Final Paper

RUNNINGHEAD: Daily Stresses and Dreams 1
The Relationship Between Daily Stresses and Dreams
Alex Turner
Baldwin Wallace University

Daily Stresses and Dreams 2
Abstract
Prior research on stress has focused mainly on causes of stress as well as some potential effects
and relationships with other factors. One main correlation that has not been studied heavily is
dream distress and dream content. Previous research has studied the relationship between
traumatic situations and dreams, but not the stress of daily life. The objective of this study was
to gain knowledge in the relationship between daily perceived stress and distress levels in
dreams. The sample consisted of 292 participants between the ages of 18 and 80. Subjects
completed a survey containing a demographic questionnaire, a Perceived Stress Scale, and a
Dream Motif Scale. The results indicated that daily perceived stress significantly positively
correlated with the level of distress in dreams (r = .222, p < .001). This main hypothesis was
followed by multiple other significant demographic findings including sex and age.

The Relationship Between Daily Stress and Dreams
Adults experience a variety of situations in their lives that can be good or bad. These
experiences may vary from social situations to test grades, which result in stress. Stress is
important to consider in each experience, as it can affect people in both psychological and
physical ways (e.g., Yang et al., 2014). In the Diagnostic and Statistical Manual, Fifth Addition,
stress disorders range from short term adjustment disorders as a reaction to a stressor, to long
term post-traumatic stress disorders (American Psychiatric Association, 2013). There are both
beneficial and detrimental forms of stress (e.g., Fernandez-Gonzalez, Gonzalez-Hernandez &
Trianes-Torres, 2015; Wang, Wang, Gaskin & Wang, 2015). This includes circumstances from
receiving ‘good’ grades to getting fired from a job. In daily life, stress can cause small
biological changes like increased heart rate, and even changes sleep patterns (Yang et al., 2014).
Past research (Kron, Hareven & Goldzweig, 2015) has even shown that extreme stress and high
levels of trauma can impact the content of dreams. For example, the most common dream theme
consists of the dreamer meeting the person they wish to see (Yu, 2015). These typical dream
themes can be greatly altered by the impact of major stress or trauma on an individual (Erlacher,
Ehrlenspiel & Schridel, 2011). However, more research is needed in the area of possibly
smaller, everyday stressors and their impact on dreams. It is hypothesized that there will be a
significantly higher level of distress in dreams of individuals experiencing life stress.
Although personal perceptions of stress may vary, there are a few things that are
important to consider. Environmental demands or events that happen in one’s life are crucial to
examine when considering stress and physiological outcomes (Munoz, Sliwinski, Scott & Hofer,
2015). Depending on the age or chosen activities of an individual, the environmental factors can
be different, causing different stressors. Culture and different traditions can also greatly affect

the causes of stress in one’s daily life (Persike & Seiffge-Krenke, 2012). Younger individuals
are more vulnerable to stressors and traumatic events that occur in life in comparison to adults
(Kron et al., 2015). Individuals experiencing these stressors from the environment have an
increase in cortisol release as well as higher heart rates (Yang et al., 2014). This shows that there
is a physiological, not just psychological, bodily reaction to stress.
College students have been tested for major stressors in life (Beiter, Nash, McCrady,
Rhoades, Linscomb, Clarahan & Sammut, 2015). Adapting to changes in location or
environment can be a stressful situation for some people. For example, in college students, the
transition can be difficult enough that stress can arise from it (Kaya, Tansey, Melecoglu &
Cakiroglu, 2015). Amounts of stress also vary between different groups of college students. For
transfer students, stress levels were significantly higher than those who did not transfer (Beiter et
al., 2015).
There are also grade level differences to take into consideration. Upperclassmen may be
considering preparing for the workforce and are concerned with meeting requirements for their
majors. Underclassmen are concerned with transitioning into college life. Beiter et al. (2015)
tested upperclassmen stress levels versus underclassmen stress levels. In their study,
upperclassmen scored higher on the given stress test when compared to underclassmen.
Specifically, seniors scored higher than sophomores, while juniors scored higher than freshmen
(Beiter et al., 2015). Age can also correlate to the severity of how a person is affected by
stressors or trauma. Younger individuals often become more negatively affected from traumatic
experiences (Kron et al., 2015). Young adults, however, have the greatest ability to cope with
trauma or stressors (Kron et al., 2015). Reactivity by any individual to minor stressors is
correlated with depressed mood (Felsten, 2002).

