The Effects of Weather on Human Emotions- FINAL

This topic submitted by Sean Barry, Lynette Inkrott, Dan O'Connell, John McCall, Mandie Valentin ( Valentaf@Muohio.edu ) on 12/9/05. [Section: Cummins]
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Natural Systems 1 Syllabus---Western Program---Miami University



Sean Barry
Lynette Inkrott
Dan O’Connell
John McCall
Mandie Valentin


Lab Proposal
THE EFFECTS OF WEATHER ON HUMAN EMOTION


WCP 121
Dr. Myers
Team: Sunny Delight

Table of Contents

I. Introduction
a. Preface
b. Actual Question and Hypothesis
c. Context

II. Methods
a. Experimental Design
b. Time Table
c. Stages
d. Variables
e. Analysis

III. Our Day


IV. Results


V. Discussion


VI. Works Cited



I. Introduction
a. Preface
It seems that everyone has a favorite season. Some like the hot summer weather, while others prefer the crisp winter air. People often enjoy the beautiful colors in the autumn trees as well as the picturesque flowers that bloom in the spring. There are many different reasons people appreciate the exquisiteness of each season.
However, recent research has suggested that aspects of certain seasons may also have characteristics that don’t affect the lives of humans in such a positive manner. Most notably, the seasonal changes may be responsible for triggering a mood disorder known as Seasonal Affective Disorder (SAD). The National Mental Health Organization defines SAD as “a mood disorder associated with depression episodes and related to seasonal variations of light (NMHA 1993).” In its more serious form, is characterized by “depressed affect, lethargy, loss of libido, hypersomnia, excessive weight gain, carbohydrate cravings, anxiety and inability to concentrate or focus during the late autumn or winter (Nelson 1990).” When these more serious symptoms occur seasonally, particularly in the fall or winter, the resulting depression is generally not simple melancholy and can be classified as bipolar disorder (Frank 1999). Different seasons may inflict different levels of severity. For example, winter depressive episodes are mostly mild to moderate severity. Patients with winter SAD seldom require hospitalization, have psychotic symptoms or are at risk of suicide. Yet, a majority of patients do experience marked impairment of functioning at work and in their social relations (Lönnqvist J., Partonen T, March 1998). When looking at the more simple form of this mood disorder, we try to analyze the cause for such a condition, most specifically in its effect on our day to day moods.
b. Question and Hypothesis
In order to test whether or not changes in the weather can affect human emotion, we want to answer the question, “Are people’s emotions positively affected or improved by increased sunlight and warmth as opposed to cold, cloudy weather?” We hypothesized that warm and/or sunny weather does elicit positive feelings within people while cold, cloudy weather does the opposite. (We will define positive feelings for our lab as relaxed, confident, optimistic, feeling a sense of well being, and overall positive outlook). This is based on our memory of others’ attitudes on days of varying weather, as well as our own personal feelings on those days and how they seem to differ based on the weather. This hypothesis is also based on our research of previous studies. For example, one study showed that “pleasant weather improves mood and broadens cognition”. This was based on a two-part study analyzing the effect on mood of both total time outdoors as well as pleasant weather, which was defined as high temperature or barometric pressure (Keller 2005). In another study, they tested a group of sixty-two university student subjects. These subjects kept structured diaries of their feelings and their productivity for six weeks in Illinois in early autumn. The study found the students to be stressed more when the weather is unstable, cloudy, warm and humid, and least stressed during sunny, dry, cool weather with rising barometric pressure (Barston, A.G. June 1988.)
In Addition, we have found compelling research that depicts our body's reaction to the intensity and exposure time of light and what type of an effect it has upon our mood. The symptoms of SAD are believed to be caused by melatonin. Melatonin is a hormone that is produced in our bodies during darkness. The primary function of this hormone is to induce sleep. For those affected with SAD, the standard indoor lighting that one encounters in the winter (indoor) months isn't bright enough to effectively inhibit its production ("Is Full Spectrum"). Therefore, because of the lack of natural light, due to the change in weather, many SAD suffers incur heavy amounts of melatonin. Consequently, this supports that weather does in fact impact mood.

