important reason (32%), distantly
followed by qualification or performance in school (19%).
Making a comparison of these results across the STEM divide however shows that notable contrasts in this
regard. For the high portion of the STEM women, the choice was on the basis of educational requirements
(42%) and deliberate ambition (24%), whereas for their non-STEM counterparts, it was mostly on the basis
of funding (33%) and educational qualifications (19%). The key message take-out here is that performance
and availability of opportunities, rather than factors associated to social and school environment, form the
main basis for STEM choices.
Table: - 4.5: Drivers for Career choices
Less competition
Education requirements
Remuneration
Career prospects/Ambition
Advice from friends/other family/peers
Career prospects
Job Satisfaction
Other
Total (%)
Prefer STEM
occupations (%)
Prefer non-STEM
occupations (%)
32
19
18
15
5
4
3
3
14
42
6
32
0
8
2
4
33
19
18
14
5
4
3
3
From the qualitative results, a view posited in terms of the factors that may influence women in their career
decisions related an early established fear of failure at school, and therefore reduced career opportunities.
This view is reinforced by views of one of the parents interviewed. The observation was that sciences
related subjects are difficult and so many students fail. A negative attitude is then developed towards the
STEM subjects, where students then settle for the arts that are perceived to be easier to pass.
"The students and most schools want to pass by doing easy subjects- the arts"~ Parent
The qualitative results, the relative influence of educational qualifications in STEM selection, in this case, on
the basis of being selected to programmes that have a competitive selection process. The limited nature of
chances to pursue higher education has also led to competitiveness. This competitiveness pegs career paths
to performance. For one of the in-depth interview participants pursuing an electrical engineering career,
we gathered that while engineering was not her dream course, her performance in Mathematics and
Physics at high school led her to taking up Engineering as a course at University and subsequently as a
career even though that was not her dream career.
Perceptions too, play an important role in the choice and pursuit of certain career paths. Findings from the
key informant discussions revealed that, negative attitudes start to develop at high school. To the chair of
the Electrical and Formation Engineering department at the University of Nairobi, male dominance in the
STEM subjects (for both instructors and classmates) reinforce the belief that STEM related courses and
hence careers are masculine and hence difficult for women. In addition, concepts taught at high school are
harder compared to concepts at elementary school. Most girls confident with STEM subjects start to lose it
at college/university.
19
"I wanted to take up engineering in Form one and Form two... I however felt that physics and
mathematics were starting to become difficult for me..." ~ Student in Computer Studies.
Results further suggest that the role of ambition in career choice begins earlier, at elementary and high
school. Asked what factors motivated them to choose the courses they were pursuing (if still at school) or
courses they pursued (if out of school), personal ambition emerged tops with a 73% score. On the other
hand, courses to pursue at University level for most women are pre-determined by the grades attained at
high school. Consequently, even for women in STEM, majority had their fate determined by grades achieved
at High School. Asked if that was her dream career, one professional Electrical Engineer had this to say:
“Generally I am good in math but I was more into medicine than engineering. I was however admitted
into engineering but basically give me biology, math and English any day... That is what was offered.
At university, most of the time you select five courses and they choose one for you... I will go back to
medicine someday. Something that I love …“~ Woman in STEM
4.3
Effect of Informative, Educational and Psychological Factors on STEM
Interest
4.3.1
Factors contributing to interest
To provide a context for the analysis, we first provide a snapshot of the level of interest in STEM study
fields. To address this, participants were asked to rate how much they liked selected careers fields on a 10point scale, ranging from 1 (Strongly dislike) to 10 (Strongly like). The t-test results for the six fields are
shown in Table 4.6. We can see that the level of interest, established using sub groups mean was moderate,
ranged from 4.10 to 4.35 (i.e. 41% to 44%) for STEM women; and 4.06 to 4.23 (i.e. 41% to 42%) for their
non-STEM counterparts.
Independent t-test on the career fields suggests that a difference does exist depending on the choice of
subject; in this case, the results find a statistically significant difference between the means in only two
fields. This evidence comes out clearly in regard to agricultural sciences and medicine/health. For both
fields, the means suggest that STEM women have more interest in these careers than their non-STEM
counterparts as noted below:

Agricultural sciences, significant difference in the scores was apparent as follows: STEM (M=4.32,
SD=1.15) and non-STEM (M=4.19, SD=1.26); t (1309) = 2.136, p = .033

Medicine/health, significant difference in the scores was apparent as follows: STEM (M=4.35,
SD=1.03) and non-STEM (M=4.17, SD=1.20); t (1379) = 3.195, p = .001.
A close examination of mean differences showed that the gaps between STEM and non-STEM women in
terms of level of interest in STEM career fields is fairly less pronounced, in any case the mean gaps
reflecting just 1 to 4% difference across the seven career domains. This was surprising because it would be
expected that interest is prerequisite to choice. For agricultural sciences and medicine/health, where the
differences were significant, the results suggest that attitudes towards these fields are generally less
positive among the non-STEM women. On the other hand, in respect of the other six fields, where the
differences were non-significant, interest levels can be considered to be the same for STEM and non-STEM
women.
20
Table: - 4.6: Scale items t-test and mean for interest in STEM careers
STEM (N=592)
Mean(SD)a
Non-STEM (N=1074)
Mean(SD)a
4.22 (1.30)
4.32 (1.15)
4.11 (1.14)
4.25 (1.11)
4.10 (1.44)
4.35 (1.03)
4.15 (1.46)
4.10 (1.41)
4.19 (1.26)
4.06 (1.26)
4.17 (1.22)
4.23 (1.44)
4.17 (1.20)
4.11 (1.46)
Engineering
Agricultural sciences
Math/Statistics
Computer science/ICT
Architecture
Medicine/Health
Construction
Mean
(difference)
t
0.12
0.13
0.05
0.08
-0.13
0.18
0.04
df
1.756
2.136
0.898
1.304
-1.749
3.195
0.528
1302
1309
1329
1321
1216
1379
1217
Sig.
.079
.033*
.369
.192
.080
.001*
.598
aSD<3
shows that the data are clustered closely around the average
* Significant difference found for means
4.3.2
Factors contributing to lack of interest
An examination of the explanations for lack of interest in a STEM career showed that lack of interest
revolves around personal factors in the form of attitude, aptitude, awareness and assurance. Figure 4.2
presents the percentage distribution for the responses. By far the most dominant factor demotivating
interest was ‘difficulty of the programme’ (75%). Lack of interest are also likely to reflect the following
reasons: ‘negative attitude towards course/practitioners’ (27%), ‘lack of familiarity’ (13%), ‘gender–STEM
stereotypes’ (8%), and ‘level of effort required to study’ (6%). Another factor worthy of discussion is
concerns about uncertainty over job opportunities, this emerging largely in relation to the field of ICT (7%).
Figure: - 4.2: Factors contributing to lack of interest in STEM
100
Total
80
Engineering
Math/Statistics
Computer Science/ICT
Medicine/Health
75
69
60
48
%
40
27
24
18
20
13
16
8 9
2
0
Difficulty o f p ro gramme/ P oor in STEM su bjects
Ne gative attitu de toward p rogramme/ praction ers
Un familiarity with pro gramme
1
Gen der-STEM stereotype s
6 5
3
Leve l o f effo rt req uired to study
1 1 0
Un certainty over job opp ortun ities
1 3 1
Oth er
.
Difficulty /
Poor in TEM
subjects
Unfamiliar
Negative
with
toward
programme/ programmes
GenderLevel of effort Uncertainty
STEM
required to
over job
stereotypes study
opportunities
Other
From the qualitative results, it is apparent that negative attitudes towards STEM can be attributed to
perceptions shaped by the school environment or individual experiences in class. For instance the belief
that STEM subjects are important yet demanding compared to arts based ones drives instructors to handle
students with sternness. This ends up creating negative attitudes towards STEM courses.
21
“When I was in school the science teachers were every strict. Those subjects were handled with
strictness to ensure seriousness on the part of the students. This created negative attitudes on the part
of the students. If they loosen up a little bit with the way they handle these subjects, students might
end up liking the science subjects” ~ Parent
4.3.3
Relating career awareness and interest (Informative factor and STEM)
For the measures related to career awareness, participants were required to indicate their level of
agreement on how much knowledge they felt they had about different STEM fields, based on prompted
awareness. For each field, participants were required to provide a score on a 3-point scale, ranging from 1
(Know nothing about it) to 3 (Know a lot about it).
Figure 4.3 presents the results for the two response options. It can be seen that, for the combined scores of
these two response options, the highest levels of awareness emerged in respect to mathematics (70%) and
agricultural sciences (68%), in relation to the STEM domains, and marketing (71%) and tourism (64%).
Other field depicting scores above the halfway mark were medicine (58%) and computer science/ICT
(55%), and journalism (54%), accounting (53%) and performing arts (51%), in respect to the non-STEM
domains.
Figure: - 4.3: Level of knowledge of career fields
Know a lot about it
Marketing
17
Tourism
54
9
Journalism
55
6
Accounting
48
8
Performing arts
39
4
Fine arts
45
8
38
3
Mathematics (including stats)
Non-STEM
45
12
Security
Law
Know something about it
36
10
Agricultural sciences
60
16
Medicine
10
Computer science/ICT
48
7
Engineering
48
5
Construction 2
Architecture 2
0
STEM
52
44
36
31
20
40
60
80
100
%
In order to determine whether differences in STEM career awareness are associated with interest, bivariate
correlation was performed on the two variables for the total sample. From the analysis showed in Table 4.7
indicate that with exception of agricultural sciences and medicine/health, the career awareness–interest
correlation was significant for all the fields at a significant level of less than .01, with correlation coefficients
ranging from 0.079 to 0.211. The results were as follows:
22