According to Nonterah et al. (2015), many students have a fear of negative evaluation,
which can include exams, presentations, and other academic stressors. In this study, Nonterah et
al. (2015) explored how the fear of negative evaluation in academic situations affected students.
Fear of negative evaluation played both beneficial and harmful roles for students in the study
(Nonterah et al., 2015). One harmful role this fear plays in students’ lives included creating a
large amount of stress. The beneficial role, in contrast, was that students studied harder due to
the fear of negative evaluation, sometimes resulting in higher grades (Nonterah et al., 2015).
Fear of negative evaluation is just one of many stressors in the college life.
The number one stressor for students, without question, is academic stress (Persike &
Seiffge-Krenke, 2012). Reaffirming the results of Persike and Seiffge-Krene (2012) concerning
the number one stressor for students, Beiter et al. (2015) found the top ten causes of stress in
college students. These sources of concern, in order of severity, include academic performance,
the pressure to succeed, post-graduate plans, financial concern, quality of sleep, relationships
with friends, relationships with family, overall health, body image, and self-esteem (Beiter et al.,
2015). Many of the stressors listed as the top ten stressors for college students are similar,
especially the top four, which all relate significantly to college life (Beiter et al., 2015). These
results suggest that major stressors correspond to a person’s priorities in their current phase of
life. Academic performance is the highest ranked cause of stress in college students according to
Beiter et al. (2015). The stress of academic performance can come from various sources,
including procrastination and poor time management skills (Moore, Burgard, Larson & Ferm,
2014). Procrastination and poor time management skills have been found to be linked with stress
(Moore et al., 2014), and can also impact academic performance.

Academic stressors have a large impact on students, but they are not the sole stressors in
an adult life. Emotional stress is another type of stressor that heavily impacts individual lives.
Optimism and pessimism are two traits that have an effect on emotional stress (Fernandez-
Gonzalez et al., 2015). Higher pessimism, and therefore lower optimism, is often correlated with
higher stress (Fernandez-Gonzalez et al., 2015). With emotional stress being significant more so
in some lives than others, Holinka (2015) tested emotional intelligence with the hopes of finding
a correlation between the two. Emotional intelligence has multiple sub-divisions within it
(Holinka, 2015). Each sub-division was tested, as well as overall emotional intelligence. Results
showed that there is only one sub-division significantly correlated to emotional stress: the
interpersonal one (Holinka, 2015). This sub-division deals with establishing relationships that
are cooperative, beneficial, and satisfying (Holinka, 2015). The correlation was found to be
significantly negative; as interpersonal skills increased, life satisfaction decreased (Holinka,
2015).
Personality traits have also been tested in correlations to stress. Neuroticism is one trait
that has been tested (Felsten, 2002). Those with higher scores of neuroticism had a higher
reactivity to stress, with reactivity being synonymous with how much a person responds to a
stressor (Felsten, 2002). This means that there is a positive correlation; the more neurotic one is,
the more stress. Neuroticism also significantly predicted drug use (Coleman & Trunzo, 2015).
This suggests that psychostimulant use increases significantly during higher stress times at
college as a reaction to stressors (Moore et al., 2014).
Negative personality traits such as neuroticism and pessimism have both been found to
correlate with stress (Felsten, 2002). Studies, such as that of Kaya et al. (2015), show that
students experiencing higher levels of stress may have other significant sources of stress as well.

