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Running Head: HIGH SELF-EFFICACY IN A PARTICULAR LIFE DOMAINS 1
Does high self-efficacy in a particular life domain offset the negative impact of low self-efficacy
in other life domains? A study of college students and workers.
Raymond Caridi
Under the supervision of Dr. Nuwan Jayawickreme
Manhattan College

HIGH SELF-EFFICACY IN A PARTICULAR LIFE DOMAINS 2
Abstract
The purpose of this study was to examine the relationship between different domains of self-
efficacy and overall well-being. Research suggests that one’s self-efficacy correlates with their
well-being, and that it is possible for a person to have distinguishably different levels of self-
efficacy in different domains of life. This study looked into these relationships, and examined
how they could potentially interact with each other. It was hypothesized that one domain of self-
efficacy would moderate the relationship between another domain of self-efficacy and overall
well-being in participants. Data was collected from 86 Manhattan College students and 32 full-
time workers. Each participant was asked to fill out a survey assessing their levels of self-
efficacy in academics, at work, and in their family, as well as a life satisfaction scale to assess
overall well-being. A linear regression showed that academic self-efficacy and familial self-
efficacy both predict well-being on their own, while work self-efficacy does not. There was no
overall moderating effect, but a moderation analysis shows that when familial and work self-
efficacy are both low, academic self-efficacy significantly predicts well-being, and when
academic and work self-efficacy are both high, familial self-efficacy significantly predicts well-
being. Future research should examine potential reasons which may explain why familial self-
efficacy is only important to well-being when other domains are high, but academic self-efficacy
predicts well-being when other domains are low.
Keywords: Self-Efficacy, Well-Being, Moderation, Domain Specific Self-Efficacy

Does high self-efficacy in a particular life domain offset the negative impact of low self-efficacy
in other life domains? A study of college students and workers.
People often have to perform many tasks throughout their lives, such as attending school,
going to work, caring for a family or loved one. These are all responsibilities that an average
person may have to take on at one point or another in life. Some people may excel in this areas,
and may be very confident in their own abilities to succeed, while some others may be less
confident in their abilities to perform these life tasks.
The psychological concept of one’s confidence in their ability to perform tasks or to
overcome obstacles is known as one’s self-efficacy. Self-efficacy is the belief in one’s own
capability to perform a task (Gist, 1987). Self-efficacy has been found to be positively associated
with overall well-being, which refers to an individual’s sense of how satisfied they are with the
trajectory of their life. Tong and Song (2004) examined this relationship between self-efficacy
and well-being in college students in China. Tong and Song (2004) defined self-efficacy as a
person’s optimistic self-belief to cope with a variety of difficult demands in life, and they defined
subjective well-being as a person’s general affect and overall life satisfaction. Students who
displayed higher levels of self-efficacy also reported having greater levels of overall general
well-being (Tong & Song, 2004).
Karademas (2005) followed up on Tong and Song’s study (2004) by examining the
relationship between self-efficacy, social support, optimism, well-being, and depressive
symptoms. These researchers defined self-efficacy using two concepts: resilience self-efficacy
and problem solving self-efficacy. Resilience self-efficacy was defined as assessing how well a
participant believes they could bear the burden of a stressful situation, and problem solving self-
efficacy was defined as assessing how well a participant believes they can solve a problem or

