Final Project: How Study Hours Per Day Impacts Sleep Hours Per Day
Manognya Vishnuvajhala
21 May, 2025
Introduction
Provide a brief introduction to your research question. Summarize the empirical articles you reviewed.
Does the amount of daily social media use predict levels of anxiety among college students? Does gender interact with this relationship and change it?
Literature Review
Article 1 Summary
Article 1 (Exploring students’ study time, sleep duration, and perceptions of course difficulty on final examination results: A cross-sectional study) Summary: Zhang and He (2025) studied the relationship between the last exam scores of college students, self reported study time, the night before taking an exam, subjective assessment of course difficulty, and grade satisfaction. 354 third-quarter students completed a four-item questionnaire in finals week. Their results indicated a positive correlation between grade satisfaction, total scores before the final exam, and sleep duration with final exam scores in both endocrinology and immunology courses. They, however, established a negative correlation between students’ perception of course difficulty and final exam scores. In contrast to expectations, the number of study hours undertaken by students did not correlate positively with their final exam scores. The authors concluded that proper sleep prior to an examination is positively associated with enhanced performance and suggested that the educational institutions make allowance for students’ sleep habits towards their achievements and advise as such.
Article 2 Summary
Article 2 (SLEEP DISTURBANCES AMONG UNIVERSITY STUDENTS : A TUNISIAN STUDY) Summary: Turki et al. (2023) conducted an online cross-sectional study to assess the prevalence of insomnia among Tunisian university students and identify related factors. Data were collected with a questionnaire sent through social media sites, and 144 students participated. The Insomnia Severity Index (ISI), was applied to assess the severity of insomnia. The outcomes indicated a high rate of insomnia among the students as 72.2% at least some degree of insomnia (subthreshold, moderate clinical, or severe clinical). Insomnia was highly prevalent among the use of psychoactive drugs, and ISI scores much higher among those that were using cannabis. The authors’ conclusion was that insomnia is common among university students and that the use of psychoactive substances appears to augment it. The authors underscored that the determinants mentioned above need to be considered while designing interventions that can improve the quality of sleep among students.
Hypothesis
State your directional hypothesis. Specify the expected relationship between your variables. This section should be clear and concise. You need one hypothesis for IV1, one hypothesis for IV2, and one hypothesis for a predicted interaction. If you are running a logistic regression, you do not need a hypothesis for an interaction.
It is hypothesized that a balanced schedule between hours of studying and hours of sleep per day for students will be associated with higher GPAs and better academic performance.
Method
I chose the Student Lifestyle Dataset: Daily Lifestyle and Academic Performance of Students. I chose this dataset because it focuses on student lifestyle patterns across an array of daily patterns such as study, extracurriculars, sleep, socializing, and physical activities. It represents the daily habits of a student and can support analyses in education, psychology, and health research fields, making it ideal for projects on lifestyle management, academic performance prediction, and well-being assessments. I am interested in studying these different aspects and how it connects to a students overall lifestyle which is why I chose this dataset.
Sample
Describe the sample used in your study. Include details about the population, sample size, and any relevant demographic information.
This dataset consists of data collected from 2,000 students via a Google Form survey. It shows how study hours, extracurricular activities, sleep, socializing, physical activity, stress levels, and CGPA impact students and their lifestyles. The data covers an academic year from August 2023 to May 2024 and can help analyze the impact of daily habits on academic performance and overall student well-being.
Variables and Operationalization
List your independent and dependent variables. Explain how each variable was operationalized, including the range for continuous variables and levels for categorical variables.
Independent Variable: Daily social media use, operationalized as the number of hours spent on social media per day. Expected range: 0 to 8 hours.
Dependent Variable: Anxiety levels, operationalized as scores on the Generalized Anxiety Disorder 7-item (GAD-7) scale. Expected range: 0 to 21, with higher scores indicating higher anxiety.
Loading Required Libraries
# Load your dataset in this chunk
library(readr)
student_lifestyle_dataset <- read_csv("student_lifestyle_dataset.csv")
View(student_lifestyle_dataset)## Error in check_for_XQuartz(file.path(R.home("modules"), "R_de.so")): X11 library is missing: install XQuartz from www.xquartz.orgDescriptive Statistics
Present the descriptive statistics for your variables. Include appropriate measures of central tendency (mean, median), variability (standard deviation, range), and frequency distributions where applicable. Use R code chunks to generate and display your results.