These sources can include high personal threat in one’s environment, as well as not having a
solid emotional support system (Kaya et al., 2015). This is shown in the example of workplace
violence, where workplace violence correlates to occupational stress (Yao, Wang, Wang & Yao,
2014). In an attempt to avoid stressors, many choose to try to suppress the stressful thoughts by
eliminating them from their minds. Although intentions can be to push away the stressful
situations or feelings, data shows that thought suppression leads to significantly more psychiatric
symptoms (Kroner-Borowik et al., 2013).
Beiter et al. (2015) included a general sample of adults, eleven percent of people
reported severe or extremely severe levels of stress, fifteen percent reporting moderate stress,
and twelve percent having mild stress. With approximately thirty-eight percent of people
surveyed reporting higher than normal stress levels, coping strategies must be developed in order
to handle the levels of stress. Some coping strategies are beneficial to users, while others can be
destructive, harming the individuals and their health. Students who indicate having more stress
may simply have less beneficial coping strategies (Kaya et al., 2015). Optimism, which is
known to negatively correlate with stress, is one characteristic that can moderate emotional
manifestations of stress (Fernandez-Gonzalez et al., 2015). According to Fernandez-Gonzalez et
al. (2015), the higher the level of optimism, the fewer emotional and behavioral manifestations
of stress. This is a positive coping mechanism, as it is not potentially harmful to the individual
who is optimistic.
Coping mechanisms have the potential to work in different ways. Optimism generally
works in most situations, such as dealing with academic stressors or work related stressors, but
other strategies may not work as well so broadly (Fernandez-Gonzalez et al., 2015).
Psychological resilience has been reported to help with overcoming psychological distress (Rees,

Breen, Cusack & Hegney, 2015). However, ego resilience specifically buffers academic stress
(Cole et al., 2015). Both ego resilience and another protective factor called mindfulness
mediates the association between academic stress and poor mental health symptoms (Cole et al.,
2015). Much like ego resilience, mindfulness buffers for academic stress, but does not buffer
anxiety as an off-shoot of stress (Cole et al, 2015).
As videogames, internet, and other technologies become more popular, new destructive
coping mechanisms are being formed. Smartphone use has become especially problematic in
recent years, and has been shown to be a possible coping strategy for life’s problems (Wang et
al., 2015). Gaming has also become a potentially overused and harmful cognitive diversion from
real life issues in recent years (Wang et al., 2015). Social support systems have been shown to
be a far more effective coping strategy (Fernandez-Gonzalez et al., 2015). Older students and
adults have significantly less satisfaction with these instrumental support systems for coping
(Fernandez-Gonzalez et al., 2015). Fernandez-Gonzalez et al. (2015) hypothesized that older
students and adults prefer to solve their own stressors due to a higher level of autonomy and self-
confidence. Regardless of the coping mechanisms, stress and life satisfaction and are negatively
correlated (Holinka, 2015).
Younger adults, such as college students, with especially low life satisfaction and high
stress levels may choose to make unexpected, and poor life decisions in an attempt to change
their unsatisfactory life (Kaya et al., 2015). For example, Kaya et al. (2015) found that college
students which have high levels of stress are more likely to make the decision to leave school
due to the stress. In studies by De Koninck (1975) and Nonterah et al. (2015), academic stress as
well as stress in general has been found to have significant positive correlations with both

depression and anxiety. Emotional state, especially after trauma, influences mental imagery
related to stress (Hartmann, Kunzendorf, Rosen & Grace, 2001).
One biological factor that plays a role in the effects of stress is sex. In many
circumstances, men and women find different aspects of life to be more stressful (Beiter et al.,
2015). Gender differences on PSS-10 (for perceived stress) scores are not significant, meaning
that both sexes have relatively similar stress levels (Kaya et al., 2015). Females are known to
show more physiological as well as emotional manifestations of stress compared to men
(Fernandez-Gonzalez et al., 2015). For example, the study done by Beiter et al. (2015) shows
that a greater portion of females compared to males reported some qualities of life to be more
stressful than males do. These qualities include sleep, academic stressors, self-esteem, and body
image. In contrast to all of the qualities that females find more stressful about life, they also
have a significantly higher reported satisfaction with life (Kaya et al., 2015). These results
demonstrate that sex is a precursor to life stress.
Regardless of sex, when shown a stressful film, De Koninck (1975) found that
participants had a significant increase in sleep latency, the length of time it takes to fall asleep.
The ability to sleep can be affected by stress (De Koninck, 1975). Once individuals are able to
fall asleep and wake up in the morning, according to Yu (2015), approximately 11.9% of people
report remembering their dreams from the night before the study was done. This is where stress
and dreams connect. A study done by Domhoff (2015) argued that dreams are what is called
embodied simulations. Embodied simulations are one’s mind creating an image or story relating
to present times, emotions, or feelings.
The feelings and emotions embodied in dreams represent and become what are known as
common dream themes (Yu, 2015). Common dream themes have been compiled into a list in the

