This study aims to examine the relationship between hours worked, sleep, gender, and employment status. A literature review found that longer work hours can reduce sleep quality and quantity due to increased stress. However, existing research has not examined how gender interacts with work hours to impact sleep. The study will distribute a 9-item questionnaire to 30 students to collect data on demographics, work hours, and sleep habits. Results will analyze correlations between independent variables (gender, work hours) and the dependent variable (hours of sleep) using statistical tests like Pearson's correlation. The study seeks to understand if gender modifies the relationship between work hours and sleep.

1. 10
Relationship Between Hours Worked and Sleep
Erica K. Fernandez
Louisiana State University Alexandria
PSYC 4017: Advanced Research Methods in BSS
Dr. Richard Elder
August 10th, 2021
ABSTRACT
While sleep is one of the most important factors of
physical, psychological, and mental health, its contributions are
still widely taken for granted. Sleep has the potential of
significantly improving or drastically diminishing one’s
productivity. As such, it is necessary for sleep to be studied
more deeply to understand how it affects employees. This study
seeks to bring out the connection between the number of hours
worked and the total number of hours slept per night, while
putting into consideration variables such as full-time or part-
time employment, and gender. According to the first hypothesis,
individuals working less hours sleep longer than those working
longer hours. As such, the variables of interest here are job
hours and hours of sleep per night. Further, the second

2. hypothesis connects gender (independent variable) with the
aforementioned dependent variable. If there is a substantial
correlation between the two, the Pearson Correlation should be
applicable. Using Factorial ANOVA, I would change the second
independent variable to the current position of the employee to
make a lead to null hypothesis acceptance.
A literature review will be conducted highlighting existing
literature in the contributions of sleep to productivity, and the
severity of the consequences of failing to get enough sleep.
Further, the differences in sleeping habits between men and
women will also be highlighted, in addition to how longer
working hours can result in fewer hours of sleep and diminish
productivity. A 9-part questionnaire will also be distributed
among students to select a target group of 30 individuals, 15 of
each gender, to provide the primary source from which
correlation between the dependent and independent variables
will be derived using the correlation coefficient. The filled
questionnaires will be compiled and grouped in terms of gender
to allow for a pattern to be established either in agreement with
or contrary to the proposed hypotheses.
Key words: sleep, productivity, longer working hours, fewer
hours of sleep
Introduction
Studies of the brain have been around for centuries now,
but the progress on our knowledge regarding its inner workings
advances at a very slow pace. However, one of the main
achievements in neurology – the study of the brain – is the
determination of the role of sleep in improving mental acuity
and alertness in tackling day-to-day tasks. It is unfortunate that
despite the importance of sleep in keeping things in our bodies
running smoothly, it is one of the most neglected aspects of
leading healthy lives. This study intends to establish a
connection between the number of hours worked per day and the
number of hours slept at night. Shedding light on this area
might help the corporate world to put more stake in ensuring
employees get enough sleep on a regular basis to improve their

3. productivity.
As such, this study aims to answer the following questions:
Is there a connection between the total hours you work and the
number of hours you sleep? Do variables such as gender and job
hours (part-time or full-time) affect the amount of sleep you
get? The working hypotheses upon which this study is based
are: Individuals who work less hours get more sleep than those
who work more hours. Those of a male gender who work 20
hours or less get more sleep than females who work the same
number of hours.
Literature Review
To accurately draw conclusions from the study, it is necessary
to first bring out existing literature on each variable. According
to the first hypothesis, individuals working less hours sleep
longer than those working longer hours. As such, the variables
of interest here are job hours (independent variable) and hours
of sleep per night (dependent variable). Further, the second
hypothesis connects gender (independent variable) with the
aforementioned dependent variable.
Variable 1: Job Hours (Independent variable)
During working hours, the mind is active as is required for
the achievement of tasks and objectives. However, for one to
fall and stay asleep, it is necessary to be in a rel axed
environment to allow the mind to ‘cool down’ in a matter of
speaking, and eventually drift off. Therefore, working long
hours as is the case in full-time employment implies that the
mind is active for a longer time, which necessitates a longer
time to ‘cool down’ and fall asleep. According to Dahlgren et
al. (2006), longer hours also imply that there is not enough time
for recuperation and relaxation, which accumulates stress,
making it difficult to fall asleep.
Further, Burgard and Ailshire (2009) explore the concept
of workplace experiences ‘following employees home’, which
can thus affect their quality of sleep. Their article builds on the
premise that the workplace exposes workers to stresses that are

