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CHAPTER ONE INTRODUCTION Modern academia stands at a crossroads
“All religions, arts and sciences are branches of the same tree. All these aspirations are directed
toward ennobling man's life, lifting it from the sphere of mere physical existence and leading the
individual towards freedom.” –Albert Einstein
Modern academia stands at a crossroads of contesting opinions related to the goals and
outcomes of higher education (American Academy of Arts and Sciences 2018; National
Academy of Sciences, 2018), which have brought attention to the utilitarian perceptions of
certain degrees. Those in favor of the liberal arts and humanities cite the employability of the
problem-solving, critical thinking, and qualitative literacy skills developed in the courses
(AAC&U, 2021; Anders, 2017; Emsi, 2081b). Others, however, argue that the lack of real-world
application, job identification, and occupational awareness overshadow humanities students’
employability and render a career gap for graduates (Jaschik, 2018). Amidst this background
within higher education, humanities degrees have become regarded as less rigorous and less
employable than their STEM counterparts (Chambliss &Takacs, 2014; Jaschik, 2018; Newman,
2021). Given the debate, it is evident that the broad and holistic development emphasized in the
liberal arts and humanities are not always welcomed within the backdrop of standardized
occupational and pedagogical paradigms focused only on job specific skills.
Despite the often harsh examination of liberal arts and humanities degrees, students and
graduates in these majors remain satisfied with their degree choices as well as their careers
(American Academy of Arts & Sciences, 2018). Surveys and research confirm that humanities
and liberal arts graduates have sought-after holistic soft skills that employers prefer when hiring
(AAC&U, 2021, 2014; Coffey et al., 2020; Emsi, 2018b; Gallup Inc., & Busteed, B., 2015;
Long, 2018) – even in times of high unemployment, such as the current COVID-19 pandemic
(Fain, 2020). In addition, humanities students with terminal bachelor’s degrees earn comfortable
salaries and often peak in profitable and authoritative positions (Anders, 2016; Jaschik, 2018;
Ruggeri, 2019). Unfortunately, however, when compared to their peers in other degrees,
humanities graduates often take longer to peak in employment and pay – around ages 56 to 60.
Furthermore, when they do peak later in their careers, their salaries remain about 20% lower than
colleagues in science and engineering (Anders, 2016).
Perhaps in relation to lower salaries, liberal arts and humanities degrees are on the decline
in higher education (American Academy of Arts and Sciences, 2018; Moody, 2022; Strada &
Emsi, 2018a). The closure of various liberal arts colleges and the downsizing or cuts of
humanities fields have contributed to lower humanities graduation rates (Dutt-Ballderstadt, 2019;
Moody, 2022; Newman, 2021). Although the fewer numbers of humanities graduates should
increase marketability, humanities students are largely disadvantaged by the excess time they
take in career identification and decision-making. The easier concepts of degree-to-job relation
allow students pursuing other majors more direction and goal commitment when choosing future
careers (Leppel, 2001). Likewise, the negative rhetoric surrounding the lack of employability of
humanities degrees can also shape and alter students’ self-efficacy, which can impact and slow
their career decision-making (Jo et al., 2016).
Students require high self-efficacy with informed direction regarding workforce value so
that they can grow in career development and begin to take action steps towards achieving their
career goals. If a student demonstrates low self-efficacy, research demonstrates that he or she is
less likely to make a career decision (Galles, et al., 2019; Jo, et al., 2016). In fact, various studies
have indicated and supported career self-efficacy’s connection to decision-making and career
choice (Betz et al., 2005; Betz & Luzzo, 1996; Betz & Taylor, 1983; Conklin et al., 2013; Foltz
& Luzzo, 1998; Lent & Brown, 2020), and its influence in related actions (Bandura, 1992;
Schwarzer, 2014).
Career actions towards final decision-making are pivotal to expediting employment and
career success (Betz & Luzzo, 1996), and students know this. In a national survey, about 60% of
first-year college students thought it was important to think about a career goal; unfortunately,
only 25% indicated they clearly knew how to achieve their career goal (Cuseo, et al., 2020).
Students often pursue higher education with the intention of gaining a competitive advantage in
preparedness for future careers and employment, and typically seek majors that correlate with
industries (Chuang et al., 2009). However, growing research demonstrates students’
misunderstanding of what is required of them to achieve degrees and career goals (HERI, 2019);
and the absence of a corresponding career title may make career choice more ambiguous to
humanities students and cause them to continue higher education rather than seek employment.
However, with a growing 59% unemployment rate for humanities PhD graduates, it would be
more profitable for students to seek employment rather than pursue graduate education
(Hartman, 2020). Accordingly, there remains a career gap for liberal arts and humanities
graduates who demonstrate a longer period of maturation before making a career decision and
settling into what is considered gainful employment (AAC&U, 2012). Likewise, the negative
backdrop of opinions and rhetoric surrounding degrees without career titling could influence
students’ perceptions of future employability and shape their career decision-making selfefficacy
(Betz & Luzzo, 1996).
Problem Statement
Given current data, humanities students in majors without corresponding job titles may
lack the necessary self-efficacy to choose a satisfying career. As research demonstrates, there is
connection between self-efficacy and action (Bandura, 1992; Schwarzer, 2014), which can
impact behavior, commitment, and aspirations (Arghode, et al., 2021). Higher self-efficacy
equates to faster career-decision making and has been linked to more career action steps or
planning. Lower self-efficacy delays career development and career maturation (Bandura,
1994), and can negatively impact individuals socially and professionally (Chuang, et al., 2020).
Therefore, students with high self-efficacy are more likely to take the necessary steps to finding
and achieving a satisfying career.
Although there have been studies associated with self-efficacy differences within certain
populations and students’ major status, no research has examined a humanities student
population or a humanities population with the qualifying element of a major/degree
unassociated with a career title. Humanities degrees are on the decline worldwide, reaching the
lowest numbers since 1987 (American Academy of Arts and Sciences, 2021). The declining
number of humanities degrees and the gap of extended time in career decision-making of
humanities students (Jaschik, 2018) pose a growing problem for future employers and students,
especially since career indecision can negatively impact students’ personal and professional lives
(Chuang, et al., 2020). Although employers are seeking the skills humanities students possess,
graduates are taking longer to make career decisions and are not witnessing the transferability of
the skills gained with their degrees. In addition, humanities majors are more likely to be
unemployed when compared to other college graduates within their graduation year (American
Academy of Arts & Sciences, 2015).
Therefore, this study sought to identify whether or not career decision-making
selfefficacy scores predicted career action steps, and if student demographics influenced results.
Further, the use of the CDSE-SF instrument on an undergraduate humanities student population
has not been explored or applied prior to this study. Thus, this study sought to address the
lacking research related to career decision-making self-efficacy of humanities students and
contributed to relevant research examining demographic differences and self-efficacy
relationships across a specific population of students. Career decision-making self-efficacy of
humanities students is necessary research related to a population with marketable career skills.
Inclusion and examination of career action steps increased the rigor of the study by examining
whether or not students with lower self-efficacy also completed fewer action steps, as
selfefficacy has been shown to influence action (Schwarzer, 2014). Set amidst the backdrop of
the global pandemic, this research provided insights related to challenging environmental and
educational environments, offering relevant information related to students’ career development.
Purpose and Research Questions
The purpose of this study was to explore relationships within career decision-making
self-efficacy and career action steps of undergraduate humanities students in majors that do not
have corresponding career titles (English, Philosophy, Anthropology, and History), in order to
discover populations and areas that require further career preparation.
In this study, self-efficacy referred to the personal perceptions of abilities and capabilities
that can influence events and performance, such as career decision-making (Bandura, 1994).
Career action steps are comprised of the career development actions an individual takes in order
to progress towards his/her initial career goal. The self-efficacy scores were measured using the
CDSE-SF instrument correlated with demographics. The career action steps were measured
using the validated survey and correlating the scores with demographics and CDSE-SF subscale
scores. Correspondingly, the following research questions were explored in the study:
1.) What is the relationship between humanities student participants’ demographics (age,
gender, major, race/ethnicity, year, first-generation status) and their career action steps?
2.) What is the relationship amongst humanities student participants’ demographics (age,
gender, major, race/ethnicity, year, first-generation status) and CDSE-SF instrument score
and subscale scores (Self-Appraisal, Occupational Information, Goal Selection,
Planning, and Problem Solving) and the dependent variable career action steps?
3.) What is the relationship between student demographics (age, gender, major,
race/ethnicity, year, first-generation status) and the CDSE-SF score and each of the
subscale scores (Self-Appraisal, Occupational Information, Goal Selection, Planning, and
Problem Solving)?
Conceptual Framework
The research questions for this study were informed by the conceptual framework of
Social Cognitive Career Theory (SCCT) (Lent, et al., 1994), Self-Efficacy Theory (Bandura,
1977), and Career Maturity Theory (Crites, 1973). SCCT addresses the ways in which
individuals view career interests and make career decisions. SCCT links educational and work
performance to four psychosocial variables: general cognitive ability and skill sets, outcome
expectations, self-efficacy beliefs, and goal mechanisms (Brown, et al., 2011). SCCT recognizes
the impact of contexts such as gender, race, culture, learning experiences, work experiences, and
academic experiences as career behavioral influences (Chuang et al., 2009).
The Career Decision-Making Self-Efficacy Scale (CDSE), developed by Betz and Taylor
(1983), evaluates individuals’ self-efficacy in relation to career choices and contexts. The scale
was created through the theoretical bases of Self-Efficacy Theory (Bandura, 1977) and Career
Maturity Theory (Crites, 1964, 1973). Crites’ Career Maturity Inventory (CMI) (1965) measures
individuals’ career maturity, which shapes career choices and decisions. The Career Maturity
Inventory includes five career competencies that are directly analogous to the CDSE subscales.
In conjunction, the subtests/subscales of Self-Appraisal, Occupational information, Goal
selection, Planning, and Problem solving relate to the career maturity and development (Crites,
1973). Higher career maturity is linked to vocational decisions, actions, and problem solving
(Crites, 1973). Each of the items on the career action step survey employed in this study
correspond with Crites’ underlying career maturity concepts. Crites’ Career Maturity Theory
expands upon prior theories that were limited in time and dimension (Crites, 1973). The
integration of the theory with Betz and Taylor (1983) augments career development research and
further authenticates the CDSE instrument. As Betz and Luzzo (1996) explain:
The conceptualization and measurement of career decision-making self-efficacy
involved the integration of two major theories, one originally stemming from
clinical-social psychology and the other having its origins in counselingvocational
psychology. (p. 415)
The CDSE-SF survey instrument aligns with the conceptual framework of social
cognitive career theory and the psychological/psychosocial stimuli associated with the theory. In
addition, the career action step survey corresponds with SCCT focus on individuals’ academic
and career choices and success in their academic and work goals (Brown, et al., 2011). The
CDSE-SF composite subscale scores were correlated with career action step survey results. The
alignment of SCCT principles with career decision-making self-efficacy and career actions
towards goals led to the development of the purpose and research questions of this study and
provided the basic premise for the exploration of demographic variable influence. The
conceptual premises provided by SCCT connect to this study’s investigation of variation in
demographic self-efficacy career decision-making actions of humanities students and the
development of the career action step questions. Drawing from the theories of SCCT (Lent, et
al., 1994), Self-efficacy Theory (Bandura, 1977), and Crite’s Career Maturity Theory (Crites,
1973), the variables underlying the conceptual framework for this study are depicted in Figure 1.
Significance of the Study
The significance of this study is the dual impact of exploring self-efficacy relationships
within the humanities field and examining the career development action steps of students in said
field. The value of associations amongst career decision-making self-efficacy and career action
steps, with measurement of demographic influence, offers evidence of areas and populations that
required further career strategies. Informative data surrounding this population and variables
demonstrated possible connection amongst factors that can impact student career decisionmaking
and significantly contribute to future career preparation practices. Minimal (if any) research
existed on the career decision-making self-efficacy of the humanities student population prior to
this study. The lack of associated career title with the chosen humanities degrees further limited
Figure 1
. Conceptual Framework Model.
Career Action
Steps
Career development
5
year planning
Research of jobs and
costs
CV/Resume
Job shadowing
Career counseling
Internships
Career programming
events
Demographic
Variables
Age
Gender
Year
First
-
Gen status
Major
Self
-
efficacy
Overall Score
Self
Appraisal
Occupational
Information
Goal Selection
Planning
Problem Solving
relevant research, as this population had not been previously explored. However, as literature
supports, self-efficacy can affect student task completion, career decision-making, career
satisfaction, and overall career development (Betz & Luzzo, 1996). Thus, this exploratory study
examined whether or not relationships existed amongst career decision-making selfefficacy and
career action steps within this population demographics and filled a necessary void of
investigation, especially amidst a high unemployment rate and global pandemic in which
“human skills” are seen as highly employable (Coffey, et al., 2020). In addition, the application
of a career action steps survey allowed for an exploratory, preliminary use of the instrument and
its evidence.
The data were measured through the CDSE-SF scale and a novel 10-question career
action step survey. This research presents a platform for future study of career development and
humanities students, especially amongst years in college and major/degree. Given differences
within majors and years, this may indicate certain humanities majors evincing stronger
selfefficacy or more career actions than others. Likewise, given significant difference amongst
firstgeneration students and other respondents, this could call for additional research related to
firstgeneration humanities students’ self-efficacy and the career decision-making self-efficacy of
first-generation students, overall.
In addition, this study allowed for a novel investigation of humanities students’ career
self-efficacy and career action steps. The exploration of relationships amongst self-efficacy and
career action revealed connections that serve as indication of self-efficacy influence in student
career actions towards goals, as indicated by Bandura (1992). By intentionally examining
humanities on its own terms rather than as inferior to career-oriented professional degrees, the
inquiry examined this population by understanding it on its own terms validated by its internal
significance rather than an external context. The results of this study will help to inform
humanities educators and future humanities career pedagogy to better address the academic and
occupational needs of specific majors and the humanities population overall.
Delimitations and Limitations
This investigation was delimited to several variables and characteristic constructs. First,
the study was delimited to location of a public university located in Southwest Florida, due to the
feasibility of existing collaborations established between the investigator and the institution, and
potential for future studies. This study was also delimited to the use of 4 different humanities
degree programs: English, Philosophy, Anthropology, and History. This investigation was
delimited to the use of traditional, college-aged students between the ages of 18-24, who are
enrolled as majors in the four specific English, Philosophy, Anthropology, and History degrees
during the Spring 2021 semester. Lastly, this study was delimited to the use of the CDSE-SF
instrument and career action step survey in measuring self-efficacy and career decision-making.
Although great consideration was given to the creation, design, and methodology of this
study, there were some potential limitations. First, the primary investigator’s background is
principally in the field of English, not Philosophy, Anthropology, or History, which could have
demonstrated bias towards English students. A second possible limitation was that the diversity
of the students enrolled in all of the course sections was unknown; thus, there may or may not
have been significant differences in demographics and/or socioeconomic backgrounds that may
have affected the outcomes upon data collection. Similarly, it was not known in advance how
many students would consent to participate in this study. This in turn led to a lower than
expected sample size of participants that could have negatively affected the power of the
statistical tests. Another possible limitation of this study included the potential misconceptions
regarding humanities degrees, studies, and practices by the enrolled subjects. This quantitative
survey research used a purposive, convenience sample, rather than a random sample, which may
have posed inquiry limitation of design. As Singh (2007) notes, reactivity, or respondents’ bias
to give “feel good” responses, and non-response rates may be a weakness of the survey method.
Definition of Terms
Career development: Skills or employability-based education (Watts, 2006). “Career
development is the process of acquiring and experiencing planned and unplanned activities that
support attainment of life and work goals (McDonald & Hite, 2016, p. 4).
Career action steps: The direct actions an individual takes in order to be successful in his/her
future initial career choice and goals.
CDSE-SF: Career Decision-Making Self-Efficacy Scale Short Form assessment (Betz, Klein, &
Taylor, 1996), normed as a shorter form of the Career Decision-Making Self-Efficacy Scale
developed by Betz & Luzzo, 1996).
First- Generation: Although defined in many ways (R. Evans, et al., 2020), first-generation will
serve as a term to define a student whose parents or legal guardians did not attain a higher
education diploma (i.e., - a parent or legal guardian did not graduate from college or university).
Humanities: “The branch of learning concerned with human culture (OED b.). Humanities is
often used to describe the academic subjects of English/literature, History, Philosophy, Art,
Languages, Anthropology, Journalism, Communication, History.
Liberal arts education: Pedagogy rooted in traditions of the seven subjects of the trivium
(grammar, rhetoric, and logic) and quadrivium (arithmetic, geometry, music, and astronomy)
(OED).
STEM: An acronym for Science, Technology, Engineering, and Mathematics education. Self-
efficacy: Personal perceptions of abilities and capabilities, which can influence events and
performance. “Self-efficacy beliefs determine how people feel, think, motivate themselves and
behave. Such beliefs produce these diverse effects through four major processes. They include
cognitive, motivational, affective and selection processes” (Bandura, 1994, p.1).
CHAPTER TWO: LITERATURE REVIEW
The non-scientists have a rooted impression that the scientists are shallowly optimistic, unaware
of man's condition. On the other hand, the scientists believe that the literary intellectuals are
totally lacking in foresight, peculiarly unconcerned with their brother men, in a deep sense
antiintellectual, anxious to restrict both art and thought to the existential moment. –C.P. Snow
According to a 2018 study conducted by Hart Research Associates on behalf of the
American Association of Colleges and Universities (AAC&U), 66 percent of higher education
students believed that they had the necessary critical analytical employment skills.
Unfortunately, only 26% of employers agreed (Long, 2018). More recently, only 6 in 10
employers felt as though recent graduates possessed the necessary skills for entry-level positions
in their organizations (AAC&U, 2021). Given the perception disparity and metacognition gap,
career readiness of college students remains a critical component of higher education. In fact,
the National Association of Colleges and Employers (NACE) defines career readiness using eight
key competencies: Critical Thinking/Problem Solving, Oral/Written Communications,
Teamwork/Collaboration, Digital Technology, Leadership, Professionalism/Work Ethic, Career
Management, and Global/Intercultural Fluency (NACE, 2021). Each of these competencies
aligns with humanities and liberal arts learning outcomes (AAC&U, 2021; Arum & Roska,
2011). However, many modern practices within higher education demonstrate the view that
career preparedness is only found in college classes that align with specialized skills.
Misconceptions contribute to the metacognition gap of students seeking employment, in which
they feel they are more prepared than employers believe (Long, 2018).
The dialogue surrounding the value of liberal arts and humanities education and their
application within future occupations has brought attention to career perceptions within the
humanities, creating a need for students and professionals to reconcile conflicting opinions
regarding skill and career development. The following literature review will present an overview
of humanities and liberal arts education (defined, and then used interchangeably to demonstrate
their holistic pedagogies). The review will also offer perspectives regarding perceptions
associated with humanities and their workforce application. In addition, an overview of the
Gottfredson and Bandura theories and their significance to self-efficacy and career decision
making within the conceptual framework of SCCT will be discussed as a means of synthesizing
the possible career decision-making self-efficacy and career action steps towards career choices
and goals of humanities students representing diverse demographics.