c. Context
Many previous studies have been done to test Seasonal Affective Disorder, including studies by past Miami students. In 2002, students studied the effects of weather on Western Campus freshmen and concluded that weather does have an impact on how we feel. However, they identified many other independent variables that affect the mood as well and, in fact, affect it more than the weather does (DuBois 2002). That same year, another student lab group studying the effects of weather on mood distributed surveys to test the mood changes among students on days of different weather (Maser 2002).
Studies to discover Seasonal Affective Disorder and its correlation with Psychiatry have been completed by professionals, such as Raymond Lam. He noted several different studies, including the questionnaire in Alaska that estimated that SAD is 9.2% widespread. Using the same screening questionnaires, a similar prevalence existed among people who lived in lower latitude areas (Current Opinion 1994). These results parallel the estimate of an 8% prevalence of SAD among the subjects of another study (Partonen 1998). Another report showed that 20% of Canadian patients treated for depression at a Canadian Mental Health Center met the criteria for winter depression. An interesting study compared the correlation between the number of psychiatric patients in accordance to the mean atmospheric pressure, air temperature, relative humidity, wind speed and minutes of sunlight and precipitation/day. The study found peaks in bed occupancy were observed in March and November, with lows in August (Maes, M. June 1993). Finally, a study in Zurich discovered 20.4% of the people in the sample as having SAD. This was diagnosed using not only self-assessments via the questionnaires, but also with follow-up analyses and reliable evaluations. (Current Opinion 1994).
These findings provide the foundation for our study, as they demonstrate the reality of this mood disorder and give us reason to believe that weather does indeed have an effect on human emotion. They also provide results that we can build off of to continue to look for answers to our question.

II. Methods
a. Experimental Design
1. Picture Test:
We will provide two pictures to stimulate each subject’s emotional reaction to the weather. The two photos will depict contrasting environments of the same location on Miami University’s campus. We chose pictures from Miami University so the subjects tested will be able to relate to the setting. The first picture is an illustration of our vision of a nice day. (A nice day being described as: blue colored sky, minimal cloud cover, and maximum amount of sunlight.) The second picture that will be provided to the subject of the study is a picture illustrating bad weather. (Bad weather in our study being described as the opposite of a nice day: gray-dark colored sky, heavy cloud cover, and minimal sunlight.) Then the subject will be asked immediately after viewing the pictures to pick five words out of the established word bank that best describe the emotional connections incurred after viewing the picture.
Next, to decipher and analyze the connection of the visual stimulants (the pictures), there will be an equation to determine the meaning of the 5 words chosen. The participants of this study will be unaware of the equation and point system in order to provide the student-generated lab with a more honest evaluation of each participant’s reaction. (Yet, this value system may be altered after further research if implications arise.)
POINTS Meaning
18 - 20 Extremely Positively Affected
13 - 17 Positively Affected
8 - 12 Indifferent
3 - 7 Negatively Affected
0 - 2 Extremely Negatively Affected


Word Bank

Extremely Positively Affected Positively Affected Indifferent Negatively Affected Extremely Negatively Affected
Point Value: 4 Value: 3 Value: 2 Value: 1 Value: 0
Delight Amusement Ok Crappy Miserable
Joy Contentment Satisfied Bad Dejected
Glee Nice Content Unpleasant Depressing
Bliss Good Good Enough Poor Dreadful
Exhilaration Pleasant Alright Distasteful Awful

To evaluate the vocabulary choices we made for determining the subject’s reaction, we thought of a list of words that could be used to describe positive and negative situations. Once we compiled a list of about thirty to forty words, we paired words up with what we thought was an appropriate level of positivism or negativity. As a group (using the opinions of the five individuals as a “mini-test pilot”), we noticed that some words seemed to be more positive or more negative than others, so we used this to decipher the difference between “Extremely Positively/Negatively Affected” and “Positively/Negatively Affected” ratings.