## vars n mean sd median trimmed mad
## Student_ID 1 2000 1000.50 577.49 1000.50 1000.50 741.30
## Study_Hours_Per_Day 2 2000 7.48 1.42 7.40 7.47 1.78
## Extracurricular_Hours_Per_Day 3 2000 1.99 1.16 2.00 1.99 1.48
## Sleep_Hours_Per_Day 4 2000 7.50 1.46 7.50 7.50 1.93
## Social_Hours_Per_Day 5 2000 2.70 1.69 2.60 2.66 2.08
## Physical_Activity_Hours_Per_Day 6 2000 4.33 2.51 4.10 4.21 2.82
## GPA 7 2000 3.12 0.30 3.11 3.11 0.31
## Stress_Level* 8 2000 1.82 0.91 1.00 1.78 0.00
## min max range skew kurtosis se
## Student_ID 1.00 2000 1999.00 0.00 -1.20 12.91
## Study_Hours_Per_Day 5.00 10 5.00 0.03 -1.18 0.03
## Extracurricular_Hours_Per_Day 0.00 4 4.00 0.00 -1.18 0.03
## Sleep_Hours_Per_Day 5.00 10 5.00 -0.01 -1.21 0.03
## Social_Hours_Per_Day 0.00 6 6.00 0.18 -1.12 0.04
## Physical_Activity_Hours_Per_Day 0.00 13 13.00 0.40 -0.44 0.06
## GPA 2.24 4 1.76 0.03 -0.38 0.01
## Stress_Level* 1.00 3 2.00 0.36 -1.69 0.02Statistical Analysis
Analysis
Perform your chosen analysis. Make sure your output shows.
mod <- "lm"(data = student_lifestyle_dataset, Sleep_Hours_Per_Day ~ Study_Hours_Per_Day + GPA)
summary(mod)##
## Call:
## lm(formula = Sleep_Hours_Per_Day ~ Study_Hours_Per_Day + GPA,
## data = student_lifestyle_dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.56613 -1.25651 0.02757 1.25450 2.55759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.79494 0.36145 21.566 <2e-16 ***
## Study_Hours_Per_Day 0.06652 0.03380 1.968 0.0492 *
## GPA -0.25385 0.16113 -1.575 0.1153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.46 on 1997 degrees of freedom
## Multiple R-squared: 0.001954, Adjusted R-squared: 0.0009547
## F-statistic: 1.955 on 2 and 1997 DF, p-value: 0.1418Post-hoc Power Analysis
Run a post-hoc power analysis with the pwr package. Use
the pwr.f2.test function for multiple regression power
analysis.
library(pwr)
pwr.f2.test(u = 1, v = nrow(student_lifestyle_dataset) - 2, f2 = 0.02, sig.level = 0.05)##
## Multiple regression power calculation
##
## u = 1
## v = 1998
## f2 = 0.02
## sig.level = 0.05
## power = 0.9999935Results Interpretation
Results are interpreted clearly using APA style; connection to hypothesis is made; statistical significance and practical implications are addressed; power level is addressed.
The sleep hours and study hours were regressed on the variable GPA, which is dependent on both these variables. The main effect of study hours per day (b = 0.06652), and its impact on students GPA (b =-0.25385), were statistically significant at the 0.05 value. nor the interaction effect of these two variables (b = -0.001882) were statistically significant at the 0.05 level. F-statistic: 1.955 on 2 and 1997 DF, p-value: 0.1418.
Graph and Table
Include at least one table and one graph that effectively summarize your analysis and findings. Use R code chunks to generate these visualizations.
#Example R code for creating a graph
# You will be performing a median split
# Median split for Experience to visualize the linear x linear interaction
iris <- iris %>%
mutate(Sepal_Length_Split = ifelse(Sepal.Length > median(Sepal.Length), "Long Sepals", "Short Sepals"))
# Plot the interaction using the median split
ggplot(iris, aes(x = Sepal.Width, y = Petal.Length, color = Sepal_Length_Split)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Effect of Sepal Width on Petal Length by Sepal Length (Median Split)",
x = "Sepal Width", y = "Petal Length") +
scale_color_manual(values = c("Long Sepals" = "green", "Short Sepals" = "orange")) +
theme_apa()
ggplot(student_lifestyle_dataset,
aes(x = Study_Hours_Per_Day , y = GPA)) +
geom_bar(stat = "identity")
ggplot(student_lifestyle_dataset,
aes(x = Sleep_Hours_Per_Day , y = GPA)) +
geom_bar(stat = "identity")
# Example R code for creating a table
# Create a summary table by Species
summary_table <- iris %>%
group_by(Sepal_Length_Split) %>%
dplyr::summarise(