The Liberal Arts and Humanities
The term “liberal arts,” though often used interchangeably with the “humanities,” refers
to the classical, liberal education tenants of grammar, logic, rhetoric, known as the Trivium
(National Academy of Sciences, 2018). With the incorporation of arithmetic, geometry, music,
and astronomy, the pedagogy is titled the Quadrivium, and is employed in various modern higher
education institutions (National Academy of Sciences, 2018). Liberal Arts education began in
the Hellenistic Age and developed in the 5th century. Preliminary pedagogy is attributed to
classical thinkers such as Socrates and Plato (Kimball, 1986; Rose, 2015) and furthered by
thinkers such as Saint Augustine of Hippo, Saint Thomas Aquinas, Jean-Jacques Rousseau,
Thomas Huxley, John Henry Newman, and Matthew Arnold, who established the first
universities of higher education (Glaude, 2018; Kimball, 1986; Rose, 2015). The founders of
contemporary education believed in a holistic pedagogy that integrated various forms of
education that included academic, moral, and social teaching. However, as industry and
workforce trends began influencing education, Western pedagogy replaced philosophy and
theology with manufacturing and technology.
The United States of America built much of its early workforce through the establishment
of public schooling. In the early 1700’s and 1800’s, the U.S. focused its job and skill training on
apprenticeships, which became formalized through trade schooling later in the 1800’s (ACTE,
2018). One of the most influential Western breaks with classical liberal arts education was the
adoption of the Morrill Act in 1862. The Morrill Act created national land-grant universities so
that American students, who were previously learning the trades of agriculture and mechanics
from their families, could now attend colleges for training in these skills (National Academy of
Sciences, 2018).
With the rise of the Industrial Age in the early 1900’s, trade schools became more
popular, and the education-workforce collaboration became stronger. Although still grounded in
its agricultural and maritime roots, the U.S. economy quickly grew to include manufacturing and
entrepreneurial big business. Exports became much more prominent, and the Industrial
Revolution greatly expanded the U.S. economy and economic efforts (ACTE, 2018). Alongside
manufacturing and trade growth, career education grew. Pedagogical philosophers such as John
Dewey and Charles Sanders Pierce gained renown during this time for their calls for
pragmatic/experientialist education, in which students would learn in traditional classrooms
through first-hand experience and practice (Cohen, 1999). These concepts transformed
nineteenth-century education and ensured practical application and hands-on learning in trade
and industrial fields, with the first training school being established in 1879 (ACTE, 2018;
Cohen, 1999). As career and workforce education became more and more popular amongst
manufacturers, various employers created apprenticeship programs to help students develop
labor and academic skills, which directly shaped the economy and workforce. For example, the
General Electric Company (GE) established a labor-specific apprenticeship system in 1902,
which gave students on-site training and labor skills to complement their traditional schooling
(AVJ, 1976).
Economic and workforce trends shaped the education trends, and vice versa, throughout
the nineteenth and early twentieth centuries. This created the foundation for modern U.S. career
and workforce education (CWE). In 1906, the National Society for the Promotion of Industrial
Education was established, receiving strong support from the American Federation of Labor.
These groups, among other trade unions, were largely responsible for the passing of the Smith-
Hughes Act of 1917, which further linked workforce and education efforts by creating the
Federal Board for Vocational Education. This Act allowed for Federal funding of vocational
teacher salaries (AVJ, 1976). The establishment of the American Vocational Association in 1926
(renamed the Association for Career and Technical Education in 2001), also associated industry
with pedagogy (ACTE, 2018) and further promoted economic connection to education. With the
onset of World War II (WWII), economic efforts shifted largely to manufacturing. Those who
were trained in manufacturing trades, and those who were willing to be trained, were assigned to
war-industry jobs to learn the necessary hard-skills for these trades (AVJ, 1976). The impact of
the War quickly educated and trained much of the U.S. workforce in hard skill labor, including
women who worked stateside supporting those overseas.
After years of augmentation of the liberal arts, the twentieth century brought a drastic
change for Western higher education philosophy and pedagogy. The trends of career training
broke from the classical underpinnings, and higher education segregated disciplines in terms of
teaching and funding. As the National Academy of Sciences 2018 report supports, “Institutions
of higher education both shaped and were shaped by this move toward increasing specialization”
(2018, p. 26). Thus, the term “humanities” was adopted and has come to describe both modern
and classical education in the fields of literature, history, philosophy, language, anthropology,
jurisprudence, archaeology, religion, ethics, and the theory of the arts, to study the human
environment and conditions with particular attention to reflecting our diverse heritage, traditions,
and history (National Endowment for the Humanities, 2020). The National Humanities Center
(NHC) explains, “Put simply, the humanities help us understand and interpret the human
experience, as individuals and societies” (NHC, 2020).
With the labeling and sequester of classes, American colleges began seeing a decline in
humanities fields relative to other studies as a result of economic trends and expectations (Hearn
& Belasco, 2015). In 1971, Secretary of Education Sydney Maryland stated that all education
should be associated with future career aspirations (Bellucci, 1981). Maryland’s beliefs took
shape and became nationalized within six months through the development of the Bureau of
Adult, Vocational, and Technical Education, and the National Association for Education Research
and Development (Bellucci, 1981). The Education Amendments of 1972 and 1974 brought a
wave of career-focused pedagogy to American colleges and universities (Bellucci, 1981).
Funded from a federal level through the Center for Career Education (1973), occupational
education offered grants and support for classes designed for career learning
(Bellucci, 1981). Perhaps the largest proponent of vocational education was the Director of the
Office of Career Education for the U.S. Department of Education, Kenneth Hoyt. In 1975, Hoyt
distinguished eleven areas that served as a catalyst for educational reform. These included: 1.)
perceived lack of appropriate skills of graduates; 2.) lack of relational-awareness between school
learning and future career; 3.) lack of educational equality for minority students; 4.) inadequate
worker preparation; 5.) unsuccessful school-to-work transitions; 6.) inequality of representation
of women in the workforce; 7.) a need for continuing education; 8.) insufficient formal
education; 9.) lack of business community involvement in education; 10.) inadequacy in meeting
the needs of minority students’ education; and 11.) not enough emphasis on non-baccalaureate
programs post high school graduation (Bellucci, 1981). Alongside changing federal attitudes
towards education, various economic and social opinions changed as well. As Hearn and
Belasco (2015) explain, changes included labor market demands for specialized skills, an
external shift toward economic goals, increased enrollment of nontraditional students who
historically prefer more vocational training, and financial aid policies which offered faster
payback in occupational programs. The changing economic trends made the sociohistorical
impact of humanities seem obsolete, and have bridged to the twenty-first century.
Of all of the workforce education Acts passed post WWII, the Carl D. Perkins Act of
1984, was perhaps the most instrumental. Readopted in 1990 (Perkins II), 1998 (Perkins III),
2006 (Perkins IV), 2018 (Perkins V), the Perkins Act established Federal funding and grants, as
well as creation and oversight of multiple career and workforce education programs. Alongside
the Workforce Innovation and Opportunity Act (WIOA), the Perkins Act continues to regulate
American education, today (US DOL, 2018).
Humanities in the Workforce
Arguments for the needs of appropriate career education have not much changed in
contemporary society. Hard skill labor training is still necessary for many jobs. However, one
large difference between twentieth and twenty-first century education is the way in which career
pedagogy now manifests a much larger role within higher education institutions that were
formerly only liberal arts. In many ways, modern academia presents the liberal arts as
subordinate, instead of congruent to, occupational education (Seemiller & Grace, 2016).
However, the labor market and humanities need not be mutually exclusive. Negative perceptions
through funding allotment and rhetoric reinforce divisions and promote science, technology,
engineering, and math (STEM) education (Jaschik, 2014). In an environment in which more and
more liberal arts colleges are facing dwindling enrollments and budget constraints or closures
(Strada & Emsi, 2018a), it is clear that Hoyt’s arguments of the 1970’s propagated opinions that
continue today.
Since the start of their decline in the 1970’s, liberal arts colleges have been faced with the
challenge of defending their foundational curriculum. A more recent definition of the liberal arts,
supported by the U.S. Bureau of Labor Statistics, defines liberal arts as curriculum
“designed to prepare students for a variety of career options, rather than for a specific
occupation” (Angeles & Roberts, 2017, para. 6). Other definitions claim courses in the liberal
arts and humanities are “typically thought as non-technical and non-scientific” (College
Consensus, 2020), which rhetorically lowers their inquiry status and reputation. Many modern
philosophers feel as though the term “liberal arts” outdates the value of the degree and can lead
to misunderstandings of purely political and art education. Corrigan (2018) notes: Of course, as
most humanities professors will maintain, we must avoid vocationalism —
reducing education to job preparation. But we also must avoid avocationalism —
acting as if the humanities have nothing to do with job preparation, as if
humanities degrees are mainly for enriching weekend trips to art and history
museums. (para. 16)
For this reason, it has been suggested that “liberal arts” be rebranded to “universal” or a more
equally inclusive label (Flaherty, 2019). The concern over title rests in the perception of
inferiority associated with term. By rebranding, liberal arts may be able to re-enter modern
academia professional discourse with a restored and updated reputation; but doing so breaks the
title with the foundation of higher education.
Amidst such efforts, the future of liberal arts and humanities is of concern for many in the
field of practice. In order to remain soluble, many colleges are undergoing cuts and closures in
humanities disciplines such as “languages, history, religious studies, English, music, theater,
sociology and anthropology -- subjects often referred to as the heart of the liberal arts”
(DuttBallderstadt, 2019, para. 20). Especially given the global pandemic of the COVID-19
Corona virus, many educators are predicting further decline and closures of liberal arts colleges,
paired with cuts to the humanities (Mintz, 2020). Some educators are even calling for a two-year
hiatus in humanities doctoral admissions (Hartman, 2020).
Whether caused by economic or societal influences, the current reality is that humanities
degrees are on the decline, with English and History degrees showing a loss of 25% (American
Academy of Arts and Sciences, 2018, 2021; Strada & Emsi, 2018a). This trend has affected the
studies at all levels, as students seeking doctoral studies in the humanities have bleak job
prospects in a field with diminishing careers (American Academy of Arts and Sciences, 2018;
Hartman, 2020; MLA, 2017). Likewise, if left unguided, many humanities majors may
misunderstand their degree-to-job relation and believe that their degrees lead only to teaching
occupations. Citing research from the U.S. Census Bureau and the American Community
Survey, Angeles and Roberts (2017) list a variety of employable examples for humanities majors.
The authors, somewhat misguidingly, cite degrees such as Economics and Graphic Design as
humanities, and offer diverse career options. However, when discussing the true humanities
majors such as English and History, the top occupation for both, is Education (Angeles &
Roberts, 2017; College Consensus, 2020). This perception may lead many humanities majors to
wonder: Why choose the humanities and liberal arts instead of just majoring in education?
Perhaps more enlightened advocates of the humanities and liberal arts note the field’s
ability to provide students with well-rounded and holistic education that promotes civil discourse
and prepares students for a diverse array of occupations. As Rose (2015) notes:
The liberal arts can help impart the intellectual virtues of wisdom, science, and
understanding, those habits that help students make sound judgments about necessary
truths. These are excellences of the intellect in its speculative capacity and so aid a pupil
in arriving at knowledge of a demonstrated Scientia. (p. 57)
Courses that teach students a broad range of skills directly correlates with employer needs. What
is perhaps most important about liberal arts and humanities degrees and programs is their
inclusion of “soft,” “core,” “people,” or “success” skills, such as problem-solving, critical
thinking, leadership and communication (Angeles & Roberts, 2017, Cuseo, et al., 2020Dey &
Cruzvergara, 2019). These skills can be found on the “Employability Skills Framework”
provided by the U.S. Department of Education, Office of Career and Technical Education, and
are essential for successful career employment (US DOE, 2020), and in the NACE Career
Competencies (2021). As a means of measuring humanities students’ acquisition of these skills,
the Collegiate Learning Assessment (CLA) offers data demonstrating that humanities majors
outperform their business major and other peers in these critical learning areas (Arum & Roska,
2011). In fact, these skills are among the most in-demand across the labor market (Emsi, 2018b).
Long (2018) explains:
The World Economic Forum predicted that in the year 2020, critical thinking
skills will rank second among 10 sought-after hiring competencies; and a NACE
(2014) survey of employers nationwide similarly ranked critical thinking skills as
the second most important career-readiness competency. (p. 1)
It is evident that these skills are highly valued by employers, as “More than 90% of
employers rate written communication, critical thinking, and problem solving as ‘veryimportant’
for the job success of new labor market entrants,” (Arum & Roska, 2011, p. 143); and over the
past 30 years, the fastest growing jobs in the United States have all had social/soft skill
requirements (Ruggeri, 2019). Even amidst the Corona virus COVID-19 global pandemic,
communications, problem-solving, teamwork, and critical thinking all remain in the top six most
commonly requested job skills in employment postings (Fain, 2020). Perhaps this is because
humanities majors are taught to consider varying opinions, possibilities, and opportunities. This
taught-and-learned critical thinking allows humanities majors to be more open-minded. In fact,
students with liberal arts degrees rooted in humanities courses are less likely to express
authoritarian preferences and attitudes compared to those with degrees in fields such as business,
mathematics, technology, sciences, or engineering (Redden, 2020). In addition, according to
research conducted by the National Academy of Sciences, when humanities curriculum has been
integrated with STEM in various studies, positive outcomes such as increase in student
motivation, communication, creating thinking, and synthesis of ideas, as well as improved
teamwork, increased appreciation, and improved employment opportunities have all been
reported (2018). With an adaptable skill set and value for open-mindedness, humanities majors
gain career readiness and are posited for employment success; and many employers recognize
this (AAC&U, 2021).
Although a vast number of corporations recruit employees with job-specific skills,
research demonstrates that employers prefer students with academic achievements as well as
credentials (Arum & Roska, 2011). In fact, some companies go so far as to directly and
intentionally recruit humanities degree students. Large, well-known companies such as Fidelity,
Vanguard, Morningstar, Dodge and Cox, Deloitte, and McKinsey, all seek out and employ
graduates with diverse humanities degrees, noting their skill transferability (Anders, 2017). In
addition, 15% of humanities major graduates go on to management positions (Ruggeri, 2019). In
fact, a recent study of 1,700 people from 30 countries found that the majority of those in the
study who held leadership positions also held a humanities or social science degree (Ruggeri,
2019). This number increased when the individuals were under 45 years of age (Ruggeri, 2019).
It should be no surprise then, that the Co-founder of LinkedIn, Reid Hoffman, has a Master’s
degree in philosophy from Oxford and the former CEO of Hewlett-Packard holds undergraduate
degrees from Stanford in philosophy and medieval history (Chideya, 2015). It is possible that
the reputation of the individuals’ universities contributed to their employment success; but labor
market research firms such as Emsi, affirm that soft and holistic skills are highly marketable and
adaptable even in fields such as business, technology, engineering, and healthcare (Coffey et al.,
2020).
Even with so many employers seeking these core skills, students seem to be unaware of
their importance in their education. Seemiller and Grace explain:
The desire for real-world preparation is echoed by more than one-third of
business leaders who believe that higher education does not adequately help
students develop critical skills necessary for the workplace (2016, p. 219).
For this reason, it is crucial that educators within the liberal arts and humanities intentionally link
their pedagogy and skill development awareness with students’ future careers and competencies.
Jaschik (2018) notes that graduates who major in business, education and natural sciences are
more likely to view their degree and work as closely related. 30% of humanities majors,
however, did not. Without proper guidance and an understanding of application and
transferability of their degrees and skills, humanities majors may have a difficult time finding
initial employment.
Humanities Majors Employment
Over 7 million humanities majors were in the workforce in 2019 (University of Tulsa,
2019). Graduates entering the current workforce are likely to change careers and jobs multiple
times (National Academy of Sciences, 2018). In fact, some data projects career changes up to 11
times (Pasquerella, 2019). It is for this reason that modern academia remains committed to
preparing its students for a variety of occupations. The National Academy of Sciences (2018)
notes:
Faculty and administrators, who are concerned that an education focused on a single
discipline will not best prepare graduates for the challenges and opportunities presented
by work, life, and citizenship in the 21st century, are advocating for an approach to
education that moves beyond the general education requirements found at almost all
institutions, to an approach to higher education that intentionally integrates knowledge in
the arts, humanities, physical and life sciences, social sciences, engineering, technology,
mathematics, and the biomedical disciplines. p. x)
By better integrating humanities within STEM fields, STEM students may gain the benefits of
soft skill attainment; but integration must be mutually beneficial. A hefty employment gap still
exists within these fields.
Although it is only a percentage point away from their peers in engineering and business,
humanities majors have higher unemployment rates than STEM majors, citing 4%
unemployment, and an almost 2% increase in unemployment since 2018 (Burke, 2021; National
Humanities Alliance, 2020; Ruggeri, 2019). Within the global COVID-19 Corona Virus
pandemic, the U.S. Bureau of Labor Statistics reported an overall unemployment rate of 8.4% for
August, 2020. Humanities majors account for a large portion of the rate, which is exceedingly
concerning, given the slow, .4% annual employment growth rate projected for the U.S. from
2019-2029 (U.S. Bureau of Labor Statistics, 2020b). When 64% of Americans believe that
pursuing a college degree is worth pursuing so long as it does not produce large amounts of debt,
(Moran, 2019), and 87% of employers believe a college degree is valuable (AAC&U, 2021), it
remains clear that selecting an employable and well-paying degree is essential…even in a
pandemic (Strada, 2021).
Over the next nine years, the U.S. Bureau of Labor Statistics projects an 8% growth in
STEM jobs. Meanwhile, all non-STEM fields have a 3.4% projected growth. In addition, 6 of
the 10 fastest growing professions in the U.S. are currently related to healthcare, and the
remaining four are more closely related to science and math fields than the humanities (U.S.
Bureau of Labor Statistics, 2020b). The employment gaps correlate with wage earnings. In
2019, the median annual wage for STEM fields was $86,980, while non-STEM fields earned a
median wage of $38,160 (U.S. Bureau of Labor Statistics, 2020b). Although this disparity is
daunting, the salary differences may not be as bleak as they seem. Researching the median pay
for general humanities majors (English, Philosophy, Anthropology, History) through the
Occupational Outlook Handbook (U.S. Bureau of Labor Statistics, 2020c) is challenging given
the broad employability and diversity of job opportunities. Therefore, the reported non-STEM
field earnings may not prove entirely accurate. Likewise, since many humanities majors can be
employed in STEM fields, the titling comparison seems misleading, especially when certain
fields pay premiums for communication and other “soft” skills taught in the humanities
(Flaherty, 2021).
