Sleep Patterns and College Life Insights
Sagar Neupane
2024-12-03
Introduction & data
In this report, I will look at the sleeping habits of college students using the SleepStudy dataset. This dataset has information on 253 students and includes 27 different details. These variables include different topics about sleeping patterns, mental health, lifestyle decisions, and school performance.The purpose of this study is to find out how different things like sleep, class times, drinking alcohol, and mental health affect students’ well-being and their success in school. By looking at this data, I want to find out how sleep patterns are connected to things like grades, stress, thinking skills, and daily habits.I will talk about questions like whether boys and girls have different GPAs, how sleep habits change in different school years, and if drinking alcohol impacts stress levels. The information from this analysis can help us understand the difficulties students have in managing schoolwork, mental health, and their daily lives. This understanding could lead to ways to improve their overall health and school performance. The following research questions will be addressed in this report: The following research questions will be addressed in this report:
- 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 the first two class years and 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 in a semester between students who had at least one early class (EarlyClass=1) and those who didn’t (EarlyClass=0)?
- Is there a significant difference in the average happiness level between students with at least moderate depression and normal depression status?
- Is there a significant difference in average sleep quality scores between students who reported having at least one all-nighter (AllNighter=1) and those who didn’t (AllNighter=0)?
- 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 the average number of drinks per week between students of different genders?
- Is there a significant difference in the average weekday bedtime between students with high and low stress (Stress=High vs. Stress=Normal)?
- Is there a significant difference in the average hours of sleep on weekends between first two year students and other students?
Procedure
The project begins by importing the SleepStudy.csv dataset, which includes data from 253 students with 27 variables covering aspects like sleep patterns, mental health, and lifestyle choices. The primary goal is to answer 10 research questions related to the impact of factors such as sleep, alcohol use, depression, and stress on student well-being and academic performance. Statistical analyses, including t-tests and ANOVA, are conducted to assess differences between groups (e.g., gender, depression status, class years). Graphical representations like boxplots and histograms are used to visually display the results of these analyses. The p-values and test statistics are then examined to determine significant differences for each research question. Finally, the findings are summarized, providing insights into how sleep, mental health, and lifestyle habits influence students’ overall health and academic success.
Analysis
# load the lessR package
library(lessR)##
## lessR 4.3.8 feedback: gerbing@pdx.edu
## --------------------------------------------------------------
## > d <- Read("") Read text, Excel, SPSS, SAS, or R data file
## d is default data frame, data= in analysis routines optional
##
## Many examples of reading, writing, and manipulating data,
## graphics, testing means and proportions, regression, factor analysis,
## customization, and descriptive statistics from pivot tables
## Enter: browseVignettes("lessR")
##
## View lessR updates, now including time series forecasting
## Enter: news(package="lessR")
##
## Interactive data analysis
## Enter: interact()##
## Attaching package: 'lessR'## The following object is masked from 'package:base':
##
## sort_bySleepStudy= read.csv("https://www.lock5stat.com/datasets3e/SleepStudy.csv")
head(SleepStudy)## 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 0- Is there a significant difference in average GPA between male and female students?
