1 Executive Summary

The main purpose of our study is to analyse the work-life balance of University students by investigating commute time to University against free time. There was a weak negative correlation between commute time and free time and as commute time increased, people used public transport over other forms of transport.


2 Full Report

2.1 Initial Data Analysis (IDA)

In this investigation an online survey was used to collect data as online surveys can reach more people faster and cheaper than an in person survey. Moreover, the data is collated immediately so can be loaded into R, the programme in which data analysis occurs, more quickly. Online surveys are completely anonymous and faster for the people completing them which might motivate more people to take the survey. Furthermore, online surveys are cheaper than surveys taken in person. This is the link to our survey: https://docs.google.com/forms/d/e/1FAIpQLSeaOG8RYTKffTgeB9WcJpy8JyUsUXEq4Vgio6nZ2HC9COaBzA/viewform?usp=sf_link.

However, with online surveys there is a higher likelihood of people submitting untruthful responses as there is no face to face contact so it’s easier for people to lie. Additionally, there is a risk of having a sampling bias as our survey is advertised over Instagram so only people who follow and interact with our accounts would see the survey link. In this report we are assuming that everyone has been honest in the survey and that their commute times do not fluctuate frequently; the average time is consistent. Furthermore, we did not consider that different degrees require different amounts of external work which would take away from students’ free time.

survey = read.csv("Project2Data.csv")

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.1     ✔ purrr   1.0.1
## ✔ tibble  3.1.8     ✔ dplyr   1.1.0
## ✔ tidyr   1.3.0     ✔ stringr 1.5.0
## ✔ readr   2.1.4     ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
x = ggplot(survey, aes(x=uni.commute, y = freetime.no,)) + geom_point(aes(shape= Transport, colour= Transport)) + stat_smooth(method = lm, se = FALSE)

axisname = x + labs(title= "Commute Time Via Different Methods to University Relating to Free Time", x = "Commute Time (min)", y = "Free Time (min)")
axisname
## `geom_smooth()` using formula = 'y ~ x'

2.2 Research Question(s)

Commuting between home and university is routinely performed by university students and the length and type of transport can impact the wellbeing of a large proportion of students (Mouratidis, 2019). The first integrated research question from the research theme is how does commute times affect students’ free time. In the scatter plot above, the data points are not that close to each other, therefore a clear trend is not present in the data. However, the linear regression shows that there is a weak negative correlation between commute time and free time; the longer the commute time of a university student, the less free time they have. This factor may not just be due to commute time however as we did not consider the fact that different university students are doing different courses which may require different time commitments outside of university hours, reducing their free time regardless of their commute time.

The second integrated research question from the research theme is how does the means of commute affect free time. As the graph above shows, when the commute time is below 25 minutes, about 71% of students choose to walk to university. When the commute takes longer than 25 minutes, 84% of students choose public transport as the way to university, and hardly no one will pick walking as the way to university. Overall, when the commute time is short, uni students will normally walk to university and slightly have more free time, and when the commute time is long ( more than 25 minutes), the students will prefer to choose public transport to university, and they normally have less free time than others. This data may not be fully accurate of a university student’s commute to university as some students may have used multiple different means of transport to get to university e.g. biking to the train station, taking the train then walking to university. Our study assumes that only one means of transport was taken.


2.3 Articles

Students with longer commute times stayed in university for longer (Kobus, M. B. et al., 2015) which might have reduced their free time aligning with what we found. In terms of leisure satisfaction, other studies indicate that a shorter commute time has a positive impact towards leisure satisfaction (Mouratidis, 2019).


2.4 References

Kobus, M. B., Van Ommeren, J. N., & Rietveld, P. (2015). Student commute time, university presence and academic achievement. Regional Science and Urban Economics, 52, 129-140.

https://www.sciencedirect.com/science/article/pii/S0166046215000216

Mouratidis, K. (2019). Built environment and leisure satisfaction: The role of commute time, social interaction, and active travel. Journal of transport geography, 80, 102491.

https://www.sciencedirect.com/science/article/pii/S0966692319302893


2.5 Acknowledgments

Our team met every Friday at 12pm to work on the project. We worked together in the Abercrombie student accommodation study rooms from 12pm to 2pm.

Nordashima Proud - npro7001
- Amended the project proposal according to feedback given
- Wrote the discussion on the source, structure and limitations of the data collection
- Planned video script

Han Nguyen - hngu4527
- Organised submission of project
- Plotted the Graph
- Editted video

Yiyi Wang - ywan2278
- Data cleaning
- Found research articles and analysed them
- Assisted in video

Qianyu Zhou - qzho0912
- Data cleaning
- Analysed the graph
- Assisted in video