Final Project: [The impact of social media usage on sleep duration for students]
[Nitya]
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
Summarize the empirical article you selected. Discuss the key findings. Summarize the empirical article you selected. Discuss the key findings. Ahmed, Oli, et al. (2024) explores a detailed review of findings on the multiple studies between social media use and various mental health factors such as depression, anxiety, sleep durations and sleep troubles. It examines the relationship between social media use and sleep in young people. The document discusses social media and the association with anxiety. The finding indicated that social media use shows association with depression, anxiety and sleep problems. Overall, the study emphasizes the relationship between social media use and mental health outcomes. investigating the relationship between social media use, mental health, and sleep has produced inconsistent findings. Younger people spend more time on social media than other age groups, and are more likely to be impacted by social media use. This systematic review with meta-analysis aimed to synthesise the evidence on the associations between social media use, mental health, and sleep of young individuals.
Article 2 Summary
Summarize the empirical article you selected. Discuss the key findings. Summarize the empirical article you selected. Discuss the key findings. Tandon, Anushree, et al explores relationship between compulsive social media use (CSMU), the fear of missing out (FOMO), psychological well-being, sleep hygiene, and problematic sleep, focusing on young adults who are either students or working professionals. It discusses the social media use on sleep patterns, particularly in the presence of anxiety and depression. This page highlights the association between FoMO and problematic sleep due to social media and its effect on sleep. It also explains how social media habits impact on sleep based on a person’s life stage, interventions regarding social media use and how it affects sleep. investigating the associations of FoMO, psychological well-being (anxiety, depression), compulsive social media use (CSMU), and sleep hygiene (habits that promote/inhibit sleep) with problematic sleep adults in both academic and employment settings.
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. 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 higher social media usage, sleep deprivation and low GPA can affect extreme levels of anxiety and stress in a student
Method
Dataset Selected: “Student Lifestyle Dataset” - This dataset includes a view of student lifestyle patterns and their correlation with academic performance, represented by GPA. It represses the daily habits across study, extracurriculars, sleep, socializing, and physical activities. A supporting analyses in education, psychology, and health research fields, and academic performance prediction.
Sample
Describe the sample used in your study. Include details about the population, sample size, and any relevant demographic information. The dataset consists of 2000 students recruited from a google form survey. It analyzes the impact of daily habits on academic performance and student well-being like information on study hours, extracurricular activities, sleep, socializing, physical activity, stress levels, and CGPA and reflects student lifestyles primarily from India.
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. 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 ~ Social_Hours_Per_Day + Stress_Level)
summary(mod)##
## Call:
## lm(formula = Sleep_Hours_Per_Day ~ Social_Hours_Per_Day + Stress_Level,
## data = student_lifestyle_dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5527 -1.1651 -0.1169 1.1203 3.1118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.52376 0.06314 119.17 <2e-16 ***
## Social_Hours_Per_Day -0.18164 0.01791 -10.14 <2e-16 ***
## Stress_LevelLow 1.06531 0.08905 11.96 <2e-16 ***
## Stress_LevelModerate 0.92148 0.06693 13.77 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 1996 degrees of freedom
## Multiple R-squared: 0.1473, Adjusted R-squared: 0.1461
## F-statistic: 115 on 3 and 1996 DF, p-value: < 2.2e-16Post-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 = 2, v = nrow(student_lifestyle_dataset) - 2, f2 = 0.02, sig.level = 0.05)##
## Multiple regression power calculation
##
## u = 2
## v = 1998
## f2 = 0.02
## sig.level = 0.05
## power = 0.999968Results 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 variables are dependent on the study hours and social hours. the main effect of stress level (b = 1.06531), the main effect of sleep duration (b =-0.18164), were statistically significant at the 0.05 level. F(3, 1996) = 115, p-value: 2.2e-16.
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 = Social_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_Hours_Per_Day.Min = min(Sleep_Hours_Per_Day),
Sleep_Hours_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_Hours_Per_Day.Min
## <dbl> <dbl> <dbl>
## 1 7.50 1.46 5
## # ℹ 1 more variable: Sleep_Hours_Per_Day.Max <dbl>summary_table <- student_lifestyle_dataset %>%
dplyr::summarise(
Social_Hours_Per_Day.Mean = mean(Social_Hours_Per_Day),
Social_Hours_Per_Day.SD = sd(Social_Hours_Per_Day),
Social_Hours_Per_Day.Min = min(Social_Hours_Per_Day),
Social_Hours_Per_Day.Max = max(Social_Hours_Per_Day)
)
summary_table## # A tibble: 1 × 4
## Social_Hours_Per_Day.Mean Social_Hours_Per_Day.SD Social_Hours_Per_Day.Min
## <dbl> <dbl> <dbl>
## 1 2.70 1.69 0
## # ℹ 1 more variable: Social_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")| Social_Hours_Per_Day.Mean | Social_Hours_Per_Day.SD | Social_Hours_Per_Day.Min | Social_Hours_Per_Day.Max |
|---|---|---|---|
| 2.70455 | 1.688514 | 0 | 6 |
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? It shows to decrease the social media usage to improve the sleep quality. The data is statistically significant.
- Limitations: Identify any limitations of your study. How might these limitations have affected your results? Perhaps maybe using variety of data resources could help with the research to better interpret.
- Future Directions: Suggest potential future research directions based on your findings. Giving more resource options to students can enable a better research findings.
References
List the articles you reviewed in APA format. Do not worry about the indentations.
Ahmed, Oli, et al. “Social Media Use, Mental Health and Sleep: A Systematic Review with Meta-Analyses.” Journal of Affective Disorders, vol. 367, 2024, pp. 701–12, https://doi.org/10.1016/j.jad.2024.08.193.
Tandon, Anushree, et al. “Sleepless Due to Social Media? Investigating Problematic Sleep Due to Social Media and Social Media Sleep Hygiene.” Computers in Human Behavior, vol. 113, 2020, pp. 106487-, https://doi.org/10.1016/j.chb.2020.106487.
https://www.kaggle.com/datasets/steve1215rogg/student-lifestyle-dataset