2. Set Scenario
We will provide three small brief stories to stimulate subjects’ emotional reaction to the text. Each scenario will include specific, varying weather conditions. (Scenario A. represents the summer, Scenario B. represents the fall, and Scenario C. represents the winter season.) The stories are meant to be simplistic and lacking any emotional ties. Consequently, the only substance left to arouse participants’ emotions are weather conditions. For example consider this sentence, “When Josie woke up the sun was already up and was shining through her curtain.” This sentence is placed in the story to generate emotions from the verbal illustration. Awakening to the sunshine, or the other weather conditions alluded toward in the passage, have different meanings and implications to different subjects.
The text will be an irrelevant story, with meaningless characters and storyline. The storyline will remain the same with the same characters, storyline, and word order. The text is purposely ambiguous, meaning the characters, setting, storyline, and plot will not be fully developed for the reader. Consequently, the reader will only have the small excerpt. This forces the subject to rely on their own intuition rather than the story to find the character’s suspected emotions.
Then, after the participant reads the brief excerpt we will ask them to answer five multiple-choice questions. These questions will focus on stress, confidence, optimism, positivism, and sense of well-being. These factors were chosen because we consider them all aspects of happiness. With the results from the scenario, it will help us to draw a conclusion of someone’s level of happiness based on weather conditions. The scenario will allow our group to determine if and how a person’s emotions are directly affected by the weather-related imagery described through our composition.
Story Scenario:

A. Madison and Josie go for a walk every morning. This has been their routine since the first day of freshman year at Miami. Today, the sky is bright blue with a few thin clouds. When Josie woke up the sun was already up and was shining through her curtain. The air is warm after stepping out of the apartment complex. Madison was late to meet Josie because she couldn’t find her green sandals. Josie comments on how Madison’s face is beginning to grow pink with color because of the summer heat. After Madison puts on her sandal, the girls are ready and head out for another walk through Pfeiffer Park.
Answer all 5 questions as If you were either Madison or Josie:
Circle the Answer that best represents your reaction toward the above scenario.
-I would feel Stressed or Relaxed.
-I would feel Confident or Insecure
-I would feel Optimistic or Pessimistic
-I would feel a Sense of Well Being or Sense of Dejection
-I would feel Positive or Lackluster


B. Madison and Josie go for a walk every morning. This has been their routine since the first day of freshman year at Miami. Today, the sky is gray and cloudy. When Josie woke up the sun was barely up and the room was still dark, with little light shining through her curtain. The air was cool and crisp after stepping out of the apartment complex. Madison was late to meet Josie because she couldn’t find her Birkenstocks. Many leaves had fallen off the trees in the past days. Josie comments on how Madison’s face, especially her nose and ears, are turning pink because of the strong, cool breeze. After Madison puts on her shoes, the girls are ready and head out for another walk through Pfeiffer Park.
Answer all 5 questions as If you were either Madison or Josie:
Circle the Answer that best represents your reaction toward the above scenario.
-I would feel Stressed or Relaxed.
-I would feel Confident or Insecure
-I would feel Optimistic or Pessimistic
-I would feel a Sense of Well Being or Sense of Dejection
-I would feel Positive or Lackluster


C. Madison and Josie go for a walk every morning. This has been their routine since the first day of freshman year at Miami. Today, the sky was dark gray and overcast. When Josie woke up the sun wasn’t up and her room was pitch-black, with no light shining through her curtain. The air was cold and chilling after stepping out of the apartment complex. Madison was late to meet Josie because she couldn’t find her snow-boots. A good amount of snow fell through the night. Josie comments on how Madison’s face is beginning to grow red with color because of the strong, chilly wind and the snow fall. After Madison laces up her shoes, the girls are ready and head out for another walk through Pfeiffer Park.
Answer all 5 questions as If you were either Madison or Josie:
Circle the Answer that best represents your reaction toward the above scenario.
-I would feel Stressed or Relaxed.
-I would feel Confident or Insecure
-I would feel Optimistic or Pessimistic
-I would feel a Sense of Well Being or Sense of Dejection
-I would feel Positive or Lackluster

Positive Answer: Point Distribution: Negative Answer: Point Distribution:
Relaxed 1 Stressed 0
Confident 1 Insecure 0
Optimistic 1 Pessimistic 0
Well Being 1 Dejection 0
Positive 1 Lackluster 0
Maximum Points: 5 Minimum Points: 0

Point System
Weather’s effect on subject:
4-5 – Positive
2-4 – Neutral
0-1 - Negative
b. Time Table

The table below shows the aspects/ factors that will be included while completing our experiment. Additionally, we will follow these time guidelines when compiling our research.
Time of Day Season of Year (correlates with Stages) Twice a Week (Twelve times in Semester – pending on weather) Seven Days Passed the last time we conducted the test