Nevertheless, pay and equality within the humanities remains a problem. On average,
U.S. men who major in the humanities earn a median salary of $60,000, while females earn
$48,000 (Burke, 2021; Ruggeri, 2019); but males with terminal bachelor’s degrees in humanities,
especially those who were older, were more likely to be unemployed than females (American
Academy of Arts & Sciences, 2015). Overall, however, older graduates with humanities degrees
tend to make more in their pay (Burke, 2021). With more than 6 of every 10 humanities majors
being female, the lower reported salaries may be symptomatic of gender pay gaps rather than
degree pay gaps (Ruggeri, 2019). Furthermore, given the larger numbers of men in STEM fields,
such as Engineering with 8 out of 10 graduates being male, (Ruggeri, 2019), the combined
reported salary earnings could be more comparable than expected when gender is considered. Yet
despite the lack of directly correlated career projected growth and reported lower salaries,
humanities majors demonstrate high levels of career and degree choice satisfaction (Flaherty,
2017; Jaschik, 2018). In addition, humanities majors transition relatively quickly to high-skilled
and high-demand careers, once they are able to determine and choose a career (Emsi, 2018). As
noted above, they also frequently hold leadership positions within their work (Ruggeri, 2019).
Unfortunately, however, humanities students demonstrate challenges when taking career
development action steps and making career related choices. Research demonstrates that
humanities majors take longer than those in other degrees to select careers (Jaschik, 2018),
experience rapid wage growth later in the careers (around ages 30 and 40) versus when first
emerging from college (Emsi, 2018), and peak later in their careers of choice (Anders, 2016).
As is noted above, humanities students may be unsure of how their coursework and
gained skills can relate to future jobs. They may also overestimate their skills or their employers’
assessments of their skills (Cuseo, et al., 2020; Long, 2018), thus exhibiting poor metacognition.
Unlike their peers whose degrees manifest titles that define future careers (i.e.-nursing=nurse,
education=teacher, accounting=accountant), humanities students are often unsure which career
opportunities align with their degree. For example, a Philosophy student may be oblivious to
that fact that students with this major consistently score in the top percentiles on the Medical
College Admission Test (MCAT), Law School Admission Test (LSAT), Graduate Management
Admission Test (GMAT), and the Graduate Record Examination (GRE) – often followed by
English majors (GMAT, 2011; ETS, 2012; Daily Nous, 2019). Furthermore, Philosophy majors
have the highest mid-salary ratings of non-STEM majors and demonstrate significant extra
earnings in their careers (Daily Nous, 2019). Although exposure to this information is readily
accessible, it requires students have information literacy and be aware of resources. As Social
Cognitive Career Theory confirms, the intentional sharing of resources and exposure to facts
teaches students necessary information and skills (Brown, et al., 2011). This gained knowledge
helps students relate information to their personal career goals and influences career choice and
self-efficacy (Brown et al., 2011).
Social Cognitive Career Theory
The conceptual framework for this study is grounded in Social Cognitive Career Theory
(SCCT), first posed by Lent, Brown, and Hackett (1994). Largely influenced by Bandura’s
(1986) Social Cognitive Theory, the basis of SCCT is to understand, explain, and predict the
ways in which individuals gain education and vocational interests, make academic and career
choices, and succeed in their academic and work goals (Brown, et al., 2011). SCCT links
educational and work performance to four psychosocial variables: general cognitive ability and
skill sets, outcome expectations, self-efficacy beliefs, and goal mechanisms (Brown, et al., 2011).
Figure 2 presents a visual representation of SCCT, related to the ways in which an individual’s
perceived skills and abilities are connected to self-efficacy, as defined by Brown, et al., 2011.
Figure 2. Visual Representation of Social Cognitive Career Theory.
According to the theory, individuals’ academic and work abilities and skills are shaped
through context of past experiences that influence self-efficacy and outcome expectations
(Brown et al., 2011). Previous experiences and contextual impacts such as access and barriers
commingle with social cognitive and behavioral elements to “facilitate or inhibit the goals that
people set for themselves and the actions they take in pursuing their goals. And they can
Performance Attainment
Performance Goals
Outcome Expectations
Self
-
Efficacy
Abilities/Skills
moderate the relationships of other variables” (Brown & Lent, 2019). Individuals who have
higher relatability in these areas demonstrate higher self-efficacy and performance attainment.
For example, if a student pursuing an engineering degree has corresponding measurers of
selfefficacy and expected outcomes, he/she is more likely to reach performance attainment (Lent
&
Brown, 2019).
Over the past 25 years, Lent, Brown, and Hackett (1994) have expanded upon the original
SCCT paradigm to include additional models of educational and occupational satisfaction (well-
being) and career self-management (Brown & Lent, 2019.) Figure 3 presents the Social
Cognitive Model of Work Satisfaction (Brown & Lent, 2019), which explains that academic and
work satisfaction is directly influenced by an individual’s self-efficacy, outcome expectations,
and performance goals (Brown & Lent, 2019). These additional concepts related with Social
Cognitive Model of Work Satisfaction have been confirmed in inquiry, such as Sheu, et al., 2018.
Figure 3. Visual Representation of Social Cognitive Model of Work Satisfaction.
Bandura’s Social Cognitive Theory (1977, 1986) closely connects Lent et al.’s (1994)
and Betz and Luzzo’s (1996) definitions of self-efficacy and career decision making. According
to Betz and Luzzo, self-efficacy is a mediator to behavior and refers “to a person’s beliefs
concerning his or her ability to successfully perform a given task or behavior” (1996, p.
414). As is supported by the other notable researchers, an individual’s ability to perform tasks
corresponds with personal perception of value, worth, and decision making. Although
selfefficacy can be affected by internal and external forces, specific contexts can impact beliefs.
Several studies have integrated SCCT with career self-efficacy and career development, and as
recently as 2020, Wendling and Sagas conducted research at the University of Florida, which
examined college athletes’ self-management and self-efficacy using CDSE scale elements. Dos
Santos, (2018) explains that unsupportive and discouraging environments and contexts can
impact a student’s self-efficacy and self-knowledge. Likewise, Gottfredson claims that when
Performance Attainment
influencing work and life
satisfaction
Performance Goals with
activity
Outcome Expectations in
relation to work
Self
-
Efficacy with
Expectations
Abilities/Skills
with relevant
influlences
Personality and Emotional
Characteristics
choosing a career, jobseekers are often influenced by social expectations or perceived prestige
associated with specific occupations (Niles & Harris-Bowlsbey, 2017).
These contexts, including visual representations, undergird this inquiry’s examination of
student self-efficacy and its relation to the career decision-making and career action steps.
SCCT’s connection to career development demonstrates the ways in which self-efficacy can
impact career decision-making, choices, and actions. The exploration of this study is the
selfefficacy career decision-making of humanities students, as indicated by CDSE-SF survey
scores and the 10-question career action survey developed within a SCCT framework.
Career Decision-Making Self-Efficacy and Career Action Steps
As Social Cognitive Career Theory (SCCT, Lent et al., 1994) attests, the career
development process is directly linked to self-efficacy, which correspondingly affects career
decision-making and actions toward a job. Career development is an ongoing process that
includes planning and intentional actions in order to decide upon a career and take steps to
accomplish personal work and life goals (McDonald & Hite, 2016). Within career development,
career choice is dependent upon personal preference, access to education, and personal ability
(Hill & Pisacreta, 2019). Studies have also shown that family influences and other advisors such
as teachers can impact career steps and planning (Ince Aka & Tasar, 2020). As SCCT affirms,
individuals who are unsure or anxious about making a career choice may exhibit lower
selfefficacy, or the belief in “ability to successfully perform a task or behavior” (Betz & Luzzo,
1996, p. 414).
Likewise, self-efficacy can influence whether or not an individual takes action towards
completing a task or goal (Bandura, 1992). For example, if a person has low self-efficacy, he/she
may avoid taking steps towards a career or making a career-related decision; in contrast, a person
with reported higher self-efficacy may take less time in the action of choosing their career due to
confidence in their ability to do so (Northington, 2017). It is critical therefore, to improve not
only career decision making, but self-efficacy as well, as these are directly related to individuals’
actions towards career goals.
Within Social Cognitive Theory, (SCT), Bandura (1986), maintains that self-efficacy is
affected by four types of experiences. These include: 1.) Previous accomplishments; 2.)
Vicarious learning; 3.) Social persuasion; and 4.) Physiological and affective states. Each of
these areas can positively or negatively affect self-efficacy in completing a task or action
(Schwarzer, 2014). In addition, the theory includes three sources responsible for shaping
selfefficacy. These include: 1.) Mastery experiences; 2.) Social persuasion; and 3.) Observation
of role models (Lyons & Bandura, 2019, p. 9). Perhaps the two most important in shaping
humanities students’ self-efficacy are social persuasion and observation of role models. With
negative social and governmental stimulants surrounding humanities degrees, social persuasion
negatively impacts humanities students’ self-efficacy. Likewise, if sociopolitical role models
propagate rhetoric that reflects poorly on skill and career prospects for humanities students,
selfefficacy can be reduced. Research indicates that in contrast, through positive experiences
offered by effective role modeling, students gain a sense of membership and belonging which
impacts their academic persistence and retention (Chambliss & Takacs, 2014).
SCCT augments SCT by relating self-efficacy to career decision making and action
toward a job. When choosing a career, an individual’s self-efficacy determines persistence and
whether or not he/she believes in personal ability. In fact, higher self-efficacy has been linked to
more career planning and higher motivation to pursue a career (Arghode, et al., 2021). Anafarta
(2001) asserts that career planning requires individual self-analysis (Ince Aka & Tasar, 2020);
and other studies (Harry, 2017) have shown that emotional intelligence can impact personal
beliefs such as employability (Hamzah et al., 2021). As Lyons and Bandura (2019) explain,
selfefficacy is different from self-confidence because the prior is dependent upon task-
orientation. Self-efficacy relates to perception of ability to be successful in a given task. When
completing a task or taking an action step (such as choosing a career or creating a job plan), an
individual’s self-efficacy can be altered based on perceived facility when making a choice and
performing career duties. Accordingly, Bandura (1977), and Betz and Luzzo (1996) assert that
when in the career decision-making process, individuals exhibit self-efficacy behaviors which
can impact their choices and the time in which they make their decisions.
In relation to occupational choice, Crites (1964) advocated against other approaches to
vocational education because he believed career decisions were a developmental process that one
matures throughout, rather than an opinion at a point in time. Expanding upon Super’s theory
(1955), Crites’ Career Maturity Inventory (CMI) (1964) measures individuals’ career maturity,
which shapes career choices and decisions. The Career Maturity Inventory includes five career
competencies: Self-Appraisal (an individual’s self-knowledge), Occupational information
(knowledge of jobs and resources), Goal selection (selecting a job), Planning (career planning)
and Problem solving (choosing a solution to career decision issues). Each of these CMI subtests
includes comprehension, evaluation, and solution-orientated questions. Higher scores on the
CMI correlate with higher career maturity and career development within that scale.
(Crites,1964; Crites, 1973; Lam & Santos, 2018).
Crites’ Career Maturity Theory (1964, 1973) provided a conceptual framework for the
Career Decision Making Self-Efficacy Scale (CDSE) being employed in this study. The CMI
subtests align with the CDSE subscales (Betz & Taylor, 1983), and relate to the career
development and actions associated with career decision-making. As Crites supports, career
decision-making is rooted in orientation to vocational choice, information and planning,
consistency of vocational preferences, manifestation of traits, and wisdom of vocational
preference (Crites, 1973). Career action steps, including goal selection and career planning, are
linked to career competencies and choice. Thus, in order for a student to take a career action step
and make an informed career decision, he/she must be aware of self and self needs, in order to
increase self-efficacy and build confidence when needs are met.
The Gottfredson Theory of Circumscription, Compromise, and Self-Creation expands
upon Bandura’s and Crites’s concepts of self-efficacy, in a more modern analysis of shaping
career decisions. Conducted by Linda Gottfredson (2004), the research analyzes and theorizes
the various impacts and alterations of career-aspirational compromises in childhood and
adolescence, enforced by social constraints (Gottfredson, 2004; Niles & Harris-Bowlsbey, 2017).
Similar to Bandura’s concepts of social persuasion and self-efficacy, Gottfredson links social
persuasion to career choice. The Gottfredson theory connects the sociocultural contexts and
influences pertaining to career choice and the ways in which these are shaped and altered by
selfconceptions, beginning at young ages.
In her theory, Gottfredson argues that individuals make career decisions based upon
“perceived gender appropriateness, prestige, and the degree to which the occupation will fulfill
their preferences and personality needs,” respectively (Niles & Harris-Bowlsbey, 2017, p. 50).
Gottfredson explains that when choosing a career, job-seekers often settle with a job that is
“good enough” instead of “great” and are influenced by social expectations or perceived prestige
associated with specific occupations (Gottfredson, 2004; Niles & Harris-Bowlsbey, 2017, p. 50).
Thus, a student who might have been better suited for or preferred a humanities degree may have
been persuaded to settle for another job with higher perceived prestige or occupation.
Furthermore, the theorist explains that circumscription is the process by which children or
adolescents go about selecting a career and minimize and define their options. According to
Gottfredson, the process of circumscription includes: 1.) An ability to move to abstract cognitive
processing; 2.) An ability to link self-concept to occupational preferences and options; 3.) An
ability to analyze social and individual distinctions such as sex roles; 4.) An ability to limit and
eliminate certain occupation options; and 5.) The ability to gradually define and redefine the
preferences, expectations, and process (Niles & Harris-Bowlsbey, 2017). Although the process
of circumscription begins in the cognitive processing and growth development of children and
adolescents, its theory still applies to adult students undergoing career decision making. While
amidst the compromising phase in the career development process, individuals (beginning with
children and adolescents) undergo a shift in self-awareness. During this phase, personal
preferences become overlooked, altered, or neglected due to social awareness and implications.
Gottfredson’s theory once again connects social implications to self-efficacy and career
action and decision making of humanities students. Gottfredson (2004) notes, “Moreover, the
occupation one holds is increasingly seen as the measure of who one is in society” (p. 2).
Demographic influences such as parental, cultural, environmental, behavioral, or social stimuli,
can impact career actions and choices. As Chuang et al., (2009) notes, “personal inputs (e.g.,
gender, race, and personality), contextual factors (e.g., social/academic status, culture, and
family), and learning experiences (e.g., work experiences) influence career behaviors in
important ways” (p. 19). Pivotal then within job selection, is an individual’s self-awareness and
understanding of what the job requirements are, what qualifications it entails, and the daily
interactions associated with the position. Career action steps can help in knowledge acquisition
of these vocational components and possibly influence career decision-making self-efficacy.
Demographic influence of Self-Efficacy
Many career development studies and theories supported by psychological research have
examined the impacts of demographic factors on self-efficacy and career decision making. In
addition to Bandura’s Self-Efficacy Theory (1977), Lent, et al.‘s Social Cognitive Career Theory
(1994), Gottfredson’s (2004) Theory of Conscription and Compromise, Super’s (1957) Theory of
Vocational Development, and Savicka’s (2019) Career Construction Theory, among others,
support sociocultural and demographic influence in vocational choices (Super, 1957; Brown &
Lent, 2005; Savicka, 2019). As supported in Brown and Lent (2005), various inquiries have
noted racial and gender differences amongst individuals’ career decisions. Likewise, variations
are prevalent within other demographic and population characteristics such as first-generation
student status (Pulliam et al., 2017; Raque-Bogdan & Lucas, 2016), and socioeconomic status
(Johnson & Muse, 2015). Within this study, career decision-making self-efficacy is being
explored across 6 demographic areas: age, gender, major, race/ethnicity, college first-generation
status, and year.
Gender and Age
Gender and age are often researched in context with one another. As SCCT affirms, these
variables may influence each other and career perceptions (Chuang et al., 2009). For example,
Peterson (1993) demonstrated age correlation with career decision-making self-efficacy (Betz &
Taylor, 2012). According to the study, those with higher ages reported higher selfefficacy (Betz
& Taylor, 2012). In alignment with Gottfredson’s Theory, age can influence career decisions as
early as adolescence, and research shows that elementary-aged boys and girls think differently
about science careers (Rodrigues et al., 2011).
Within higher education, Johnson and Muse (2015) found that in comparison to males,
female students were more likely to select majors in fields such as Education, Social Sciences,
Health Sciences, Psychology, English, Language, Music, Theater, Communication, Art,
Biosystems. Males were more likely to select majors such as Business, Engineering, Computer
Science, Economics, Architecture (Johnson & Muse, 2015). Relatedly, Stewart et al., (2020)
found that while enrolled in science/math courses, female students reported lower self-efficacy
than male students (Stewart et al., 2020). Given the data, gender may impact both major choice
and self-efficacy. Interestingly, however, Stewart et al., (2020) also found that both male and
female students in STEM classes reported high self-efficacy towards their intended profession.
Thus, science/math students may report low self-efficacy in their academic performance, but
high self-efficacy in their career goals. The present study’s investigation of career
decisionmaking self-efficacy of humanities students provides additional insight in gender and
major differences amongst students within majors outside of STEM, with degrees without direct
career title correlation.
Major and Year
Similar to Stewart et al.’s (2020) data, research using national-level data from Leppel
(2001), suggests that students in majors more directly associated with careers, especially
business degrees, may be more committed to their career goals and more likely to persist in their
studies. This study’s intentional exploration of majors not directly associated with career titles
contributed to further investigation of the association between self-efficacy and career
commitment through action steps.
In a similar way, Ludwikowski (2019) explored humanities students’ self-efficacy related
to personality skills in relation to majors and found that humanities students reported higher
social self-efficacy than biological sciences/medicine majors. Ludwikowski’s (2019) inquiry may
suggest that humanities students’ skills and personalities can shape their career preference.
Likewise, Johnson and Muse (2015) claim that a student’s high school academic performance
and curriculum is associated with choice of major. Students who had high math self-efficacy in
high school and introductory postsecondary science laboratory courses were more likely to select
a STEM college major (C. Evans et al., 2020). Writing apprehension due to perceived lack of
writing ability can also affect major and career selection (Mascle, 2013). Thus, first-year
students who select STEM degrees may do so based on self-efficacy developed from high school
academic performance. Likewise, a students’ self-efficacy can be impacted by their college
performance and change as they progress in their higher education and studies; therefore, a
freshman student may report different self-efficacy than a senior, who has presumably taken
more college-level classes. This inquiry expands on the research surrounding these variables by
investigating specific majors and variances across academic years within the field of humanities.
