STAT353 Project 2: Sleep Patterns and Lifestyle Among College Students
JB
2025-04-13
Introduction
This report will focus on analyzing sleep patterns, lifestyle habits, and mental well being among college students using the SleepStudy data set from Lock5Stat (https://www.lock5stat.com/datapage3e.html). This data includes 253 observations on 27 variables considering a wide range of academic, behavioral, and other important factors.
We will focus on 10 research questions that will give us more insight in our report goal.
Research Questions
Is there a significant difference in the average GPA between male and female college students?
Is there a significant difference in the average number of early classes between first- and second-year students vs. other class years?
Do students who identify as “larks” have significantly better cognitive skills (cognition z-score) compared to “owls”?
Is there a significant difference in the average number of classes missed between students with and without early classes?
Is there a significant difference in happiness levels between students with moderate/severe depression and those with normal status?
Is there a significant difference in sleep quality between students who pulled at least one all-nighter and those who didn’t?
Do students who abstain from alcohol use have significantly better stress scores than those who report heavy alcohol use?
Is there a significant difference in drinks per week between male and female students?
Is there a significant difference in average weekday bedtime between high-stress and normal-stress students?
Is there a significant difference in average weekend sleep hours between first-/second-year students and others?
Data
The dataset contains 253 observations and 27 variables, which include demographics (Gender, ClassYear), academic performance (GPA, Cognition), mental health (Happiness, Depression, Stress), and lifestyle (AllNighter, AlcoholUse, SleepQuality, etc.).
According to Lock5Data’s website, the data was collected via surveys and self reports. All Analysis will be done using R!
Analysis
We will explore the questions in detail.
## Gender ClassYear LarkOwl NumEarlyClass EarlyClass GPA ClassesMissed
## 1 0 4 Neither 0 0 3.60 0
## 2 0 4 Neither 2 1 3.24 0
## 3 0 4 Owl 0 0 2.97 12
## 4 0 1 Lark 5 1 3.76 0
## 5 0 4 Owl 0 0 3.20 4
## 6 1 4 Neither 0 0 3.50 0
## CognitionZscore PoorSleepQuality DepressionScore AnxietyScore StressScore
## 1 -0.26 4 4 3 8
## 2 1.39 6 1 0 3
## 3 0.38 18 18 18 9
## 4 1.39 9 1 4 6
## 5 1.22 9 7 25 14
## 6 -0.04 6 14 8 28
## DepressionStatus AnxietyStatus Stress DASScore Happiness AlcoholUse Drinks
## 1 normal normal normal 15 28 Moderate 10
## 2 normal normal normal 4 25 Moderate 6
## 3 moderate severe normal 45 17 Light 3
## 4 normal normal normal 11 32 Light 2
## 5 normal severe normal 46 15 Moderate 4
## 6 moderate moderate high 50 22 Abstain 0
## WeekdayBed WeekdayRise WeekdaySleep WeekendBed WeekendRise WeekendSleep
## 1 25.75 8.70 7.70 25.75 9.50 5.88
## 2 25.70 8.20 6.80 26.00 10.00 7.25
## 3 27.44 6.55 3.00 28.00 12.59 10.09
## 4 23.50 7.17 6.77 27.00 8.00 7.25
## 5 25.90 8.67 6.09 23.75 9.50 7.00
## 6 23.80 8.95 9.05 26.00 10.75 9.00
## AverageSleep AllNighter
## 1 7.18 0
## 2 6.93 0
## 3 5.02 0
## 4 6.90 0
## 5 6.35 0
## 6 9.04 01. Is there a significant difference in the average GPA between male and female college students?
##
## Welch Two Sample t-test
##
## data: male_gpa and female_gpa
## t = -3.9139, df = 200.9, p-value = 0.0001243
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.30252780 -0.09982254
## sample estimates:
## mean of x mean of y
## 3.123725 3.324901
p-value: 0.0001 (fairly significant)
Male and female students differ quite significantly in GPA. The boxplot shows a higher median GPA for females.
2. Is there a significant difference in the average number of early classes between first- and second-year students vs. other class years?
##
## Welch Two Sample t-test
##
## data: early and late
## t = 4.1813, df = 250.69, p-value = 4.009e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.4042016 1.1240309
## sample estimates:
## mean of x mean of y
## 2.070423 1.306306
p-value: 0.0001 (significant)
Underclassmen (1st/2nd year) take significantly more early classes than upperclassmen.
3. Do students who identify as “larks” have significantly better cognitive skills (cognition z-score) compared to “owls”?