Engineering; modest positive correlation (r = 0.187; p< 0.000)
Math/Statistics; weak positive correlation (r = 0.079; p< 0.001)
Computer science/ICT; modest positive correlation (r = 0.124; p< 0.000)
Architecture; modest positive correlation (r = 0.158; p< 0.000)
Construction; modest positive correlation (r = 0.211; p< 0.000)
The results showed a relationship between awareness and interest across all the career fields examined,
with exception of medicine and agricultural sciences. From this analysis, we can see that the technically
oriented fields of construction, engineering and architecture had the strongest awareness–interest
bivariate relationship, perhaps indicating that with more information, women are likely to make these
careers more as their first choice. On the contrary, math/statistics was less predictive of awareness–
interest relationship, in this case suggesting that, as compared with the other fields, awareness is still
slightly likely to motivate interest to pursue a career in this field.
Table: - 4.7: Correlation between career awareness and STEM interest
Engineering
Agricultural sciences
Math/Statistics
Computer science/ICT
Architecture
Medicine/Health
Construction
Cor. Coef.
Sig. (2-tailed)
.187(**)
.033
.079(**)
.124(**)
.158(**)
-.023
.211(**)
.000
.177
.001
.000
.000
.356
.000
** Correlation is significant at the 0.01 level (2-tailed).
4.3.4
Relating academic self-concept and interest (Educational factor and STEM)
For the measures related to academic self-concept, participants were required indicate their own perceived
learning ability in terms of how confident they felt to succeed in studying science and mathematics. The
responses were scored on a 10-point scale, ranging from 1 (Not confident) to 10 (Very confident). To
further determine whether differences in STEM career awareness are associated with interest, bivariate
correlation was performed on these two variables for the total sample. Table 4.8 shows the results of the
bivariate correlation analysis between measures of these variables. Here, the academic self-conceptinterest correlation was significant for all the fields at a significant level of less than .01, with correlation
coefficients ranging from 0.065 to 0.162. The results were as follows:

Engineering; modest positive correlation (r = 0.143; p< 0.000)

Agricultural sciences; modest positive correlation (r = 0.102; p< 0.000)

Math/Statistics; modest positive correlation (r = 0.162; p< 0.000)

Computer science/ICT; modest positive correlation (r = 0.134; p< 0.000)

Architecture; weak positive correlation (r = 0.065; p< 0.008)

Medicine/health; modest positive correlation (r = 0.132; p< 0.000)

Construction; weak positive correlation (r = 0.072; p< 0.003)
The results showed a relationship between academic self-concept and interest across all the career fields.
More surprising, perhaps, is that math/statistics and engineering, in that order had the strongest had the
strongest awareness–interest relationship. From a self-efficacy perspective, these results suggest that with
positive self-evaluations of academic their capabilities, women are likely to place math-related and
technical fields more as first-choice career selection.
23
Table: - 4.8: Correlation between career self-concept and STEM interest
Engineering
Agricultural sciences
Math/Statistics
Computer science/ICT
Architecture
Medicine/Health
Construction
Cor. Coef.
Sig. (2-tailed)
.143(**)
.102(**)
.162(**)
.134(**)
.065
.132(**)
.072(**)
.000
.000
.000
.000
.008
.000
.003
** Correlation is significant at the 0.01 level (2-tailed).
4.3.5
Relating career self-concept and interest (Psychological factor and STEM)
For the measures related to career self-concept, participants were required indicate their own perceived
job ability in terms of how confident they felt to perform competently in STEM career fields. The responses
were scored on a 10-point scale, ranging from 1 (Not confident) to 10 (Very confident). Similarly, to further
determine whether differences in STEM career awareness are associated with interest, bivariate
correlation was performed on these two variables for the total sample. In Table 4.9, all the fields showed
statistically significant correlations between the two at a significant level of less than .01, with correlation
coefficients ranging from 0.068 to 0.148. The results were as follows:

Engineering; modest positive correlation (r = 0.107; p< 0.000)

Agricultural sciences; modest positive correlation (r = 0.113; p< 0.000)

Math/Statistics; modest positive correlation (r = 0.145; p< 0.000)

Computer science/ICT; modest positive correlation (r = 0.148; p< 0.000)

Architecture; weak positive correlation (r = 0.097; p< 0.008)

Medicine/health; modest positive correlation (r = 0.133; p< 0.000)

Construction; weak positive correlation (r = 0.068; p< 0.006)
The results showed a relationship between academic self-concept and interest across all the seven career
fields, indicating that having a higher level of career self-concept allows an individual to possess a higher
level of interest in STEM fields. For this aspect, it was found that math/statistics, computer science/ICT and
medicine/health, in that order had the strongest career self-concept–interest relationship. This shows that
with positive self-evaluations of job performance their capabilities, women are likely to make these fields
more as first-choice career selection.
Table: - 4.9: Correlation between career self-concept and STEM interest
Engineering
Agricultural sciences
Math/Statistics
Computer science/ICT
Architecture
Medicine/Health
Construction
Cor. Coef.
Sig. (2-tailed)
.107(**)
.113(**)
.145(**)
.148(**)
.000
.000
.000
.000
.000
.000
.006
.097(**)
.133(**)
.068(**)
** Correlation is significant at the 0.01 level (2-tailed).
24
4.3.6
Relating socio-cultural influence and interest (Social factor and STEM)
Lastly, for the measures related to socio-cultural influence, here, participants were required indicate their
perceptions of social, cultural and religious factors in terms of the extent they thought these influenced or
were likely to influence their interest to pursue a career in the fields of science, technology and
mathematics. The responses were scored on a 10-point scale, ranging from 1 (Strongly disagree) to 10
(Strongly agree). Similarly, to further determine whether differences in STEM career awareness are
associated with interest, bivariate correlation was performed on these two variables for the total sample. In
Table 4.10, all the fields showed statistically significant correlations at a significant level of less than .01,
with correlation coefficients ranging from 0.082 to 0.209. The results were as follows:

Engineering; modest positive correlation (r = 0.107; p< 0.000)