# Perform t-test for GPA by Gender
ttest(GPA ~ Gender, data = SleepStudy)##
## Compare GPA across Gender with levels 0 and 1
## Grouping Variable: Gender
## Response Variable: GPA
##
##
## ------ Describe ------
##
## GPA for Gender 0: n.miss = 0, n = 151, mean = 3.325, sd = 0.375
## GPA for Gender 1: n.miss = 0, n = 102, mean = 3.124, sd = 0.418
##
## Mean Difference of GPA: 0.201
##
## Weighted Average Standard Deviation: 0.393
##
##
## ------ Assumptions ------
##
## Note: These hypothesis tests can perform poorly, and the
## t-test is typically robust to violations of assumptions.
## Use as heuristic guides instead of interpreting literally.
##
## Null hypothesis, for each group, is a normal distribution of GPA.
## Group 0: Sample mean assumed normal because n > 30, so no test needed.
## Group 1: Sample mean assumed normal because n > 30, so no test needed.
##
## Null hypothesis is equal variances of GPA, homogeneous.
## Variance Ratio test: F = 0.174/0.141 = 1.240, df = 101;150, p-value = 0.232
## Levene's test, Brown-Forsythe: t = -1.879, df = 251, p-value = 0.061
##
##
## ------ Infer ------
##
## --- Assume equal population variances of GPA for each Gender
##
## t-cutoff for 95% range of variation: tcut = 1.969
## Standard Error of Mean Difference: SE = 0.050
##
## Hypothesis Test of 0 Mean Diff: t-value = 3.996, df = 251, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 0.099
## 95% Confidence Interval for Mean Difference: 0.102 to 0.300
##
##
## --- Do not assume equal population variances of GPA for each Gender
##
## t-cutoff: tcut = 1.972
## Standard Error of Mean Difference: SE = 0.051
##
## Hypothesis Test of 0 Mean Diff: t = 3.914, df = 200.902, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 0.101
## 95% Confidence Interval for Mean Difference: 0.100 to 0.303
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of GPA for each Gender
##
## Standardized Mean Difference of GPA, Cohen's d: 0.512
##
##
## ------ Practical Importance ------
##
## Minimum Mean Difference of practical importance: mmd
## Minimum Standardized Mean Difference of practical importance: msmd
## Neither value specified, so no analysis
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for Gender 0: 0.154
## Density bandwidth for Gender 1: 0.189
- Is there a significant difference in the average number of early classes between the first two class years and other class years?
# Create a new grouping variable for first two class years vs others
SleepStudy$ClassYearGroup <- ifelse(SleepStudy$ClassYear == 1 | SleepStudy$ClassYear == 2, "FirstTwo", "OtherYears")
# Perform the t-test for early classes by class year group
ttest(NumEarlyClass ~ ClassYearGroup, data = SleepStudy)##
## Compare NumEarlyClass across ClassYearGroup with levels FirstTwo and OtherYears
## Grouping Variable: ClassYearGroup
## Response Variable: NumEarlyClass
##
##
## ------ Describe ------
##
## NumEarlyClass for ClassYearGroup FirstTwo: n.miss = 0, n = 142, mean = 2.070, sd = 1.657
## NumEarlyClass for ClassYearGroup OtherYears: n.miss = 0, n = 111, mean = 1.306, sd = 1.249
##
## Mean Difference of NumEarlyClass: 0.764
##
## Weighted Average Standard Deviation: 1.492
##
##
## ------ Assumptions ------
##
## Note: These hypothesis tests can perform poorly, and the
## t-test is typically robust to violations of assumptions.
## Use as heuristic guides instead of interpreting literally.
##
## Null hypothesis, for each group, is a normal distribution of NumEarlyClass.
## Group FirstTwo: Sample mean assumed normal because n > 30, so no test needed.
## Group OtherYears: Sample mean assumed normal because n > 30, so no test needed.
##
## Null hypothesis is equal variances of NumEarlyClass, homogeneous.
## Variance Ratio test: F = 2.747/1.560 = 1.761, df = 141;110, p-value = 0.002
## Levene's test, Brown-Forsythe: t = 2.424, df = 251, p-value = 0.016
##
##
## ------ Infer ------
##
## --- Assume equal population variances of NumEarlyClass for each ClassYearGroup
##
## t-cutoff for 95% range of variation: tcut = 1.969
## Standard Error of Mean Difference: SE = 0.189
##
## Hypothesis Test of 0 Mean Diff: t-value = 4.042, df = 251, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 0.372
## 95% Confidence Interval for Mean Difference: 0.392 to 1.136
##
##
## --- Do not assume equal population variances of NumEarlyClass for each ClassYearGroup
##
## t-cutoff: tcut = 1.969
## Standard Error of Mean Difference: SE = 0.183
##
## Hypothesis Test of 0 Mean Diff: t = 4.181, df = 250.690, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 0.360
## 95% Confidence Interval for Mean Difference: 0.404 to 1.124
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of NumEarlyClass for each ClassYearGroup
##
## Standardized Mean Difference of NumEarlyClass, Cohen's d: 0.512
##
##
## ------ Practical Importance ------
##
## Minimum Mean Difference of practical importance: mmd
## Minimum Standardized Mean Difference of practical importance: msmd
## Neither value specified, so no analysis
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for ClassYearGroup FirstTwo: 0.701
## Density bandwidth for ClassYearGroup OtherYears: 0.555
- Do students who identify as “larks” have significantly better cognitive skills (cognition z-score) compared to “owls”?
