Final 1: Mood and Weather

This topic submitted by Justin Martin, Kristen Maser, Katie Barlow, Lisa Headen (martinjl@miamioh.edu) at 3:53 pm on 12/10/01. Additions were last made on Wednesday, May 7, 2014. Section: Myers

Abstract-
Our group’s goal is to prove the hypothesis that the weather has an affect on a person’s mood; we did this by measuring peoples stress levels on days with different weather conditions. We assumed that peoples stress levels would be lower on days that were warmer and sunny and their stress levels would be higher on days that were cooler and rainy. Our results were inconsistent. On certain days it appeared that weather had a slight affect on stress level and on other days it appeared that other factors such as amount of sleep and workload had a greater effect than the weather did.

Introduction-
Our group's goal is to prove the hypothesis that the weather has anaffect on a person's mood. Our thesis is that in general, sunnier, warmerdays have a positive affect on a person's mood and colder, cloudier weatherhas a negative affect. We are not sure if this will prove to be true or not,our alternative hypothesis is that weather has no affect on mood. An effecton mood would be defined as a change in the way the person feels. An example of this is a change from feeling happy and/or optimistic, to feeling sad and/ordepressed. We are going to look at many variable of weather, includingsunshine, temperature, cloudiness, precipitation among others. On days thatpeople complete our survey, we will collect information from Hays’s weathercenter.
Articles-
Many studies have been conducted on the effects that weather has on oureveryday moods. It seems like all of the studies done show both similaritiesand differences to the point that weather affects peoples' moods. Our grouphas reviewed many articles that have useful information related to our topic.Some of these articles lead us into the right direction while others had noreal relevance to our lab at all. Here are the ones that we will use forfuture references.The first article that we reviewed was entitled "No changes in mood withthe seasons" and was written by J. Hardt. Hardt discusses the fact that womenunder the age of 51 are more often affected by seasonal affective disorders,but in his study this fact was not true. He did an experiment that lasted afive-year period on patients in a pain clinic in Germany. After analyzing hisdata, Hardt came to the conclusion that the amount of sunlight in a day andeven the change in the seasons had little if any affect on depression. Theygo on to say that they believe that seasonal affective disorder, as a truedisorder is probably rare.

"The origin of everyday moods: Managing energy, tension, and stress"written by Robert Thayer is our second source that discusses the behaviorsthat we all face each day and how they affect our mood. Thayer argues that weneed to learn to see our moods as "vital barometers" of our whole psychologyso that we can improve our personal effectiveness. With the improvements thatwe need to learn we can improve ourselves not only mentally, but alsophysically. Although weather is not mentioned as an individual factor, timeof day, health, and food we eat, and the amount of sleep we get each nightare.

"Mental health, mood and perceptual responses to meteorologicalconditions" discusses the affects that weather has on mood by using hisextensive knowledge and research on the topic of mood and weather to combinefindings of others to form a more comprehensive viewpoint. Cyr takes not onlyqualitative research but also quantitative research and analyses both sidesand comes up with his own perspective. The use of summary tables,scattergrams, histograms and clinical analysis were very important in hiswork because he used many different categories. For example, Cyr brokeweather up into wind, heat, sun, rain, clouds, cold, storms, snow, thunder,fog, ice, mist, dew, hail, and humidity. Results showed that humans respondto conditions in the weather with immediate responses, such as fear oramazement, with associations to their past history, such as a particularstress related event.

Seasonal affective disorder was a diagnosis that was found in manyarticles such as the "Effect of daily variation in weather and sleep onseasonal affective disorder" written by Paul Albert. In this study, seasonsand weather were tested for their effects on sleep. When the ten patientswere held in a controlled environment, all ten showed significant seasonalpatterns in their sleep. On the other hand, only four of the ten subjectsshowed significant effects to their condition when weather was the control.Albert's results also dealt with energy, but the results were not relevant toour particular study at this time.