Agricultural sciences; modest positive correlation (r = 0.113; p< 0.000)

Math/Statistics; modest positive correlation (r = 0.145; p< 0.000)

Computer science/ICT; modest positive correlation (r = 0.148; p< 0.000)

Architecture; weak positive correlation (r = 0.097; p< 0.008)

Medicine/health; modest positive correlation (r = 0.133; p< 0.000)

Construction; modest positive correlation (r = 0.068; p< 0.006)
Likewise, the results showed a relationship between socio-cultural perception and interest across all the
seven career fields, indicating that interest in STEM is likely to increase if an individual felt that the society
has a positive perception towards the fields. For this particular aspect, it was found that medicine/health
and construction had the strongest socio-cultural perception–interest relationship. This shows that with
positive evaluation of the socio-cultural environment, women are likely to make these fields more as firstchoice career selection.
Table: - 4.10: Correlation between socio-cultural perception and STEM interest
Engineering
Agricultural sciences
Math/Statistics
Computer science/ICT
Architecture
Medicine/Health
Construction
Cor. Coef.
Sig. (2-tailed)
.169(**)
.082(**)
.162(**)
.158(**)
.116(**)
.196(**)
.186(**)
.000
.001
.000
.000
.000
.000
.000
** Correlation is significant at the 0.01 level (2-tailed).
4.4
Factors Predicting STEM and Non-STEM Choices
Based on the above observations, an interesting question naturally poses itself which we plan to address is
that of causal inference. Under this section, we make further analysis to understand the factors that predict
STEM and non-STEM choices. Analysis and discussions are provided according to three broad themes,
namely: self-efficacy, outcome expectations and social-contextual experiences
4.4.1
Self efficacy
i) Competence beliefs
The first measure of self-efficacy competence beliefs, and comprised ten items (in this case, study fields),
designed to measure agreement about participants’ level of confidence that they could perform well in
these disciplines. Table 4.11 illustrates the results of the ANCOVA test. More specifically focusing on STEM
25
fields, the adjusted average means ranged from 3.91 to 4.17, but tended to be higher for the non-STEM than
for the STEM fields. The observations made from this analysis can be summarized as follows:

Significant effect of competence beliefs in biology on STEM choice, F=10.00, df=1, p = 0.002.
STEM women gave higher scores than non-STEM women- M=4.27 (STEM) against M=4.11 (nonSTEM)

Significant effect of competence beliefs in Computer science/ICT on STEM choice, F=5.51, df=1, p
= 0.019. STEM women gave higher scores than non-STEM women- M=4.20 (STEM) against
M=4.08 (non-STEM)

Significant effect of competence beliefs in math on STEM choice, F (1, 1660) = 13.01, p = 0.000.
STEM women gave higher scores than non-STEM women- M=4.17 (STEM) against M=3.96 (nonSTEM)

Significant effect of competence beliefs in chemistry on STEM choice was evident, F=15.30, df=1,
p = 0.000. STEM women gave higher scores than non-STEM women- M=4.07 (STEM) against
M=3.85 (non-STEM)

Significant effect of competence beliefs in physics on STEM choice was evident, F=4.19, df=1, p =
0.034. STEM women gave higher scores than non-STEM women- M=3.99 (STEM) against M=3.86
(non-STEM)
Making a comparison across the sub groups, the analysis showed that the average competence beliefs score
for STEM women in the STEM fields was significantly higher than those for their non-STEM women
counterparts. On the other hand, with exception of English, where the scores were statistically significantly
higher for STEM women, the analysis did not differ significantly between the sub groups. This pattern
suggests that women who see themselves good at STEM tend to also have confidence that they could
perform well in non-STEM areas, or more generally, can be seen to have more confidence about their ability
in academic pursuit.
Table: - 4.11: ANCOVA results for competence belief scale
Q: “Indicate how confident you currently are or were that you could do it and perform well in each of these subjects/
disciplines on a scale of 1 to 10, where 1 means "Not at all confident at all" and 10 means "Very Confident," assuming you
are motivated to do your best”
Total
STEM
Non-STEM
Partial
M (SD)
M (SD)
M (SD)
F
Sig
η2
STEM fields
Biology
4.17 (0.95)
4.27 (1.05)
4.11 (1.02)
10.00
0.002*
0.006
Computer science/ICT
Math
Chemistry
Physics
4.12 (1.08)
4.03 (1.00)
3.93 (1.02)
3.91 (1.18)
4.20 (1.14)
4.17(1.19)
4.07 (1.18)
3.99 (1.35)
4.08 (1.12)
3.96 (1.13)
3.85 (1.13)
3.86 (1.29)
5.51
13.01
15.30
4.49
0.019*
0.000*
0.000*
0.034*
0.003
0.008
0.009
0.003
Non-STEM fields
Languages
English
History
Social Studies
Business subjects3
4.42 (0.79)
4.37 (0.83)
4.25 (1.00)
4.16 (0.94)
4.15 (1.00)
4.44 (0.80)
4.44 (0.82)
4.22 (0.98)
4.17 (0.92)
4.25 (1.03)
4.41 (0.80)
4.34 (0.82)
4.26 (0.98)
4.15 (0.93)
4.10 (1.02)
0.73
4.60
0.21
0.21
8.89
0.394
0.032*
0.646
0.650
0.003*
0.000
0.003
0.000
0.000
0.005
*Statistically significant between STEM and non STEM women
df (within group) = 1
df (between groups) =1660
3
Although business studies is not designated as STEM fields, it is closely related to STEM field such as math and accounting
26
ii) Personal effort
Nine items were used to collect data on the second measure of self-efficacy, which addressed aptitude and
interest, and was designed to measure agreement with the statements relating to interest in learning or to
succeed in science & math. From Table 4.12, it is seen that the overall adjusted average means ranged from
3.99 to 4.25. When comparing the scores of STEM and non-STEM women, significant differences were
found across all the nine items, with the scores emerging significantly higher for STEM than non-STEM
women. The observations made from this analysis are provided below:

Significant effect of preparation on STEM choice (‘I study/studied hard and prepared well for
every test or exam’), F=15.54, df=1, p = 0.000. STEM women gave higher scores than non-STEM
women- M=4.38 (STEM) against M=4.19 (non-STEM)

Significant effect of consulting on STEM choice (‘When confronted with a problem, I can/could
usually consult my teacher/other students’), F=6.67, df=1, p = 0.009. STEM women gave higher
scores than non-STEM women- M=4.32 (STEM) against M=4.20 (non-STEM)

Significant effect of practical relevancy of science and math on STEM choice (‘I can/could relate
science & math subjects to my career interest’), F=9.95, df=1, p = 0.002. STEM women gave higher
scores than non-STEM women- M=4.29 (STEM) against M=4.11 (non-STEM)

Significant effect of extra effort on STEM choice (‘I can/could relate science & math subjects to my
career interest’), F=15.95, df=1, p = 0.000. STEM women gave higher scores than non-STEM
women- M=4.29 (STEM) against M=4.10 (non-STEM)

Significant effect of problem solving on STEM choice (‘I manage/managed to solve most problems
if I invest the necessary effort’), F=20.96, df=1, p = 0.000. STEM women gave higher scores than
non-STEM women- M=4.24 (STEM) against M=4.00 (non-STEM)

Significant effect of knowledge acquisition on STEM choice (‘I manage/managed to solve most
problems if I invest the necessary effort’), F=13.01, df=1, p = 0.000. STEM women gave higher
scores than non-STEM women- M=4.20 (STEM) against M=4.01 (non-STEM)

Significant effect of good grades on STEM choice (‘I am/was able to get good grades in my science
& math class), F=17.51, df=1, p = 0.000. STEM women gave higher scores than non-STEM womenM=4.20 (STEM) against M=3.96 (non-STEM)

Significant effect of confidence on STEM choice (‘Each lesson increase/increased my confidence in
dealing with science & math’), F=8.92, df=1, p = 0.003. STEM women gave higher scores than nonSTEM women- M=4.11 (STEM) against M=3.95 (non-STEM)