Dream Motif Scale (DMS; Yu, 2015). Although typical dream themes sometimes vary between
cultures (Yu, 2015), evidence supports cross-cultural stability of many dream characteristics
(Mazandarani, Aguilar-Vafaie & Domhoff, 2013). Dreams that are more intense are had by
‘thin’ boundaried subjects, creating more contextual images (Hartmann et al., 2001). Dreams
that have more contextual images are more vivid and ‘dreamlike’ (Hartmann et al., 2001). The
boundary of the mind is so thin that it can let in those images, whereas individuals with ‘thick’
boundaries are not able to do so (Hartmann et al., 2001).
According to Hartmann et al. (2001), dreams when asleep also contain more
contextualizing images than do day dreams when awake. Emotions reported when dreaming are
often very similar to emotions had when in an awake state during actual events of a day (Nielsen,
Deslauriers & Baylor, 1991). This means that the emotions while awake may impact emotions
had in dreams (Nielsen et al., 1991). Emotions are as important in dreams as they are in real life
when experiencing them (Nielsen et al., 1991). Bad dreams have more emotional references,
while nightmares have more contextual images (Fireman, Levin & Pope, 2014). In regards to
negative dream types, bad dreams happen four times more often than nightmares (Fireman et al.,
2014).
Arousal affects dreams, no matter if the dream is pleasant or a nightmare (Hartmann &
Basile, 2003). Being threatened when in an awake state increase the incidence of threats in
dreams (Valli, Revonsuo, Palkas & Punamaki, 2006). After watching a stressful video, those
with anxiety from the video incorporated the film into their dreams (De Koninck, 1975).
Traumatic events such as the terrorist attack on 9/11/01 has affected a great number of people
involved in the event, as well as throughout the nation of the Unites States. In the United States,
after 9/11/01, dreams began to trend towards increased inclusion of fear and/or terror, as well as

increased intensity of dream imagery (Hartmann & Basile, 2003). Dreams that relate to some
sort of trauma, such as the 9/11 attack, were found to be longer than the control group’s dreams
(Valli et al., 2006). Not only are the dreams themselves different in length with stress or trauma
involved, but trauma also improves dream recall after the fact (Valli et al., 2006). This means
that once a person is exposed to a stressor or traumatic event, the dreams which that individual
has after the fact will be remembered more easily in comparison to individuals who did not
undergo stressors or trauma.
People who have undergone traumatic experiences have prevalent dream themes, which
include situational stress, fear, anxiety, and helplessness (Kron et al., 2015). For women, the
most prevalent were togetherness and active ego, possibly as strategies to cope with the trauma
(Kron et al., 2015). There is a theory called Disruptive Avoidance Adaptation (DAA) in regards
to dreams after a stressful event (Stewart & Koulack, 1993). The DAA theory involves
oscillation between mastery dreams and pleasant avoidant dreams. Mastery dreams are typically
more upsetting for the person involved, and are composed of situations in which the individual
attempts to learn or ‘master’ specific areas of life, or stressors (Stewart & Koulack, 1993). This
implies that avoidance dreams provide the dreamer with brief times of relief between the
disruptive and negative mastery dreams in which the mind attempts to master the stress (Stewart
& Koulack, 1993).
Even though trauma greatly affects individuals in a number of ways, they do not have to
have a traumatic event in order to affect dreams. For example, a study done by Erlacher et al.
(2011) of German athletes shows that stressful situations can also cause dream changes. In this
study, athletes reported a significant likelihood of having a bad dream or nightmare the night
before a big game in whatever sport they were participating in. The exact statistic is that fifteen