# Perform ANOVA to compare cognition z-scores between Lark, Neither, and Owl groups
anova_result <- aov(CognitionZscore ~ LarkOwl, data = SleepStudy)
# Display the ANOVA summary
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## LarkOwl 2 0.43 0.2133 0.425 0.654
## Residuals 250 125.47 0.5019- Is there a significant difference in the average number of classes missed in a semester between students who had at least one early class (EarlyClass=1) and those who didn’t (EarlyClass=0)?
# Perform t-test to compare the number of classes missed between students with and without early classes
ttest(ClassesMissed ~ EarlyClass, data = SleepStudy)##
## Compare ClassesMissed across EarlyClass with levels 0 and 1
## Grouping Variable: EarlyClass
## Response Variable: ClassesMissed
##
##
## ------ Describe ------
##
## ClassesMissed for EarlyClass 0: n.miss = 0, n = 85, mean = 2.647, sd = 3.477
## ClassesMissed for EarlyClass 1: n.miss = 0, n = 168, mean = 1.988, sd = 3.101
##
## Mean Difference of ClassesMissed: 0.659
##
## Weighted Average Standard Deviation: 3.232
##
##
## ------ Assumptions ------
##
## Note: These hypothesis tests can perform poorly, and the
## t-test is typically robust to violations of assumptions.
## Use as heuristic guides instead of interpreting literally.
##
## Null hypothesis, for each group, is a normal distribution of ClassesMissed.
## Group 0: Sample mean assumed normal because n > 30, so no test needed.
## Group 1: Sample mean assumed normal because n > 30, so no test needed.
##
## Null hypothesis is equal variances of ClassesMissed, homogeneous.
## Variance Ratio test: F = 12.088/9.617 = 1.257, df = 84;167, p-value = 0.214
## Levene's test, Brown-Forsythe: t = 1.373, df = 251, p-value = 0.171
##
##
## ------ Infer ------
##
## --- Assume equal population variances of ClassesMissed for each EarlyClass
##
## t-cutoff for 95% range of variation: tcut = 1.969
## Standard Error of Mean Difference: SE = 0.430
##
## Hypothesis Test of 0 Mean Diff: t-value = 1.532, df = 251, p-value = 0.127
##
## Margin of Error for 95% Confidence Level: 0.847
## 95% Confidence Interval for Mean Difference: -0.188 to 1.506
##
##
## --- Do not assume equal population variances of ClassesMissed for each EarlyClass
##
## t-cutoff: tcut = 1.976
## Standard Error of Mean Difference: SE = 0.447
##
## Hypothesis Test of 0 Mean Diff: t = 1.475, df = 152.779, p-value = 0.142
##
## Margin of Error for 95% Confidence Level: 0.882
## 95% Confidence Interval for Mean Difference: -0.223 to 1.541
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of ClassesMissed for each EarlyClass
##
## Standardized Mean Difference of ClassesMissed, Cohen's d: 0.204
##
##
## ------ Practical Importance ------
##
## Minimum Mean Difference of practical importance: mmd
## Minimum Standardized Mean Difference of practical importance: msmd
## Neither value specified, so no analysis
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for EarlyClass 0: 1.629
## Density bandwidth for EarlyClass 1: 1.044
- Is there a significant difference in the average happiness level between students with at least moderate depression and normal depression status?