4. otherwise not available at home, which in turn causes them poor
quality of sleep. Building on this logically, longer exposure to
such stresses in the case of full-time employment causes
workers to take these stresses home, minimizing time needed to
relax, a claim substantiated by Dahlgren et al. (2006) article.
Therefore, drawing from these two articles, part-time
employment gives workers more time to engage in leisurely
activities such as going to the gym or taking yoga classes which
reframe the mind and prepare it for the following day’s tasks.
This is the kind of recuperation that Dahlgren et al. (2006) talks
about.
Variable 2: Gender
Much like most other biological aspects, sleep is affected
by gender differences, beginning from the most basic concept of
circadian rhythms – mental, physical and behavioral changes
concurrent with daily cycles often in response to light and
darkness. As such, men have longer circadian rhythms as
compared to women, which is noticed in the fact that women are
often more active in the morning, while men are more active at
night (Santhi et al., 2016). Further, men spend less time in deep
sleep as compared to women, a statistic which varies depending
on age. Also, women get up to 11 minutes more sleep than men,
but are more likely to experience sleep problems such as
insomnia. This might be due to hormonal changes associated
with the female reproductive cycle such as menstruation,
pregnancy and menopause (Stallings, 2021).
Therefore, by most objective merits, women sleep longer
and better than men, assuming sleeping problems and
disturbances are at a minimum or entirely absent. However,
this goes against the second hypothesis which states that men
working 20 hours or less per week get more sleep than women
working the same hours, which implies that the aspect of daily
activities has a stronger correlation than that of gender in terms
of hours slept every night. By analyzing these three variables, a
reason for this discordance can be derived and understood.
Variable 3: Hours Slept

5. As the dependent variable, the concept of hours slept is
affected both by gender and by hours worked per day. This
aspect is heavily researched on, with articles such as one by
Philibert (2005) which explores the relationship between sleep
loss and the performance of non-physicians and residents in a
hospital. Philibert (2005) expresses the importance of cognitive
skills in ensuring high quality patient care, bringing out how
sleep loss and sleep deprivation can cause life-threatening
errors not only to patients but also to fellow workers. According
to WELCOA (2018), failure to get adequate sleep affects one’s
mood, which in turn impairs concentration and focus, thus
drastically reducing productivity. Further, failure to get enough
sleep resulted in more than 40% of adults falling asleep during
work or even while driving.
The effects of reduced productivity are not only felt in the
workplace, but also in the economy. According to the RAND
Corporation, more than $400 billion is lost annually in the US
economy due to poor sleep. Additionally, poor sleep causes a
vicious cycle in which an employee fails to get enough sleep,
gets overwhelmed by work the next day, which increases their
anxiety and stress, thus affecting their ability to fall asleep at
night. Therefore, according to existing literature, it is necessary
to adopt positive sleeping practices and behaviors not only for
employees’ benefits, but also for the good of the economy at
large.
How this Study Fits into Existing Literature
This study seeks to establish a connection between and
among the three aforementioned variables. Previous study only
correlate two of these variables. For example, the articles by
Dahlgren et al. (2006) showcases the relationship between
working long hours and sleep and stress levels, which are two
of the desired variables for this study. Moreover, Burgard and
Ailshire (2009) express how employees facing stresses at work
end up carrying them home, which implies that such stresses are
always with them even when they are supposed to be unwinding
and relaxing. Due to this, they end up sleeping poorly. This