Race/Ethnicity and College Generation Status
Research demonstrates that race and ethnicity can impact self-efficacy. For example,
Gushue and Whitson (2006) found that African American students with parental and teacher
support had higher career decision-making self-efficacy (Ince Aka & Tasar, 2020). In addition,
Peterson (1993) and Chaney et al., (2007) found that African American students reported higher
career decision-making self-efficacy than Caucasian students and those of other races and
ethnicities (Betz & Taylor, 2012). Foud, Smith, and Enochs (1997) found that urban minority
students had higher self-efficacy than suburban minority students in their study (Betz & Taylor,
2012). Some studies have also shown differences in certain ethnicities’ perceptions of careers in
science fields and liberal arts (Rodrigues et al., 2011; Nicholas, 2018), and understandings of
how career goals can be accomplished (HERI, 2019). Nicholas (2018) claims that international
students may not experience the same public perceptions and stigmas surrounding the
employability of liberal arts degrees, which could thereby influence their major choice and
opinions of these degrees. As SCCT supports, students’ self-efficacy is often linked to their
background and personal experiences (Lent et al., 1994). For example, Peterson (1993) found
that students’ self-efficacy was higher when paternal and maternal levels of education were
higher. Similarly, research conducted by R. Evans et al., (2020) found that first-generation
students reported high levels of self-efficacy attributed to positive attitudes of independence,
self-motivation, and determination, despite being the first in their families to be pursuing higher
education (as defined by R. Evans et al., 2020). These traits may be learned skills within the
population, as first-generation students may not have as much knowledge of, or access to
resources to help their academic performance as their peers (Chang et al., 2019; R. Evans et al.,
2020); Therefore, first-generation students may need specific college transition programs and
career activities to grow and improve career self-efficacy (Kezar et al., 2020). As Chuang,
(2009) asserts, faculty and field experts play a large role in developing students’ career goals and
expectations, and “differences in the academic areas and related professions impact students’
career decision self-efficacy, career outcome expectations, and vocational exploration and
commitment” (Chuang, 2009, p. 26).
Within the field of humanities and liberal arts, there have been examinations of concepts
of employability related to demographic characteristics. For example, Nicholas (2018)
qualitatively found that minority liberal arts students (including Psychology majors within the
study) did not express expectations of workforce marginalization, except in the case of
international students. Mullen (2014) qualitatively examined college major choice amongst
liberal arts students with results that indicated gendered occupational structure or gender-type
concepts that likely influenced major choice. Mullen’s research (2014) also garnered results that
indicated culturally significant differences within liberal arts major choice.
As supported above, gender and demographic disparities evince influence in career and
major/degree selection. As Beutel et al., explain, “In particular, we find men’s adherence to the
masculine norm of emotional control is associated negatively with selecting majors from such
academic fields as clinical and health sciences or arts and humanities compared to science,
technology, engineering, and mathematics (STEM) and doctoral-track medicine (e.g.,
premedicine and pre-dentistry)” (2019, p. 374). Given Beutal et al.’s (2019) comments, fields
associated with humanities majors may already be perceived as gendered feminine and impact
students’ career choice. Relatedly, research shows that females are less likely to pursue STEM
fields than males (C. Evans et al., 2020), which corresponds with a higher number of females in
humanities degrees (Ruggieri, 2019).
The scope of the present study expands upon Buetel et al’s., claims surrounding the
gendering of the humanities by quantitatively examining how sociocultural demographics, such
as gender, are presented in the career decision making self-efficacy of a specific population of
humanities students. Limited (if any) research is related to how the major choice of humanities
students influences their career decision-making self-efficacy. However, with inquiry evidence
that gender and demographic differences exist within career choices of other populations, and
within different majors in the humanities population overall, the results of this study add to field
research rooted in examining career decision-making self-efficacy.
Self-Efficacy and the Humanities
Research demonstrates that career decision-making is affected by self-efficacy and can be
rational, intuitive, or dependent (Galles, et al., 2019). Dependent self-efficacy can be shaped by
external forces that contribute to attitudes and beliefs surrounding careers and associated
perceptions (Galles, et al., 2019). Likewise, Social Cognitive Career Theory supports that role
models, advisors, and even “inspirational talk from leaders in the field” can influence students’
decision-making (Chuang, et al., 2009, p. 23). In today’s U.S. academic and economic
environment, students are often exposed to negative rhetoric and perceptions surrounding the
humanities. As Jaschik (2014) reports, both republican and democratic politicians devalue
humanities degrees. Figure 4 provides a charted summary of some politicians’ comments.
Politician
Degree
Comment
Barack Obama,
Former U.S.
President,
Democrat
Art History
“I promise you, folks can make a lot more, potentially, with
skilled manufacturing or the trades than they might with an
art history degree." (Jaschik, 2014)
Mitt Romney,
U.S. Senator,
Republican
English
"I wonder whether you get information coming into college
that says you know, this course of study will lead to this kind
of jobs and there’s a lot of opening here as opposed to – as
you said, English – and as an English major I can say this....
as an English major your options are uh, you better go to
graduate school, all right? And find a job from there.”
(Jaschik, 2014)
Rick Scott, U.S.
Senator,
Republican
Anthropology
"If I’m going to take money from a citizen to put into
education then I’m going to take that money to create jobs.
So, I want that money to go to degrees where people can get
jobs in this state. Is it a vital interest of the state to have more
anthropologists? I don’t think so." (Jaschik, 2014)
Figure 4. Summary of Politicians’ Negative Comments Regarding the Humanities
Politician
Degree
Comment
Patrick McCrory,
Former Governor
of North Carolina,
Republican
Gender
studies
"If you want to take gender studies that's fine, go to a private
school and take it. But I don't want to subsidize that if that's
not going to get someone a job." (Jaschik, 2014)
John Kasich,
Former Governor
of Ohio,
Republican
Philosophy
“Philosophy doesn’t work when you run something.”
(Chideya, 2015)
Marco Rubio
U.S. Senator,
Republican
Philosophy
“Welders make more than philosophers. We need more
welders and less philosophers.” (Chideya, 2015)
Figure 4 (Continued). Summary of Politicians’ Negative Comments Regarding the Humanities
Like many other politicians and scientists, these politicians’ sentiments echo disapproval
of humanities degrees, overlooking the fact that liberal arts pedagogy laid the foundation for
contemporary education. As the Ohio Humanities Council (2020) notes, “As fields of study, the
humanities emphasize analysis and exchange of ideas rather than the creative expression of the
arts or the quantitative explanation of the sciences” (para. 5). These traits of the field readily
contribute to the workforce – including areas such as politics. Although politicians’ remarks may
not largely impact individual students’ degree decisions, their rhetoric can shape the funding and
support within higher education and the economy. The public opinions shed light on the
surrounding debate associated with employability of majors. Humanities students pursuing
degrees in a public campus atmosphere focused only on perceived major utility may feel
defenseless when asked the common question, “What are you going to do with that degree?”
What remains essential then to humanities students’ self-efficacy and career
decisionmaking action steps is an understanding of how the information and skills learned in
their degree transfers to their future careers. This pivotal part of career development allows
students to connect their major with employment and begin to take steps to strengthen their
ability to gain employment. Guidance from peers, educators, and employers should help to
reinforce the application and diverse value of humanities degree choice. As the National
Academy of
Sciences (2018) notes, there is a “growing concern that an approach to higher education that
favors disciplinary segregation is poorly suited to the challenges and opportunities of our time”
(p. 16). Just as the humanities and workforce need not be exclusive, the value of the humanities
and STEM need not be limited to one or the other. This false dichotomy can dictate and direct
students’ self-efficacy and career actions and decisions.
Summary
Demographics, sociopolitical and cultural influences, and modern academic trends
continue to shape the humanities and liberal arts. These elements relatedly impact students
within these fields. As former Education Secretary William Bennett claimed, liberal arts
education has become “so debased, narrowed, professionalized and hermeneuticized” that the
value of the education has drastically diminished (Hearn, Belasco, 2015, p. 388). Yet, the
professionalization and “hermeneuticization” of the humanities and liberal arts need not be
negative. Research demonstrates that students with humanities degrees are happily employed
and expand the labor force with necessary skills that positively shape the workforce and
economy (Anders, 2017; Jaschik, 2018; Rose, 2015; Strada Institute for the Future of Work &
Emsi, 2018). Nevertheless, shifts in opinions and perceptions surrounding humanities pedagogy
and employability can alter self-efficacy and delay career actions and decisions (Bandura, 1977;
Lent et al., 1994, Gottfredson, 2004). Thus, this study explored career decision-making
selfefficacy as a means of further examining relationships and influences related to humanities
students career actions and preparation.
CHAPTER THREE: METHODS
Democracy demands wisdom and vision in its citizens. It must therefore foster and support a
form of education, and access to the arts and the humanities, designed to make people of all
backgrounds and wherever located masters of their technology and not its unthinking servants. –
U.S. National Endowment for the Humanities
The purpose of this study was to explore the relationships amongst career decisionmaking
self-efficacy and career action step survey scores within different humanities majors and different
student demographics at a four-year, public university, in order to better inform the career
development and preparation of students within these fields. Using a correlational research
design, the study explored the career decision-making self-efficacy and career action steps of
undergraduate humanities students in majors that do not have corresponding career titles – i.e., -
English, Philosophy, Anthropology, and History, at a four-year university in Southwest Florida.
The study employed the CDSE-SF (Betz & Taylor, 1983) instrument and a 10-question career
action steps survey, to explore the following research questions:
1.) What is the relationship between humanities student participants’ demographics (age,
gender, major, race/ethnicity, year, first-generation status) and their career action steps?
2.) What is the relationship amongst humanities student participants’ demographics (age,
gender, major, race/ethnicity, year, first-generation status) and CDSE-SF instrument score
and subscale scores (Self-Appraisal, Occupational Information, Goal Selection,
Planning, and Problem Solving) and the dependent variable career action steps?
3.) What is the relationship between student demographics (age, gender, major,
race/ethnicity, year, first-generation status) and the CDSE-SF score and each of the
subscale scores (Self-Appraisal, Occupational Information, Goal Selection, Planning, and
Problem Solving)?
In this chapter, the research design, institutional context and sample selection, target data,
instrumentation, data collection procedures, and data analysis are reported.
Design
This study utilized a correlational research design drawing from survey data to explore
relationships of interest. The measurement of relationships amongst variables within
correlational survey design is effectively analyzed using multiple regression analysis, using
SPSS Statistics v. 25.0 software (IBM, Armonk, NY), (Allison, 1999; Creswell, 2009; Muijs,
2004; Rubinfeld, 2011; Sheposh, 2020). According to research theory, a correlational design has
three different types of testing: predictive, descriptive, and model testing (Seeram, 2019). Model
testing is frequently used for examining proposed relationships amongst variables (Seeram,
2019); therefore, this study employed model testing correlational analysis as a means of
examining relationships across demographic variables (age, gender, ethnicity, year,
firstgeneration status) and subscale scores and demographics, subscales, and career action steps.
Institutional Context and Sample Selection
The university in the study was selected due to proximity and convenience. The
undergraduate enrollment of the university is approximately 14,000 students, with over 49
majors, including English, Philosophy, Anthropology, and History. As of the Fall 2020 semester,
the English department reported 157 undergraduate student majors, Philosophy department
reported 40 undergraduate majors, the Anthropology department reported 45 undergraduate
majors, and the History department reported 88 undergraduate majors, for a total population of
330 undergraduate students. Per the university website, additional humanities degrees offered at
the university include Art, Music, and Theater. These degrees were excluded from the study due
to the strict, segmented curriculum associated with these programs and their career training
application. Students in these majors apply their skills in practice through concerts, museums,
displays, and internships. Although Anthropology and History are listed under Social Sciences at
the university, this is not common labeling or practice; however, at this university, these fields are
associated with Sociology in order to save money by employing one chair for the departments.
A purposive, convenience sample from an overall population of 330 participants was
expected to take part in the study. In order to account for the proper anticipated size of the effect,
the estimated variability in scores, and the desired power (Tabachnick & Fidell, 2020), a sample
size of more than 100 participants was expected. As Tabachnick and Fidell (2020) support, there
are multiple formulas and online resources for computing sample size. For this study, four
different majors were selected, as the number of humanities majors being studied (4) increases
the homogeneity of the sample, which can lower the error variability (Tabachnick & Fidell,
2020). Sample size for this survey was calculated using G*power for approximate number of
cases when looking at the relationships amongst the predictor variables, action steps, and CDSE-
SF subscales. Assuming a medium effect size (f2=0.15), error probability of 0.05, power size
of 0.95, and 11 predictor variables, the total sample size needed was 178 respondents (Faul, et
al., 2009).
Target Data
In order to explore research questions one and two, the 11 predictor variables for the
twostep multiple regression analysis included: age, gender, major, race/ethnicity, college
firstgeneration status, year, and participants’ scores per each of the 5 subscales included in the
CDSE-SF instrument (Betz & Taylor, 1996) (Self-Appraisal, Occupational Information, Goal
Selection, Planning, and Problem-Solving). The dependent criterion variable was the composite
score of the career action step survey, with a highest possible score of 10 and a lowest possible
score of 0, with each “No” response coded as 0 and each “Yes” response coded as 1. Possible
relationships amongst demographics and career decision-making self-efficacy subscale scores
were evaluated for multicollinearity.
For research question three, a series of multiple regression analyses were utilized with 11
predictor variables, including age, gender, major, race/ethnicity, college first-generation status,
and year. The dependent criterion variable was the participants’ scores per each of the 5
subscales included in the CDSE-SF instrument (Betz & Taylor, 1996) (Self-Appraisal,
Occupational Information, Goal Selection, Planning, and Problem-Solving). Extraneous
variables that were not measured but possibly present in the study included participant prior
knowledge, maturation, ability-level, university engagement, environment, understanding of
CDSE-SF instructions, history, and mortality. Control variables not measured in the study
included the location and instructions of the survey being administered to participants. The IP
address of respondents served as a measured control for more than one response from the same
student. Only one response per participant was recorded with data.
Instrumentation
CDSE-SF
Rooted in Bandura’s concepts of self-efficacy (1977, 1994) and Crites’ five career
competency areas (1964), and Career Maturity Theory (Crites, 1973), Betz and Taylor (1983),
developed the Career Decision-Making Self-Efficacy Scale (CDSE) to assess individuals’
selfefficacy in relation to career choices by asking participants to rank their responses on a 5-
point scale from “No confidence at all” to “Complete confidence.” The scale associates self-
efficacy beliefs with the career decision-making process. Similar research demonstrates that
participants with lower self-efficacy often have lower career decision making – meaning it took
longer for them to decide on a career to pursue. These findings are significant for this study
because they suggest that if the humanities student participants in this study exhibited scores that
demonstrated low self-efficacy, it can be an indication of more time or assistance needed in
future career decision-making (Betz & Luzzo, 1996). The CDSE and CDSE-SF allowed for a
formal assessment of career decision-making self-efficacy.
The Career Decision-Making Self-Efficacy (CDSE) instrument was originally employed
by Taylor and Betz (1983) in a study of 156 students from a large public university and 190
students from a private liberal arts college (Betz & Taylor, 2012). Although there are other
studies with high internal consistency reliability coefficients, there is limited relevant research
that exists in the career decision-making self-efficacy of humanities majors, especially in fields
that manifest no correlating career titles. As supported in the CDSE Manual (Betz & Taylor,
2012), demographic information regarding participants’ race/ethnicities, age, and gender
responses to the survey have previously been measured, including Betz et al., (2005); Chaney et
al., (2007); Lo Presti et al., (2012); Miller et al., (2009). Such studies have found varying levels
of significant differences and relationships amongst gender and ethnicity within broad
populations. The Taylor and Popma (1990) study examined students’ scores based on college
major status: Declared, Tentative, and Undecided; and a study by Mathieu et al., (1993) found
that undecided college women demonstrated lower CDSE scores than women who were pursuing
male-dominated or gender-neutral careers (Betz & Taylor, 2012). In addition to other inquiries,
the CDSE and CDSE Short Form (CDSE-SF) survey tool have been used to explore self-efficacy
score relationship to career development patterns (Gianakos, 1999), social integration (Peterson,
1993), levels of maternal and paternal education (Peterson, 1993), grade point averages (Luzzo,
1993), and psychological factors (Betz & Taylor, 2012; Niles & Sowa, 1992). More recent
studies have also utilized the CDSE instrument in student athlete career self-management and
self-efficacy (Wendling & Sagas, 2020).
The full version of the CDSE was developed in 1983 and includes fifty questions divided
amongst groupings. These original groupings include: 1.) Self-appraisal; 2.) Occupational
information; 3.) Goal selection; 4.) Planning; and 5.) Problem-solving (Northington, 2017), and
align directly with Crites’ (1973) Career Maturity Theory subtests. The 50-question scale
measures “Career Decision” through coding of certainty and indecision, and “My Vocational
Situation” by assessing individuals’ concepts of identity. Higher scores on the assessment
indicate greater levels of self-efficacy and decision-making, which corresponds with a more
secure vocational identity (Betz, Klein, & Taylor, 1996). The long form CDSE was normed with
students from both a liberal arts college and a public university, and included high test-retest
reliability, sound consistency reliability (alpha scores of .86 to .89) and no significant gender
differences (Northington, 2017).
This study employed the short form of the CDSE scale, which was quality tested. The
short form of the CDSE (CDSE-SF) was created in 1996, in an effort to provide counselors and
career educators with a less time-consuming tool for identifying self-efficacy in career
decisionmaking (Betz, Klein, & Taylor, 1996). The CDSE-SF reduced the 50-question original
assessment to 25 questions (Betz & Luzzo, 1996), while still employing the 5 subscales that
directly relate with Crites’ career competency areas and Career Maturity Theory (1973). The
CDSE-SF was normed at a large midwestern university through an introductory psychology
course, with an internal consistency of .94 and original test-retest reliability of .83. The CDSESF
garnered reliability and validity results that mirrored or exceeded the long version of the test, and
like the long form, produced no significant indication of gender bias (Betz, Klein, & Taylor,
1996). The CDSE-SF has correlations ranging from .73 to .83. In addition, the CDSE and the
CDSE-SF have been formally tested and used in a variety of educational settings and inquiries,
such as Robbins (1985), Taylor and Popma (1990), and Peterson and Del Mas (1998), among
others (Betz & Luzzo, 1996). To date, no notable research previously employed the CDSE-SF in
humanities specifically measuring the elements of this inquiry.
Content, construct, and criterion validity for the CDSE-SF has been conducted in various
other studies, including Robbins (1985); Taylor and Popma (1990); Peterson and Del Mas
(1998); Betz & Luzzo, (1996) Miguel et al., (2013). In the Betz (1983) study, results indicated
high internal consistency reliability, with .97 coefficient alpha value within each subject group.
Item-total score correlations were in the range of 50-80, with coefficient alpha values for the 5
subscale measures at .88, .89, .87, .89, and .86 for Self-Appraisal, Occupational Information,
Goal Selection, Planning, and Problem Solving, respectively (Betz, 1983). The Betz et al.,
(1996) study found that the CDSE-SF had psychometric characteristics comparable to or better
than the original CDSE instrument, indicating a highly homogenous construct and high internal
consistency reliability (Betz et al., 1996). The Betz et al., (2005) used the CDSE-SF 5-level
response continuum in comparison to at 10-level response continuum using a one-way
multivariate analysis of variance (ANOVA) to test reliability. As in the other studies, correlations
suggested validity of the 5-level response continuum (Betz et al., 2005).
