Final Project: [The Effects of Daily Lifestyle Habits on Academic Performance and Well-Being]
[Kathleen Pobrero]
23 May, 2025
Introduction
This dataset examines how various daily lifestyle patterns correlate with the academic performance and well-being of college students in different cities in India. The daily lifestyle habits that this dataset measures include study, extracurricular, sleep, social, and physical activity hours; it also measures the students’stress level and cumulative GPA. The data was collected through a Google form survey and was possibly self-reported. I chose this dataset because I would like to learn how different daily lifestyle habits affect the academic performance of students and if there’s a strong correlation between the two. As I studied this dataset, I did some research and found 2 empirical articles that helped to provide a good, additional insight into my chosen dataset. Both articles talk about the physical, mental, and emotional effects on college students when balancing daily lifestyle habits with academics. Moreover, both articles highlight the relationship between academic performance and daily lifestyle habits, and in doing so, gives us insight into whether or not these habits positively or negatively affect college students’ overall academic performance.
Literature Review
Article 1 Summary
In a cross-sectional study, Lumley et al. (2015) examines the relationship between academic performance, extracurricular activities, and quality of life levels among 4,478 final-year medical school students across 20 different schools in the United Kingdom. Lumley et al. (2015) collected self-reported data from the students through an electronic questionnaire and found varying results such that students who had better study habits/skills and participated in extracurricular activities (e.g. participating in research or sports), reported higher academic performance and higher quality of life, while those who had higher study hours, but lower studying skills reported a lower quality of life and academic performance. The dataset I chose for my final project discusses how various daily lifestyle patterns correlate with the academic performance and well-being of college students in different cities in India. My research question is as follows: do college students’ daily lifestyle habits affect their academic performance and well-being? Both the research article I chose and the dataset I chose explore how the varying lifestyle habits of college students affects their overall well-being and academic performance. In my chosen dataset, the researcher found that balancing daily lifestyle habits and academics were associated with higher levels of stress among college students and this relationship resulted in B average GPAs.
Article 2 Summary
Bilušić et al. (2021) conducted a cross-sectional quantitative study from a small sample of 235 students in Croatia and examined how working college students deal with their work-study-life-balance (WSLB) and its effects on their overall emotional well-being (happiness, unhappiness, and relaxation), while also paying close attention to specific sociodemographic factors like age, gender, GPA, and field of study. Interestingly, in this study, the researchers found varying results between male and female students, where male students had an overall better work-study-life-balance than females. Additionally, one of the key findings was that overall, both male and female students achieved a better WSLB and had higher happiness when they had a better balance of studying and working, specifically. However, age seemed to play a significant role in overall WSLB, where the older the students were, the lower balance they had. Also, aside from having a better balance of studying and working, two other important factors that played a major role in the students’ overall emotional well-being were their study-life balance. The findings from this study were similar to the results of the dataset that I found because both studies conclude that while it is possible to balance school with one’s private life and work life, it may increase stress and unhappiness from the demands of trying to maintain a proper work-study-life-balance while also trying to take care of yourself.
Hypothesis
Independent Variable #1: It is hypothesized that among college students, balancing socializing for an average of 2.7 hours a day with academics is associated with having an above-average GPA.
Independent Variable #2: It is hypothesized that among college students, balancing extracurricular activities for an average of 2 hours a day with academics is associated with having an above-average GPA.
Hypothesis for Predicted Interaction: It is hypothesized that among college students, balancing the daily lifestyle habits of socializing and extracurricular activities with academics is associated with having an above average cumulative GPA.
Method
Sample
This dataset consists of 2,000 college students from various colleges in different cities in India. The only other information provided regarding the sample is that the data covers the academic year between August 2023 and May 2024.
Variables and Operationalization
Independent Variables: Socializing and extracurricular activities – operationalized as number of hours spent on each habit per day. Expected range: 0 to 6 hours.
Dependent Variable: Academic performance – operationalized as cumulative grade point averages, with higher GPAs indicating better academic performance and lower GPAs indicating lower academic performance. Range: 2.24 GPA to 4.0 GPA.