Significant effect of ability to understand science & math concepts on STEM choice (‘I find/found
science & math concepts easy to understand’), F=8.63, df=1, p = 0.003. STEM women gave higher
scores than non-STEM women- M=4.11 (STEM) against M=3.92 (non-STEM)
In relation to this dimension, the above patterns suggest two possibilities; first, STEM women’s ability to
acquire critical STEM skills shape their academic choices that lead to the STEM careers. Alternatively it is
quite possible that their personal effort serve to strengthen their interest in STEM. The interest differences
apparent suggest STEM women have a greater tendency to persist in studies when the material is difficult
than their non-STEM counterparts, making them potentially less likely to choose STEM study or career
fields.
27
Table: - 4.12: ANCOVA results for interest and aptitude scale
Q: “ Considering your ability, interest and efforts in science & mathematics, rate yourself on a scale of 1 to 10, where 1
means "Strongly Disagree" and 10 means "Strongly Agree" with each of these statements based on how you think it is or was
true for you as a student “
Total
STEM Non-STEM
Partial
M (SD)
M (SD)
M (SD)
F
Sig
η2
I study/studied hard and prepared well
for every test or exam
When confronted with a problem, I
can/could usually consult my
teacher/other students
I can/could relate science & math subjects
to my career interest
I do/did extra exercises and readings in
order to get full understanding of the
subjects
I manage/managed to solve most
problems if I invest the necessary effort
Each lesson increase/increased my
knowledge in science & math
I am/was able to get good grades in my
science & math class
Each lesson increase/increased my
confidence in dealing with science & math
I find/found science & math concepts
easy to understand
4.25 (0.84)
4.38 (0.97)
4.19 (0.93)
15.54
0.000*
0.009
4.24 (0.92)
4.32 (0.96)
4.20 (0.95)
6.76
0.009*
0.004
4.17 (0.96)
4.29 (1.19)
4.11 (1.12)
9.95
0.002*
0.006
4.17 (0.90)
4.29 (0.98)
4.10 (0.96)
15.95
0.000*
0.010
4.08 (0.94)
4.24 (1.04)
4.00 (1.01)
20.96
0.000*
0.012
4.08 (1.01)
4.20 (1.09)
4.01 (1.06)
13.01
0.000*
0.008
4.04 (0.98)
4.20 (1.16)
3.96 (1.11)
17.51
0.000*
0.010
4.01 (1.03)
4.11 (1.12)
3.95(0.90)
8.92
0.003*
0.005
3.99 (1.10)
4.11 (1.28)
3.92 (1.22)
8.63
0.003*
0.005
*Statistically significant between STEM and non STEM women
df (within group) = 1
df (between groups) =1660
4.4.2
Outcome expectations
i) Achievement and esteem
On the first measure of outcome expectations, four items were used to collect data on the aspect of
achievement and esteem, which was designed to measure agreement with the statements around realization
of success and prestige. From Table 4.13, it is seen that the overall adjusted average means of the items
ranged from 4.35 to 4.43. When comparing the scores of STEM and non-STEM women, significant
differences were found on two items, with the scores emerging significantly higher for STEM than nonSTEM women. The observations made from this analysis can be summarized as follows:

Significant effect of interest for recognition on STEM choice (‘It will/would make me to be
respected by others’), F=6.34, df=1, p = 0.012. STEM women gave higher scores than non-STEM
women- M=4.46 (STEM) against M=4.34 (non-STEM)

Significant effect of interest for family's economic uplift on STEM choice (‘It will/would lift the
socio-economic status of my family’), F=10.62, df=1, p = 0.001. STEM women gave higher scores
than non-STEM women- M=4.46 (STEM) against M=4.31 (non-STEM)
The above results demonstrate a significant influence of ‘extrinsic non-monetary’ motivations at personal
level, relating to the social rewards associated with the job. The key element surfacing in relation to this
was: ‘It will/would make me to be respected by others’, which underlines recognition as a selection driver. At
familial level however, choice is driven by both intrinsic and extrinsic motivations, as noted: ‘It will/would
lift the socio-economic status of my family’. Thus, although reflecting money goals this is not a clear-cut
motive. Rather, money is only seen as beneficial under certain circumstances, where in this case, money
goals are driven by intrinsic motivation of family success and image.
28
Table: - 4.13: ANCOVA results for outcome expectations
Q: “Thinking about your career expectations, rate your agreement on a scale of 1 to 10, where 1 means "Strongly
Disagree" and 10 means "Strongly Agree" with each of these statements based on what you always think or thought
about a career in the fields of science, technology and math”
Total
M (SD)
It will/would give me a career I feel proud of
It will/would make me to be respected by
others
It will/would lift the socio-economic status
of my family
It will/would make my family to be
respected by others
4.43 (0.83)
4.38 (0.85)
4.37 (0.80)
4.35 (0.91)
STEM
M (SD)
4.46
(0.93)
4.46
(0.98)
4.46
(0.95)
4.37
(0.98)
Non-STEM
M (SD)
F
Sig
Partial
η2
4.42 (0.89)
0.90
0.344
0.001
4.34 (0.94)
6.34
0.012*
0.004
4.31 (0.90)
10.62
0.001*
0.006
4.35 (0.96)
0.14
0.705
0.000
*Statistically significant between STEM and non STEM women
df (within group) = 1
df (between groups) =1660
ii) Financial and personal goals
The second measure of outcome expectations addressed financial and personal goals, and consisted of eight
items designed to measure agreement with the statements relating to intended long-term financial and
personal goals. As shown in Table 4.14, the overall adjusted average means ranged from 4.19 to 4.42. When
comparing the scores of STEM and non-STEM women, significant differences were found in three items,
with the scores emerging significantly higher for STEM women. The observations made can be summarized
as follows:

Significant effect of intrinsic motivations consistent interests on STEM choice (‘It will/would
allow me to obtain a job I like doing’), F=12.01, df=1, p = 0.001. STEM women gave higher scores
than non-STEM women- M=4.53 (STEM) against M=4.36 (non-STEM)

Significant effect of career goals on STEM choice (‘It will/would enable you to achieve your future
goals’), F=7.06, df=1, p = 0.006. STEM women gave higher scores than non-STEM women- M=4.41
(STEM) against M=4.28 (non-STEM)

Significant effect of career success on STEM choice (‘It will/would have failed if I don’t/didn’t
pursue this career’), F=4.03, df=1, p = 0.045. STEM women gave higher scores than non-STEM
women- M=4.27 (STEM) against M=4.15 (non-STEM)
On this aspect, the results attest to the significant influence of intrinsic rewards on aspirations to enter
STEM career. Essentially, these constitute non-monetary motivations inherent to the nature of the job. The
key reasons why women choose STEM careers comprise the combination of ‘intrinsic preferences’- linked
to the satisfaction obtained from doing the job itself, and ‘intrinsic goal’- associated with long term career
expectancy.
29
Table: - 4.14: ANCOVA results for outcome expectations
Q: “Thinking about your career expectations, rate your agreement on a scale of 1 to 10, where 1 means "Strongly
Disagree" and 10 means "Strongly Agree" with each of these statements based on what you always think or thought
about a career in the fields of science, technology and math”
It will/would allow me to obtain a job I like
doing
It will/would make me more employable
It will/would allow me to obtain a wellpaying job
It will/would give me the kind of lifestyle I
want
Abundant opportunities that
await/awaited me
It will/would enable you to achieve your
future goals
It will/would allow me to get a job where I
can use my talents & creativity
I will/would have failed if I don’t/didn’t
pursue this career
Total
M (SD)
STEM
M (SD)
Non-STEM
M (SD)
F
Sig
Partial
η2
4.42 (0.77)
4.53 (0.99)
4.36 (0.93)
12.01
0.001*
0.007
4.42 (0.81)
4.47 (0.94)
4.39 (0.90)
2.96
0.085
0.002
4.40 (0.86)
4.45 (0.96)
4.38 (0.92)
2.16
0.142
0.001
4.40 (0.87)
4.44 (0.96)
4.38 (0.93)
1.39
0.238
0.001
4.39 (0.89)
4.38 (0.94)
4.39 (0.92)
0.10
0.752
0.000
4.32 (0.85)
4.41 (1.02)
4.28 (0.96)
7.60
0.006*
0.005
4.31 (0.88)
4.37 (1.05)
4.27 (1.00)
3.13
0.077
0.002
4.19 (1.22)
4.27 (1.29)
4.15 (1.27)
4.03
0.045*
0.002
*Statistically significant between STEM and non STEM women
df (within group) = 1
df (between groups) =1660
4.4.3
Social-contextual experiences
i) Aspirations and support
The first measure of social-contextual experiences addressed aspirations and support, and consisted of a
wide range of fifteen items relating to social support and emotional capacity. From Table 4.15, it is seen
that the overall adjusted average means of the items ranged from 4.06 to 4.37, with the scores emerging
significantly higher for STEM women. The observations made can be summarized as follows:

Significant effect of personal disposition on STEM choice (‘Your personality and character’),
F=6.62, df=1, p = 0.010. STEM women gave higher scores than non-STEM women- M=4.43
(STEM) against M=4.33 (non-STEM)

Significant effect of underlying interests and preferences on STEM choice (‘Your personal interest
in particular occupation’), F=4.28, df=1, p = 0.039. STEM women gave higher scores than nonSTEM women- M=4.38 (STEM) against M=4.29 (non-STEM)

Significant effect of environment on STEM choice (‘Conducive environment to pursue this course’),
F=10.82, df=1, p = 0.001. STEM women gave higher scores than non-STEM women- M=4.36
(STEM) against M=4.19 (non-STEM)

Significant effect of environment on STEM choice (‘Charisma of teacher(s)’, F=4.67, df=1, p =
0.031. STEM women gave higher scores than non-STEM women- M=4.28 (STEM) against M=4.17
(non-STEM)

Significant effect of environment on STEM choice (‘Financial success of family’), F=4.46, df=1, p =
0.035. STEM women gave higher scores than non-STEM women- M=4.23 (STEM) against M=4.10
(non-STEM)
Drawing from this analysis, the results suggest that the motivators of STEM choice comprise the
combination of personal, learning/educational and familial factors. The larger portion of the overall
aspiration is attributable to personal factors, i.e. personality & character and interest. Learning
environment relates to the context of the classroom and school, and represents the external variables that
30
influence STEM preference. Implication for learning environment on students’ cognitive engagement
include perceptions of teaching quality, characteristics of tasks and learning activities, teachers’ behaviors
during instruction, classroom goal structures, the integration of student oriented learning, action learning,
problem-based learning, and constructivist learning, and academic fields. 4Family’s socio-economic status is
the third factor that is likely to influence STEM choices. The underlying premise here is that STEM fields are
more expensive than other disciplines, and therefore decision about whether to pursue a STEM occupation
is influenced by family’s ability to pay the programme fee.
Table: - 4.15: ANCOVA results for aspirations and support
Q: “Thinking of the way people make career choices, rate your agreement on a scale of 1 to 10, where 1 means "Strongly
Disagree" and 10 means "Strongly Agree" with each of these statements based on the extent you think they have
influenced or are likely to influence your interest to pursue a career in the fields of science, technology and math”
Total
M (SD)
STEM
M (SD)
Non-STEM
M (SD)
F
Sig
Partial
η2
Your personal values
4.37 (0.86)
4.41 (0.860
4.35 (0.86)
2.37
0.124
0.001
Your personality and character
Your personal interest in
particular occupation
Influence (persuasion) of your
parents
Conducive environment to pursue
this course
4.37 (0.84)
4.43 (0.850
4.33 (0.85)
6.62
0.010*
0.004
4.32 (0.88)
4.38 (0.96)
4.29 (0.93)
4.28
0.039*
0.003
4.29 (1.01)
4.36 (1.10)
4.26 (1.07)
3.18
0.075
0.002
4.25 (0.93)
4.36 (1.06)
4.19 (1.02)
10.80
0.001*
0.006
The kind of school you go/went to
Good relationship with
trainers(Teachers)
4.22 (1.06)
4.27 (1.07)
4.19(1.07)
2.39
0.123
0.001
4.21 (0.97)
4.24 (1.05)
4.20 (1.02)
0.76
0.382
0.000
Charisma of teacher(s)
Opportunity - including bursary,
scholarship
Interesting curriculum content
Pressure from parents/family for
financial support
4.21 (1.07)
4.28 (1.13)
4.17 (1.110
4.67
0.031*
0.003
4.19 (1.20)
4.18 (0.96)
4.25 (1.27)
4.22 (1.07)
4.16 (1.25)
4.16 (1.04)
2.37
1.52
0.124
0.217
0.001
0.001
4.17 (1.22)
4.19 (1.27)
4.16 (1.25)
0.22
0.636
0.000
Financial success of family
4.14 (1.21)
4.23 (1.26)
4.10 (1.25)
4.46
0.035*
0.003
Education status of parent(s)
Influence (persuasion) of your
friends
4.12 (1.25)
4.15 (1.29)
4.10 (1.27)
0.69
0.407
0.000
4.11 (1.16)
4.16 (1.22)
4.09 (1.20)
1.20
0.273
0.001
Educational status of peers/friends
4.06 (1.20)
4.06 (1.25)
4.06 (1.23)
0.01
0.912
0.000
*Statistically significant between STEM and non STEM women
df (within group) = 1
df (between groups) =1660
ii) Career guidance and information
The second measure of social-contextual factors addressed career guidance and information, and comprised
four items designed to measure agreement with the statements relating to career development assistance.
As shown in Table 4.16, the overall adjusted average means ranged from 4.19 to 4.42. Significant difference
was observed on only one item, with the score emerging significantly higher for STEM than non-STEM
women. The key observation made can be summarized as follows:
4Less,
Sunghye (2014). Educational Technology International, Vol 15, No 2
31

Significant effect of parental messaging on STEM choice (‘Informal counseling coming from your
parents’), F=12.01, df=1, p = 0.001. STEM women gave higher scores than non-STEM womenM=4.53 (STEM) against M=4.36 (non-STEM)
In relation to this dimension, the results demonstrate that STEM interest may be based, at least in part, on
parental messaging. Earlier we noted (see Table 4.14 above) that parental influence (persuasion) does not
seem to predict STEM career preferences. It is therefore possible to conclude that, it is how frequently
students talk with their parents about careers rather than persuasion that are likely to predict interest in
STEM.
Table: - 4.16: ANCOVA results for career guidance and information
Q: “Thinking of the way people make career choices, rate your agreement on a scale of 1 to 10, where 1 means "Strongly
Disagree" and 10 means "Strongly Agree" with each of these statements based on extent you think they have influenced or are
likely to influence your interest to pursue a career in the fields of science, technology and math”
Informal counseling coming from your
parents
Professional career guidance and
counseling provided by other people
Knowledge about particular occupation
Career guidance and counseling you
receive in school
Total
M (SD)
STEM
M (SD)
Non-STEM
M (SD)
F
Sig
Partial
η2
4.28 (0.98)
4.36 (1.17)
4.23 (1.11)
5.75
0.017*
0.003
4.24 (1.11)
4.29 (1.21)
4.22 (1.17)
1.54
0.215
0.001
4.24 (1.01)
4.31 (1.05)
4.21 (1.04)
3.73
0.053
0.002
4.24 (1.09)
4.29 (1.17)
4.20 (1.14)
2.35
0.126
0.001
*Statistically significant between STEM and non STEM women
df (within group) = 1
df (between groups) =1660
iii) Barriers and obstacles
The third measure of social-contextual factors addressed barriers and obstacles, and comprised five items
designed to measure agreement with the statements relating to factors that hinder entry into STEM career.
From Table 4.17, it is seen that the overall adjusted average means of the items ranged from 4.11 to 4.30,
with the scores emerging significantly higher for STEM women. Significant barriers to STEM choice are
academic grades and cultural/religious sentiments. The observations made can be summarized as follows:

Significant effect of performance on STEM choice (‘Academic ability/ grades’), F=12.98, df=1, p =
0.000. STEM women gave higher scores than non-STEM women- M=4.38 (STEM) against M=4.21
(non-STEM)

Significant effect of gender norms and the socio-cultural context on STEM choice
(‘Cultural/religious sentiments about girls’), F=8.23, df=1, p = 0.004. STEM women gave higher
scores than non-STEM women- M=4.31 (STEM) against M=4.14 (non-STEM)
Given the distinction between STEM and non-STEM women regarding their intent for STEM careers, it is
interesting and even surprising to see that the scores are particularly pronounced among the former.
Ultimately, though, when viewed in the appropriate context, it is quite possible that women whose explicit
career goal is join a STEM career face higher levels of these obstacles than their non-STEM counterparts on
account of these two factors.
32
Table: - 4.17: ANCOVA results for barriers and obstacles
Q: “Thinking of the way people make career choices, rate your agreement on a scale of 1 to 10, where 1 means "Strongly
Disagree" and 10 means "Strongly Agree" with each of these statements based on extent you think they have influenced or are
likely to influence your interest to pursue a career in the fields of science, technology and math”
Total
M (SD)
STEM
M (SD)
Non-STEM
M (SD)
F
Sig
Partial
η2
Health or physical condition
4.30 (1.12)
4.29 (1.18)
4.31 (1.16)
0.12
0.729
0.000
Academic grades
4.27 (0.92)
4.38 (1.02)
4.21 (0.99)
12.98
0.000*
0.008
Availability of learning resources
4.25 (0.99)
4.25 (1.04)
4.25 (1.02)
0.00
0.952
0.000
Cultural/religious sentiments about girls
Necessary hands on laboratory
experience
4.20 (1.21)
4.31 (1.26)
4.14 (1.24)
8.23
0.004*
0.005
4.11 (1.10)
4.15 (1.17)
4.08 (1.14)
1.15
0.283
0.001
*Statistically significant between STEM and non STEM women
df (within group) = 1
df (between groups) =1660
4.5
Gender Issues in Career selection
An important goal of the present study was to identify how gender factors may affect perceived personal
capacities, interest and sensitivities in both study field and career choice, by items with a nominal response
scale.5 This line of investigation consisted of two parts. The first part referred to “programme choice
inclinations.” The second part referred to “career choice inclinations.” Participants’ perceptions of careers
were assessed by asking them to relate various attributes to selected STEM and non-STEM specialties.
For the analysis, categorical principal components analysis using CATPCA was applied to investigate
whether or not an a priori idea about gendered assignment of items to subscales was supported by the
data. Separate analyses were performed for the each part with 3 components to extract maximum variance
from a data set with a few components from the data set. “None” and “Don’t know” responses constituted
missing values. Comparisons of the differences among gender issues obtained with different scaling levels
were based on the ‘Variance Accounted For’ (VAF)6, and Component Loadings computed as the sum of the
transformed variables with rotated loadings.
4.5.1
Gender factors associated with STEM study choice
The initial step in the analysis was to examine the reliabilities of the scores of the three components
derived. Cronbach's alpha for all the items demonstrated that the data was reliable to be utilized for
CATPCA, with coefficient of 0.944 for the sixteen items examined. The total sum of variance attributable to
the factors was 8.681 (= 72.34%), indicating acceptable reliabilities, indicating that the relation between
gender and STEM study field selection is relatively strong.
It is worth noting at this point that, a cut-off value of 4.0 was adopted to determine the composition of the
dimensions. Table 4.18 presents the components extracted from the dataset. As seen, the output of
component loadings showed that two variables- “I will look silly in this class” and “Women can excel in this
5
Scales that do not have any numerical significance, and could simply be called “labels”
Expected contribution is calculated by dividing the eigenvalue by the number of items included in the analysis, in this case
16, multiplied by 100
6
33
course just like men” failed to attain scores above the threshold, and were thus excluded from further
analysis. One other variable- “I know very little about this course” emerged as a complex variable 7(see item
#15 marked with an asterisk in the table), and was similarly excluded. On this basis, thirteen items qualified
to be retained in the CATPCA dimensional model.
Table: - 4.18: CATPCA dimensions output associated with STEM study choice
Cronbach's Alpha
VAF
1. It is a course for people like me
2. I interacted (Interact) well with the teachers
3. I'm (was) very interested in this course
4. I Know a lot about this course
5. It is a course for men
6. It is a very difficult course for women
7. It requires a lot of studying for one to pass
8. A course that I'm disinterested in
9. A course with good career prospects
10. The course segregates women
11. Women have limited ability to pursue this course
12. An easy course for women to pass
13. It is a course for women
14. I will look silly in this class
15. I know very little about this course*
16. Women can excel in this course just like men
Dimension 1
Dimension 2
Dimension 3
0.818
4.296
0.980
0.926
0.943
0.960
-0.095
-0.049
0.050
-0.242
0.370
-0.036
-0.088
0.480
0.369
-0.092
-0.212
0.173
0.692
2.847
0.098
0.094
0.098
0.072
0.595
0.576
0.638
0.446
0.477
0.577
0.584
-0.241
-0.261
0.394
0.466
0.347
0.373
1.538
-0.131
-0.132
-0.117
-0.108
-0.176
-0.037
0.024
0.384
0.034
0.002
-0.053
0.618
0.791
0.144
0.455
0.242
The next step of our analyses was to determine the contribution and interpretation of each dimension to
the overall CATPCA model. As depicted in Figure 4.3, it is immediately apparent that much of the variance
was retained in Dimension 1, but dropped widely in the subsequent dimensions. Dimension 1 provides the
highest indicator of the associations in the data, explaining about half (49%) of the total sum of variance,
and seems to suggest that“learning environment” is the most significant predictor of study choice. The four
contributors relating to this dimension appeared more salient to aspects of classroom and teaching context,
namely: “It is a course for people like me” (0.980), “I interacted (Interact) well with the teachers (0.926),
“I'm (was) very interested in this course (0.943), and “I Know a lot about this course (0.960).
Dimension 2 explains one-third (35%) of the total sum of variance, and seems to contain variables that
relate to “Ability & performance.” The seven contributors relating to this dimension seemed to be concerned
with attitudinal barriers to women’s STEM study selection, as seen from the following statements: “It is a
course for men” (0.595), “It is a very difficult course for women” (0.576), “It requires a lot of studying for
one to pass” (0.638), “A course that I'm disinterested in” (0.446), “A course with good career prospects”
(0.477), “The course segregates women” (0.577), and “Women have limited ability to pursue this course”
(0.584).
Dimension 3 is the least reliable indicator of the associations in the data, explaining just below one-fifth
(18%) of the total sum of variance, and seems to point to variables that relate to “Value & interest.” Here,
the two contributors relating to this dimension appeared to be concerned with positive expectancy of the
7
Complex variables have a score of 5 and above on more than one component (dimension)
34
careers, as noted from the following statements: “An easy course for women to pass” and “It is a course for
women.”
Figure: - 4.5: Contribution of dimensions to study decision-making processes
18%
49%
Dimension 1: Learning
environment
33%
Dimension 2: Ability &
performance
Dimension 3: Values &
interests
Next, we turn to a discussion of the responses categories for the academic fields examined along the
dimensions, as measured by the centroid coordinate8 Table 4.19, taken from the CATPCA ‘Quantifications’
output for the 5 pre-determined study fields, namely: Science, Math/ Statistics, Computer studies/ICT,
Languages and Social Science. The main conclusions of this analysis can be summarized as follows: On
Dimension 1, it can be seen that the scores for two STEM fields examined, namely science (-2.19 to -2.48)
and math/statistics (-0. -0.19 to -0.26) fall on the negative, the negative association being strongest in
respect to math/statistics. On the other hand, in relation to ICT (0.21 to 0.40), the scores are seen to fall on
the positive side across all the variables. The above analysis indicates potential negative effects of learning
environment on selection of a STEM study field.