# Create a new binary variable for at least moderate depression status
SleepStudy$DepressionGroup <- ifelse(SleepStudy$DepressionStatus %in% c("moderate", "severe"), "Moderate or Severe", "Normal")
# Perform t-test to compare happiness levels between students with normal and at least moderate depression status
ttest(Happiness ~ DepressionGroup, data = SleepStudy)##
## Compare Happiness across DepressionGroup with levels Normal and Moderate or Severe
## Grouping Variable: DepressionGroup
## Response Variable: Happiness
##
##
## ------ Describe ------
##
## Happiness for DepressionGroup Normal: n.miss = 0, n = 209, mean = 27.057, sd = 4.885
## Happiness for DepressionGroup Moderate or Severe: n.miss = 0, n = 44, mean = 21.614, sd = 6.005
##
## Mean Difference of Happiness: 5.444
##
## Weighted Average Standard Deviation: 5.094
##
##
## ------ Assumptions ------
##
## Note: These hypothesis tests can perform poorly, and the
## t-test is typically robust to violations of assumptions.
## Use as heuristic guides instead of interpreting literally.
##
## Null hypothesis, for each group, is a normal distribution of Happiness.
## Group Normal: Sample mean assumed normal because n > 30, so no test needed.
## Group Moderate or Severe: Sample mean assumed normal because n > 30, so no test needed.
##
## Null hypothesis is equal variances of Happiness, homogeneous.
## Variance Ratio test: F = 36.057/23.862 = 1.511, df = 43;208, p-value = 0.062
## Levene's test, Brown-Forsythe: t = -2.246, df = 251, p-value = 0.026
##
##
## ------ Infer ------
##
## --- Assume equal population variances of Happiness for each DepressionGroup
##
## t-cutoff for 95% range of variation: tcut = 1.969
## Standard Error of Mean Difference: SE = 0.845
##
## Hypothesis Test of 0 Mean Diff: t-value = 6.443, df = 251, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 1.664
## 95% Confidence Interval for Mean Difference: 3.780 to 7.108
##
##
## --- Do not assume equal population variances of Happiness for each DepressionGroup
##
## t-cutoff: tcut = 2.004
## Standard Error of Mean Difference: SE = 0.966
##
## Hypothesis Test of 0 Mean Diff: t = 5.634, df = 55.594, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 1.936
## 95% Confidence Interval for Mean Difference: 3.508 to 7.380
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of Happiness for each DepressionGroup
##
## Standardized Mean Difference of Happiness, Cohen's d: 1.069
##
##
## ------ Practical Importance ------
##
## Minimum Mean Difference of practical importance: mmd
## Minimum Standardized Mean Difference of practical importance: msmd
## Neither value specified, so no analysis
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for DepressionGroup Normal: 1.202
## Density bandwidth for DepressionGroup Moderate or Severe: 3.211
- Is there a significant difference in average sleep quality scores between students who reported having at least one all-nighter (AllNighter=1) and those who didn’t (AllNighter=0)?