##
## Welch Two Sample t-test
##
## data: lark and owl
## t = 0.80571, df = 75.331, p-value = 0.4229
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1893561 0.4465786
## sample estimates:
## mean of x mean of y
## 0.09024390 -0.03836735
p-value: 0.4128 (not Significant)
There’s no significant cognitive performance difference between larks and owls.
4: Is there a significant difference in the average number of classes missed between students with and without early classes?
##
## Welch Two Sample t-test
##
## data: with_early and without_early
## t = -1.4755, df = 152.78, p-value = 0.1421
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.5412830 0.2233558
## sample estimates:
## mean of x mean of y
## 1.988095 2.647059
p-value: 0.1268 (not Significant)
The number of classes missed does not significantly differ based on having early classes.
5. Is there a significant difference in happiness levels between students with moderate/severe depression and those with normal status?
##
## Welch Two Sample t-test
##
## data: happy_normal and happy_severe
## t = 5.6339, df = 55.594, p-value = 6.057e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 3.507836 7.379724
## sample estimates:
## mean of x mean of y
## 27.05742 21.61364
p-value: < 0.0001 (Significant)
Students with normal depression status report significantly higher happiness scores.
6. Is there a significant difference in sleep quality between students who pulled at least one all-nighter and those who didn’t?
##
## Welch Two Sample t-test
##
## data: yes_allnighter and no_allnighter
## t = 1.7068, df = 44.708, p-value = 0.09479
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1608449 1.9456958
## sample estimates:
## mean of x mean of y
## 7.029412 6.136986
p-value: 0.0974 (not Significant)
While the trend suggests worse sleep quality with all-nighters, the result is not statistically significant.
7. Do students who abstain from alcohol use have significantly better stress scores than those who report heavy alcohol use?
##
## Welch Two Sample t-test
##
## data: abstain and heavy
## t = -0.62604, df = 28.733, p-value = 0.5362
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.261170 3.327346
## sample estimates:
## mean of x mean of y
## 8.970588 10.437500
p-value: 0.5301 (not Significant)
There’s no significant stress score difference between abstainers and heavy drinkers.
8. Is there a significant difference in drinks per week between male and female students?
##
## Welch Two Sample t-test
##
## data: male_drinks and female_drinks
## t = 6.1601, df = 142.75, p-value = 7.002e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.241601 4.360009
## sample estimates:
## mean of x mean of y
## 7.539216 4.238411
p-value: < 0.0001 (significant)
Male students drink significantly more than female students on average.
9. Is there a significant difference in average weekday bedtime between high-stress and normal-stress students?
##
## Welch Two Sample t-test
##
## data: bed_high and bed_normal
## t = -1.0746, df = 87.048, p-value = 0.2855
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4856597 0.1447968
## sample estimates:
## mean of x mean of y
## 24.71500 24.88543
p-value: 0.2771 (not Significant)
Weekday bedtime is not significantly different between high-stress and normal-stress groups.
10. 10. Is there a significant difference in average weekend sleep hours between first-/second-year students and others?
##
## Welch Two Sample t-test
##
## data: weekend_early and weekend_other
## t = -0.047888, df = 237.36, p-value = 0.9618
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3497614 0.3331607
## sample estimates:
## mean of x mean of y
## 8.213592 8.221892
p-value: 0.9619 (not Significant)
Weekend sleep duration does not differ significantly between underclassmen and upperclassmen.
Summary
This analysis examined 10 research questions using the SleepStudy dataset, focusing on how student demographics, sleep habits, mental health, and lifestyle behaviors relate to academic performance and wellness.
Significant differences were found in GPA by gender (Q1), early classes by class year (Q2), happiness by depression status (Q5), and alcohol consumption by gender (Q8). These findings may suggest that both academic and lifestyle outcomes may vary meaningfully across gender and mental health status.
In contrast, no significant differences were found for cognition scores between larks and owls (Q3), class attendance (Q4), sleep quality from pulling all-nighters (Q6), stress by alcohol use (Q7), weekday bedtime by stress (Q9), or weekend sleep by class year (Q10). This implies that while some behavioral and demographic factors are associated with student performance or well-being, others may not have as clear an impact.
Overall, this analysis highlights that gender and mental health status appear to play significant roles in shaping students’ academic success and well-being. However, many sleep-related behaviors (like all-nighters or being a “lark” vs. “owl”) may not lead to statistically significant differences across certain outcomes.
These findings highlight important associations between mental wellness, academic performance, and lifestyle among college students.