percent of all German athletes included in the survey have had a nightmare the night before a big
game in the past year (Erlacher et al., 2011). Females were more likely to succumb to this than
men (Erlacher et al., 2011). These nightmares often presented distressing content for the
individual dreaming that was predominantly related to the upcoming game (Erlacher et al.,
2011). According to Erlacher et al. (2011), common themes of these nightmares included
athletic failure or losing the game.
In the presence of stress, recurrent dreams are more present in an actively recurrent group
(Duke & Davidson, 2002). The group that was actively having recurrent dreams recalled more
recurrent dreams than the group who was not currently active in the presence of stress (Duke &
Davidson, 2002). When participants were found to suppress thoughts on stressors, they had a
significant amount of more target-related dreams than those who did not suppress their stressful
thoughts (Kroner-Borowik et al., 2013). In those dreams, there was also a higher level of dream
distress due to the negative thoughts in the dream. In a case study done by Domhoff (2015), a
recently widowed man continued to dream of his deceased wife. The dreams that followed her
death consisted of all of the emotions and feelings he had towards her, as well as sexual
frustrations from their relationship. As the dreams continued, there were diminishing
occurrences of his wife coming back to life or of her illness and death (Domhoff, 2015). From
survey studies to case studies, previous research on the variables of stress and dreams have
shown significant correlations. In this study, the researcher predicts that there will be a
significantly higher level of distress in dreams of adult individuals experiencing stress.

Methods
Participants
Participants were obtained in two ways. The final survey of all tests were displayed in
Qualtrics for the students of Baldwin Wallace University’s Spring 2016 semester taking the
Intorduction to Psychology – 100 course to potentially choose for extra credit. The survey link
was also posted in a Facebook event open to the public. Potential subjects were invited and
encouraged to also invite their friends in a snowball effect. Using both methods, the total number
of participants was 292. There were a total of 91 male participants and 200 female participants
recorded, while the remaining one participant chose to omit this information.
Materials
In the survey, three measures were used to account for the main hypothesis as well as all
ex post facto variables. Perceived stress was measured using the Perceived Stress Scale (PSS;
Cohen, Kamarck & Mermelstein, 1983). Distress during dreams was measured using the Dream
Motif Scale (DMS; Yu, 2011). Age, sex, year in school, race, and family income were all
measured using the Demographic Survey in Appendix A.
Procedures
All subjects completed the survey provided to them online using a device that can access
the internet. The first page shown on the survey was the informed consent agreement. By
clicking the ‘next’ button, the research participant agreed to the terms of the study. After the

informed consent, this survey first consisted of the demographic survey provided in Appendix A.
Following that was the Percieved Stress Scale (PSS; Cohen, Kamarck & Mermelstein, 1983).
The final part of the survey was the Dream Motif Scale (DMS; Yu, 2011).

Results
In this study, the main hypothesis tested perceived stress scores with distress levels in
dreams using a Correlation Regression. The Pearson Correlation revealed that there is a
significant relationship between perceived stress and distress in dreams, r = + .222, p < .001. See
Figure 1 for a scatter plot of this relationship. The Linear Regression demonstrated that one can
significantly predict a person’s distress level in dreams from his/her level of perceived stress
(F1,290 = 15.036, p < .001). The Regression equation is:
Distress in Dreams = Perceived Stress (.279) + 89.574
(r2 = .046, t = 30.705, p < .001)
Figure 1: Scatterplot of Distress in Dreams and Perceived Stress.
The age of participants in this study ranged from eighteen to eighty. A Pearson
Correlation evinced that there is a significant relationship between perceived stress and age, r = -
.320, p < .001. See Figure 2 for a scatter plot of this relationship. A Linear Regression

demonstrated that one can significantly predict a person’s perceived stress from his/her age
(F1,275 = 31.455, p < .001). The Regression equation is:
Perceived Stress = Age (-.182) + 45.131
(r2 = .099, t = 44.221, p < .001)
Figure 2: Scatterplot of Perceived Stress and Age.
According to a One-Way F-Test, there was a significant difference among the perceived
stress scores of the given five school groups: Freshmen, Sophomores, Juniors, Seniors, and Not
Currently in School (F1,4 = 4.646, p = .001). In a post-hoc Tukey analysis, the only two groups
with significant differences were Freshmen who were significantly more stressed than those not
currently in school at p = .001. See Figure 3 for a graph of this finding.