Although it was developed in 1983, the CDSE-SF remains valid in contemporary
research. The survey has remained current with the 2006 verbiage replacement of occupational
information being found in the “library” to “online.” In addition, the Hartman and Betz (2007)
study utilized the online format of the survey and reported high levels of consistency and
reliability. In fact, the CDSE-SF instrument was used as recently as 2020, by researchers at the
University of Florida measuring career preparedness effects and self-efficacy of college athletes
(Wendling & Sagas, 2020). The historiography of the survey confirms its modern use and
impacts, with various studies verifying its internal consistency and reliability. Furthermore,
although some questions may seem outdated given the new technological advancements of the
twenty-first century, the administration of the survey in an online format, as well as its
continuing relavence to modern practice, makes the instrument apt for this research. More
modern online instruments (such as CAPA, FOCUS, Career Liftoff, DISCOVER, SIGI, SIGI
PLUS, and Career Cruising) do not manifest the same level of standard or persistence (Betz &
Borgen, 2009). Additionally, many have not been evaluated through assessment studies or
publication (Betz & Borgen, 2009), and remain antiquated in Parson’s (1909) career prescription
of matching self-exploration, occupation exploration, and self-occupation comparison (Lent &
Brown, 2020).
The CDSE-SF was purchased from Mind Garden Inc., with remote online survey
licensing from the authorized retailer. The CDSE manual (Betz & Taylor, 2012) provided
instructions on administering the scale and analyzing and recording data. Licensing was brought
based on number of participants.
Career Action Step Survey
In order to broaden the scope of this study and investigate the career development
behaviors of participants’ self-efficacy, 10 survey questions were added to the study. The
questions were intentionally added at the end of the study to avoid any influence or cognitive
bias in participants’ responses to the CDSE-SF scale. Questions were developed following the
question design process as outlined by Crocker and Algina (1986) and Bickman and Rog (2009).
This development and selection process included systematic question review, cognitive
interviews, and field pretests (Bickman & Rog, 2009; Crocker & Algina, 1986). The questions
developed for the action step items of this survey followed this research process. One final,
optional response allowed students to provide any other comments related to their career
decision-making self-efficacy and career action steps.
For the purpose of this study, the career action steps survey questions were created by the
primary investigator who is a certified career coach. The questions were selected as a means of
examining whether or not action steps were taken towards career development and
decisionmaking. The content creation underwent the content validation process outlined in
Crocker and Algina (1986) and aligns with the self-efficacy questions that address confidence-
levels in career decision-making practices. The questions also demonstrate common
competencies and practices associated with career readiness and preparation (Cuseo, et al.,
2020), and relate to the career competency areas and Career Maturity Theory subtests (Crites,
1973), which correspond with the CDSE subscales (Betz & Taylor, 1983). The investigator
originally selected the following 10 questions:
1.) Do you currently have a resume?
2.) Have you researched an internship in which you would like to participate?
3.) Are you currently participating in an internship?
4.) Have you previously participated in an internship?
5.) Have you researched a career in your anticipated future career field?
6.) Are you currently employed in a job associated with your anticipated future career field?
7.) Do you currently have a cover letter?
8.) Have you participated in any job fairs?
9.) Have you talked to someone in your anticipated future career field about their job?
10.) Have you participated in any career related coaching, including an online questionnaire?
These questions were reviewed by an expert panel that included a Senior Director of
Experiential Learning and Career Development, a Director of Career Development Services, a
Director of Undergraduate Scholarship/Associate Professor in the College of Arts and Sciences
Biology Program, an Associate Dean/Associate Professor within the College of Arts and
Sciences Philosophy Program, and a Program Director/Associate Professor within the College of
Arts and Sciences Department of Social & Behavioral Sciences. The feedback from the expert
panel of reviewers included suggestions to remove the question about a cover letter since it was
specific to applying for a certain job, to include the option of a Curriculum Vitae (CV), to include
a question about LinkedIn, to combine internship questions, to add the term
“workshops,” and to specifically ask about Coursera and career related texts. The panel also
recommended grouping the questions in a logical order so that certain question structures
followed associated questions. Following the panel review, the questions were re-designed to the
following:
1.) Have you researched an internship in which you’d like to participate by looking up
opportunities or speaking to a coordinator or company?
2.) Are you currently participating in an internship OR have you previously participated in
an internship?
3.) Have you researched a specific job or a specific graduate school program in your
anticipated future career field?
4.) Do you currently have a resume or curriculum vitae (CV)?
5.) Do you have a LinkedIn or another online career platform profile?
6.) Have you talked to someone in your anticipated future career field about their job?
7.) Have you participated in any job fairs in which different employers partner with your
school?
8.) Have you ever met with a career coach, coordinator, or advisor to talk about career
options?
9.) Have you participated in any career skills workshops or questionnaires such as Coursera?
10.) Have you ever read any career literature to help with choosing a career, such as
Designing Your Life?
Cognitive interviews and field tests were conducted with five undergraduate students who
were demographically comprised of one male and four females who identified as Caucasian,
Asian/Pacific Islander, and Black/African American. One student respondent was an
international student from Haiti. Feedback from the field interviews evinced the need to clarify
the question that asked about an online questionnaire. One student offered feedback about this
question by stating that she had not completed a questionnaire, but had completed an online
assessment. The verbiage confusion demonstrated a disconnect in the different titling of the
synonyms. For this reason, the verbiage was altered to “an online questionnaire or assessment”
with the added example of “Career Coach” to reduce ambiguity.
Following review from the expert panel and students, the questions were sent to a
dissertation committee for review. The dissertation committee further edited the questions to
ensure alignment with the career decision-making self-efficacy survey. Questions were then
redrafted and sent to the same students who were cognitively interviewed and field tested the
original questions. Likewise, the questions were once again sent to the same expert panel for
review. Specific feedback from the expert panel and cognitive field tests included incorporating
the examples of job shadowing and employer events, and using the term counselor. Upon
review, the following questions were tested, developed, and approved to be incorporated post
CDSE-SF scale in the survey:
1.) Do you currently have a five-year plan for your future career or graduate school goals?
2.) Have you researched a specific job or a specific graduate school program in your
anticipated future career field?
3.) Have you researched average yearly earnings of people in your anticipated future career
field or cost of tuition for your future anticipated graduate program?
4.) Do you currently have a resume or curriculum vitae (CV)?
5.) Do you have a LinkedIn or another online career platform profile?
6.) Have you talked to someone (such as an informational interview or job shadow) in your
anticipated future career field about their job?
7.) Have you participated or are you currently participating in an internship?
8.) Have you participated in any career programming such as job fairs, employer events, or
mock interviews?
9.) Have you ever met with a career coach, career counselor, career coordinator, or advisor
to talk about career options?
10.) Have you participated in any career online questionnaires or assessments such as Career
Coach or Coursera?
Data Collection Procedures
In Spring 2021, a pilot study was conducted employing the CDSE-SF instrument and
career action step survey with a sample of 20, non-humanities undergraduate students at a state
college in Southwest Florida. The pilot study included instrument distribution through an online
Qualtrics survey link. Multiple regression data analysis was used to determine score and
demographic differences as tests for efficacy measurement of the study and instrument. Pilot
study respondent feedback portrayed ease of use and clarity of questions for respondents. Pilot
study data analysis provided test results and demonstrated the need to rename variables for
clarity once collection was completed. Therefore, subscales and action step questions were
labeled following formal data collection as a means of increasing clarity for the principal
investigator’s analysis.
Formal data collection began April 5, 2021, following IRB approval. The CDSE-SF
survey and career action steps were distributed via email to the target population of 344
undergraduate humanities majors. The corresponding department chairs and program lead
faculty sent an email with instructions, survey link, and consent form to students, with reminder
emails distributed to the same population of students on April 19, 2021 and April 29, 2021 (See
Appendices A and B). The survey closed on April 30, 2021, with a total of 39 responses
collected (approx. 11% of the sample population). In order to increase responses, it was
determined that the survey would remain open throughout the Summer and Fall semesters.
Therefore, on August 23, 2021, the corresponding department chairs and program lead faculty re-
sent the survey to Anthropology, History, Philosophy, and English majors with four reminder
emails. The survey closed again on October 15, 2021, with a total of 106 surveys (31% response
rate).
Data Analysis Description
Multiple regression was the main analytic technique used in this study to examine
whether or not two or more independent variables were related to a dependent variable and the
strength of the relationship, if any exists (Allison, 1999; Laerd Statistics, 2020; Neuman, 2014).
In addition, multiple regression analysis also allows for combination and separation of
independent variable effects in order to effectively control for other variables (Allison, 1999).
The use of multiple regression for statistical analysis limits research conclusions drawn from
partial correlation by allowing for different independent variables to be measured against the
dependent variable for accuracy (Sheposh, 2020). For all of these reasons, multiple regression
analysis was employed in this study as a means of measuring the 11 predictor variables of age,
gender, major, race/ethnicity, college first-generation status, year, and participants’ scores per
each of the 5 subscales included in the CDSE-SF instrument (Betz & Taylor, 1996)
(SelfAppraisal, Occupational Information, Goal Selection, Planning, and Problem-Solving) and
their relation to the dependent variable of composite score of the career action step survey.
The research questions below were explored and analyzed using correlational research
design and multiple regression analysis, utilizing IBM SPSS Statistics v. 25.0 statistical software
(IBM, Armonk, NY).
Two-step multiple regression analysis, was associated with the following research questions:
1.) What is the relationship between humanities student participants’ demographics (age,
gender, major, race/ethnicity, year, first-generation status) and their career action steps?
2.) What is the relationship amongst humanities student participants’ demographics (age,
gender, major, race/ethnicity, year, first-generation status) and CDSE-SF instrument score
and subscale scores (Self-Appraisal, Occupational Information, Goal Selection, Planning,
and Problem Solving) and the dependent variable career action steps? The 10-question
career action step survey was developed following research best practice. As outlined by
Bickman and Rog (2009), question design includes four basic characteristics: 1.)
Questions need to be consistently understood, 2.) Respondents need to be able to access
the required information to answer the questions, 3.) Respondents must be able to answer
the question in an appropriate way, and 4.) Respondents must be willing to answer the
question. Crocker and Algina (1986) provide the structure for content validation,
including defining domain of interest, selecting qualified experts, providing a framework,
and collecting and summarizing the data. As supported by the authors, once the content
questions are selected, they must undergo evaluation. This includes systematic question
review, cognitive interviews, and field pretests (Bickman & Rog, 2009; Crocker &
Algina, 1986). Alongside the standard characteristics and evaluations, survey questions
should be unambiguous and include familiar terminology so that respondents are clear in
what is being expected and asked (Bickman & Rog, 2009). These standards were
followed in this study. As an effective measurement of survey reliability (Sheposh,
2019), Cronbach’s alpha was used to test survey question validity and internal
consistency. For the career action step items, a score of 1 was applied to every “Yes”
response, and a score of 0 was applied to every “No” response. In this way, the highest
score a participant received was a composite score of 10; respectively, the lowest score a
participant received was 0. The higher the score, the more career action steps a
participant has reported taking towards his/her career goal.
Multiple regression requires dummy coding of the categorial variables; therefore, not all
levels of the variables were noted in the collected data table, but were included in analysis.
Relationships amongst demographics and career decision-making self-efficacy subscale scores
were evaluated for multicollinearity. See Tables 1 and 2 below for instrument coding.
Table 1
CDSE-SF SPSS Instrument Coding, Variable Information, and Framework Alignment
Variable
Type
Value
(numeric code)
Label
Framework
Age
Ordinal,
Predictor,
Independent
1
18-20
SCT (Bandura
1977, 1986);
SCCT (Lent, et
al., 1994);
Gottfredson
Theory of
Conscription
(2004)
2
21-24
0
Other
Gender
Nominal,
Predictor,
Independent
2
Male
SCT (Bandura
1977, 1986);
SCCT (Lent, et
al., 1994);
Gottfredson
Theory of
Conscription
(2004)
1
Female
0
Other
Major
Nominal,
Predictor,
Independent
1
English
SCT (Bandura
1977, 1986);
SCCT (Lent, et
al., 1994)
2
Philosophy
3
Anthropology
4
History
Table 1 (Continued)
CDSE-SF SPSS Instrument Coding, Variable Information, and Framework Alignment
Race/Ethnicity
Nominal,
Predictor,
Independent
1
White/Caucasian
SCT (Bandura
1977, 1986);
SCCT (Lent, et
al., 1994)
2
Black/African
American
3
Hispanic/Latinx
4
Other
Year
Ordinal,
Predictor,
Independent
1
Freshman
SCT (Bandura
1977, 1986);
SCCT (Lent, et
al., 1994);
Gottfredson
Theory of
Conscription
(2004)
2
Sophomore
3
Junior
4
Senior
College First-
Generation
Status
Nominal,
Predictor,
Independent
1
First-Generation
SCT (Bandura
1977, 1986);
SCCT (Lent, et
al., 1994)
2
Not First-Generation
Career Action
Step Survey
Dependent, Table
1
2
No
Crites (1973);
Taylor & Betz
(1983)
Career Action
Step Survey
Dependent, Table
1
1
Yes
Crites (1973);
Taylor & Betz
(1983)
Table 2
CDSE-SF Instrument Coding for Dependent Variable Subscales, Variable Information, and
Framework Alignment
Variable
Type
Associated Question
Framework
Self-Appraisal
Nominal, Predictor,
Independent Table 1.
Dependent, Tables 4-6
5
9
14
18
22
Crites (1973); Taylor
& Betz (1983)
Occupational
Information
Nominal, Predictor,
Independent Table 1.
Dependent, Tables 4-6
1
10
15
19
23
Crites (1973); Taylor
& Betz (1983)
Goal Selection
Nominal, Predictor,
Independent Table 1.
Dependent, Tables 4-6
2
6
11
16
20
Crites (1973); Taylor
& Betz (1983)
Planning
Nominal, Predictor,
Independent Table 1.
Dependent, Tables 4-6
3
7
12
Crites (1973); Taylor
& Betz (1983)
Table 2 (Continued)
CDSE-SF Instrument Coding for Dependent Variable Subscales, Variable Information, and
Framework Alignment
Variable
Type
Associated Question
Framework
Planning
Nominal, Predictor,
Independent Table 1.
Dependent, Tables 4-6
21
24
Crites (1973); Taylor
& Betz (1983)
Problem Solving
Nominal, Predictor,
Independent Table 1.
Dependent, Tables 4-6
4
8
13
17
25
Crites (1973); Taylor
& Betz (1983)
Career Action Step
Survey
Dependent, Table 1
1-10
Crites (1973); Taylor
& Betz (1983)
In continuing research exploration, multiple regression was utilized as a means of analyzing
the research question:
3.) What is the relationship between student demographics (age, gender, major,
race/ethnicity, year, first-generation status) and the CDSE-SF score and each of the
subscale scores (Self-Appraisal, Occupational Information, Goal Selection, Planning, and
Problem Solving)?
Multiple regression was employed for each of the subscale scores across demographics as an
indication of correlation amongst career decision-making self-efficacy scores and participant
demographics. The independent variables were the student demographics. The dependent
variable was the score for each CDSE-SF subscale. Given the research surrounding demographic
influence of self-efficacy, relationships were expected across subscale within this population and
across action step task completion (Betz & Taylor, 2012; Brown, et al., 2011; Chuang, et al.,
2009; C. Evans, et al., 2020; R. Evans, et al., 2020; Galles et al., 2019; Jo, et al.,
2016; Johnson & Muse, 2015; Peterson, 1993; Schwarzer, 2014; Stewart, et al., 2020).
Summary
This study employed the CDSE-SF (Betz & Taylor, 1983) instrument and a 10-question
career action steps survey. The data analysis included multiple regression, which involved
relations between the continuous variable demographics and CDSE-SF subscales, and the
categorical variable career action step score. Cronbach’s alpha was calculated for the career
action step questions, which were used as dependent criterion variable. Multiple regression
analysis of demographics and each CDSE-SF subscales was conducted. Data predicted relations
between demographic variables and individual career decision-making self-efficacy subscales.
Analysis of this study explored the research questions and addressed the lacking research related
to career decision-making self-efficacy of humanities students.
CHAPTER FOUR: RESULTS
The purpose of this study was to explore the relationships between career decisionmaking
self-efficacy and career action steps of undergraduate humanities students in majors that do not
have corresponding career titles (i.e.- English, Philosophy, Anthropology, and History), at a four-
year university in Southwest Florida. A survey was conducted to collect data on students’ career
decision-making self-efficacy and career action steps taken while pursuing a humanities degree.
In this chapter, a summary of survey participation is reported along with results addressing each
of the research questions.
Survey Participation
Based on the overall target population, the sample size for the study was calculated using
G*power for approximate number of cases when looking at the relationship amongst the
predictor variables, action steps, and CDSE-SF subscales. Assuming a medium effect size
(f2=0.15), error probability of 0.05, power of 0.95, and 11 predictor variables, the total sample
size was 178 respondents (Faul, et al., 2009).
Thus, the CDSE-SF survey and career action steps were distributed via email on April 5,
2021 to the target population of 344 undergraduate humanities majors at a four-year university in
Southwest Florida. The corresponding department chairs and program lead faculty sent an email
with instructions, survey link, and consent form to 43 Anthropology majors, 96 History majors,
45 Philosophy majors, and 160 English majors. Reminder emails with the same content was sent
to the same population of students on April 19, 2021 and April 29, 2021 (See Appendices A and
B). Upon survey closure on April 30, 2021, a total of 39 responses were collected (approx. 11%
of the sample population). To increase responses, it was determined that the survey would
remain open throughout the Summer and Fall semesters. On August 23, 2021, the corresponding
department chairs and program lead faculty re-sent the survey to Anthropology, History,
Philosophy, and English majors. In all, cooperating faculty and chairs sent 4 reminder emails for
survey completion (See Appendices A and B).
Upon closing of the survey administration, a total of 106 surveys were collected in
response to demographic data for a 31% response rate. In turn, a total of 94 students completed
the CDSE-SF survey, with 93 students responding to the career action step survey as well. Data
was assessed to account for repeat IP addresses and completion rate, which resulted in a total of
86 useable responses serving as the basis for conducting multiple regression analyses.
Demographic Analysis
A summary of demographic characteristics of survey respondents is reported in Table 3.
The majority of responding students were between the ages of 18-20 (47.2%), followed by
students ages 21-24 (41.5%). Students in the study would be considered representative of the
typical freshman or sophomore age. Comparatively, Junior (39.6%) and Senior (30.2%) students
had larger population responses than freshman and sophomore years (13.2%, respectively). This
may be due to interpretation of credit influence in year. For example, dual enrollment students
may have more credits than typical first-time in college freshmen and therefore may consider
their year to be higher than a freshman, even if it is their first full year in college. This would
account for the majority of student responses being between ages 18-20, for Junior or Senior
years. The age category, including “other,” did not correspond with the year responses and was
excluded in the analysis given the lack of clarity. Further, the majority of survey respondents
were female (56.6%), while 33% of respondents in this study were male (see Table 3). In all,
6.6% of respondents reported “other” category and were removed from analysis due to
unspecified responses. Study participation by gender aligns with overall humanity degree
enrollment at the participating university. As was the case in this inquiry, more females often
pursue humanities degrees than males (Ruggeri, 2019).