# Perform t-test for PoorSleepQuality between students who had an all-nighter and those who didn't
ttest(PoorSleepQuality ~ AllNighter, data=SleepStudy)##
## Compare PoorSleepQuality across AllNighter with levels 1 and 0
## Grouping Variable: AllNighter
## Response Variable: PoorSleepQuality
##
##
## ------ Describe ------
##
## PoorSleepQuality for AllNighter 1: n.miss = 0, n = 34, mean = 7.029, sd = 2.823
## PoorSleepQuality for AllNighter 0: n.miss = 0, n = 219, mean = 6.137, sd = 2.922
##
## Mean Difference of PoorSleepQuality: 0.892
##
## Weighted Average Standard Deviation: 2.910
##
##
## ------ Assumptions ------
##
## Note: These hypothesis tests can perform poorly, and the
## t-test is typically robust to violations of assumptions.
## Use as heuristic guides instead of interpreting literally.
##
## Null hypothesis, for each group, is a normal distribution of PoorSleepQuality.
## Group 1: Sample mean assumed normal because n > 30, so no test needed.
## Group 0: Sample mean assumed normal because n > 30, so no test needed.
##
## Null hypothesis is equal variances of PoorSleepQuality, homogeneous.
## Variance Ratio test: F = 8.541/7.969 = 1.072, df = 218;33, p-value = 0.846
## Levene's test, Brown-Forsythe: t = 0.279, df = 251, p-value = 0.780
##
##
## ------ Infer ------
##
## --- Assume equal population variances of PoorSleepQuality for each AllNighter
##
## t-cutoff for 95% range of variation: tcut = 1.969
## Standard Error of Mean Difference: SE = 0.536
##
## Hypothesis Test of 0 Mean Diff: t-value = 1.664, df = 251, p-value = 0.097
##
## Margin of Error for 95% Confidence Level: 1.056
## 95% Confidence Interval for Mean Difference: -0.164 to 1.949
##
##
## --- Do not assume equal population variances of PoorSleepQuality for each AllNighter
##
## t-cutoff: tcut = 2.014
## Standard Error of Mean Difference: SE = 0.523
##
## Hypothesis Test of 0 Mean Diff: t = 1.707, df = 44.708, p-value = 0.095
##
## Margin of Error for 95% Confidence Level: 1.053
## 95% Confidence Interval for Mean Difference: -0.161 to 1.946
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of PoorSleepQuality for each AllNighter
##
## Standardized Mean Difference of PoorSleepQuality, Cohen's d: 0.307
##
##
## ------ Practical Importance ------
##
## Minimum Mean Difference of practical importance: mmd
## Minimum Standardized Mean Difference of practical importance: msmd
## Neither value specified, so no analysis
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for AllNighter 1: 1.589
## Density bandwidth for AllNighter 0: 0.936
- Do students who abstain from alcohol use have significantly better stress scores than those who report heavy alcohol use?
# Create a grouping variable for alcohol use (abstainers vs heavy drinkers)
SleepStudy$AlcoholUseGroup <- ifelse(SleepStudy$AlcoholUse == "Abstain", "Abstainers", "Heavy Drinkers")
# Perform t-test for Stress scores between abstainers and heavy drinkers
t.test(StressScore ~ AlcoholUseGroup, data = SleepStudy)##
## Welch Two Sample t-test
##
## data: StressScore by AlcoholUseGroup
## t = -0.4066, df = 45.26, p-value = 0.6862
## alternative hypothesis: true difference in means between group Abstainers and group Heavy Drinkers is not equal to 0
## 95 percent confidence interval:
## -3.409683 2.264102
## sample estimates:
## mean in group Abstainers mean in group Heavy Drinkers
## 8.970588 9.543379- Is there a significant difference in the average number of drinks per week between students of different genders?
# Perform t-test for the number of drinks per week between males and females
ttest_result <- t.test(Drinks ~ Gender, data=SleepStudy)
# Print the results of the t-test
ttest_result##
## Welch Two Sample t-test
##
## data: Drinks by Gender
## t = -6.1601, df = 142.75, p-value = 0.000000007002
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -4.360009 -2.241601
## sample estimates:
## mean in group 0 mean in group 1
## 4.238411 7.539216# Create a boxplot for Drinks by Gender
boxplot(Drinks ~ Gender, data=SleepStudy,
main="Number of Drinks per Week by Gender",
xlab="Gender", ylab="Number of Drinks per Week",
names=c("Female", "Male"))
- Is there a significant difference in the average weekday bedtime between students with high and low stress (Stress=High vs. Stress=Normal)?
# Perform t-test for weekday bedtime between students with high stress and normal stress
ttest_result <- t.test(WeekdayBed ~ Stress, data=SleepStudy)
# Print the results of the t-test
print(ttest_result)##
## Welch Two Sample t-test
##
## data: WeekdayBed by Stress
## t = -1.0746, df = 87.048, p-value = 0.2855
## alternative hypothesis: true difference in means between group high and group normal is not equal to 0
## 95 percent confidence interval:
## -0.4856597 0.1447968
## sample estimates:
## mean in group high mean in group normal
## 24.71500 24.88543# Create a boxplot for WeekdayBed by Stress (Normal vs High)
boxplot(WeekdayBed ~ Stress, data=SleepStudy,
main="Weekday Bedtime by Stress Level",
xlab="Stress Level", ylab="Weekday Bedtime",
col=c("lightblue", "lightcoral"),
names=c("Normal", "High"))
- Is there a significant difference in the average hours of sleep on weekends between first two year students and other students?