stressful situation effectively. Social support in this study was defined as how many friends and
family members a participant has to give them emotional support in times of need. One’s general
well-being was measured as overall satisfaction with life. The level of social support and self-
efficacy a person has was found to be directly related to the level of optimism that person has.
Levels of optimism were also directly related to the participant’s overall well-being. Optimism
was also shown to be indirectly related to depressive symptoms, meaning that the more
optimistic a person is, the less depressive symptoms that person displays (Karademas, 2005).
Thus, higher self-efficacy predicts being happier, more satisfied with life, more optimistic, and
less depressed.
These previous studies support the claim that self-efficacy is correlated to overall well-
being. However, a limitation of these studies is that they only examined general self-efficacy –
individuals’ overall sense of how competent they were in their lives - and its relationship to
subjective well-being. An individual may not feel competent in every one of their roles; rather,
individuals often feel varying levels of competence and confidence in different areas of life. A
person may have particularly high self-efficacy in one area of life, and low self-efficacy in
another area. This focused sense of self-efficacy is referred to as domain self-efficacy. Domain
self-efficacy is defined as one’s belief in their abilities to perform a task in only one specific area
or domain, such as school or work for example. Lent, Brown, and Gore Jr. (1997) attempted to
differentiate domain self-efficacy from general self-efficacy. In their study, 205 university
students answered questions which revealed their personal levels of academic self-concept,
global academic self-efficacy, and domain-specific mathematics self-efficacy. Their results
showed that students reported having latent dimensions of self-perception and different levels of
self-efficacy and self-concept in different areas of life which were distinguishable from one

another (Lent et al., 1997). Thus, people can have different levels of confidence in different areas
of life - such as in school, at work, at home, or in hobbies. Many studies examine self-efficacy in
a general sense, where people are assumed to have a general sense of overall self-efficacy. While
this is true, one’s self-efficacy is complex, and can be broken down into different parts within
different domains.
It is unknown whether high self-efficacy in one domain acts as a buffer against the
negative effects of low self-efficacy in another domain on subjective well-being. The negative
effects of feeling unable to perform a task in one area of life may be easier to cope with if there
is another area in life which makes a person feel good about themselves. The purpose of the
current study is to examine the relationship between domain self-efficacy and subjective well-
being in two distinct samples: Manhattan College students who have full- or part-time
employment, and people who work full-time in the workforce. It was hypothesized that high self-
efficacy in one domain (e.g., at school) will moderate the relationship between low self-efficacy
in a second domain (e.g., the workplace) and overall subjective well-being, such that high self-
efficacy in one domain reduces the negative impact of low self-efficacy in the other domain on
subjective well-being. For example, students at Manhattan College who have low self-efficacy at
their jobs but high self-efficacy in school will report greater subjective well-being than those
students who have lower self-efficacy in both the classroom and at work. Similarly, full-time
workers who have low self-efficacy at work will report higher subjective well-being if they
report higher self-efficacy when it comes to being an effective family member.
Method
Participants

Participants consisted of 87 Manhattan College students and 32 full-time (i.e., working
40-hour weeks) workers. The ages of the students ranged from 18 to 25 (M = 20.36, SD =
1.447). These data from Manhattan College students was collected online using Qualtrics. The
adult full-time workers were employed by either Wappingers Central School District as
custodians, carpenters, mechanics and electricians, or were employed by Eastern Account
Systems as debt collection specialists. Their ages ranged from 18 to 62 (M = 41.03, SD =
12.122). Data from full-time workers were collected by distributing hard copies of the survey to
the participants. The participants in this group were family friends, and then those family friends
who participated were able to recruit more participants through word of mouth (i.e., snowball
sampling). Of the 119 participants surveyed for this study, 89 were female (74.8%) and 30 were
male (25.2%).
Measures
In order to measure participants’ academic self-efficacy, the Patterns of Adaptive
Learning Survey (PALS: Midgley et al, 2000) was used. This is a five question, 5 point likert-
type scale survey asked questions that specifically related to how confident a participant is in
their ability to perform tasks in an academic setting, and has been validated to determine self-
efficacy in the domain of academia with an alpha level of .78 (PALS: Midgley et al, 2000). To
measure work self-efficacy and familial self-efficacy (the degree to which one believes they
fulfill their role as a family member) in participants, an adaptation of Schwarzer and Jerusalem’s
General Self-Efficacy Scale (1995) was used. This scale contains ten questions in relation to
one’s general self-efficacy, which is a measurement of overall confidence in one’s ability to
perform daily tasks. All ten questions have 4 point likert-type scale responses. These scales were
validated in order to measure general self-efficacy, with alpha levels ranging from .76 to .90,