Figure 3: Bar Graph of Perceived Stress and Year in School.
Differences in sex were also examined in this study. First, an Independent Samples T-
Test indicated that women have a higher level of perceived stress than men, t289 = - 2.686, p =
.004. See Figure 4 for a graph of this finding.
Figure 4: Bar Graph of Sex and Perceived Stress.

However, an Independent Samples T-Test indicated that men and women do not have
significantly different levels of distress in dreams (see Table 1 for Group Statistics), t289 = -
1.283, p = .1005.
Table 1: Group Statistics of Sex and Distress in Dreams.
Two other demographics were tested in this study; race and income level. A One-Way F-
Test revealed that there was not a significant difference in distress in dreams among the five
given races: White, Multiracial, Black/African American, Asian, Hispanic, and Other (F1,5 =
1.400, p = .224). Another One-Way F-Test discovered that there was not a significant difference
in perceived stress among the four listed income levels: less than $50,000, $50,001-$75,000,
$75,001-$100,000, and greater than $100,000 (F1,3 = .802, p = .493).

Discussion
This study strengthened prior research dealing with stress. According to Kron et al.
(2015), extreme stress and traumatic events can impact the content of dreams. In this study,
perceived daily stress was considered instead, and was predicted to positively correlate with level
of distress in dreams as the main hypothesis. As hypothesized, higher daily perceived stress
significantly positively correlated with distress in dreams. The main null hypothesis was rejected
as the correlation had a significance above what was needed for the set alpha level of five
percent. This study had other significant findings dealing with demographics listed in the survey
as well. One demographic that was tested with perceived stress was age.
Age was found to significantly negatively correlate with daily perceived stress. This
means that eighteen year old freshmen in college were significantly more stressed than the
participant of eighty years old. The variable age relates to the variable of year in school, as
younger individuals were typically in lower years in school, such as freshmen and sophomores.
A post-hoc comparison of year in school with daily perceived stress also determined that the
only significant finding when considering the college level groups was between freshmen in
college and those not currently in school. The findings of both age and year in school relating to
stress have the same result in this study, increasing its relevance.
However, the findings pertaining to year in school contradict previous research stating
that juniors were more stressed than freshman, and seniors more stressed than sophomores
(Beiter et al., 2015). This could be due to issues with the number of participants (i.e., power) in
the study. This study had fewer upperclassmen college students than underclassmen, specifically
freshmen. Also, although this study had a wide age range, there were many more participants

that were in the age range of eighteen to twenty-five than many other groups (i.e., 25-35, or 40-
50). The lack of power in other age ranges and years in school may have an effect on the
outcome of their correlations. In the future, closer to equal amounts of subjects in each age
range should be considered.
Another demographic in question was sex. According to previous research, females have
a greater physiological and emotional manifestations of stress (Fernandez-Gonzalez et al., 2015).
This study has strengthened that known finding. According to the data from the current study,
females have significantly higher perceived stress compared to men. Past research also found
that females were more likely to succumb to bad dreams compared to men (Erlacher et al.,
2011). In contradiction, this study found that there was no significant difference in level of
distress in dreams between the two sexes. With that being said, future studies should focus on
which result is indeed significant when considering sex and dreams.
The remaining two demographics studied were race and income level. Despite personal
preconceptions, income level did not lead to significantly different perceived stress levels. Also,
differences in race did not lead to significantly different levels of distress in dreams. As with
age, both findings could pertain to lack of power in these demographics, or simply in the number
of subjects. Concerning income level, each category had a similar number of participants. With
race, a strong majority of subjects were Caucasian, while many other groups were
underrepresented in the sample. In future research, correlations between race and distress in
dreams, as well as between income level and perceived stress, should be investigated further.

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Appendix A
Demographic Survey
1. What is your age? ________
2. What sex do you identify with?
a. Male
b. Female
3. Year in school?
a. Freshman
b. Sophomore
c. Junior
d. Senior
e. Not currently in school
4. What is your race? ________
5. In what range is your best estimate of your household/family income?
a. Less than $50,000
b. $50,001-$75,000
c. $75,001-$100,000
d. Greater than $100,000

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