11 Relationship Between Hours Work & Sleep Relationship Between Hours Worked and Sleep Erica K. Fernandez Louisiana State University Alexandria PSYCH 3017 Dr. Richard Elder 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. 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 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 o ...

1. 11
Relationship Between Hours Work & Sleep
Relationship Between Hours Worked and Sleep
Erica K. Fernandez
Louisiana State University Alexandria
PSYCH 3017
Dr. Richard Elder
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.
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

2. 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
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

3. 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 relaxed
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
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’s 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 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

4. 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
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 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

5. 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 article by
Dahlgren 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 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 article also highlights
similar variables to those by Dahlgren.
Nevertheless, the differences in sleep in men and women
have been discussed by Santhi et al. and Stallings, 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

6. 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
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

7. 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
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

8. 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.
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

9. should focus on the amount of sleep received by men and
women in working conditions.
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.
Fernandez, E. How Much Sleep Are You Getting? (2021).
Retrieved May 22, 2021, from
https://docs.google.com/forms/d/1C7gICLWXH9WMYOxzU4rA
kGgzMSnKKvi6-dkEshEYKVc
Philibert, I. (2005). Sleep loss and performance in residents and
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/
WELCOA. (2018, December 14). The Effects of Poor Sleep in
the Workplace. WELCOA. https://www.welcoa.org/blog/effects-
poor-sleep-workplace/
Assignment Instructions
OPERATING SYSTEMS, SOFTWARE, AND HARDWARE
Assignment Instructions
This assignment requires familiarity with productivity software,

10. computer hardware, and operating systems. These components
are designed to fit together, and IT professionals must be able
to evaluate and recommend an optimum combination of them to
potential users.
Consider this scenario: A family-run bike shop is growing, but
all of its data is currently on paper. This data includes expenses,
sales, wages for workers, and so on. The shop owners want
software to better organize and use this information, as well as
information on how software could help as the store expands to
new locations.
Research alternatives for operating systems, software, and
hardware and make a recommendation to the shop owners that
addresses the following:
· Compare the features of operating systems.
. Be sure to consider both Mac and Windows operating systems,
comparing the advantages and disadvantages of each for the
bike shop.
· Recommend productivity software for a specific business
context.
. The shop will need basic word processing and spreadsheet
software. Given its growth, it will also need productivity
software to keep track of expenses. In addition, the store will
need to design posters, signs, and labels to highlight
merchandise, promotions, sales, and special events.
· Apply appropriate evidence to support a recommendation.
. Return to the Undergraduate Information Technology Research
Guide list of IT industry resources, in addition to other
resources found through research. Select authoritative evidence
to support your comparisons and recommendations.
Assignment Requirements
· Written communication: Write with few spelling, grammatical,
or mechanical errors.
· Length of paper: Submit 2–3 typed, double-spaced pages (in
addition to title and reference pages).
· Sources: Cite at least two sources, in addition to your textbook
and the media pieces. These may include the industry sources

11. investigated during the course. Format resources and citations
according to current APA guidelines. See Evidence and APA for
more information.
· Font and font size: Use Times New Roman, 12-point font.