with a majority of scores being in the high .80s (Schwarzer & Jerusalem, 1995); however, each
question was modified in order to be more specific to the domain in which data was being
collected (work and familial self-efficacy, specifically). Finally, participants completed the
Satisfaction with Life Scale (Diener, Emmons, Larsen & Griffin, 1985) in order to measure
overall well-being in participants. This scale has five questions, with 7 point likert-type scale
responses as well, and is validated to measure one’s overall well-being, with a two-month test
retest reliability coefficient of .82 and a coefficient alpha of .87 (Diener, Emmons, Larsen &
Griffin, 1985).
Procedure
This study was brought to and approved by the Institutional Review Board at Manhattan
College. Participants first signed an informed consent form, which explained their rights as
participants and explained some details about the data being collected. The first page of the
survey collected demographic information about the participants, with questions asking about
participants’ age, gender, ethnicity, marital status, if they were enrolled in academic courses, if
they had children, who they live with, if they have financial dependents, and their job title. The
survey then collected data on participants’ self-efficacy. In order to collect data on domain-
specific self-efficacy, several validated scales were modified and used. After the survey was
completed by the participants, they were debriefed about the purpose of the study.
Results
Reliability for the work self-efficacy, academic self-efficacy, and familial self-efficacy
scales was high (α= .87, .94, and .91, respectively). The means and standard deviations for each
scale can be seen in Table 1. Pearson correlations between all of the variables were calculated.

Table 2 presents the correlations found between the three domains of self-efficacy which were
examined (work, academics, and family), as well as participants’ overall well-being. Table 2
shows that work self-efficacy, academic self-efficacy, and familial self-efficacy are significantly
correlated with each other, and each of these domains of self-efficacy is correlated with overall
well-being.
Results from comparative descriptive statistics and an independent samples t-test showed
that workers’ self-efficacy at work (M = 35.31, SD = 3.36) was significantly higher than college
students’ self-efficacy at work (M = 32.27, SD = 3.92) t = 4.200, p = .000. The workers’ also had
significantly higher scores for their self-efficacy in academics (M = 22.31, SD = 3.16) than
students (M = 19.20, SD = 4.53) t = 3.43, p = .002. Workers also had significantly higher
familial self-efficacy (M = 34.75, SD = 3.70) than students (M = 30.88, SD = 5.63) t = 4.55, p =
.000. Workers in this sample had higher well-being (M = 27.63, SD = 6.38) than students (M =
23.70, SD = 6.82), although these results were only marginally significant, t = 1.92, p = .058.
These results can be viewed in Table 3.
In order to test the hypothesis that high levels of self-efficacy in one domain can
moderate the negative effects of self-efficacy in another domain, moderation analyses were
conducted using the PROCESS Macro for SPSS (Hayes, 2016; Figure 1). The dependent
variable in each model was overall well-being, while the independent variable was one of the
domains which was measured for self-efficacy, and the moderator of the situation was a different
domain which was measured. For example, in one situation self-efficacy at work was the
independent variable and the measured, or dependent, variable was well-being. Academic self-
efficacy was the examined to see if it moderated, or had an impact on, the relationship between
work self-efficacy and well-being. That process was then repeated for each combination of the