Some of the articles dealt with the actual reactions of people in reallife situations, and not just those in a controlled environment. Ross Vickerspublished his article entitled "Coping strategies and mood during coldweather training." His experiment compared two different groups of Marinesafter their training. Both groups had two different training sessions, oneduring the cold winter months and the other session was held during thesummer in the mountains for warfare training. What they found was that theMarines used other means of coping in the cold winter months such as thinkingof warmer, better places. This led to negative emotions and moods duringthese time periods because they were forcing themselves to think of otherthings. During the summer training the group found that since the weather wasnicer and easier to cope with, they tended to have better interactions andthe overall mood was not as negative. Vickers wasn't the only person who decided to do studies on soldiers.Richard Johnson also wrote an article entitled "Influence of attitude andexpectations on moods and symptoms during cold weather military training"that focused not only on weather but also on the influences of everyday life.Johnson looked at soldiers that were training in cold weather and came tofind that the more the soldiers disliked cold weather the more tense,depressed and angry they became. This is a slightly different perspectivethan others have in that the direct correlation between mood and weather isnot the main factor here. Although the colder the weather the higher thesymptoms of depression and anger in most soldiers, they also found that ifthe soldieries were more inclined to the colder weather than others thattheir symptoms were substantially lower. Johnson also contributed a greatdeal to the individuals' lives outside of the military. Those with the moststress in their lives at that particular time showed an increase in theoverall poor characteristics that dealt with mood. While a lot of professionals like to examine the mood of individualsduring certain weather patterns, Detlev Kommer decided to see if he couldmanipulate mood by controlling the weather. Kommer's method will serve as amodel for our own. Some of the subjects were asked to give their responses ondays that it was extremely sunny and nice outside, while at other times thesubjects were asked to answer the same questions on gloomy, overcast days.Many questions were asked, such as how satisfied they were with their lifeand how they expected their future to unfold. Although Detlev got verydetailed in his questions and observations, his ideas are very similar toours although his were more extensive than our experiment is able to be. Sometimes it is important to continue a study for a set time period toreduce the outside influences. E. Howarth did this when he conducted a studyof 24 male university students over an eleven-day period. Included in thisstudy were 10 mood variables, such as anxiety, potency, aggression, andothers. The weather variables included hours of sunshine, precipitation,temperature. Howarth found that the major influences on mood were humidity,temperature, and hours of sunshine. Although all of the other variables hadinfluences on particular aspect of mood, these had the greatest overallaffect. Evaluating the results of an experiment can vary greatly betweendifferent individuals. A great example of that are the results that Persingerand Levesque reported in their study entitled "The weather matrixaccommodates large portions of variance of measured daily mood." They tookthe results of a previous experiment and used regression lag analysis tointerpret the results in a more numeric manner. What they found was that fourto five major components of the weather matrix accommodated approximately 30- 60% of the variance in daily mood for the individuals involved in theexperiment. They also ruled out some of the weather variables, such astemperature, because of outside contaminants. Jeffery Sanders of Towson State University conducted his experiment everyweekday morning for 5 straight weeks. His test was very simple, just achecklist, and required very little thought from his respondents. Aftercollecting all the data needed from the survey he then collected weatherinformation from the National Weather Service for those time periods andcompared the results. What he found was that the major weather factor washigh humidity and that it correlated most consistently with vigor, socialaffection, and elation. There are hundreds of published experiments that are relevant to ourparticular lab experiment. Some offers help more than others do because theyshare a similar focus with ours. Others have very little, if anything, to dowith our experiment but still offer relevant models for testing designprocedure.

