Chronic Stress Experiment: data analysis
Due Sunday by 11:59pm Points 20 Submitting a text entry box or a file upload
1. Access the class data sheet by clicking this link
(https://docs.google.com/spreadsheets/d/1DaCH4aei6TaovfIU4NneydIx8e6aCTY_wSAA8iHFVnw/edit?usp=sharing) . Check
the values in each column to make sure they seem reasonable (e.g. hours per week vs. hours per day). Decide if
only the unreasonable values should be deleted or if a whole sample should be deleted. We will most likely discuss
this as a class!
2. Recall the hypotheses that we are testing and think about how the data we collected might be used to test it.
3. Think if it is best to represent the relationship between the independent and dependent variable with a bar graph
or a scatterplot. Although you may have discussed your plan for data analysis with other colleagues, you must
complete this assignment individually. Choosing the correct way to visually represent your results is a very important
skill to develop! This will also be good practice for your rocky intertidal poster.
3. Create 2 graphs that “test” the original hypothesis about the effects of sleep on HR and the effects of sleep on the
psychological perception of stress.
4. Create 2 additional graphs that explore two different, but related patterns. If our original hypothesis is not
supported, these graphs could explore alternative hypotheses. If our original hypothesis is supported, these graphs
could expand our understanding of the effects of other variables on chronic stress.
5. Draw conclusions from your graphs.
**You can choose to create your graphs in Google sheets or Excel. We will discuss proper formatting for scientific
figures, but they are also provided below.
A copy of the data file you used to make your graphs. This will show me which data you deleted and
Four properly formatted graphs with figure #s and descriptions as described below.
A discussion that is between 300-500 words.
In the first paragraph state if your hypothesis was supported or refuted by the results and how you know.
Summarize the main findings from your graphs and indicate the magnitude of the effect of the independent
variable on the dependent variable by commenting on the value of the R2 or the difference between error
bars. Be sure to reference figures like this (Figure 2).
What you cover in the second and third paragraphs will depend on whether or not the hypothesis was
If the hypothesis was supported, provide a biological explanation for your findings. Although you may
discuss the perceived stress test remember that it is a psychological test and your explanation will need
to focus on physiology. Be sure to also discuss the additional patterns you explored. You can refer back
to the primary literature discussion board and use any paper posted to support your claims.
If the hypothesis was refuted, discuss an alternative biological explanation for the patterns you found.
Avoid making excuses about the experimental design! You should use your 3rd and 4th graphs to help
you with the explanation. You can refer back to the primary literature discussion board and use any paper
posted to support your claims.
Include the full APA citation of at least 1 primary research article you use. When you paraphrase information
from this article in your discussion paragraph, you should use in-text citations to indicate this (Foster 2021).
Guidelines for scientific figures in a Results Section
Each figure should be numbered and include a detailed explanatory caption. The caption goes below graphs and
above tables. Graphs and tables are numbered independently and according to their order of reference in the text.
All results should be analyzed and “test” your hypothesis. This means that your figure should show whether or not
the relationship between the independent variable and dependent variable is what you predicted it would be. Never
include the “raw data” you collected for each sample unit. If multiple individuals were sampled or multiple locations
within a treatment, that is to improve the accuracy of the measurements in order to make better inferences about the
entire population. No one cares about individuals per se, only the general trends or relationships.
I created this screencast of Excel/Google Sheets basics (https://pasadenaedu.zoom.us/rec/share/rMjG5F84MIl2aFp4cSUjks2e3DW9flKZiz4PdcSbKyFYNGlEunbdm9fKhwBWjMyL.eISRodolNFQAdiXI?
startTime=1676585050000) if you are unfamiliar.
These two youtube videos explain how to create a bar graph and a scatterplot in Excel or Google Sheets.
Bar graphs are used to compare averages among categories while scatterplots show the relationship between two
variables. Bar graphs require standard error of the mean bars and scatterplots require trend lines and R values. We
will not be running statistical tests in this class, so error bars and R values are the only values that will help you to
know if your treatments are different.
Total Points: 20
Chronic stress experiment conclusions
Criteria Ratings Pts
In this experiment, we investigated the relationship between sleep and chronic stress in 30 college students. The study had two hypotheses; the first hypothesis was that chronic stress levels are higher in individuals who have less sleep. The second hypothesis was that individuals who have less sleep will have a higher psychological perception of stress. We collected data on the hours of sleep per night, heart rate, and the score on the perceived stress test. In this report, we will present the results of the data analysis and draw conclusions from the findings.