measured domains of self-efficacy. This concept was used in order to study the relationship
between the three domains of self-efficacy which were being measured in this study, and to see if
any two domains of self-efficacy moderated each other when the dependent variable was well-
being. When examining the moderation between each domain on one’s well-being, the results
showed that there was no significant moderation between any of the tested domains (work and
familial self-efficacy, p = .74, work and academic self-efficacy, p = .18, familial and academic
self-efficacy, p = .49). However, a linear regression model where well-being was predicted using
the various domains of self-efficacy was significant with an R-squared value of .252 (p = .000).
In the model, familial self-efficacy and academic self-efficacy significantly predicted a
participant’s overall well-being (β = .24, p = .012 & β = .27, p = .009, respectively), while work
self-efficacy did not significantly predict well-being (β = .144, p = .183). The moderation
analysis conducted using the PROCESS Macro for SPSS (Hayes, 2016) was used at different
levels of domain self-efficacy, and found that at these different levels, there was some evidence
of moderation. When scores for familial and work self-efficacy were both low, results indicated
that academic self-efficacy was significantly more likely to predict overall well-being. This
analysis also showed that when academic and work self-efficacy were both high, familial self-
efficacy then significantly predicted well-being. These results can be seen in Table 4.
Discussion
We hypothesized that high levels of self-efficacy in one of the measured domains (self-
efficacy at work, at home, and at school) would moderate the negative effects of low self-
efficacy in another domain. There were no significant results to support any domain of self-
efficacy as a moderator. Although none of the domains significantly moderated any of the other
domains of self-efficacy, academic self-efficacy and familial self-efficacy were both shown to

significantly predict well-being on their own, while self-efficacy at work did not predict well-
being on its own. The results showed the levels of academic self-efficacy was the domain which
was most likely to predict overall well-being, while one’s self-efficacy at work did not appear to
significantly predict well-being. One reason for this finding could be the fact that a majority of
the participants in this study were college students. According to data collected in the Annual
Survey of Colleges from The College Board (2016), students spend 10 – 30 thousand dollars on
tuition per year alone, plus about 10,000 dollars on books and a little more than 1,000 dollars on
transportation. Students in college would need to work to pay for rent, gas for their car,
insurance, or student loans. While the students are most likely studying a subject they enjoy in
order to eventually have a career they enjoy, at this point in their lives a job is likely held by a
student strictly for a paycheck so that these expenses can be paid. The full time workers which
participated in this study were maintenance workers, custodians, and debt collectors, which some
may consider to be jobs done more for a paycheck than for “passion”. Future studies should
examine not only self-efficacy at work, but perhaps how happy employees are at work, and data
on how close the employees’ current jobs are to their ideal career. These could be variables
which can be compared and perhaps see if there is a difference among self-efficacy at work for
employees who love their jobs versus employees who dislike their job.
Even though the original hypothesis was not supported, and different domains of self-
efficacy did not appear to significantly moderate well-being, moderating effects between
different levels of domain self-efficacy on well-being were identified. Specifically, when either
self-efficacy at work or at home was average or below average, academic self-efficacy
significantly predicted well-being. This finding seems to give some support for the original
hypothesis, suggesting that when one has low or average self-efficacy in other domains,

academic self-efficacy can offset the negative effects on well-being. Similarly, when academic
self-efficacy and self-efficacy at work was average or above average, familial self-efficacy
significantly predicted well-being. What was interesting about this particular finding was that it
shows the opposite of what the hypothesis suggested. This finding suggests that as the other
domains of self-efficacy become higher, one’s familial self-efficacy also increases. This domain
of self-efficacy does not moderate the other domains, but it increases and decreases with them.
This may occur because of the responsibilities one takes on when part of a family. One may not
feel like a productive member of their family if they are struggling at work or school because
they are expected to do a job in order to give their family a better life. Not meeting these goals
can in turn cause someone to feel as though they are also not meeting their goals of taking care
of and supporting their family. Another reason for this could stem from traditional gender roles,
where a man is only considered a good father and/or husband if he does well at work. The
limited sample size of this study restricted our ability to examine this potential gender difference.
Our results indicate that one domain of self-efficacy does not necessarily moderate
another domain, the more domains of self-efficacy you have high levels of, the higher you score
in overall well-being. Future research should examine more domains (i.e., hobbies, athletics,
intelligence, socializing, etc.) in order to get a greater understanding of domain specific self-
efficacy, and then the data could be analyzed in order to see if more domains of high self-
efficacy are related to high levels of well-being in participants.
Full-time workers reported having significantly higher levels of self-efficacy at work, at
school, at and home, than student participants. Full-time workers also reported significantly
greater levels of overall well-being than the students. For all domains, the lower bounds of the
workers’ scores were still greater than the upper bounds of the students’ scores. These results