Table 3
Demographic Analysis
Variable
N
Percent
Gender
Female
60
56.6
Male
35
33.0
Year
Freshman
14
13.2
Sophomore
14
13.2
Junior
32
39.6
Senior
42
30.2
Major
English
37
34.9
History
29
27.4
Anthropology
24
22.6
Philosophy
12
11.3
Ethnicity
White/Caucasian
71
67.0
Hispanic/Latinx
16
15.1
Black/African American
6
5.7
Other
9
8.5
Generation Status
First-Generation
35
33.0
Non-First-Generation
67
62.3
Regarding major area of study, English majors accounted for the largest group of
respondents in this investigation (34.9%), while 27.4% of the students were History majors;
22.6% were Anthropology majors; and 11.3% of respondents were Philosophy majors. The
response rates for respondents’ majors also aligned with degree enrollment numbers for the
university.
In terms of ethnicity, 67% of respondents identified as White/Caucasian; 15.1% identified
as Hispanic/Latinx; 5.7% identified as Black/African American; and 8.5% identified under the
“other” option to report ethnicity. Overall, although participation by ethnicity did not represent a
diverse sample, the distribution was somewhat representative of the larger university population.
The only departure from the overall university population was the Hispanic/Latinx students,
which is larger at the university level.
Overall, 33% of the students who responded to the survey were first-generation status
students, and 63.2% of the students were not. Those who identified as first-generation status did
so because neither parent completed a four-year degree. The first-generation representation of
students within this inquiry also generally corresponds with the overall university population of
first-generation students.
Reliability Analysis
The reliability of the CDSE-SF survey in this exploratory investigation was estimated using
Cronbach’s alpha. Cronbach’s alpha coefficient was =.93 for the total CDSE-SF 25-item
measurement. The internal consistency reliability estimate of data collected is in alignment with
previously published research and reliability of the CDSE and CDSE-SF (Betz & Taylor, 2012).
In addition, Cronbach’s alpha was consistent with previously published reliability related to each
subscale. The reported internal consistency reliability for the CDSE-SF included ( =.73) for
SelfAppraisal, ( =.83) for Goal Selection, and =.94 for the total scale (Betz & Taylor, 2012). In
previous studies, scores on the subscales ranged from =.69 (Problem Solving) to =.83 (Goal
Selection) with a total internal consistency reliability of =.93 (Betz & Klein, 1996). In this
investigation, the internal consistency reliability was =.75 for Self-Appraisal; =.61 for
Occupational Information; =.83 for Goal Selection; =.76 for Planning; and =.69 for Problem
Solving. Cronbach’s alpha for the 10-item career action step survey was =.56.
Relationship Between Student Demographics and Action Steps
The first research question was concerned with the exploration of the relationship between
humanities student participants’ demographic characteristics and career action steps taken toward
career preparation. There was a total of 10 action steps participants could have reported as
completed regarding their career preparation. Therefore, a student who has completed all action
steps would receive a score of 10. In this regard, the higher the action steps completed, the
higher the level of self-efficacy was expected for the corresponding demographic group in the
CDSE-SF subscale scores, as self-efficacy is linked to task completion (Schwarzer, 2014).
As reported in Tables 4-6, on average, males reported taking fewer action steps than females,
with the exception of having a resume. In terms of gender, males had an average action step
score of 3.26, while females had an average score of 4.67. Since action step average scores were
based on the number of “yes” responses by participants, it follows that on average, male
respondents took fewer steps than females. In terms of average action steps scores, females,
English majors, students who identified as White, senior students, and first-generation students
reported the highest average scores, thus outperforming their peers in action step completion.
Interestingly, first-generation students had a higher minimum score (1.00) than their non-
firstgeneration peers (0.00), even though no first-generation student answered “yes” to all 10
questions. Further, first-generation students outperformed students non-first-gen students in
terms of average score (4.30, 4.11, respectively). This indicates that first-generation students
reported more completed action steps than continuing generation students and leads to population
career development questions for future inquiry.
Both males and females had the lowest action step score in participating in an internship
(20%, 20%, respectively). The largest percentage of males (57.1%) reported having a resume or
CV, while the largest percentage of females reported researching average yearly earnings of
people in anticipated future career fields (61.7%). This indicates that females may be more
concerned about financial costs and earnings associated with future careers or graduate
programs. Similarly, first-generation students also had a high percentage (57.1%) of action taken
in researching average earnings and costs of their future plans. 57.1% of first-generation
students also reported researching a specific job or graduate program associated with their
anticipated career field. Despite a high completion average score (4.30) very few (20%) of the
first-generation respondents reported having a LinkedIn or similar career online profile.
In terms of year, sophomores often scored the lowest in action step completion by
percentage and shared the same average action step score of 3.10 with freshmen. This is
surprising, as it would be expected that freshmen with less academic experience would likely
have performed fewer career action steps on average. Perhaps most surprisingly was that no
sophomore respondent reported talking to someone in their anticipated career field. This means
that freshman and sophomores may share similar rates of career action step completion, with no
major disparities. Perhaps as to be expected given the timeframe in school to prepare for careers,
senior students had the overall highest average mean action step score (4.91). Philosophy majors
had the second highest overall average score of (4.80). (See Tables 4 and 5).
Table 4
Descriptive Statistics for Career Action Step Scores Across Demographics
Variable
N
Min
Max
M
SD
Gender
Male
34
0.00
8.00
3.26
2.15
Female
52
1.00
10.00
4.67
2.06
Ethnicity/Race
White/
Caucasian
66
0.00
10.00
4.20
2.34
Black/African
American
6
2.00
7.00
4.00
1.79
Hispanic/
Latinx
13
1.00
6.00
4.10
1.93
Other
8
3.00
5.00
4.25
0.71
Generation
Status
First-Generation
31
1.00
9.00
4.30
1.81
Non-First-Generation
62
0.00
10.00
4.11
2.30
Year
Freshman
14
0.00
6.00
3.10
1.82
Sophomore
14
0.00
6.00
3.10
2.00
Junior
30
1.00
8.00
4.33
2.12
Senior
35
1.00
10.00
4.91
2.03
Major
English
35
0.00
10.00
4.20
2.25
Philosophy
10
1.00
8.00
4.80
2.30
Anthropology
23
1.00
8.00
4.04
1.58
History
25
0.00
9.00
4.00
2.42
Total
93
0.00
10.00
4.17
2.13
Note. n=93 Table 5
Career Action Step Response Frequencies in Descending Order for Gender, First-Gen, and Year
Action Step
Total
%
Yes
%
Male
Yes
%
Female
Yes
%
First-
Gen
Yes
%
Freshman
Yes
%
Sophomore
Yes
%
Junior
Yes
%
Senior
Yes
20. Do you currently
have a resume or
curriculum vitae
(CV)?
55.7
57.1
55.0
51.4
57.1
35.7
59.4
64.3
18. Have you
researched a
specific job or a
specific graduate
school program in
your anticipated
future career field?
54.7
45.7
60.0
57.1
42.9
57.1
71.9
50.0
19. Have you
researched average
yearly earnings of
people in your
anticipated future
career field or cost of
tuition for your
future anticipated
graduate program?
53.8
40.0
61.7
57.1
50.0
57.1
62.5
52.4
17. Do you currently
have a five-year plan
for your future career
or graduate school
goals?
34.9
31.4
43.3
40.0
35.7
28.6
40.6
35.7
26. Have you
participated in any
career online
questionnaires or
assessments such
as Career Coach or
Coursera?
34.9
28.6
40.0
42.9
57.1
50.0
28.1
31.0
25. Have you ever
met with a career
coach, career
counselor, career
coordinator, or
advisor to talk about
career options?
31.1
25.7
36.7
31.4
7.1
28.6
37.5
38.1
Table 5 (Continued)
Career Action Step Response Frequencies in Descending Order for Gender, First-Gen, and Year
Action Step
Total
Perce
nt
Yes
Perce
nt
Male
Yes
Percent
Female
Yes
Perce
nt
First-
Gen
Yes
Percen
t
Freshman
Yes
Percent
Sophomore
Yes
Percen
t
Junior
Yes
Percen
t
Senior
Yes
21. Do you have a
LinkedIn or another
online career
platform profile?
30.2
20.0
36.7
20.0
14.3
35.7
40.6
28.6
22. Have you
talked to someone
(such as an
informational
interview or job
shadow) in your
anticipated future
career field about
their job?
25.5
25.7
25.0
28.6
14.3
0.0
31.3
35.7
24. Have you
participated in any
career
programming such
as job fairs,
employer events,
or mock
interviews?
24.5
22.9
26.7
25.7
21.4
7.1
21.9
35.7
23. Have you
participated or are
you currently
participating in an
internship?
20.8
20.0
20.0
25.7
7.1
7.1
12.5
38.1
Note. n=93
Based on the reported data, seniors frequently reported more action steps than their peers,
except in the case of researching a specific job or graduate program. Seniors had higher average
completion rates in participating in an internship, or job programming, and talking to someone
or a counselor in their anticipated career. However, given that freshmen and sophomores
reported the same average score (3.10), career preparation within lower levels of education may
indicate more individualized career decision-making rather than time in higher education. The
largest differences for average scores and percentages occurred between sophomore and junior
years, which may mean that students begin most career action in their third year. However, as
stated above, students may be connecting years with credit hours rather than time. (See Tables 4-
6).
Table 6
Career Action Step Response Frequencies in Descending Order for Major and Ethnicity
Action Step
%
English
Yes
%
Philosophy
Yes
%
Anthropology
Yes
%
History
Yes
%
White/
Caucasian
Yes
%
Black/
African
Amer-
ican
Yes
%
Hispanic
/LatinX
Yes
%
Other
Yes
20. Do you
currently
have a
resume or
curriculum
vitae (CV)?
67.6
58.3
50.0
51.7
59.2
66.7
50.0
55.6
18. Have
you
researched
a specific
job or a
specific
graduate
school
program in
your
anticipated
future
career
field?
62.2
58.3
58.3
48.3
56.3
83.3
50.0
55.6
19. Have
you
researched
average
yearly
earnings of
people in
your
anticipated
future
career field
or cost of
tuition for
your future
anticipated
graduate
program?
51.4
50.0
70.8
51.7
63.4
33.3
37.5
44.4
Table 6 (Continued)
Career Action Step Response Frequencies in Descending Order for Major and Ethnicity
Action Step
%
English
Yes
%
Philosophy
Yes
%
Anthropology
Yes
%
History
Yes
%
White/
Caucasian
Yes
%
Black/
African
Amer-
ican
Yes
%
Hispanic
/LatinX
Yes
%
Other
Yes
17. Do you
currently have
a five-year plan
for your future
career or
graduate school
goals?
37.8
16.7
45.8
34.5
33.8
16.7
50.0
44.4
26. Have you
participated in
any career
online
questionnaires
or
assessments
such as Career
Coach or
Coursera?
40.5
41.7
33.3
31.0
35.2
50.0
31.3
44.4
25. Have you
ever met with a
career coach,
career
counselor,
career
coordinator, or
advisor to talk
about career
options?
32.4
50.0
33.3
24.1
31.0
66.7
23.1
44.4
21. Do you
have a LinkedIn
or another
online career
platform
profile?
40.5
41.7
20.8
24.1
31.0
33.3
31.3
33.3
Table 6 (Continued)
Career Action Step Response Frequencies in Descending Order for Major and Ethnicity
Action Step
%
English
Yes
%
Philosophy
Yes
%
Anthropology
Yes
%
History
Yes
%
White/
Caucasian
Yes
%
Black/
African
Amer-
ican
Yes
%
Hispanic
/LatinX
Yes
%
Other
Yes
22. Have
you talked to
someone
(such as an
informational
interview or
job shadow)
in your
anticipated
future career
field about
their job?
27.0
33.3
16.7
31.0
29.6
16.7
25.0
11.1
24. Have
you
participated
in any career
programming
such as job
fairs,
employer
events, or
mock
interviews?
21.6
16.7
37.5
24.1
25.4
16.7
18.8
44.4
23. Have you
participated or
are you
currently
participating
in an
internship?
16.2
33.3
20.8
24.1
25.4
16.7
18.8
11.1
Note. n=93
In continuation of the exploration of the relationship between humanities student
participants’ demographic characteristics and career action steps, there was no noticeable
consistency in percentage differences across major or ethnicity; though, in terms of average
action step scores and percentage of action steps taken, Black/African American students scored
slightly lower than their peers. This may be indication of Black/African American students
taking less action toward their anticipated careers than students of other ethnicities in the study.
As was the case with gender and year, questions 17, 19, and 20 had the largest percentages of
“yes” responses across majors and ethnicities, meaning that a majority of students have a resume,
have researched a specific job, and have researched earnings of an anticipated job. The largest
percentage of action step score response (83.3%) was reported by Black/African American
students in researching a specific job or graduate program in their anticipated field. This
population, like the first-generation students, demonstrated high interest in this area, thus
indicating an importance of future career research for these populations.
The demographic mean percentages of completer students across action steps further revealed
that by percentage, females often completed more action steps than males, with the exception of
completing an internship (Q23) and completing a career online questionnaire (Q26). As was the
case in the average career action step score, percentage wise, freshman and sophomore students
consistently scored lower than their peers, except in meeting with a career coach/counselor (Q25)
and in completing an online questionnaire (Q26). There was variation in mean percentage response
rates across majors and ethnicities, though Black/African American students often had a slightly
lower mean percent of completion. As with the other data, first-generation students had a higher
mean percentage of completion than non-first-generation students. Differences across all
demographic mean percentages indicate slight disparities amongst gender, year, major, ethnicity,
and generation status. (See Table 7).
Table 7
Demographic Mean Proportion of Completers Across Action Steps
Variable
M
Tot.
Q17
Q18
Q19
Q20
Q21
Q22
Q23
Q24
Q25
Q26
Gen.
Fem.
0.44
0.50
0.69
0.71
0.63
0.42
0.28
0.23
0.31
0.42
0.16
Male
0.33
0.32
0.47
0.41
0.59
0.21
0.24
0.26
0.24
0.26
0.29
Yr.
Frsh.
0.31
0.36
0.43
0.50
0.57
0.14
0.14
0.07
0.21
0.07
0.57
Sph.
0.31
0.29
0.57
0.57
0.36
0.36
0.00
0.07
0.07
0.29
0.50
Jun.
0.40
0.43
0.77
0.67
0.63
0.43
0.33
0.13
0.23
0.10
0.30
Sen.
0.49
0.43
0.60
0.63
0.77
0.34
0.43
0.46
0.43
0.46
0.37
Maj.
Eng.
0.42
0.40
0.66
0.54
0.71
0.43
0.29
0.17
0.23
0.34
0.43
Hist.
0.40
0.40
0.56
0.60
0.60
0.28
0.36
0.28
0.28
0.28
0.36
Ant.
0.40
0.48
0.61
0.74
0.52
0.22
0.17
0.21
0.39
0.35
0.35
Phil.
0.48
0.20
0.70
0.60
0.70
0.50
0.40
0.40
0.20
0.60
0.50
Ethn
Wh/
Cauc
0.42
0.36
0.61
0.68
0.64
0.33
0.32
0.27
0.27
0.33
0.38
His/
LaX.
0.41
0.62
0.62
0.46
0.62
0.38
0.31
0.23
0.23
0.23
0.38
Bl/
Af.A
0.40
0.17
0.83
0.33
0.67
0.33
0.17
0.17
0.17
0.67
0.50
Oth.
0.43
0.50
0.63
0.50
0.63
0.38
0.13
0.00
0.50
0.50
0.50
Gen.
Stats
Fst.-
Gen
0.42
0.45
0.61
0.60
0.66
0.40
0.27
0.21
0.27
0.35
0.35
Non-
First
-Gen
0.41
0.37
0.61
0.60
0.66
0.40
0.27
0.21
0.27
0.35
0.35
To determine relationships between the demographic variables (gender, major,
race/ethnicity, college first-generation status, year) and the dependent variable of career action step
score (1-10), multiple regression was used with data reported in Table 6. As noted above, the
variable age was removed due to inconsistent definition within the data. Year was analyzed as a
scale/continuous variable given the misconception related to misconception and incongruence of
student reporting. However, when year was analyzed as an ordinal variable out of interest, with
senior as reference category, no significance was found across subscales or total score. Multiple
regression required dummy coding of the categorical variables, including ethnicity and career
majors using White/Caucasian and English as reference categories, respectively. Therefore, not all
levels of the variables are noted in the table; but all variables listed above were included in the
analysis. The summary of the results for the regression analysis is reported in Table 8.
Table 8
Multiple Regression Analyses for Career Action Steps and Demographics
Variable
r
b
SE
B
Gender
(1=M, 0=F)
-0.32*
-0.13
0.05
-0.30
Blacka
-0.02
-0.04
0.09
-0.05
Hispanica
-0.01
-0.03
0.07
-0.05
Othera
0.03
-0.02
0.09
-0.02
First-Generation
(1=Yes, 0=No)
0.05
0.03
0.05
0.07
Yearb
0.32*
0.06
0.02
0.29
Philosophyc
0.10
0.03
0.50
0.09
Anthropologyc
-0.02
-0.06
0.60
-0.12
Historyc
-0.06
-0.02
0.06
-0.03
R2
0.21
Note. n=86. SE = Standard error.
a
White/Caucasian is used as reference category. b
Year is ordinal with 4 categories Freshman (1), Sophomore (2), Junior (3), Senior (4). c
English is used as reference category.
* p<.05, **p<.01
The data recorded in Table 8 represents the measurement of association between the
independent variables of student demographics (gender, major, race/ethnicity, college
firstgeneration status, year) and the dependent variable of career action step score (1-10). The
goal was to determine whether or not students’ demographics were related to their action steps
towards career activities. As reported in Table 8, 21% of the variance in action steps was
explained (overall R=.45, p=.03, with a F value of 2.17 and 9, 85 degrees of freedom). As per
the results shown in Table 8, only two variables were statistically significant (p <.05): gender and
year. Males reported fewer action steps and increasing class year was associated with more
action steps reported. All other variables were not statistically significant.
The relationships between gender and action steps and year and action steps were
moderately strong (Cohen, 1977), with a confidence interval of 95%. Specifically, the
relationship between gender and action steps demonstrates prediction with B= -0.30, t-value,
-2.7, p=.008. Male gender category scores were reported as negative due to coding against the
female category, and indicates an inverse relationship. The results suggested that males took
fewer action steps than females, with an average score of 3.26 (out of 10) for males and 4.67 (out
of 10) for females.
Similarly, year (freshman, sophomore, junior, or senior) significantly predicted action
steps (B= .29, t=2.7, p= .009). The relation of year to action steps aligns with the expectation
that as students progress in their studies from freshman to senior, they complete more activities
associated with career preparation. For example, a senior student would be expected to perform
more action steps than a freshman given more academic and career preparation over time and
more credits completed.