Turning to Dimension 2, again the scores for the two STEM fields aforementioned- science (-0.63 to -0.81)
and math/statistics (-0.07 to 0.39) are seen to fall on the negative at a relatively high level of magnitude,
while ICT (0.09 to 0.86) fall on the positive at relatively low level of magnitude. The high negative scores on
science and math/statistics reflect low confidence in ability to pursue these fields; and alternately, low
positive scores on ICT (note that the statements are in the negative); reflect high confidence in ability to
pursue the field.
Lastly, on Dimension 3, it is observed that the scores for science (-2.39 to -2.79) and math/statistics (-0.94
to -1.90) are seen to fall on the negative at a relatively high level of magnitude. On the other hand, for ICT (0.17 to 0.03), there is a split across the two variables, with the negative score more generally reflecting a
low interest in pursuing these fields; whereas, the positive score reflect both high interest and confidence
in ability to pursue this field.
8
Represent the average of the object scores of objects for the response category (in this case academic courses)
35
Table: - 4.19: Responses categories along the dimensions associated with STEM study choice
Science
Math/
Statistics
Computer
studies/ICT
Languages
Social
Science
Dimension 1
1. It is a course for people like me
2. I interacted (Interact) well with the teachers
3. I'm (was) very interested in this course
4. I Know a lot about this course
-2.39
-2.48
-2.19
-2.39
-0.26
-0.20
-0.19
-0.22
0.21
0.31
0.31
0.40
0.53
0.49
0.56
0.53
0.57
0.54
0.63
0.63
Dimension 2
5. It is a course for men
6. It is a very difficult course for women
7. It requires a lot of studying for one to pass
8. A course that I'm disinterested in
9. A course with good career prospects
10. The course segregates women
-0.63
-0.66
-0.69
-0.78
-0.64
-0.70
-0.17
-0.29
-0.07
-0.31
0.11
-0.39
0.83
0.82
0.86
0.85
0.09
0.76
1.58
1.39
1.47
0.88
0.71
1.70
1.72
1.63
1.47
0.69
0.92
1.61
11. Women have limited ability to pursue this
course
-0.81
-0.32
0.78
1.66
1.43
Dimension 3
12. It is a course for women
13. An easy course for women to pass
-2.79
-2.39
-1.90
-0.94
-0.17
0.03
0.32
0.24
0.32
0.21
4.5.2
Gender factors associated with STEM career choice
First, considering the reliability of the data, Cronbach's alpha for all the items showed that the scores were
reliable to be utilized for CATPCA, with coefficient of 0.946 for the twelve items. The total sum of variance
attributable to the factors was 7.522 (= 62.68%), similarly indicating relatively strong correlation between
gender and STEM career selection.
Table 4.20 presents the components extracted from the dataset. Based on a cut-off value of 4.0, one
variable- “It is a well-paying career” was excluded from the dimensional model because it emerged as a
complex variable (see item #12 marked with an asterisk in the table), leaving twelve items qualified to be
retained in the model.
36
Table: - 41.20: CATPCA dimensions output associated with STEM career choice
Dimension 1
Dimension 2
Dimension 3
Cronbach's Alpha
0.717
0.674
0.543
VAF
2.917
2.614
1.992
1. It is a career for people like me
1.055
0.208
-0.421
2. It is my dream career
1.081
0.205
-0.427
3. That career kills the feminity
4. My parents would never want me in that career
-0.258
-0.244
0.625
0.691
-0.073
-0.196
5. I will not get a spouse if I pursued that career
6. This career is not supportive of family- oriented women
-0.243
-0.227
0.666
0.644
-0.148
-0.186
7. I need extra effort to make it in that career
8. Both men and women can excel in this career
9. I will never pursue that career
0.300
0.382
-0.270
0.359
0.391
0.278
0.727
0.695
-0.418
10. It is a masculine career
-0.139
0.352
-0.178
11. It is a feminine career
-0.122
0.339
-0.084
0.232
0.451
0.554
12. It is a well-paying career*
The interpretation and contribution of each dimension to the overall CATPCA dimensional model are
presented in Figure 4.4. It is apparent here that the proportion of variance differ narrowly between
Dimensions 1 and 2, but relatively wider in Dimension 3. Dimension 1 explains about two-fifth (39%) of the
total sum of variance, and appears to capture variables that relate to “Personal interests.” The two
contributors related to this dimension addressed both intrinsic aspects of choice and notions of
accomplishment, as noted from the following statements: “It is a career for people like me” (1.055) and “It
is my dream career” (1.081).
Dimension 2 explains just above one-third (35%) of the total sum of variance, and seems to point to
variables that reflect “Societal norms.” A closer look at the three contributors related to this dimension
showed that they were largely concerned with gender stereotypes and notions of sex or gender acceptable
roles & expectancies. These were identified as follows: “That career kills the feminity” (0.625), “My parents
would never want me in that career” (0.691), “I will not get a spouse if I pursued that career” (0.666) and
“This career is not supportive of family- oriented women” (0.644).
Dimension 3 explains just above one-quarter (26%) of the total sum of variance, and seems to point to
variables that relate to “Abilities & skill.” The three contributors related to this dimension seemed to be
concerned with both the fear of failure and motivation to succeed, as noted from the following statements:
“I need extra effort to make it in that career” (0.727), “Both men and women can excel in this career”
(0.695) and “I will never pursue that career” (-0.418).
37
Figure: - 4.6: Contribution of dimensions to career decision-making processes
26%
39%
Dimension 1: Personal
interests
Dimension 2: Societal
norms
35%
Dimension 3: Abilities
& skills
The discussion of the responses categories, in this case, for the career fields examined along the three
dimensions as measured by the centroid coordinates in relation to each dimension can next be inspected.
Here again, we look at the patterns of occurrence of the responses as measured by the centroid coordinates
in relation to the relevant dimension. Table 4.21 presents theresponse scores for the seven pre-determined
career areas, referencing math/statistics, ICT, engineering, medicine, teaching, law, social science and
politics, in relation to each variable. The main conclusions of this analysis can be summarized as follows:
On Dimension 1, the scores for the four STEM fields, namely math/statistics (-0.14 to & 0.31), ICT (-0.15 & 0.07), and engineering (-0.23 & -0.34) fall on the negative at a relatively high level of magnitude. These high
score, in a negative sense, suggest weak negative association between these three careers and ‘career
preference.’ The scores for medicine (0.01 for both variables) on the other hand fall on the positive, but as
noted, at a low level of magnitude, suggesting positive, although weak association with career preferences.
On Dimension 2, the scores for all the four STEM fields – i.e. math/statistics (-1.69 to -1.81), ICT (-1.48 to 2.29), engineering (-0.03 to -1.08), and medicine (-0.65 to -1.31) fall on the negative at a relatively high
level of magnitude; suggest weak negative association between these three careers and ‘societal norms’.
This negative association is strongest in respect to engineering on three accounts – it kills feminity, hinder
one from getting a spouse, and does not supportive of family- oriented women. In respect to medicine, it is
strongest on two accounts: It kills feminity and does not supportive of family- oriented women.
On Dimension 3, it is seen that the scores for four STEM fields fall on both the negative and positive sides,
making comparisons of the response across the variables difficult, as was the case in our study. The three
career areas of Math/statistics (-0.80 to 1.00), ICT (-0.674 & 0.47), engineering (-0.88 to 0.24) fall on the
negative at a relatively high level of magnitude in respect to two variables - “I need extra effort to make it in
that career” and “Both men and women can excel in this career,” and on the positive in relation to “I will
never pursue that career,” which correspond to strong negative association with perceived ‘abilities’ and
‘motivation’ to pursue the careers. Medicine (0.40 to 1.33) on the other hand, is characterized by a weak
positive relationship with all the 3 variables, which correspond to a positive, but weak association with
both perceived ‘abilities’ and ‘motivation’ to pursue the career.
38
Table: - 4.21: Responses categories along the dimensions associated with STEM career choice
Math/
Statistics
ICT
Engine
ering
Medicine
Teaching
Law
Social
Science
Political
Science
Dimension 1
1. It is a career for people like
me
2. It is my dream career
-0.14
-0.31
-0.15
-0.07
-0.23
-0.34
0.01
0.01
-0.13
-0.19
-0.10