Loading Required Libraries
# Load your dataset in this chunk
library(readxl)
student_lifestyle_dataset <- read_excel("~/PSYCH 214/student_lifestyle_dataset.xlsx")
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.
model <- lm(GPA ~ Social_Hours_Per_Day + Extracurricular_Hours_Per_Day, data = student_lifestyle_dataset)
summary(model)##
## Call:
## lm(formula = GPA ~ Social_Hours_Per_Day + Extracurricular_Hours_Per_Day,
## data = student_lifestyle_dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9084 -0.2125 -0.0031 0.2094 0.8976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.183059 0.018122 175.649 < 2e-16 ***
## Social_Hours_Per_Day -0.016261 0.003978 -4.087 4.54e-05 ***
## Extracurricular_Hours_Per_Day -0.011617 0.005812 -1.999 0.0457 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2974 on 1997 degrees of freedom
## Multiple R-squared: 0.009323, Adjusted R-squared: 0.008331
## F-statistic: 9.396 on 2 and 1997 DF, p-value: 8.675e-05Post-hoc Power Analysis
Run a post-hoc power analysis with the pwr package. Use
the pwr.f2.test function for multiple regression power
analysis.
## [1] 0.009410736##
## Multiple regression power calculation
##
## u = 2
## v = 1997
## f2 = 0.01
## sig.level = 0.05
## power = 0.9850909Results 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 statistical analysis that I chose was linear aggression. Through this analysis, I found that the residuals seem to be small and random, indicating a good model of fit. Overall, there wasn’t too much variation among social hours per day as well as extracurricular hours per day (RSE = 0.2974 on 1997 df). The p-values of each variable (p-value: 0.00008675) indicate high statistical significance, as it is lower than 0.05. Furthermore, the multiple regression post-hoc power analysis I ran indicated that the sample size of 2000 from this study increased the power to 0.99, showing that there is a significant relationship between my independent variables (social hours per day and extracurricular hours per day) and the dependent variable (GPA). Therefore, my hypothesis for the predicted interactions was proven to be true, due to the results from my linear analysis.
Social_Hours_Per_Day: For every 1 hour increase in social hours per day, GPA decreases by 0.02.
Extracurricular_Hours_Per_Day: For every 1 hour increase in extracurricular hours per day, GPA decreases by 0.01.
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
student_lifestyle_dataset <- student_lifestyle_dataset %>%
mutate(Social_Hours_Per_Day_Split = ifelse(Social_Hours_Per_Day > median(Social_Hours_Per_Day), "0 Hours", "6 Hours"))
# Plot the interaction using the median split
ggplot(student_lifestyle_dataset, aes(x = Social_Hours_Per_Day, y = GPA, color = Social_Hours_Per_Day_Split)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Effect of Social Hours on Grade Point Average",
x = "Social Hours Per Day", y = "GPA") +
scale_color_manual(values = c("0 Hours" = "lightblue", "6 Hours" = "pink")) +
theme_apa()
# Median Split for Extracurricular Activities
student_lifestyle_dataset <- student_lifestyle_dataset %>%
mutate(Extracurricular_Hours_Per_Day_Split = ifelse(Extracurricular_Hours_Per_Day > median(Extracurricular_Hours_Per_Day), "0 Hours", "4 Hours"))
# Plot the interaction using the median split - Extracurricular Hours
ggplot(student_lifestyle_dataset, aes(x = Extracurricular_Hours_Per_Day, y = GPA, color = Extracurricular_Hours_Per_Day_Split)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Effect of Extracurricular Hours on Grade Point Average",
x = "Extracurricular Hours Per Day", y = "GPA") +
scale_color_manual(values = c("0 Hours" = "lightblue", "4 Hours" = "black")) +
theme_apa()
ggplot(student_lifestyle_dataset, aes(x = Social_Hours_Per_Day, y = GPA)) +
stat_summary(fun = "mean", geom = "line", color = "darkseagreen3",size = 1.2) +
labs(x = "Social Hours Per Day", y = "GPA", title = "Does the number of hours spent per day socializing affect GPA?") +
theme_apa()
ggplot(student_lifestyle_dataset, aes(x = Extracurricular_Hours_Per_Day, y = GPA)) +
stat_summary(fun = "mean", geom = "line", color = "lightskyblue3",size = 1.2) +
labs(x = "Extracurricular Hours Per Day", y = "GPA", title = "Does the number of hours spent per day participating in extracurricular activities affect GPA?") +