may be because a majority of the workers surveyed were middle aged and set in a career, while
the students surveyed were in their late teens and early twenties. This age gap may be a factor
which causes a significant difference in scores between the two groups. The full-time workers
are older and set in their careers and more comfortable and certain with themselves and their
abilities, while students are young and still unsure of their career paths and not yet settled in life
like the workers are. This comfort and settled feeling in workers could potentially cause them to
be more confident in all aspects of life, and more satisfied with life overall. Future research could
be done in order to examine if one’s age effects their overall well-being, hypothesizing that the
older a person is, the more satisfied with life they are.
Although the results showed that students score significantly lower than workers, is this
because of the fact that they are college students, or because of their actual age? In other words,
does being a college student cause lower self-efficacy and lower satisfaction with life, or is just
being young a cause of this? Further research could look at whether age mediates the relationship
between self-efficacy and well-being, and examine some potential variables which may explain
further why students tend to have lower self-efficacy and life satisfaction than workers. More
research could also be done to examine if there is a difference in self-efficacy and well-being
among people in their late teens and early twenties who go to school or go directly into the work
force.
The present study was limited to only 119 participants, 32 of which were full-time
workers. Having a small sample can potentially cause limitations to the results, considering there
is greater chance for outliers and skewed data. Many of the full-time workers surveyed were also
considered to be more “blue collar” workers, who make a living by working with their hands
(maintenance workers, custodians, carpenters, electricians, etc.). Perhaps employees of this type

of work have different levels of well-being and self-efficacy than employees who are considered
“white collar” workers, who make a living working in an office or cubical, and rarely with
physical labor (i.e., business administrator, sales person, etc.). The present study lacks enough
white collar workers to compare answers, but it could be a variable which should be examined in
a future study.
In conclusion, this study examined different domains of self-efficacy, including self-
efficacy at work, at home, and at school, and how these different domains moderate each other
when it comes to well-being in college students and full-time workers. The data from this study
failed to support the original hypothesis, and showed that different domains of self-efficacy do
not appear to moderate other domains. However, it was found that academic self-efficacy
seemed to predict well-being when familial and work self-efficacy scores were low, which is a
finding that supports our hypothesis. Similarly, familial self-efficacy appeared to predict well-
being when self-efficacy at school and work was high, which means this particular domain
shows evidence for the opposite of the original hypothesis. It was also found that older, full-time
workers are significantly more confident in all domains of life, and are significantly more
satisfied with life than younger, college students. These findings need to be studied and analyzed
further in order to fully understand their implications, but the data here does show some potential
for future findings in the area of self-efficacy.

References
Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale.
Journal of Personality Assessment, 49, 71-75.
Gist, M. E. (1987). Self-Efficacy: Implications for Organizational Behavior and Human
Resource Management. The Academy of Management Review, 12(3). pp. 472-485.
Hayes, A. F. (2016). The PROCESS Macro for SPSS and SAS. Retrieved from
http://processmacro.org/index.html
Karademas, E. C. (2006). Self-Efficacy, Social Support and Well-Being: The Mediating Role of
Optimism. Personality and Individual Differences, 40(6). pp. 1281-1290
Lent, R. W., Brown, S. D., & Gore Jr., P. A. (1997). Discriminant and Predictive Validity of
Academic Self-Concept, Academic Self-Efficacy, and Mathematics-Specific Self-
Efficacy. Journal of Counseling Psychology, 44(3). pp. 307-315
Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman, L., Freeman, K. E., et al.
(2000). Manual for the Patterns of Adaptive Learning Scales. Ann Arbor, MI: University
of Michigan.
Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy scale. Measures in health
psychology: A user’s portfolio. Causal and control beliefs (pp. 35-37)
The College Board (2016). Average Estimated Undergraduate Budgets, 2015-16. Retrieved from
https://trends.collegeboard.org/college-pricing/figures-tables/average-estimated-
undergraduate-budgets-2015-16