6. article also highlights similar variables to those by Dahlgren et
al. (2006).
Nevertheless, the differences in sleep in men and women
have been discussed by Santhi et al. (2016) and Stallings (2021)
which provide a baseline regarding how gender defines sleeping
habits and patterns. The effect of poor sleep on productivity in
the workplace has also been explored separately. As such, each
of the sections above in this literature review only describe how
the variables are related with each other, but not how the
independent variables work in tandem to determine the nature of
the dependent variable. This study thus fits into existing
research by filling this gap, and by adding to existing
knowledge regarding sleep and relaxation.
Method
Participants
My participants will include 30+ (15 male and 15 female
selected at random) students who are currently enrolled at
Louisiana State University Alexandria and at the local college
in my area Georgia State University ages 18 and up. The equal
number of male and female participants is intended for
highlighting the correlation between sleep, hours worked, and
gender. I intend on recruiting them using both of the campuses
Facebook group pages and some student email listings.
Partakers will have the option to freely take part in a survey
that will assist with a psychological research study.
There will be no age maximum on this study, nor will race
and/or gender be a deciding factor in picking the participants
although they will be asked about the latter at the end of the
survey process for the results. While I assume that individuals
who work full or part-time hours may be more beneficial to this
study, anyone one who is employed and a college student over
the age of 18 are encouraged to participate. However, the
inclusion of individuals from different ages might cause
problems correlating the data because each variable is affected
differently according to age. Therefore, further research is
required to accurately examine the variables across different

7. age groups for better results.
Materials
The survey consists of 9 multiple-choice questions that
should take no more than 3 minutes to complete. The survey
was created by Fernandez (2021) and utilizes questions relating
to demographics and sleep habits. Also available upon start of
the survey, questions 1-3 will be the Informed consent, that
contains the researcher’s contact information, guarantee that
there are nor will be any penalties for not completing and/or
participating in the survey, and ensure all information retained
from survey will stay anonymous and will not be used for any
other purposes outside of this study. Following consent, the
second portion of questions (3-6) refer to participant’s
employment status, job classification (part-time or full-time)
and average hourly amount of sleep on a daily basis. The
remaining questions focus on race, age, and gender. The
assessment has a significant level of legitimacy due to the
questions aiming to find the connection between work and
sleep. Limitations that could affect the reliability and validity
of the study would be if participants choose to answer questions
with no honest and/or honorable intention.
Procedure
The purpose of this study will be discovered using a non-
experimental design and Factorial ANOVA. This study consists
of 2 independent variables (gender and hours worked) and 1
dependent variable (sleep). The survey, which will be posted on
Google Forms, will be distributed via a post on Facebook with
the link attached and can be accessed from their personal
Facebook app. First, participants who attend LSUA will receive
an email Dr. Elder in the psychology department that invites
them to take part in various surveys from students who are
currently enrolled in PSYC 4017 to complete their research. If
the student wishes to participate, a list of survey links will be
populated on the screen and students will have the option to
select from the selection of surveys offers. At the conclusion of
the survey, there will be no additional requirements from

8. participants.
The filled forms will then be examined and analyzed for any
similarities and correlations important to the establishment of a
pattern. Selection of filled forms will be done by collecting the
first 15 forms filled by male students, and the first 15 forms
filled by female students, while checking for their validity. It
might be impossible to divide them according to age since the
targeted demographic is that of college students who are
between ages 22 and 34. Further, the small size of the group
might make it impossible to divide them according to age
groups, which can also be attributed to the fact that doing so
would fall outside the scope of objectives that this project
intends to achieve.
Results
In this study, the first research question is whether or not the
number of hours you sleep is affected by the number of hours
you work. It is to be expected that there will be some
considerable correlations between the two variables, which also
shows a relationship with the hypothesis for this study that
states that employees who work less hours get more sleep than
those employees who work more hours. The second hypothesis
states that female employees will receive less sleep per night
than male employees. If there is a substantial correlation
between the two, the Pearson Correlation should be applicable
since I would like to find the correlation between the number of
hours working and the hours of sleep. I do not feel that I can
make use of Factorial ANOVA since we will only be comparing
the sleep hours between male employees and female employees,
which can be analyzed by an independent-samples t-test.
However, if necessary, using Factorial ANOVA, I would change
the second independent variable to the current position of the
employee, whether or not the employee is freelance, regular,
supervisor, and manager, so it would truly be a factorial design
on its own. Therefore, if the correlation is indeed proved to be
positive, it would lead to the accepting the null hypothesis as
the experimental hypothesis.