-0.10
-0.15
-0.16
7.62
7.24
Dimension 2
3. That career kills the feminity
-1.69
-1.48
-0.03
-0.65
-1.09
-0.74
-1.77
0.78
4. My parents would never want
me in that career
-1.74
-2.28
-1.08
-1.31
-1.96
-1.13
-1.46
0.63
5. I will not get a spouse if I
pursued that career
-1.95
-2.29
-0.93
-1.23
-1.88
-1.00
-2.15
0.65
6. It does not supportive of
family- oriented women
-1.81
-2.06
-0.76
-0.83
-1.35
-0.76
-1.94
0.62
Dimension 3
7. I need extra effort to make it
in that career
-0.80
-0.64
-0.66
1.33
0.28
0.49
-0.39
0.71
-1.00
-0.67
-0.88
0.99
0.00
0.17
-0.66
1.32
0.40
0.47
0.24
0.40
0.47
0.43
0.14
-0.69
8. Both men and women can
excel in this career
9. I will never pursue that
career
Discussion
The above findings represent an analysis of the data gathered about participants’ interest in STEM, as well
as important influences on the chances to follow a STEM career. This study focused on participants’ precollege characteristics rather than their experiences during their college years. Throughout more of this
analysis, we disaggregate findings by STEM and non-STEM sub groups. The analyses were performed using
descriptive statistics to be able to make inferences and predictions based on data. To obtain a snapshot of
group differences in levels of interest in STEM, an independent t-test was conducted to determine if there
was any significant difference between STEM and non-STEM women in terms of interest in STEM. Bivariate
correlations were then conducted to assess STEM interest in relation to the informative, educational,
psychological and social factors. Analysis of covariance (ANCOVA) was used to explore which factors
(independent variables) best predicted STEM choice. Lastly, CAPTCA was performed to analyze the
dimensional patterns and quantify the study fields and their correspondence to gender-related constructs.
Considering these results more broadly, however, the sub group differences are not quite dramatic as
would be expected. This stemmed primarily from the narrow range of scoring, between Likert's scale 2 and
6 points, with high cluster around average reflected. This narrow discrepancy suggests that interest levels
are practically the same for STEM and non-STEM women. The bivariate correlations have shown that the
relationship between informative, educational, psychological and social and interest to pursue a STEM field
was statistically significant for practically all the STEM fields, although as noted the correlations across all
of the fields examined were at best modest.
Using the theoretical framework of social cognitive theory, a total of 55 variables were developed and
ANCOVA used to examine the differences in the preferences of STEM and non-STEM women associated
with the effect of these factors, controlled for parental education, geographical location and LSM. The
attendant sub groups’ views about significant factors in career choice indicated that STEM preference is a
combination and interaction of various influences. The most critical factors in STEM choice appeared to be
39
the aspects associated with self-efficacy. The analysis indicated that uptake of STEM careers is likely to be
influenced not only by competence beliefs, but also by personal efforts. On outcome expectations, STEM
choice is influenced, on the one hand by recognition and extrinsic aims of familial success and image, and
on the other hand, by the combination of ‘intrinsic preferences’- linked to the satisfaction obtained from
doing the job itself, and ‘intrinsic goal’- associated with long term career expectancy. On socio-contextual
experiences, STEM choice was predicted, on the one hand, by learning environment and teaching style, and
family’s socio-economic status, in terms of ability to pay the programme fee, and on the other by parental
messaging. Regarding the barriers and obstacles, the results showed that academic performance and
societal norms constitute the key barriers.
The above findings raise pertinent questions as which gender related factors influence STEM choices, and
how women can be motivated to select STEM career paths. Some useful and interesting insights came from
CAPTCA outputs. The CAPTCA comprised sixteen and twelve dependent variables that addressed study and
career choices respectively. In respect to study choice, gender issues emerged in terms of learning
environment in relation to positive classroom and teaching context; perceived ability and performance in
relation to attitudinal barriers; and value & interest in relation to positive expectancy of the career. On the
other hand, in respect to career choices, gender issues emerged in terms of personal interests in relation to
intrinsic aspects of choice and notions of accomplishment; societal norms in relation to gender stereotypes
and notions of gender acceptable roles; and abilities & skills relating to both the fear of failure and
motivation.
40
C
onclusion
This survey was designed to identify important influences on the intent to pursue STEM careers among
women. One important finding from this research was that, other than in medicine/health and agricultural
sciences, no apparent difference existed between STEM and non-STEM women in terms of attitude towards
STEM, which contradict the assumption that non-women are not drawn to STEM fields.
The series of statements used to measure participants’ views about personal capabilities showed that
women are likely to select a STEM careers in concurrence with their self-belief. Women who were
interested in STEM are generally more positive about STEM disciplines, than those who were not. There
was also a significant gap in perceptions of personal capabilities, with those who are interested in STEM
tend to rate themselves higher for positive self-image than those who are not.
The results of the motivating factors demonstrated that expectations of STEM careers are influenced more
by non-monetary than monetary considerations. Intrinsically motivating factors were identified as
recognition and long-term career fulfillment motivators. It also seems that opportunity to improving the
family economic status will motivate STEM choice. As prior noted, although reflecting money goals, money
is only seen as beneficial under certain circumstances, where in this case, money goals are driven by
intrinsic motivation of family success and image.
Analysis of responses addressing the social-contextual factors demonstrated that parental messaging and
charisma of teaching or curricular experiences are important sources of support in making a choice about
STEM careers. Those with higher educational qualifications may feel better equipped to give effective
educational support. Beyond this, there was also significant difference in terms of family’s socio-economic
status, on account that programme determines the family’s ability to pay the programme fee.
Lastly, the results of this study have shown that contextual barriers are more likely to relate to both
internal and external barriers. Internal barriers reflect the limitations of the participants in regard to selfefficacy, while external barriers reflect the socio-cultural norms, primarily in relation to stereotypes that
consider STEM subjects as male domains.
Recommendations
From all that has been discussed, the following recommendations are underlined in relation to these
results:
i.
Foster young women’s confidence levels in STEM to make them feel capable. By enhancing
confidence in their abilities, they will be more likely build ability to overcome obstacles, and also to
develop and sustain interest in STEM study fields.
ii.
Provide college and career counseling to increase the students' awareness of STEM fields. In so
doing, they are able to understand the relevance of the programmes of study, and also to create
awareness of the wide variety of career opportunities open to them post-qualification.
iii.
Role of parental guide to career decision. The results showed that informal counseling coming from
parents predicted STEM choice. It is therefore important for parents to give young women support
and encouragement to explore the STEM options available to find the career that interests them. In
relation to this, it is important in this respect for parents to avoid obvious or subtle stereotypes
about girls’ and women’s abilities in STEM fields.
iv.
Provide and support effective learning environments. As noted, charisma of teacher and conducive
learning environment constitute important motivators in STEM choice. The quality of learning
environment is therefore important, in terms of the delivery of effective STEM instruction, on the
one hand; and providing a social environment for students that encourages dialogue with teachers
and other students on the other.
41
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