Time of Day: the research will be administered to participants at two different times of day. The first time will be approximately 12:30 in the afternoon (or around the lunch hour.) The second study will take place at 5:00pm or later in the evening (striving for around the dinner hour or later.) The two times of day will allow the group to determine if the time and length of day (because as our research time progresses the days grow shorter) affect the participants’ emotions toward weather.
c. Stages

Administer tests throughout the change of seasons. Stages are not based on their solstices (beginnings) due to the fact that the semester ends before the Winter solstice. For data purposes, these dates are about the same time periods, but each period should display their respective weather events.
Beginning of Fall
September 20th – October 15th Weather is still summer-like (temperature > 80, green grass, and tree leaves.)
Mid Fall
October 16th – November 10th Cool days and nights (temperature < 70, change in leaf and grass color but still visually pleasing)
Late Fall
November 11th – November 30th Colder days and nights, trees beginning to look bare.


d. Variables
The variables of the experiment all deal with time periods. Our first variable is the number of times per week we collect data. We planned to collect data twice on the same day, one collection at 12:30pm and second collection at 5:30pm. This would decipher any difference in the time of the day that would affect our results. We also assumed that the effect of the shorter daylight time as the months pass might impact our results. This collection will be done once a week, on varying days, based upon the weather. We will conduct the experiment on one day of the week that fits our positive definition for the weather, and another day of the week that fits our negative definition for the weather. If the weather remains constant throughout one week, there may be a variable in which we take a day out of another week to obtain data from different weather.
The next set of changing variables is the different stages and amount of time passed we have chosen to conduct our experiment in. We have chosen three stages in which to collect data: Beginning of Fall, Mid-Fall, and Late-Fall. We assume these as variables because the amount of time in the day will decline as the winter season comes, and we are assuming that this may affect our results. Also, the weather has a chance of changing abnormally, which could affect our results. For example, in the Late Fall Stage, there could be a chance that the weather becomes abnormally warm.
e. Analysis

We plan to analyze our data according to the results we obtain throughout our experiment. Since we have two different methods to our experiment, we will have separate data tables, and then compare the data tables of the two methods to see if our results agree with our hypothesis. The point systems we devised will give us accurate numbers to use for our data.
To organize the data earned from the results, we plan to use a couple of formats. The formats will be done for each experiment, and then the data will be compared. First, we plan to depict the data in a quantitative form. We will write out our statistics using percentages and numbers, more specifically, the percentages of the choices made in each experiment. The numbers obtained will also include averages (mean), the range of possible results, the median, and the mode. We figured this would abide by the formal guidelines of statistics. We then plan to depict our data using graphs and charts. Specifically, bar graphs, pie charts, and a stem-and-leaf display will be used for both methods. The use of these formats will make our data as exact as possible and help determine if our hypothesis is correct. If we only had one format, our data may not look correct due to many factors, such as abnormal ranges from numbers outside a standard deviation, and skew-ness of the numbers.


III. Our Day

As a group, we feel that one of the most important parts of our semester-long student generated lab report is our day to present it to the class. This will be our time to present our results that we have compiled throughout our experiments all semester long and their significance to science. The structure we have come up with to use our time appropriately will be essential to the final success of our project.
We will begin our day of class by introducing two very important parts of our lab: the purpose and the hypothesis. By introducing these two topics first, we will be able to get the listeners’ attention right away about what we will be talking during our time. After we introduce the topic of our lab, we will pass out our surveys that we handed out to the students on campus to the listeners, and ask that they complete them. This will be a great hands-on opportunity to show the class how we conducted our lab. Once they have completed the surveys, we will then collect them and half of our group will score them. The other half of our group will explain to the class how we have been scoring all the surveys throughout the semester to compile our results. Once we have done this, we will then reveal to the class the results we have compiled throughout the semester. After discussing the results, we will reveal the class results from the day and compare them to the results from our previous surveys.
By this time in our day, the class will have a good understanding of our experiment and how we conducted it. We will conclude our day by discussing if we were correct or not with our hypothesis that we came up with at the beginning of our experiment. Our day will end and we will offer up time for either questions or comments to finish.