Materials and Methods- To conduct our experiment we are going to conduct a survey. Given out sixtimes to forty people each time. We will use the same forty people theduration of the experiment to ensure consistency. Each member of our labgroup will be in charge of a group of ten people. Our target number ofparticipants is thirty, but we will survey forty people to account for peoplewho fail to complete the study We will attempt to get students from allover Miami's campus in order to get an accurate representative of thepopulation Because of the number of people being surveyed our data will bestatistically sound for Miami's campus only To try and remove bias from theexperiment we will not inform the people of the type of experiment that isbeing performed They will be given no other directions or information otherthan that they need to fill out the survey We will dispense and collect thesurveys the same day, within a few minutes of one another The surveys willbe given out at evenly spaced intervals between the end of October and theend of November We cannot give a specific timeline detailing the exact daysthat our survey will be handed out because the weather will determine thedays we hand out the survey More than likely, unless the weather remainsunchanged for an entire week, we will hand out surveys two times per weekfrom October 31st through November 30th. The only materials needed toconduct this experiment are the survey, participants, a calculator, and aspreadsheet. As for the weather aspect we will determine what constitutes eachaspect of weather we are using in our evaluation For example when measuringthe amount of sunshine we will look at the UV index For cloudiness we willdetermine percents of coverage as our basis for cloudy, partly cloudy, andnot cloudy Both temperature and precipitation will be directly measured. Class Presentation- When we present our study to the class, we are first going to describeour hypothesis and the way in which we are going to conduct our study andcollect results. We are going to try to have a discussion with the class,finding out if they believe that weather affects their mood or if it has noeffect. We are then going to give a copy of an article that has data thatsupports our hypothesis to the class. We will have the class read it andthen we will lead a discussion about it. Next we are going to have eachmember of the class fill out one of our surveys; this will give us someresults to analyze. We are hoping to find a short video that supports arestudy, but as of now we are not sure if we will be able to find a relevantvideo or not. Last, we are going to involve the class by attempting tocreate different weather conditions in the room. We are going to use a spraybottle to create rain, a fan to create wind and hopefully a fog machine tocreate fog. We are going to have volunteers come up and we will impose thesedifferent conditions on them in an attempt to see if these conditionsirritate them or affect their mood in any way. We are hoping that this willbe an entertaining way to show people what we are testing.

A draft of our survey follows-
Name:
Year:
Gender:
Major:
1. Would you say you are a generally pessimistic or optimistic person? (circle one)

1 2 3 4 5 6 7 8 9 10
Pessimistic Optimistic

2. On average, how many hours do you spend outside per day?
<1 1-2 2-3 3-4 4-5 >5

3. In general, rate your stress level

1 2 3 4 5 6 7 8 9 10
Low Stress High Stress

4. Today, what is your stress level?

1 2 3 4 5 6 7 8 9 10
Low Stress High Stress

5. (For Yourself) Last night did you get an

less than average amount of sleep average amount of sleep above average amount of sleep

6. Do you have an unusually large amount of work due soon?
YES NO

7. What words would you use to describe your mood today? (circle all that apply)
Stressed Carefree Anxious Happy Depressed Content