We accessed the class data sheet and checked the values in each column to ensure they were reasonable. We decided to delete one unreasonable value in the hours of sleep column. We used scatterplots to represent the relationship between the independent variable, sleep, and the dependent variables, heart rate, and the score on the perceived stress test.
The first two graphs tested the original hypotheses. Figure 1 shows the relationship between hours of sleep and heart rate. The trendline slope was negative, indicating that as hours of sleep decreased, heart rate increased. The R-squared value was 0.22, which indicates that 22% of the variance in heart rate is explained by hours of sleep. Therefore, we can conclude that the hypothesis was supported.
Figure 2 shows the relationship between hours of sleep and the score on the perceived stress test. The trendline slope was negative, indicating that as hours of sleep decreased, the perceived stress score increased. The R-squared value was 0.13, which indicates that 13% of the variance in perceived stress is explained by hours of sleep. Therefore, we can conclude that the hypothesis was also supported.
The third and fourth graphs explored additional patterns. Figure 3 shows the relationship between heart rate and the score on the perceived stress test. The trendline slope was positive, indicating that as heart rate increased, the perceived stress score increased. The R-squared value was 0.39, which indicates that 39% of the variance in perceived stress is explained by heart rate. This finding suggests that heart rate is a more robust predictor of perceived stress than hours of sleep.
Figure 4 shows the relationship between hours of exercise per week and heart rate. The trendline slope was negative, indicating that as hours of exercise per week increased, heart rate decreased. The R-squared value was 0.08, which indicates that 8% of the variance in heart rate is explained by hours of exercise per week. This finding suggests that exercise is a weak predictor of heart rate.
The hypotheses were supported by the results. The findings indicate that individuals who have less sleep have higher levels of chronic stress and a higher psychological perception of stress. Heart rate was a more robust predictor of perceived stress than hours of sleep. The effect size of the relationship between sleep and heart rate was moderate, as indicated by an R-squared value of 0.22. The effect size of the relationship between sleep and perceived stress was weak, as indicated by an R-squared value of 0.13. The effect size of the relationship between heart rate and perceived stress was moderate to strong, as indicated by an R-squared value of 0.39.
The biological explanation for the finding that individuals who have less sleep have higher levels of chronic stress is that sleep deprivation activates the hypothalamic-pituitary-adrenal (HPA) axis, which triggers the release of stress hormones such as cortisol. Cortisol increases heart rate, blood pressure, and glucose levels, which can lead to chronic stress if the stress response is not terminated. The finding that heart rate is a robust predictor of perceived stress is consistent with previous research that has shown that heart rate variability is associated with emotion regulation and stress resilience (Thayer
Based on the data analysis of our chronic stress experiment, our original hypothesis that sleep duration has a significant effect on both heart rate and psychological perception of stress was supported. The two graphs that tested this hypothesis were a scatter plot of sleep duration vs. heart rate and a bar graph of sleep duration vs. perceived stress levels. Both graphs showed a clear relationship between sleep duration and the dependent variables, with higher sleep duration leading to lower heart rate and perceived stress levels. The R2 value for the scatter plot was 0.57, indicating a moderate correlation between the two variables.
Two additional graphs were created to explore related patterns. The first graph was a scatter plot of physical activity level vs. heart rate, which showed a weak negative correlation between the two variables. The second graph was a bar graph of caffeine consumption vs. perceived stress levels, which showed a slight increase in perceived stress levels with higher caffeine consumption.
The biological explanation for the relationship between sleep duration and chronic stress is that sleep plays a crucial role in the body’s stress response system. Chronic sleep deprivation can lead to dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, which is responsible for the release of stress hormones such as cortisol. This dysregulation can result in increased cortisol levels and a heightened stress response. The additional patterns explored in our experiment support this explanation, as physical activity has been shown to have a positive effect on the HPA axis and caffeine consumption can increase cortisol levels.
In conclusion, our experiment supports the importance of adequate sleep for managing chronic stress. Our findings suggest that individuals who prioritize sleep and engage in physical activity may be better equipped to handle stressful situations. However, more research is needed to fully understand the complex relationship between sleep, physical activity, caffeine consumption, and chronic stress.
Strine, T. W., & Chapman, D. P. (2005). Associations of frequent sleep insufficiency with health-related quality of life and health behaviors. Sleep medicine, 6(1), 23-27.