Tong, Y., Song, S. (2004). A Study on General Self-Efficacy and Subjective Well-Being of Low
SES College Students in a Chinese University. College Student Journal, 28(4). pp. 637-
642
Torres, J. B., Solberg, V. S. (2001). Role of Self-Efficacy, Stress, Social Integration, and Family
Support in Latino College Student Persistence and Health. Journal of Vocational
Behavior, 59(1). pp. 53-63.

Table 1. Mean scores and standard deviations of participants on each scale
N Mean Standard Deviation
Academic Self-Efficacy
Total Score
103 19.63 4.49
Work Self-EfficacyTotal
Score
119 33.08 3.92
Familial Self-Efficacy
Total Score
118 31.93 5.39
Well-Being Total 118 24.40 6.58
N 102

Table 2. Correlations between different domains of self-efficacy and well-being.
** = p<0.01 * = p<0.05
Work Self-
Efficacy Score
Academic
Self-Efficacy
Score
Familial Self-
Efficacy Score
Well-Being
Score
Work Self-
Efficacy Score
Pearson
Correlation
1 .49** .41** .34**
Academic
Self-Efficacy
Score
Pearson
Correlation
.49** 1 .23* .40**
Familial Self-
Efficacy Score
Pearson
Correlation
.41** .23* 1 .34**
Well-Being
Score
Pearson
Correlation
.34** .40** .34** 1

Table 3. Upper and Lower bounds of students and workers in each of the
measured domains of self-efficacy and well-being.
Work Self-
Efficacy
Total Score
Worker Mean 35.31
95% Confidence
Interval
Lower Bound 33.52
Upper Bound 37.10
Standard Deviation 3.36
Student Mean 32.27
95% Confidence
Interval
Lower Bound 31.43
Upper Bound 33.11
Academic
Self-
Efficacy
Total Score
Worker Mean 22.31
95% Confidence Interval Lower Bound 20.63
Upper Bound 23.99
Student Mean 19.20
Familial
Self-
Efficacy
Total Score
Worker Mean 34.75
Upper Bound 36.72
Student Mean 30.88
Upper Bound 32.09
Well-Being
Total Score
Worker Mean 27.63
Lower Bound 24.23

95% Confidence
Interval
Lower Bound 18.23
Upper Bound 20.17
95% Confidence
Interval
Upper Bound 31.02
Student Mean 23.70
95% Confidence
Interval
Lower Bound 22.24
Upper Bound 25.16
Table 4. Examples of moderation at different levels of self-efficacy
If… Then…
Family self-efficacy is low AND work self-
efficacy is low
Academic self-efficacy significantly predicts
well-being (p = .01)
Family self-efficacy is average AND work self-
efficacy is low
Family self-efficacy is average AND work self-
efficacy is average
Academic self-efficacy approaches
significance (p = .07)
Family self-efficacy is high AND work self-
efficacy is low
Family self-efficacy is high AND work self-
efficacy is average
Work self-efficacy is low AND academic self-
efficacy is average
Familial self-efficacy approaches significance
(p = .07)
Work self-efficacy is average AND academic
self-efficacy is average
Familial self-efficacy approaches significance
(p = .06)
Work self-efficacy is average AND academic
self-efficacy is high
Familial self-efficacy significantly predicts
Work self-efficacy is high AND academic self-
efficacy is high
Familial self-efficacy significantly predicts

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