References
- Lock5Stat (SleepStudy Data): https://www.lock5stat.com/datapage3e.html
- Lock5Stat (NHANES Data): https://www.lock5stat.com/datapage3e.html
- R t.test(): https://stat.ethz.ch/R-manual/R-devel/library/stats/html/t.test.html
Appendix: R Code Used
1. Is there a significant difference in the average GPA between male and female college students?
male_gpa <- sleep\(GPA[sleep\)Gender == 1] female_gpa <- sleep\(GPA[sleep\)Gender == 0] t.test(male_gpa, female_gpa) boxplot(GPA ~ Gender, data=sleep, names=c(“Female”, “Male”), main=“GPA by Gender”)
2. Is there a significant difference in the average number of early classes between first- and second-year students vs. other class years?
early <- sleep\(NumEarlyClass[sleep\)ClassYear %in% c(1, 2)] late <- sleep\(NumEarlyClass[!sleep\)ClassYear %in% c(1, 2)] t.test(early, late) boxplot(NumEarlyClass ~ (ClassYear <= 2), data=sleep, names=c(“3rd/4th”, “1st/2nd”), main=“Early Classes by Class Year”)
3. Do students who identify as “larks” have significantly better cognitive skills (cognition z-score) compared to “owls”?
lark <- sleep\(CognitionZscore[sleep\)LarkOwl == “Lark”] owl <- sleep\(CognitionZscore[sleep\)LarkOwl == “Owl”] t.test(lark, owl) boxplot(CognitionZscore ~ LarkOwl, data=subset(sleep, LarkOwl %in% c(“Lark”, “Owl”)), main=“Cognition Z-Score: Lark vs. Owl”)
4: Is there a significant difference in the average number of classes missed between students with and without early classes?
with_early <- sleep\(ClassesMissed[sleep\)EarlyClass == 1] without_early <- sleep\(ClassesMissed[sleep\)EarlyClass == 0] t.test(with_early, without_early) boxplot(ClassesMissed ~ EarlyClass, data=sleep, names=c(“No Early”, “Early”), main=“Classes Missed by Early Class”)
5. Is there a significant difference in happiness levels between students with moderate/severe depression and those with normal status?
happy_normal <- sleep\(Happiness[sleep\)DepressionStatus == “normal”] happy_severe <- sleep\(Happiness[sleep\)DepressionStatus != “normal”] t.test(happy_normal, happy_severe) boxplot(Happiness ~ DepressionStatus, data=sleep, main=“Happiness by Depression Status”)
6. Is there a significant difference in sleep quality between students who pulled at least one all-nighter and those who didn’t?
yes_allnighter <- sleep\(PoorSleepQuality[sleep\)AllNighter == 1] no_allnighter <- sleep\(PoorSleepQuality[sleep\)AllNighter == 0] t.test(yes_allnighter, no_allnighter) boxplot(PoorSleepQuality ~ AllNighter, data=sleep, names=c(“No”, “Yes”), main=“Sleep Quality by All-Nighter”)
7. Do students who abstain from alcohol use have significantly better stress scores than those who report heavy alcohol use?
abstain <- sleep\(StressScore[sleep\)AlcoholUse == “Abstain”] heavy <- sleep\(StressScore[sleep\)AlcoholUse == “Heavy”] t.test(abstain, heavy) boxplot(StressScore ~ AlcoholUse, data=subset(sleep, AlcoholUse %in% c(“Abstain”, “Heavy”)), main=“Stress by Alcohol Use”)
8. Is there a significant difference in drinks per week between male and female students?
male_drinks <- sleep\(Drinks[sleep\)Gender == 1] female_drinks <- sleep\(Drinks[sleep\)Gender == 0] t.test(male_drinks, female_drinks) boxplot(Drinks ~ Gender, data=sleep, names=c(“Female”, “Male”), main=“Alcohol Consumption by Gender”)
9. Is there a significant difference in average weekday bedtime between high-stress and normal-stress students?
bed_high <- sleep\(WeekdayBed[sleep\)Stress == “high”] bed_normal <- sleep\(WeekdayBed[sleep\)Stress == “normal”] t.test(bed_high, bed_normal) boxplot(WeekdayBed ~ Stress, data=sleep, main=“Weekday Bedtime by Stress Level”)
10. 10. Is there a significant difference in average weekend sleep hours between first-/second-year students and others?
weekend_early <- sleep\(WeekendSleep[sleep\)ClassYear %in% c(1,2)] weekend_other <- sleep\(WeekendSleep[!sleep\)ClassYear %in% c(1,2)] t.test(weekend_early, weekend_other) boxplot(WeekendSleep ~ (ClassYear <= 2), data=sleep, names=c(“3rd/4th”, “1st/2nd”), main=“Weekend Sleep by Class Year”)