9. Based on data collected from the literature review, it is possible
to draw the conclusion that individuals that work less hours
such as part-time employee sleep more than those working full-
time jobs. Therefore, this study accepts the first the hypothesis
which connects these two variables, describing that long
working hours imply less hours of sleep which increase stress
carried forward to the next day and affect sleep even further.
Therefore, companies are encouraged to implement measures
such as maximum working hours to prevent employees from
overworking, which might cause more harm than good.
Further, the articles discussed above also lead to the conclusion
that gender affects the number of hours slept per night. Without
considering the aspect of working, women sleep longer and
better than men. However, the second hypothesis stipulates that
males working less than 20 hours a day sleep better than
females working the same hours. The discord between existing
literature and the findings of this study indicates the presence of
another variable which is unaccounted for. Future studies
should focus on the amount of sleep received by men and
women in working conditions.
Data analysis (statistical)
Fig 1. Employment status of the respondents.
Table 1. Relationship between the percentage of hours worked
in a week and sleep.

10. Discussion
From the results of data analysis, it is found that, most of the
respondents for this research were unemployed individuals. This
is as it is shown in figure 1, indicating the employment
percentage of the respondents. The results indicate that 87% of
the respondents are unemployed, while only 13% are employed.
On the number of hours that the respondents work in a week,
3.4% of the respondents indicated that they work between 0-38
hours. The highest percentage recorded for people who work
between 38 to 40 hours is 41%. Those who work for 40 hours
are only 3.4%, 40-43 hours makes up to 3.4%, 42-50 hours is
6.9%, 50 and more hours are 3.4%, 60 hours in a week is 6.9%,
those who indicated that there is no any hour spent in a week
for work is 3.4%.
From the data collected, it is clear that the majority of the
respondents lie between 27-34 years. 54.7% of the respondents
indicated they ae 27-34 years of age. Followed by those who are
over 35 years of age who makes up 35.5% whereas 22-26 years
make up to 6% and 18-21 years making up only 4%.
The researcher also wanted to know the race of the respondents,
from the data collected and analyzed, 12.9% of the respondents
indicated that they are African Americans and the whites are
6.5%.

11. Respondents were also asked to indicate the number of hours
that they sleep at night. Here, 45.2% of the respondents noted
that they sleep for 1-5 hours at night, while 51.6 of the
respondents noted that they sleep for more than 5 hours. The
remaining 3.2% noted that they sleep for more than six hours
per night. On this, the percentage of women respondents was
71% whereas men were 29%. There was no one who preferred
not to indicate their sex.
Hypothesis
The hypothesis of this study was that women sleep for longer
hours in a night and that sleeping improves the health of an
individual. it was also hypothesized that sleep enables the
minds to remain active during working hours. From the results
analysis, it is realized that it is true that having enough sleep at
night helps to keep the minds active to enhance the achievement
of tasks. the results from the research show that women are able
to work for longer hours as compared to men because they sleep
enough at night as compared to men, therefore, it is true that
sleeping is directly proportional to job performance. The
hypothesis is thus supported by this research.
Relationship of research with the past research
According to Burgard, (2009) sleep deprivation impact work
performance negatively lowering productivity and quality of
work done. This also effects work relationship negatively. When
employees miss out on enough sleep at night, they will find it
more difficult to concentrate on the job, meaning that the end
results will not be as expected from them. learning is also
affected by lack of sleep, when someone fails to have enough
sleep at night, they will have a difficult time learning and
communicating with others. The results of this research are thus
valid as they indicate that the respondents who indicated that
they had more hours of sleep at night were able to work for
longer hours, this means that the productivity of the companies
they work for improved and also the quality of the products will
be high as the workers would concentrate more on the job.