IV. Results / Statistical Analysis

A. Picture Test

When analyzing the first part of our experiment, we wanted to observe differences not only between the sunny and rainy pictures, but also with the different times of season that the surveys were distributed. Therefore, we looked at the results at the three different time periods: early fall, mid fall, and late fall.

a. Early Fall: The results for our early fall picture test can be seen below:

As you can see, the p-value is <.0001 which shows that our data has extremely strong evidence that there is statistical significance. Therefore, there is a very small chance that the differences observed can be explained by chance alone. In looking at the graph at the top, the mean of the responses on the Sunny Picture survey is 13.8, which means the participants were “positively affected” by the survey in general. The mean for the rainy picture is 3.0, which means the survey participants were mostly “negatively affected.” Also, there doesn’t appear to be much variance with either picture.
b. Mid Fall: The results from our mid fall picture test can be seen below:

The print-out above shows a p-value of <.0001 again, as the early fall test did. Again, this shows that our data has statistical significance and that there is a very little chance that the differences observed can be explained by chance alone. The mean of each test is spread fairly fair apart, with the rainy picture having a mean of 2.88 (participants were mostly “negatively affected”) and the sunny picture having a mean of 14.28 (participants were mostly “positively affected”). The variance appears to be very small according to the graph at the top.
c. Late Fall: The results from our late fall picture test can be seen below:

Looking at the statistical print-out above, you can see that the data is once again statistically significant with a p-value of .0003. That means that there’s only a .03% chance that the differences we observed can be explained by chance alone. The means for our late fall test rested more closely together than for early and mid fall. The rainy picture had a mean response of 10.24 (most participants were “indifferent”), while the sunny picture had a mean of 14.56 (most participants were “positively affected”). Neither appear to have significant variance, but there is more variance present here than in the two picture tests earlier in the semester.

B. Set Scenario Test

As we did with the results of the picture test, we wanted to look at the statistical results of our story tests relative to the time of the semester they were given out. Again, we divided our survey collection dates into three time periods: early fall, mid fall, and late fall. The set scenarios were identical plot outlines yet were written to represent these different seasons. We then observed the differences in the reactions to the three different stories for each time period. Scenarios involved in the experiment were a positive one/summer scenario (Story A), a neutral one/fall scenario (Story B) and a negative/winter scenario (Story C).
a. Early Fall: The results from our early fall story test can be seen below:
As you can see in the mid fall statistical results, the p-value of <.0001 shows statistical significance of our data. Thus, there is a very small chance that the differences observed can be explained by chance alone. The mean scores were close for Story B and C, at 2.16 and 2.52, respectively. The mean scores for both Story B and C represented weather had a neutral effect on subjects. The mean score for story A/ summer scenario was higher at 4.6. Story A represented a positive affect based on the weather. All story scores appear to have similar variance.


b. Mid Fall: The results from our mid fall scenario test can be seen below:

The results above from our mid fall scenario test show a p-value of <.0001. Like the results from the early fall test, these results have statistical significance and there is a very small probability that the differences observed can be explained by chance alone. The means also reacted similarly to our early fall test, with Story B and C being close again at 1.52 and 1.44. Story A’s mean remained high at 3.96. The variances for each story seem to be smaller than those for the first scenario test of the semester.


c. Late Fall: The results from our late fall scenario test can be seen below:

According do the results above, the late fall scenario test was statistically significant with a p-value of <.0001. Thus, there is a very small probability that the differences observed can be explained by chance alone. The means are more spread out for this test, with 4.56 for Story A, 2.36 for Story B, and 1.72 for Story C. The variance of the results of each story is similar to that from our early fall test.