Petal.Length.Mean = mean(Petal.Length),
Petal.Length.SD = sd(Petal.Length),
Petal.Length.Min = min(Petal.Length),
Petal.Length.Max = max(Petal.Length)
)
summary_table <- student_lifestyle_dataset %>%
dplyr::summarise(
Sleep_Hours_Per_Day.Mean = mean(Sleep_Hours_Per_Day),
Sleep_Hours_Per_Day.SD = sd(Sleep_Hours_Per_Day),
Sleep_Duration_Per_Day.Min = min(Sleep_Hours_Per_Day),
Sleep_Duration_Per_Day.Max = max(Sleep_Hours_Per_Day)
)
summary_table## # A tibble: 1 × 4
## Sleep_Hours_Per_Day.Mean Sleep_Hours_Per_Day.SD Sleep_Duration_Per_Day.Min
## <dbl> <dbl> <dbl>
## 1 7.50 1.46 5
## # ℹ 1 more variable: Sleep_Duration_Per_Day.Max <dbl>summary_table <- student_lifestyle_dataset %>%
dplyr::summarise(
Study_Hours_Per_Day.Mean = mean(Study_Hours_Per_Day),
Study_Hours_Per_Day.SD = sd(Study_Hours_Per_Day),
Study_Hours_Per_Day.Min = min(Study_Hours_Per_Day),
Study_Hours_Per_Day.Max = max(Study_Hours_Per_Day)
)
summary_table## # A tibble: 1 × 4
## Study_Hours_Per_Day.Mean Study_Hours_Per_Day.SD Study_Hours_Per_Day.Min
## <dbl> <dbl> <dbl>
## 1 7.48 1.42 5
## # ℹ 1 more variable: Study_Hours_Per_Day.Max <dbl>## vars n mean sd median trimmed mad
## Student_ID 1 2000 1000.50 577.49 1000.50 1000.50 741.30
## Study_Hours_Per_Day 2 2000 7.48 1.42 7.40 7.47 1.78
## Extracurricular_Hours_Per_Day 3 2000 1.99 1.16 2.00 1.99 1.48
## Sleep_Hours_Per_Day 4 2000 7.50 1.46 7.50 7.50 1.93
## Social_Hours_Per_Day 5 2000 2.70 1.69 2.60 2.66 2.08
## Physical_Activity_Hours_Per_Day 6 2000 4.33 2.51 4.10 4.21 2.82
## GPA 7 2000 3.12 0.30 3.11 3.11 0.31
## Stress_Level* 8 2000 1.82 0.91 1.00 1.78 0.00
## min max range skew kurtosis se
## Student_ID 1.00 2000 1999.00 0.00 -1.20 12.91
## Study_Hours_Per_Day 5.00 10 5.00 0.03 -1.18 0.03
## Extracurricular_Hours_Per_Day 0.00 4 4.00 0.00 -1.18 0.03
## Sleep_Hours_Per_Day 5.00 10 5.00 -0.01 -1.21 0.03
## Social_Hours_Per_Day 0.00 6 6.00 0.18 -1.12 0.04
## Physical_Activity_Hours_Per_Day 0.00 13 13.00 0.40 -0.44 0.06
## GPA 2.24 4 1.76 0.03 -0.38 0.01
## Stress_Level* 1.00 3 2.00 0.36 -1.69 0.02# Display the table using knitr::kable()
kable(summary_table, caption = "Descriptive Statistics for Iris Sepal Length")| Study_Hours_Per_Day.Mean | Study_Hours_Per_Day.SD | Study_Hours_Per_Day.Min | Study_Hours_Per_Day.Max |
|---|---|---|---|
| 7.4758 | 1.423888 | 5 | 10 |
Discussion
Discuss the implications of your results for psychological theory or practice. Address the following points:
- Implications: What do your findings mean in the context of existing research? Study hours and sleep hours definitely had an impact on students overall GPA and the data was statistically significant. Further testing can give more accurate results.
- Limitations: Identify any limitations of your study. How might these limitations have affected your results? The only limitation was I only utilized the data I was provided with when I could have implemented a different data set for this project. If I was provided with a vast category of studies and research beyond what is provided in this class and given more time, I could conduct different research and potentially have more in depth and accurate research questions.
- Future Directions: Suggest potential future research directions based on your findings. Given more resources and a wider variety of options to students can provide better research findings and more measurable results.
References
List the articles you reviewed in APA format. Do not worry about the indentations.
Zhang, Niu, and Xiaohua He. “Exploring Students’ Study Time, Sleep Duration, and Perceptions of Course Difficulty on Final Examination Results: A Cross-Sectional Study.” The Journal of Chiropractic Education, vol. 39, 2025, https://doi.org/10.7899/JCE-24-5.
Turki, M., et al. “SLEEP DISTURBANCES AMONG UNIVERSITY STUDENTS : A TUNISIAN STUDY.” European Psychiatry, vol. 66, no. S1, 2023, pp. S1101–S1101, https://doi.org/10.1192/j.eurpsy.2023.2339.