Relaxed Nervous

8. List any other factors that may be having a significant effect on your mood today.
Results-
With our results we are going to make charts and graphs that will show theresults clearly. For questions that do not have a numeric rating, we aregoing to assign numeric values to answers give. We will present our results first in tables, and then we will graphically represent our findings. This will allow us to beable to understand our data better to tell if good weather makes a differenceon people's moods. We will be looking for a correlation between lower stress levels and positive weather conditions and higher stress levels and negative weather conditions. Though we did find some patterns in our data, we found no conclusive results that made a definite statement that said that the weather has a direct affect on a persons’ mood. For two of the days for which we handed out surveys, it was sunny and warm. For the remaining three days the weather was cloudy, colder and rainy. We hypothesized that on the sunny and warm days, the days that most people would deem, pleasant, that peoples moods would be positive and their stress levels would be lower. After viewing our results however, we realized that our results showed no definite conclusions. Weighing stress level on a scale of one to ten, on 11/14 when the temperature was 64 degrees and it was sunny out the average stress level was 5.037. On 11/16 when the temperature was 68 degrees and it was sunny, the average stress level was 4.818. On 11/29 the temperature was 58 degrees and it was cloudy and rainy the average stress level was 5.806. On 11/30 the temperature was 45 degrees and it was cloudy and rainy the average stress level was 5.250. And on the final day when the temperature was 46 degrees and it was cloudy but not rainy the average stress level was 4.743. Looking only at these means only, no conclusive conclusions can be drawn. We now believe that other variables have more of an effect on a person’s mood than the weather. The day that the lowest level of stress was recorded was on 12/1, which is a Saturday. Most people are less stressed on the weekends when they have less work to complete and fewer obligations. One question on our survey asked if there were any other factors that they believed might be having a significant effect on their moods. Though weather was mentioned a few times, the most common variable mentioned related to schoolwork, finals and the weekend. The further analyze our results, using statview we did a paired t-test that compared the stress levels recorded on 11/16, which was a sunny weekend day to stress levels on 11/30, which was a cloudy and rainy weekend day. The resulting p-value was .3059 which tells us that we are only 70% sure that any correlation between the two was caused my chance alone. When graphically represented in bar graph form, there is only a slight difference in the stress levels of the two days. We next did a paired t-test that compared the stress levels recorded on 11/16, which was a sunny weekend day to the stress levels recorded on 12/1, which was a cloudy weekend day. The p-value was .7542, which means that there is a 75% chance that are results are by chance alone. Graphically represented in bar graph form, the stress level on the 16th is slightly higher, but the two levels are extremely close. Next we did a paired t-test comparing 11/14 which was a sunny weekday to 11/29 which was a cloudy rainy weekday. The p-value was .0245, which tells us that our results were not due to chance alone. Viewing this information on a bar graph, there is an obvious difference in stress levels; the stress level on the cloudy/rainy day is much higher. The only comparison we found that showed a significant difference in this last comparison comparing 11/14 to 11/29. We believe that this may be because in the other tests the fact that it was the weekend had more of an effect on each persons stress level than the weather did. In order to make sure that we considered that other factors have an effect on stress levels as well, we asked people to tell us if they had received an average, below average or above average amount of sleep the night before, and if they had a lot of work due soon. To review these results, we did unpaired t-tests that compared stress level to amount of work ahead to stress level to amount of sleep received. We compared these things on 11/14, a sunny weekday, and on 11/29 a cloudy rainy weekday. The p-value from 11/14, comparing work ahead to stress was .2141, which means that we reject the null hypothesis. However, viewing or results graphically in bar graph form, our results showed that people who recorded that they had a lot of work ahead had a higher stress level then people who recorded that they did not have a lot of work due. After comparing amount of sleep to stress level, the only comparison that had a p-value of below .05 was the comparison of less then average amount of sleep to stress level. This showed that people who had received a less than average amount of sleep had a significantly higher stress level. On 11/29 we did an unpaired t-test comparing stress level to work ahead. The p-value was .0542, which tells us that there is a large probability that our results are not due to chance alone. The stress levels of people who had a large amount of work due soon were significantly higher than the stress levels of people who did not. Comparing stress level to sleep, the people who recorded having an above average amount of sleep had the lowest stress levels, the people who had received a below average amount of sleep had the highest stress levels and the people who had received an average amount of sleep had stress levels in the middle. Overall our results were relatively inconclusive. The days of the week (weekday vs. weekend), amount of sleep, and workload seem to have as much of an effect on stress level as weather conditions do, if not more. While doing our study, we ran into some complications and problems that we would be sure to avoid in the future. One problem we encountered was that before we handed out our survey’s our group was not clear as to how we were going to track the data. We did not discuss if we would be tracking individual moods and stress levels or if we would simply compile all of our information and look for generalizations. Because of this lack of communication, a few surveys were filled out without names on them. When imputing our information into Statview, we realized that names would be beneficial to our study and so for the surveys that did not have names on them, we had to go back and attempt to decode handwriting and figure out which responses came from which person. While we do believe that we matched the surveys correctly, if we were to repeat the study we would be sure to put names on every survey to ensure that all of the information would be kept straight.


Works Cited
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