C. Overall Statistics Findings
From both our picture and our set scenario tests, we’ve gathered strong data with high statistical significance for each test. Just using these results alone, we would conclude that there is a strong correlation between change in mood and feelings with days of cloudy/clear, day length, and temperature. While this may suggest a successful observance of differences among the various pictures and scenarios throughout different times of the semester, this also raises some concern within our group. Because the results are so statistically significant, we believe there is a reason behind it. Most importantly, we feel that the survey respondents looked at the survey and immediately knew what response we were seeking. Thus, regardless of their true mood at the time of the survey and how it was affected by the weather, they appeared to respond in a very predictable way to both surveys.
* Complete data results from the picture test and story scenario are attached at the end of the lab manual.
V. Discussion
When we first came up with the idea for a lab revolving around human emotion and how it is affected by the weather, we thought that the methods would be fairly simple. All we figured we would have to do is create a survey that we can give to many different people on different ways of varying weather conditions. Although this appeared as a viable solution, we soon discovered the endless counts of independent variables present in such methods. Thus, we had to narrow our focus and specifically design the methods in accordance with minimizing the extraneous independent variables.
Through this process, many questions and a few alternating hypotheses became very prevalent in our minds. We were working with arguably one of the most complex systems occurring in nature, the human brain. Dealing with human emotion, there are so many different variables that mend together to form even the most basic of emotions. So when we begin to hypothesize that weather has a direct affect on human emotion, skepticism quickly forms. An alternative, and probably a safer hypothesis, is that weather tends to enhance emotions, rather than create them. For example, if a person were in a fairly confident and optimistic state, a crisp, clear, sunny day would enhance the person’s emotional state to ecstatic. Conversely, if a person were in a pessimistic, lethargic emotional state, then a cold, cloudy, rainy day would enhance their negativity to extreme depression. Although this alternative hypothesis seems to be more valid, we preferred to take a bold stance that would be much more interesting if the results backed our hypothesis.
Some questions that arouse while we were forming our methods were the following: How many variables should be kept and how many should be eliminated from contaminating our data? Which variables will have the largest effect on our data? Should the same subjects be tested multiple times, or should a large number of random subjects be tested? Which words should be used to represent both positive and negative emotional states? These questions made the development of our methods a very meticulous and well thought-out process. Overall, we think that our methods were created effectively in creating results that back our hypothesis. We predict that the weather will play a significant role in creating emotional states of the average human being. They will not be the only factor in the mental process, but they will contribute to the overall positive/negative emotional condition.

VI. Works Cited
1. Barnston, A.G. The effect of weather on mood, productivity, and frequency of emotional crisis in a temperate continental climate. International Journal of Biometeorology: June 1988. Volume 32, Number 2. pp 134-143. Springer-Verlag GmbH.

2. Dubois, Jones, Bokar, D’Elia. “The Effects of Weather on Western Campus Freshmen” November 2002. http://jrscience.wcp.muohio.edu/nsfall01/
FinalArticles/TheEffectsofWeatheronWest.html

3. Frank, Thase. “Natural History and Preventative Treatment of Recurrent Mood Disorders” Annual Review of Medicine. February 1999. 50: 453-468.

4. "Is Full Spectrum Light Important?" http://ns.isaac.net/ksmith/fulspec.html (24 Feb 1997).

5. Keller, Frederickson, Ybarra. “A Warm Heart and a Clear Head” Psychological Science. September 2005. Volume 16, Issue 9: 724-731.

6. Lönnqvist J., Partonen T. Seasonal Affective Disorder: A Guide to Diagnosis and Management. CNS Drugs, Volume 9, Number 3, pp. 203-212(10) March 1998. Adis International. els/0924977x;jsessionid=17dn57abb4io0.victoria>

7. Maes M., F. De Meyer, D. Peeters, H. Meltzer, C. Schotte, S. Scharpe and P. Cosyns. The periodicities in and biometeorological relationships with bed occupancy of an acute psychiatric ward in Antwerp, Belgium. International Journal of Biometeorology: June 1993. Volume 37, Number 2, pp 78-82. Springer-Verlag GmbH

8. Maser, Martin, Barlow, Headen. “Affects of Weather on Mood” November 2002. http://jrscience.wcp.muohio.edu/nsfall01/labpacketArticles/AffectsOfWeatheronMood.html

9. Nelson, Badura, Goldman. Mechanisms of Seasonal Cycles of Behavior. Annual Review of Psychology. 1990. 41: 81-108.

10. Partonen, Timo, Lonnqvist. Seasonal Affective Disorder. Psychology and Behavioral Sciences Collection. October 1998. Volume 352, Issue 9137.

11. “Seasonal Affective Disorder” Current Opinion in Psychiatry. January 1994. http://www.mentalhealth.com/book/p40-sad.html#Head2

12. “Seasonal Affective Disorder” The Harvard Mental Health Letter. February 1993. http://www.nmha.org/infoctr/factsheets/27.cfm


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