12. Limitation to study
Some of the limitations to this study was that some of the
respondents did not return the questionnaires and thus the
researcher relied on a small number of data received from the
few respondents who retuned the questionnaires. Another
limitation to this study was the study area, the researcher was
only limited to one area hence getting results from a small
sample and generalizing the results. The other limitation was
inadequate funds, if the researcher had enough funds, they could
have covered a larger geographic area, collecting data from a
number of workers on how sleep had affected their work.
Future research
The research is recommending research to be done on sleep
deprivation and work performance. This will help in analyzing
how people who are deprived of sleep will be affected on work
performance and look for solutions to this problem.
Another recommendation for further study is the effect of sleep
deprivation on physical health. Researchers have to find out
how sleep deprivation impact people’s physical health, this will
help in determining the ways by which people ensure to have
enough sleep so that they can have good physical health as this
is what is needed for work.
References
Burgard, S. A., & Ailshire, J. A. (2009). Putting work to bed:
stressful experiences on the job and sleep quality. Journal of
health and social behavior, 50(4), 476-492.
Dahlgren, A., Kecklund, G., & Åkerstedt, T. (2006). Overtime
work and its effects on sleep, sleepiness, cortisol and blood
pressure in an experimental field study. Scandinavian journal of
work, environment & health, 318-327.
Philibert, I. (2005). Sleep loss and performance in residents a nd

13. non-physicians: a meta-analytic examination. Sleep, 28(11),
1392-1402.
Santhi, N., Lazar, A. S., McCabe, P. J., Lo, J. C., Groeger, J.
A., & Dijk, D. J. (2016). Sex differences in the circadian
regulation of sleep and waking cognition in
humans. Proceedings of the National Academy of
Sciences, 113(19), E2730-E2739.
Stallings, M. (2021, March 30). Men's and Women's Sleep
Habits. Sleep.org. https://www.sleep.org/sleep-for-men-and-
women/
Suni, E. (2020, November 13). How Is Sleep Different For Men
and Women? Sleep Foundation.
https://www.sleepfoundation.org/how-sleep-works/how-is-
sleep-different-for-men-and-
women#:%7E:text=In%20general%2C%20women%20and%20me
n,younger%20children%20need%20more%20sleep.
WELCOA. (2018, December 14). The Effects of Poor Sleep in
the Workplace. WELCOA. https://www.welcoa.org/blog/effects-
poor-sleep-workplace/
Appendix
hours of sleep per night
1-5 hours more than 5 hours 6 hours 0.45200000000000001
0.51600000000000001 3.2000000000000001E-2

14. sex female male prefer not to say 0.71
0.28999999999999998 0
employment status
employed not employes 0.877 0.129
hours
0 38 40 40-43 42 50+ 60 none
3.4000000000000002E-2 3.4000000000000002E-2
3.4000000000000002E-2 0.41399999999999998
3.4000000000000002E-2 3.4000000000000002E-2
3.4000000000000002E-2 3.4000000000000002E-2
6.9000000000000006E-2 6.9000000000000006E-2
3.4000000000000002E-2 3.4000000000000002E-2
3.4000000000000002E-2 3.4000000000000002E-2
3.4000000000000002E-2
hours worked in a week
response percentage
Age range
18-21 22-26 27-34 35 or older 4 5.7 54.8
35.5

15. racial percentage
African American African American African American
Black Black/Asian Blackitty black Blackitty black
everything N/A White 0.129 0.161
3.2000000000000001E-2 0.32300000000000001
3.2000000000000001E-2 3.2000000000000001E-2
3.2000000000000001E-2 3.2000000000000001E-2 0.129
6.5000000000000002E-2
race
race percentage