theme_apa()
# Example R code for creating a table
# Create a summary table by Species
summary_table2<- student_lifestyle_dataset %>%
group_by(Social_Hours_Per_Day_Split) %>%
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_table2## # A tibble: 2 × 5
## Social_Hours_Per_Day_Split Social_Hours_Per_Day_Mean Social_Hours_Per_Day_SD
## <chr> <dbl> <dbl>
## 1 0 Hours 4.20 0.939
## 2 6 Hours 1.28 0.764
## # ℹ 2 more variables: Social_Hours_Per_Day_Min <dbl>,
## # Social_Hours_Per_Day_Max <dbl># Example R code for creating a table
# Create a summary table by Species
summary_table <- student_lifestyle_dataset %>%
group_by(Extracurricular_Hours_Per_Day_Split) %>%
dplyr::summarise(
Extracurricular_Hours_Per_Day_Mean = mean(Extracurricular_Hours_Per_Day),
Extracurricular_Hours_Per_Day_SD = sd(Extracurricular_Hours_Per_Day),
Extracurricular_Hours_Per_Day_Min = min(Extracurricular_Hours_Per_Day),
Extracurricular_Hours_Per_Day_Max = max(Extracurricular_Hours_Per_Day)
)
summary_table## # A tibble: 2 × 5
## Extracurricular_Hours_Per_Day_…¹ Extracurricular_Hour…² Extracurricular_Hour…³
## <chr> <dbl> <dbl>
## 1 0 Hours 3.02 0.566
## 2 4 Hours 1.03 0.604
## # ℹ abbreviated names: ¹Extracurricular_Hours_Per_Day_Split,
## # ²Extracurricular_Hours_Per_Day_Mean, ³Extracurricular_Hours_Per_Day_SD
## # ℹ 2 more variables: Extracurricular_Hours_Per_Day_Min <dbl>,
## # Extracurricular_Hours_Per_Day_Max <dbl># Display the table using knitr::kable()
kable(summary_table, caption = "Descriptive Statistics for Extracurricular Hours")| Extracurricular_Hours_Per_Day_Split | Extracurricular_Hours_Per_Day_Mean | Extracurricular_Hours_Per_Day_SD | Extracurricular_Hours_Per_Day_Min | Extracurricular_Hours_Per_Day_Max |
|---|---|---|---|---|
| 0 Hours | 3.022614 | 0.5663340 | 2.1 | 4 |
| 4 Hours | 1.029344 | 0.6041786 | 0.0 | 2 |
# Display the table using knitr::kable()
kable(summary_table2, caption = "Descriptive Statistics for Social Hours")| Social_Hours_Per_Day_Split | Social_Hours_Per_Day_Mean | Social_Hours_Per_Day_SD | Social_Hours_Per_Day_Min | Social_Hours_Per_Day_Max |
|---|---|---|---|---|
| 0 Hours | 4.196414 | 0.9389342 | 2.7 | 6.0 |
| 6 Hours | 1.282617 | 0.7639805 | 0.0 | 2.6 |
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?
- In the context of existing research, my findings indicate a clear relationship between daily lifestyle habits and academic performance. Based on the results from this study, they show that as hours spent socializing and hours spent participating in extracurricular activities increases, these increases may decrease GPA. This shows that college students should continue to decrease distractions that may negatively affect their academic performance.
- Limitations: Identify any limitations of your
study. How might these limitations have affected your results?
- The only limitation that I found was that the data was collected through a Google form survey, indicating that the data was self-reported. Self-reported data has lots of room for bias and false reporting, which could in turn skew results.
- Future Directions: Suggest potential future
research directions based on your findings.
- Some further research that may be done based on these findings could be determining what kind of extracurricular activities students participate in that would possibly decrease their academic performance. More research that could be done is researching how consistent college students participate in their lifestyle habits to see how strong the effects of that are on academic performance.
References
List the articles you reviewed in APA format. Do not worry about the indentations.
Lumley, S., Ward, P., Roberts, L., & Mann, J. P. (2015). Self-reported extracurricular activity, academic success, and quality of life in UK medical students. International journal of medical education, 6, 111–117. https://doi.org/10.5116/ijme.55f8.5f04
Vokić, N. P., Bilušić, M. R., & Perić, I. (2021). Work-Study-Life Balance - the Concept, its Dyads, Socio-Demographic Predictors and Emotional Consequences. ZIREB Zagreb International Review of Economics & Business, 24, 77–94. https://doi-org.dvc.idm.oclc.org/10.2478/zireb-2021-0021