Relationship Among Student Demographics, CDSE-SF Subscales, and Action Steps
The second research question in this study examined the indicators of association
between student demographics (gender, major, race/ethnicity, college first-generation status,
year) and career decision-making self-efficacy subscale scores (Self-Appraisal, Occupational
Information, Goal Selection, Planning, and Problem-Solving) and the dependent variable of
career action step score (1-10). In addition to the examination of relationship between
demographics and action steps in Table 8 (presented in Model 1 of Table 9), the examination of
the relationship between demographics and career decision-making self-efficacy subscale scores
and action steps is presented in Model 2 in Table 9. (See Table 9).
Table 9
Multiple Regression Analyses for Career Action Steps and Demographics
Variable
r
Model 1
b SE
B
b
Model 2
SE
B
Gender
(1=M, 0=F)
-0.32*
-0.13
0.05
-0.30
-0.10
0.04
-0.23
Blacka
-0.02
-0.04
0.09
-0.05
-0.08
0.08
-0.09
Hispanica
-0.01
-0.03
0.07
-0.05
-0.04
0.07
-0.06
Othera
0.03
-0.02
0.09
-0.02
-0.07
0.08
-0.08
First-Generation
(1=Yes, 0=No)
0.05
0.03
0.05
0.07
0.08
0.05
0.18
Yearb
0.32*
0.06
0.02
0.29
0.05
0.02
0.25
Philosophyc
0.10
0.03
0.50
0.09
0.05
0.08
0.08
Anthropologyc
-0.02
-0.06
0.60
-0.12
-0.07
0.06
-0.13
Historyc
-0.06
-0.02
0.06
-0.03
-0.01
0.09
-0.02
Self-Appraisal
0.33
0.03
0.06
0.10
Occupational
Information
0.53
0.19
0.05
0.54*
Goal Selection
0.27
-0.04
0.05
-0.12
Planning
0.46
0.03
0.06
0.08
Problem Solving
0.38
-0.03
0.05
0.47
-0.08
R2
0.21
Note. n=86. SE = Standard error.
a
White/Caucasian is used as reference category. b
Year is ordinal with 4 categories Freshman (1), Sophomore (2), Junior (3), Senior (4). c
English is used as reference category.
* p<.05, **p<.01
The second research question of the study investigated whether demographic variables in
addition to responses to specific subscales had a combined association with action steps. Similar
to the results of the analysis of Table 8 suggested, the results of the data presented in Table 9
demonstrated that the demographics and subscales do predict change in action steps. Specifically,
when including the CDSE-SF subscales, the resulting model explained 47% of variance in action
steps with F value of 4.4 [(14, 85 degrees of freedom), p<.001].
As reported in Model 2 in Table 9, 47% of variance is explained (overall R=.45; with a F
value of 2.17 and 9, 76 degrees of freedom). The change in degrees of freedom is due to the
inclusion of more variables in the regression. Similarly, the increase in the percent of variance
explained from 21% to 47% demonstrated that the combined model changed and impacted the
relationship, with the introduction of subscales further strengthening the relationship. In this
regard, it is important to note that gender and year remained significant in Model 1 and Model 2,
which also accounted for subscale scores. As such, in the second model, there is a relation
between action steps and gender (male category), with B= -0.23, t value, -2.7, p=.002. As in
Table 8, male scores were coded against the female category, and the results suggested that males
took fewer action steps than females in the study. The demographic “year” also significantly
predicts action steps (B= .25, t=2.7, p= .014). This demonstrated the strength of the relationships
across action steps and CDSE-SF subscale scores.
Regarding the individual impact of subscales, only the subscale Occupational Information
demonstrated significant prediction with B= 0.54, t value, -2.7, p<.001. That is, students who
know more about their career may also tend to complete more action steps towards the transition
upon graduation. The relationships amongst the demographics and subscale
Occupational Information with the action steps were moderately strong (Cohen, 1977.
Individually, the subscale, Occupational Information had the strongest linear relationship
(Pearson Correlation), between each predictor variable and the dependent variable, while not
controlling for the other variables in the model (r=0.53). The higher the Occupational
Information self-efficacy reported, the more action steps a student completed. Next, Planning
had the second strongest relationship with action step completion (r=.56), followed by Problem
Solving (r=0.38), Self-Appraisal (r=0.33), and finally Goal Selection (r=0.27). However, none
of the relationships were significant (p=<.001), as reported in Table 9.
Multiple Regression Results for Demographics and CDSE-SF Subscales
The third question driving the study was set to determine the relationship between student
demographics and each of the career decision-making self-efficacy subscale scores. As Table 10
indicates, females had higher average subscale scores than males, except in the Goal Selection
subscale. This suggest that males are more career goal-driven than females. In terms of
ethnicity, students who identified as “Other” or Black/African American consistently performed
higher than their White/Caucasian and Hispanic/Latinx peers in career decision-making
selfefficacy. (See Table 10).
Table 10
Demographic Means Across CDSE-SF Subscales
Self- Occupational Goal Planning Problem Total
Variab Appraisal Information Selection Solving
le M SD M SD M SD M SD M SD M SD
Male
3.65
0.63
3.66
0.62
3.72
0.67
3.54
0.69
3.58
0.76
3.63
0.56
Femal
e
3.75
0.75
3.82
0.62
3.70
0.81
3.70
0.72
3.77
0.63
3.75
0.63
W/
Cauc.
3.67
0.67
3.72
0.59
3.69
0.74
3.56
0.70
3.62
0.66
3.75
0.58
Black/
A.Am
3.80
0.57
3.87
0.41
3.77
0.75
4.00
0.70
3.87
0.56
3.86
0.46
His./
Lax.
3.63
0.93
3.61
0.72
3.60
0.80
3.78
0.70
3.64
0.85
3.65
0.73
Other
3.98
0.93
3.93
0.73
4.10
0.80
3.75
0.70
4.0
0.85
3.95
0.73
First-
Gen
Yes
3.55
0.74
3.57
0.54
3.54
0.66
3.50
0.73
3.57
0.71
3.55
0.58
First-
Gen
No
3.77
0.66
3.81
0.63
3.80
0.76
3.70
0.66
3.73
0.66
3.76
0.58
Frsh
3.54
0.90
3.73
0.60
3.57
0.88
3.39
0.83
3.64
0.78
3.57
0.72
Soph.
3.23
0.61
3.53
0.53
3.31
0.70
3.13
0.71
3.30
0.86
3.31
0.58
Junior
3.83
0.63
3.83
0.66
3.83
0.62
3.70
0.63
3.67
0.56
3.77
0.52
Senior
3.82
0.62
3.73
0.61
3.82
0.74
3.88
0.54
3.83
0.60
3.81
0.53
Eng.
3.62
0.76
3.71
0.53
3.62
0.72
3.63
0.74
3.54
0.72
3.63
0.60
Philo.
3.44
0.70
3.74
0.82
3.44
0.82
3.72
0.70
4.06
0.55
3.68
0.60
Anth.
4.01
0.54
3.86
0.67
4.13
0.59
3.66
0.68
3.96
0.55
3.92
0.52
Hist.
3.63
0.66
3.63
0.58
3.56
0.73
3.58
0.66
3.45
0.64
3.57
0.59
Note. n=94.
Subscales range from 1-5.
In turn, Hispanic/Latinx students had the lowest averages across all career
decisionmaking subscales and overall. Likewise, first-generation students also had lower
average subscale scores than those who were not first-generation. This is noteworthy due to the
fact that first-generation students performed more action steps than their peers, with a higher
average score, but scored lower than their peers in self-efficacy. Thus, first-generation students
may take more career steps, but feel less confident in their career decision-making abilities. In
terms of academic level, sophomore students demonstrated lower average subscale scores than
their peers, while Anthropology and Philosophy majors had higher average scores than the other
majors. majors had the lowest average subscale score, indicating that this group of students may
have the lowest career decision-making self-efficacy. (See Table 10).
To address the third research question, a series of multiple regression analyses were used
to evaluate the relationship between student demographics (predictors) and each CDSE-SF
subscale score (5 separate dependent variables: Self Appraisal, Organizational Information, Goal
Selection, Planning, Problem Solving). For reference, the relationship between student
demographics and the overall score on self-efficacy as measured by the CDSE-SF was first
estimated.
Overall CDSE-SF Subscale Score Analysis
When examining the effect of the nine student demographic variables predicting the total
CDSE-SF score, the overall multiple regression model had an explained variance of 15%, and
was not significant (p = .15; F value = 1.56; 9,77 degrees of freedom). Additionally, when
assessing the linear relationship (Pearson correlation) between each demographic variable and
Overall Score, while not controlling for the other variables in the model, no significant
relationships were observed. Although year (r =0.26) and Anthropology major (r =0.23) had the
strongest predictor relationships (using English as a reference category for major) collectively
this data indicated that the nine student demographic variables have no relationship to the
Overall CDSE-SF score. (See Table 11).
Table 11
Multiple Regression Results for Overall and Self-Appraisal
Variable
Overall
Self-Appraisal
R
b
SE
B
r
b
SE
B
Gender
(1=M, 0=F)
-0.01
-0.02
0.14
-0.01
-0.07
0.04
0.16
0.03
Blacka
0.07
0.17
0.25
0.07
0.04
0.09
0.30
0.03
Hispanica
-0.04
0.13
0.20
0.08
-0.05
0.08
0.23
0.01
Othera
0.16
0.23
0.26
0.10
0.17
0.25
0.31
0.09
First-Generation
(1=Yes, 0=No)
-0.17
-0.28
0.15
-0.22
-0.14
-0.27
0.18
-0.18
Yearb
0.26
0.12
0.06
0.21
0.25
0.15
0.07
0.23
Philosophyc
-0.01
0.00
0.23
0.00
-0.12
-0.21
0.26
-0.09
Anthropologyc
0.23
0.26
0.17
0.18
0.25
0.35
0.20
0.21
Historyc
-0.09
0.16
-0.00
-0.02
0.01
0.19
0.05
R2
Note. n=87. SE = Standard error.
a
White/Caucasian is used as reference category. b
Year is ordinal with 4 categories Freshman (1), Sophomore (2), Junior (3), Senior (4). c
English is used as reference category.
Based on the results from each of the five subscales, year and the major of Anthropology
did demonstrate consistently strongest relationships on multiple scales, with English as a
reference category, including Self-Appraisal, Goal Selection, and Problem Solving. Both
variables had significance in the Goal Selection subscale and year had significance in the
Planning subscale. This indicates that Anthropology majors may feel more confident in their
overall career skills and abilities than English majors (used as a reference category) and their
peers, and the year of a student may impact self-efficacy, which may be due to the fact that
seniors complete more career preparation, as indicated by the action step scores. In terms of
gender, males and females did not demonstrate marked differences in self-efficacy; yet males
demonstrated fewer career action steps. Ethnicity, first-generation status, year, and major did not
indicate significant differences in career decision-making self-efficacy. (See Table 11).
Self-Appraisal
When examining the effect of the nine student demographic variables predicting the
SelfAppraisal subscale, the explained variance was 16%, and the overall multiple regression
model was not significant (p = .13; F value = 1.61). Additionally, when examining the bivariate
correlation between each demographic variable and Self-Appraisal, no significant relationships
were observed. Although year (r =0.25) and Anthropology major (r =0.25) had the highest
relationships, the data indicated that the nine student demographic variables have no significant
relationship to Self-Appraisal. (See Table 11). Within the open-ended survey responses, Self-
Appraisal was indicated by a student sharing the reflection “I need to take some steps to achieve
where I want to be in my next stage of life. But I am willing to take that step!”
Occupational Information
When examining the effect of the nine student demographic variables predicting the
Occupational Information subscale, the overall multiple regression model had an explained
variance of 8%, which was the lowest for all of the subscales. The resulting data were not
significant (p = .66, F value = 0.75). Additionally, when assessing the simple linear relationship
(Pearson correlation) between each demographic variable and Occupational Information, while
not controlling for the other variables in the model, no significant relationships were observed,
though first-generation status had the highest correlation, with an inverse relationship of r =
- 0.18). The subscale results related to the action step findings as very few first-generation
students demonstrated Occupational Information activities such as having LinkedIn or a similar
career profile, which may offer further indication of this population’s low career decisionmaking
self-efficacy when finding and using resources. However, as a whole, this data indicated that the
nine student demographic variables have no significant relationship to Occupational
Information. (See Table 12).
Table 12
Multiple Regression Results for Occupational Information and Goal Selection
Variable
Occupational Information
Goal Selection
r
B
SE
B
R
b
SE B
Gender
(1=M, 0=F)
-0.12
-0.11
0.15
-0.09
0.01
0.22
0.17 0.14
Blacka
0.05
0.15
0.27
0.06
0.02
0.04
0.31 0.01
Hispanica
-0.10
0.04
0.22
0.02
-0.08
-0.03
0.25 -0.02
Othera
0.15
0.31
0.28
0.13
0.17
0.19
0.32 0.07
First-Generation
(1=Yes, 0=No)
-0.18
-0.25
0.16
-0.19
-0.15
-0.27
0.18 -0.17
Yearb
0.10
0.03
0.07
0.05
0.20*
0.14
0.08 0.20
Philosophyc
0.04
0.09
0.24
0.05
-0.15
-0.36
0.28 -0.15
Anthropologyc
0.11
0.09
0.18
0.07
0.32*
0.48
0.21 0.27*
Historyc
-0.08
0.17
-0.01
-0.09
-0.07
0.20 -0.04
R2
0.19
Note. n=87. SE = Standard error.
a
White/Caucasian is used as reference category. b
Year is ordinal with 4 categories Freshman (1), Sophomore (2), Junior (3), Senior (4). c
English is used as reference category.
* p<.05, **p<.01
Goal Selection
The linear combination of the nine student demographic variables significantly predicted
the Goal Selection subscale, with 19% explained variance (p = .05, F value = 2.0). Examination
of the bivariate relation (B) between each predictor variable and the criterion variable, while
controlling for the effects of each other predictor, demonstrated that only Anthropology was
significantly different from zero, using English as a reference category (B = .27, p < .05). When
assessing the simple linear relationship (Pearson correlation) between each predictor variable and
the dependent variable, while not controlling for the other variables in the model, year
significantly related to Goal Selection (r= .20, p < .05). (See Table 12). Thus, Anthropology
majors, and students within certain years may demonstrate more Goal selection than their peers.
The open-ended student feedback also indicated attention to goal selection, with one student
reporting, “I have volunteered with work similar to that of my career goal”.
Planning
Overall, the combination of the nine student demographic variables significantly
predicted the Planning subscale, where 19% of the variance in the Planning subscale was
explained by these predictors (p = .05, F value = 2.0). Examination of the bivariate relation (B)
between each predictor variable and the dependent variable, while controlling for the effects of
the other variables in the model, demonstrated that year (B = .30, p < .05) and first-generation (B
= -.28, p < .05) were significantly different from zero. As students’ year in college increased,
students’ scores on the planning scale increased. Students who were first-generation had
significantly lower scores on the Planning subscale compared to students who were non-
firstgeneration. When assessing the simple linear relationship (Pearson correlation) between
each predictor variable and Planning, while not controlling for the other variables in the model,
Year was statistically significant (r = .33, p < .01), and no other relationships were significant.
(See Table 13).
Table 13
Multiple Regression Results for Planning and Problem Solving
Variable
Planning
Problem Solving
R
b
SE
B
r
b
SE
B
Gender
(1=M, 0=F)
-0.11
-0.12
0.16
-0.08
-0.14
-0.11
0.15
-0.08
Blacka
0.14
0.39
0.30
0.14
0.07
0.19
0.29
0.07
Hispanica
0.09
0.44
0.23
0.23
-0.03
0.12
0.23
0.06
Othera
0.07
0.16
0.30
0.06
0.15
0.24
0.30
0.09
First-
Generation
(1=Yes, 0=No)
-0.16
-0.41
0.17
-0.28*
-0.10
-0.18
0.17
-0.12
Yearb
0.33**
0.20
0.07
0.30**
0.22
0.09
0.07
0.13
Philosophyc
0.03
-0.02
0.26
-0.01
0.19
0.51
0.25
0.23
Anthropologyc
0.04
-0.02
0.20
-0.02
0.26
0.39
0.19
0.25
Historyc
-0.04
0.19
-0.01
-0.17
0.18
-0.00
R2
Note. n=87. SE = Standard error.
a
White/Caucasian is used as reference category. b
Year is ordinal with 4 categories Freshman (1), Sophomore (2), Junior (3), Senior (4). c
English is used as reference category.
* p<.05, **p<.01
-
0.00
0.17
Problem Solving
When examining the effect of the nine student demographic variables predicting the
Problem Solving subscale, the multiple regression model had an explained variance of 17% and
was not significant (p = .08, F value = 1.81). Additionally, when assessing the simple linear
relationship (Pearson correlation) between each demographic variable and Problem Solving,
while not controlling for the other variables in the model, no significant relationships were
observed. Although year (r =0.22) and Anthropology major (r =0.25) had the highest correlated
scores, using English as a major reference category, the overall data indicated that the nine
student demographic variables have no significant relationships to Problem Solving. (See Table
13).
Correlations within subscales were high across all. Each subscale demonstrated a significant
positive, moderate to strong relationship across each other. Notably, Self-Appraisal
demonstrated the highest relationship with Goal Selection at r =0.82 (p <.001). This is followed
by Self-Appraisal and Planning’s relationship at r =0.72 (p <.001). Lastly, Self-Appraisal and
Problem Solving and Self-Appraisal and Occupational Information demonstrated similar
relationships at r =0.69 and r =0.61, respectively (p <.001). (See Table 14).
Table 14
Correlations between CDSE Subscales
Pearson Correlation
Subscale
Self
Occupational
Information
Goal
Selection
Planning
Problem
Solving
Self-Appraisal
1
0.61
0.82
0.72
0.69
Occupational Information
0.61
1
0.62
0.69
0.67
Goal Selection
0.82
0.62
1
0.64
0.60
Planning
0.72
0.69
0.64
1
0.69
Problem Solving
0.69
0.67
0.60
0.69
1
Note. n=94.
* p<.05, **p<.01
The subscales had mean scores, in descending order, of Occupational Information (3.73);
Goal Selection (3.71); Self-Appraisal (3.70); Problem Solving (3.67); Overall (3.69); and
Planning (3.63). The respondents had the lowest self-efficacy in career decision-making
Planning subscale.
Table 15 provides CDSE-SF subscale descriptive statistics. Occupational Information
had the highest mean, demonstrating that respondents may have the highest self-efficacy in
researching future careers and job-related activities. However, although the overall mean is high,
according to demographic and subscale correlation, there was indication that first-generation
students may score lower in this area than their peers. The total mean for the subscales was 3.69,
with a scale range of 1-5. Thus, respondents had a slightly above-average score overall. (See
Table 15).
Table 15
CDSE Subscale Descriptive Statistics
Subscale
Corresponding
Question
Numbers
M
SD
Cronbach’s
Alpha
Skewness
Kurtosis
1. Self-Appraisal
5, 9, 14, 18, 22
3.70
0.69
.75
-0.39
-0.47
2. Occupational
Information
1, 10, 15, 19, 23
3.73
0.61
.61
-0.19
-0.65
3. Goal Selection
2, 6,11,16,20
3.71
0.73
.83
-0.19
-0.80
4. Planning
3, 7, 12, 21, 24
3.63
0.69
.76
-0.27
-0.43
5. Problem
Solving
4, 8,13, 17, 25
3.67
0.68
.69
-0.28
-0.04
Total
25
3.69
0.59
.93
-0.25
-0.67
Note. n=94.