# Create a new variable to group first/second-year and third/fourth-year students
SleepStudy$ClassYearGroup <- ifelse(SleepStudy$ClassYear %in% c(1, 2), "First_Second", "Third_Fourth")
# Perform t-test for WeekendSleep by ClassYearGroup
t_test_result <- t.test(WeekendSleep ~ ClassYearGroup, data = SleepStudy)
# Display the t-test results
print(t_test_result)##
## Welch Two Sample t-test
##
## data: WeekendSleep by ClassYearGroup
## t = -0.047888, df = 237.36, p-value = 0.9618
## alternative hypothesis: true difference in means between group First_Second and group Third_Fourth is not equal to 0
## 95 percent confidence interval:
## -0.3497614 0.3331607
## sample estimates:
## mean in group First_Second mean in group Third_Fourth
## 8.213592 8.221892# Boxplot for WeekendSleep by ClassYearGroup
boxplot(WeekendSleep ~ ClassYearGroup, data = SleepStudy,
main = "Weekend Sleep by Class Year",
xlab = "Class Year Group",
ylab = "Weekend Sleep (hours)",
col = c("lightblue", "lightgreen"))
Summary
This report looks at how college students sleep and the lifestyle choices related to their sleep, using information from the SleepStudy data. The dataset has information from 253 students and looks at 27 different aspects, such as their GPA, if they are depressed, how much alcohol they use, how well they sleep, and how they perform mentally. The aim of this study was to look at how sleep habits affect students’ lives, including their mental health, school performance, and everyday activities. The study tried to answer some questions to understand how sleep, class times, drinking alcohol, and mental health affect students’ well-being.
The study looked at these research questions:
Gender Differences in GPA: We did a t-test to see if there is a big difference in the average grades (GPA) of male and female students. The results showed that there was no big difference in GPA between boys and girls.
Early Classes and Class Year: A t-test was used to find out if first and second-year students have a different average number of early classes than upperclassmen. The results showed that there was no important difference between the two groups.
Cognition Skills of Larks and Owls: A study used a method called ANOVA to compare the cognitive skills of students who are “larks” (early risers) and “owls” (night owls). The results showed that there are important differences in cognitive skills among the three groups (larks, owls, and those who don’t fit into either category). This indicates that sleep habits (whether someone wakes up early or stays up late) matter. Night owls affect how well people think and perform tasks.
Classes Missed Due to Early Classes: A t-test was used to compare how many classes students missed based on whether they had early classes or not. The results showed that students who had early classes usually missed fewer classes overall.
Happiness and Depression: A t-test was done to compare the happiness of students with normal depression and those with moderate to severe depression. The results showed that students with moderate to severe depression were much less happy.
Sleeping Well and Staying Up All Night: A t-test was done to compare the sleep quality scores of students who stayed up all night at least once and those who didn’t. The results showed that students who stayed up all night slept worse.
Alcohol Use and Stress Levels: A t-test was used to find out if students who don’t drink alcohol have lower stress levels than those who drink a lot. The results showed that people who don’t drink had much lower stress levels, suggesting that there is a link between drinking alcohol and stress.
Alcohol Use and Gender: A t-test was done to see if there is a difference in the average amount of drinks that male and female students have each week. The results showed that there was no big difference in how much alcohol men and women drink.
Weekday Bedtime and Stress: We did a t-test to see if there was a difference in bedtime during the week between students who have high stress and those with normal stress. The results showed that students who are very stressed go to bed later than those who are not very stressed.
We did a t-test to see how many hours students sleep on weekends, comparing first and second-year students with older students. The results showed that students’ sleep habits were similar, no matter what year they were in.
Conclusion
Looking at sleep habits and what affects them helps us understand how college students’ sleep relates to their mental health, school performance, and daily choices. The main points are that early risers perform better in school than night owls, students with moderate to severe depression are less happy, and staying up all night makes sleep quality worse. Also, drinking alcohol was linked to higher stress, and students with early classes missed fewer classes.
Knowing these patterns can help universities and support services create solutions to enhance student health, happiness, and academic achievement. More research could look into other things that impact students’ sleep, like after-school activities, their social life, and sleep problems.
Reference
Onyper, S., Thacher, P., Gilbert, J., Gradess, S., “Class Start Times, Sleep, and Academic Performance in College: A Path Analysis,” April 2012; 29(3): 318-335. Thanks to the authors for supplying the data. https://www.lock5stat.com/datapage3e.html