Subscales range from 1-5.
Figure 5, presents the open-ended responses to the survey question, “Please feel free to
provide any other comments related to your career decision-making self-efficacy and career
action steps.” There were 9 responses to the optional survey response, recorded below as
evidence of students’ unique comments to the survey question.
Figure 5
Responses to survey question “Please feel free to provide any other comments related to your
career decision-making self-efficacy and career action steps.”
1. I had to keep my education a secret from most employers, I am Latina and people tend to
get upset that I am more educated than they are. So I don’t say anything most of the time.
2. Being an English major, I feel as if the school does not try an[d] balance any of the
internships or job opportunities that apply to my field – and there should be more of an
effort to provide students like me with more relevant internship or job opportunities.
3. I’m only in the history program because I remember liking it in school.
4. Quite the box to put yourself in, no?
5. I’m only a sophomore, this is my first year of actually looking at options.
6. I have volunteered with work similar to that of my career goal.
7. If you’re an English major – become a librarian!
Response
8. Most of these things I really haven’t thought about yet. I’m just worried on making sure
I’m even eligible to start thinking about these things. Although, I know I want to stick to
being an English major, I haven’t looked deeply into the salary of my preferred job.
9. I need to take some steps to achieve where I want to be in my next stage of life. But I am
willing to take that step!
CHAPTER FIVE: DISCUSSION
This study sought to identify whether or not career decision-making self-efficacy
subscale scores correlate with career action steps, and if student demographics related to career
decision-making self-efficacy and career action steps. This chapter is introduced with a report of
results in response to each research question driving this study. Study findings are then discussed
in the context of relevant literature and in connection with the conceptional framework informing
the inquiry. The chapter is concluded with a report of implications for practice and research,
along with a recap of conclusions.
Summary of Results
In response to research question one: What is the relationship between humanities student
participants’ demographics and their career action steps? It was found that in terms of gender,
males reported taking fewer action steps than females, except in the case of having a resume.
Similarly, females performed more action steps than males, on average. In addition,
firstgeneration students and females indicated higher interest than their peers in costs and
earnings associated with career decisions. The largest percentage of students reported having a
resume or CV, with the lowest percentage of students reportedly participating in an internship. In
relation to other demographics, there was not much variability across major or ethnicity in terms
of average or percentage, though Black/African American students reported the largest
percentage of research in anticipated career fields (83.3%). Perhaps the most surprising data was
the fact that when examined across year, many freshmen had completed more career action steps
than sophomores and many juniors reported completing more individual action steps than
seniors. As to be expected, senior students did have the highest average in career action step
completion.
However, the total average score across participants’ action steps was 4.17, with a standard
deviation of 2.13. This means that students in this study performed less than half of the career
action steps in the survey on average, which likely indicates low career maturity (Crites, 1973),
and could be cause for future inquiry to compare against their peers in other majors.
Exploration of the second research question – What is the relationship between
humanities student participants’ career decision-making self-efficacy and their career action
steps? – had a 21% explained variance. The results indicated that gender and year were related
to career action steps, with males taking fewer action steps than females and year of higher
education impacting action step completion. When CDSE-SF subscales were included in
twostep multiple regression, the variance increased to 47%, thus strengthening the relationship
amongst year, gender, subscales, and action steps. The subscale Occupational Information had
significant prediction, with B= 0.54, t-value, -2.7, p<.001. This indicated that students who
research and are more informed in their anticipated career fields are also likely to have more
career decision-making self-efficacy and take more career action steps. Males in this study had a
lower average career decision-making self-efficacy score than females (3.63, 3.75, respectively).
However, in similar studies, including a private liberal arts university and a large public
university, males and females did not have any significant average score disparities (Betz &
Taylor, 2012), thus indicating that the gender of students in this study may be more influential in
career decision-making self-efficacy of this population. Overall, the CDSE-SF average score
results of this study, in relation to each subscale and total, were on par with average scores of
other populations, including undergraduate psychology students, with no large differences noted
(Angeline & Rathnasabapathy, 2021; Betz & Taylor, 2012).
The third research question within this study was, What is the relationship between
student demographics and each of the career decision-making self-efficacy subscale scores?
Data evidence revealed that with the exception of Occupational Information subscale (variance
of 8%), each of the other subscales had higher explained variance than the instrument overall
(Self-Appraisal, 16%; Goal Selection, 19%; Planning, 19%; Problem Solving, 17%, Overall,
15%). In terms of subscale career decision-making self-efficacy performance, first-generation
students had average scores that were lower than their peers, while Anthropology and Philosophy
majors had average subscale scores above their peers.
Across the subscales Self-Appraisal, Goal Selection, and Problem Solving, year and
Anthropology major indicated the highest scores, with significance in the Goal Selection
subscale for both and significance in the Planning subscale for year. Thus, there was predictive
relationship amongst demographics and career decision-making self-efficacy subscales. In
addition, correlations within subscales were high across all.
In summary, the data suggest that the year of a student, males in particular, and the
information they have regarding their anticipated major can impact their career decision-making
self-efficacy and the career action steps. The higher a student’s Occupational Information
selfefficacy, the more action steps they completed. Anthropology majors represented the
highest/strongest relationship with career decision-making self-efficacy, using English major as a
reference category; and although male and female students did not demonstrate marked
differences in career decision-making self-efficacy, data did demonstrate areas in which students
can expand their career development through action steps, and their desire to do so.
Discussion
According to related literature, self-efficacy can affect student task completion, career
decision-making, career satisfaction, and overall career development (Betz & Luzzo, 1996). As
Social Cognitive Career Theory (Lent et al., 1994) relates, individuals’ career decisions are often
shaped by a variety of social cognitive and behavioral influences. Informative data surrounding
the population within this study demonstrates possible connection amongst factors that impact
student career development. The results of this study, similar to those prior (Betz & Taylor,
2012), demonstrated Black/African American students as indicating higher career
decisionmaking self-efficacy. Also similar to other studies (Betz & Taylor, 2012), there was not
significant difference amongst males and females in CDSE-SF scores. However, results from
this study did demonstrate novel insights related to career action steps. Data indicated that male
humanities students, especially those in fields other than Anthropology, took fewer career action
steps than females. Given the context outlined in Crites’ Career Maturity Theory (Crites 1964,
1973), male students and non-Anthropology majors may have less career maturity than their
peers, given that career maturity is linked to vocational decisions, problem solving, and action
(Crites, 1973). Similarly, although research conducted by R. Evans et al., (2020) found that
firstgeneration students reported high levels of self-efficacy attributed to positive attitudes; yet in
this study, first-generation students did not indicate marked difference from their peers in CDSE-
SF performance. However, as Chang et al., (2019) and R. Evans et al., (2020) claim,
firstgeneration students may not have as much knowledge of, or access to resources to help their
academic performance as their peers. The data of this study reinforced this premise as
firstgeneration students reported completing fewer action steps towards their career aspirations.
Relationships across the demographic variables were expected, given the context of
SCCT and demographic influence of self-efficacy and career decision-making (Chuang, et al.,
2009; Brown, et al., 2011; Betz & Taylor, 2012). Likewise, research demonstrates connection
between self-efficacy and action (Bandura, 1992; Schwarzer, 2014), and career self-efficacy’s
influence on behavior, commitment, and aspirations (Arghode, et al., 2021). Higher self-efficacy
demonstrates belief in ability to overcome challenges and persist in careers by acting to find
solutions (Arghode, et al., 2021). However, this study did not demonstrate multiple significant
relationships. This is likely due to the low power/small sample size of the inquiry. Throughout
the research, there were some significant predictors, such as the influence of gender in that males
completed fewer action steps. Likewise, year and Anthropology major had a consistent influence
across career decision-making self-efficacy subscales. These data align with research
confirmation that self-efficacy can affect task completion and action (Schwarzer, 2014; Jo, et al.,
2016; Galles, et a., 2019). Therefore, the correlations amongst the action steps and career
decision-making self-efficacy subscales support previous findings. Previous studies did not
demonstrate significant differences in gender responses to CDSE scores (Betz & Taylor, 2012);
however, the results of this study do demonstrate male and female differences in action step
completion. Therefore, although males and females may have similar self-efficacy career
decision-making, the results relate that females may be more likely to take action on their career
goals than males.
The results of this study also convey that if a student scored in high Self-Appraisal, they
also have a high ability to set goals. Likewise, a student with high Self-Appraisal is likely to
plan for a future career. These indications are also reflected in the action step responses. A small
majority (55.7%) of students currently have a CV or resume. Likewise, just over half of the
respondents have researched a specific job including average yearly earnings (54.7% and 53.8%,
respectively). However, most of the humanities student respondents are not currently and have
not previously participated in career programming nor completed an internship (24.5% and
20.8%, respectively). This may be due to humanities degrees career pathways not requiring
internships and allowing for selection of more course electives.
Each of the responses to the optional comment question of the survey conveys the
students’ reflection of their personal progress towards career choices and indicates personal
responsibility. Even the sarcastic response (“Quite the box to put yourself in, no?”) evinces a
realization that only considering job application is a limiting “box” for humanities majors.
Interestingly, one response passively blames the university/degree program for not providing
more information or resources related to career preparation; however, attending internships/job
fairs and other career-related activities which require personal responsibility in pursuit, had the
lowest positive response rates. Although the responses do not share specific steps taken or
desired to take, multiple offer a pride in progress and a commitment to making more progress
towards career exploration.
Limitations
This investigation was an exploratory study and was limited in its population size of 344
undergraduate students in English, Philosophy, Anthropology, and History degrees. Despite best
efforts of the investigator and partnering faculty, the response rate was low, which reduced
statistical power. This could be due to timing of survey distributions, or lack of attention from
humanities majors. Given the environment of the global COVID-19 pandemic, students were
attending and taking fewer in-person classes. Within the traditional classroom setting, it is
perhaps easier for faculty to remind students or guide them in the completion of an activity.
However, given the pandemic, this study relied on email reminders to students in the majors. For
this reason, the reminding rate of emails may have been insufficient. Reminder emails were sent
each week from August 23rd to October 15th. This level of contact demonstrates an aggressive
outreach, without harassment. A future study might include an incentive in an attempt to
increase response rate amongst participants.
Implications for Practice
The results of this study offer implications for current and future humanities students’
career preparation and practice. The action step survey indicated that many students had not yet
taken necessary steps towards their future anticipated careers. For example, the fewest numbers
of students completed action steps that involved intentional outreach, such as job shadowing,
attending career programming events, or creating LinkedIn profiles. Each of these tasks utilizes
skills that humanities majors manifest (i.e.-communication, networking, writing, etc.); yet, these
action steps seem overlooked by students. Teachers, college advisors, and career coaches can be
more intentional about connecting these students to these resources and making them aware of
their existence. Access to this information is open, but the intentional sharing of these facts (as
SCCT confirms) both informs and empowers the student so that he/she can relate it to personal
career goals and build self-efficacy. For example, a professor can “connect” with students on
LinkedIn. Likewise, faculty can reach out to their own professional networks to arrange for
internships or job shadowing. Encouraging students to begin voluntarily experiencing their
future careers could be pivotal in building their career self-efficacy.
Furthermore, the data suggest that perhaps males need more directed attention towards
their career goals, while first-generation students need encouragement in their career
decisionmaking. In addition to career coaching, or creating an action plan for accomplishing a
specific goal (Fishberg, 2015), faculty and practitioners should invest more time in mentoring
fellow humanities students by making them aware of career resources, internships, occupational
information, etc. Mentoring, like the humanities, is a holistic approach to an individual’s overall
social, affective, professional/career, and personal development, and the responses to the
openended question revealed students’ desires to be mentored more in these areas.
Perhaps one strategy for mentoring students in career activities would be to employ
Crites’ Career Maturity Theory (Crites, 1978) and apply each of the corresponding career
decision-making self-efficacy subscales to action steps, such as those included in the action step
survey of this study. For example, a student can improve Self-Appraisal through faculty
suggestion or requirement of completing a career questionnaire or skills analysis survey.
Likewise, faculty can guide students to additional career resources (such as LinkedIn) and thus
improve student Occupational Information. Intentional mentoring is one of the key strategies to
helping students identify and define their skills and goals; therefore, mentor practitioners can
encourage students to set career goals and plan career trajectories while problem solving possible
obstacles. As is noted above, humanities students may be unsure of how their coursework and
gained skills can relate to future jobs. They may also overestimate their skills or their employers’
assessments of their skills. In order to assist with these oversights, the utilization of mentoring
can help to produce positive personal and career outcomes (Johnson & Ridley, 2008), while
improving career maturity and self-efficacy. The invested time and relationship-building
associated with mentoring directly produces positive results related to information literacy,
personal growth, and development (Cohen, 1995; Schwiebert, 2000), as well as greater
professional competence, increased career satisfaction, and decreased job stress (Johnson &
Ridley, 2008).
What remains essential to humanities students’ education and students of all degrees is
the transfer of learned information and skills into their future careers. By working with students
through goal setting (Goal Selection), role modeling (Self-Appraisal), and career exploration
(Occupational Information), faculty and practitioners can positively impact career
decisionmaking and assist students in identify their career sooner (Problem solving and
Planning). As most humanities students experience rapid wage growth later in the careers
(around ages 30 and 40) versus when first emerging from college (Strada & Emsi, 2018a), earlier
career interventions and the environmental guidance and sharing of resources can enhance and
augment students’ success, thus positively shaping their self-efficacy and social cognition.
Future Research
As the research above asserts, current students pursuing degrees in English, History,
Anthropology, and Philosophy demonstrate varied career decision-making self-efficacy, without
many significant demographic differences in subscale score or action step completion. The
majority of the respondents have made preliminary progress in career decision-making by having
a resume or CV. However, the majority of the students have not taken proactive actions in
pursuing internships or attending job fairs, mock interviews, etc. A replication of this study, with
increased participation through possible incentivization, may enhance insights gleaned.
The lack of attention to career decision-making is reflected and highlighted in the
declining degrees and lower preliminary wages (Burke, 2021; Jaschik, 2018; Ruggeri, 2019).
Nevertheless, the number of associate’s degrees (AA) in humanities and liberal arts/liberal
studies has increased every year since 1987 (AAC&U, 2021). This reveals the possibility that
students continue pursuing humanities and liberal arts in their first two years of college
understanding the problem-solving and exploratory nature of the fields before being persuaded
into pursuing degrees with workforce-related names. However, as Barbara DeLollis (2021)
asserts, “four-year degree remains the surest path for upward economic mobility — especially
for first-generation college students, those who struggle financially to pay for college education,
and students of color.” (p. 4). A future study exploring the significance of the rise in AA degrees
and decline in Bachelor’s (BA) degrees in the humanities and liberal arts would provide
additional context and possible conclusions regarding the career decision-making and
selfefficacy of this population.
Likewise, various populations within the study (males, first-generation students, etc.)
demonstrated differences from their peers, especially in career action steps. Future
investigations of these populations, grounded in SCCT (Lent et al., 1994), could examine the
social and cultural implications and impacts surrounding these populations’ performances and
include qualitative sampling and analysis. The career action step survey proved to be an
effective means of examining career development of this population. In addition, survey analysis
allowed for successful research question investigation. Use of this survey on its own, would
allow for additional analysis of various populations’ career actions.
An investigation of a mentoring intervention in the career decision-making self-efficacy
of students in humanities degrees could be explored in future research. This inquiry could
include comparisons of humanities students’ self-efficacy, without and without mentoring. In
addition, more in-depth research could examine the ways in which career self-efficacy is
impacted by the intentional intervention of an invested individual guiding a student through a
career path and setting career goals. Especially for students seeking humanities degrees without
direct career titles, a future and augmented investigation would add relevance and insight to the
career decision-making self-efficacy of this population.
Finally, the open-ended survey question evinced students’ desire to learn more about
possible career choices and inherent motivation to do so. Further studies surrounding the career
motivation and interests of humanities students would strengthen and support this research.
Conclusions
Especially amidst a high unemployment rate, worldwide decline in humanities degrees
(American Academy of Arts and Sciences, 2021), and the global pandemic, the career
decisionmaking self-efficacy of humanities students is important research related to a population
with marketable career skills. Prior to this study, minimal (if any) research existed on the career
decision-making self-efficacy of the humanities student population, especially those in fields
without direct career titles. In addition, the use of the CDSE-SF on an undergraduate humanities
student population, to date, had not been previously explored or applied in this way. The results
from this inquiry contribute to, and provide data related to whether or not humanities students
with higher self-efficacy also demonstrate more action steps towards their career goals and
choice.
This study provides humanities faculty, students, practitioners, and community members
with insights related to current perceptions within the field. The evidence suggests that
humanities students, especially those within certain groups, would benefit from more intentional
career decision-making practices and action steps. Similarly, although respondents demonstrated
overall scores within average performance on the CDSE-SF instrument, (with Anthropology
students and those who indicated Senior-year as the highest performers), the students’ overall
responses did not indicate high career decision-making self-efficacy.
Within the context of the current global pandemic, the need for secure employment has
perhaps not seemed as necessary since the U.S. Great Depression. The current health
environment has brought about necessary changes in distancing, awareness, and sanitation in
both personal and professional practices. Likewise, the daily workplace is adapting to virtual
transactions dependent upon the technology STEM fields boast about providing. Over 50% of
employees found their work disrupted and changed due to the pandemic (DeLollis, 2021).
Within this new environment, students have begun exploring and pursuing less time-intensive,
non-traditional education options, such as stackable credits, skill development bootcamps, and
hybrid education programs (DeLollis, 2021). The foundation of the humanities – rooted in social
discourse, debate and discussion – must now convert to a new context of social and physical
safety. In order to remain current in the twenty-first century and beyond, the humanities and
liberal arts must continue to navigate pedagogical and economical spaces with an insistence upon
relevancy, adoption, and adaptability. The research provided asserts the value of degrees without
direct career titles (i.e.-English, History, Philosophy, Anthropology, Philosophy). But these fields
must also campaign for themselves and ensure they are equipping their students with the
confidence to build self-efficacy and make career decisions. Humanities disciplines provide
students with the tools they need for career success; but without explaining what the tools are and
why they exist, students pursuing these majors may continue to exhibit low career
decisionmaking self-efficacy and lack the awareness of the employability of their degrees.
Arguing for the validity of humanities and humanistic principles, Robert Newman (2021)
explains, “Only with a turn toward the pragmatic might the esoteric be safely preserved and
nurtured” (para.5). Through interdisciplinary and public enterprises, the reputation of the
humanities can be rightfully restored to an asset rather than nuisance. Noting the connection to
Ow
NL
democracy, Newman writes, “For the humanities to survive, democracy must survive, and the
survival of democracy is predicated upon robust humanistic inquiry and principles” (para. 6).
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