Technology Use Amongst University Students
Project 1
500488395 | 500474402 | 490611861
University of Sydney | DATA1001 Foundations of Data Science | March 2020
1 Executive Summary
The aim of this report was to gain an insight into the phone habits of university students. By using the data collected by conducting a survey, we sought to answer two research questions:
Are university students easily distracted by their phones?
How do university students use their phones?
The survey revealed that social media was the most popular use for phones among university students. Phones have become part of everyday life, with most students surveyed saying they used their phones for over three hours every day and beside them while studying.
2 Full Report
2.1 Initial Data Analysis (IDA)
2.1.1 Source of Data
The data is sourced from an online survey, supported by Google Forms, that was sent to 25 students that attended universities in the Sydney area. In accordance with ethics, all answers are anonymous and the link to the survey sent out to the community with a clear message that it was an optional survey that did not have to be completed unless the participants chose to do so. Unfortunately this did create some slight bias, due to the sending of the link directly by the researchers to people they knew and to the broader University of Sydney DATA1001 Ed page. This meant that the people answering the survey were often from similar backgrounds and with similar experiences, which may have skewed the data and not accurately represented the community as a whole.
2.1.2 Complexity of Data and Classification of Variables
The name of the variables were the questions from the survey. However, these were too long for easy and unconvoluted analysis, so prior to the import of the dataset, the variables were renamed:
“How old are you?” to “age”
“Which gender do you identify yourself as?” to “gender”
“What brand of mobile phone do you use?” to “brand”
“What model do you use?” to “model”
“How many years have you been using your phone for?” to “phoneAge”
“What is the average amount of hours you spend on your phone per day?” to “screenTime”
“How often would you say you reach for your phone?” to “reach”
“Do you keep your phone nearby when you’re studying/working?” to “study”
“Do you think your phone is more helpful or a distraction?” to “distraction”
“Where/when do you often use your phone?” to “usageLocation”
“What do you your phone most often for?” to “use”
Size of data, name of variables and their classification
library(readxl)
TechUse = read_excel("/Users/emilyralph/Documents/R Studio/Tech Usage for University students (Responses).xlsx")
## Size of data
dim(TechUse)## [1] 25 11## Name of variables
names(TechUse)## [1] "age" "gender" "brand" "model"
## [5] "phoneAge" "screenTime" "reach" "study"
## [9] "distraction" "usageLocation" "use"## R's classification of variables
str(TechUse)## Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 11 variables:
## $ age : chr "20-24" "20-24" "Less than 20" "20-24" ...
## $ gender : chr "Male" "Female" "Female" "Female" ...
## $ brand : chr "Apple" "asus" "Apple" "Apple" ...
## $ model : chr "iPhone 7" "idk" "iphone x" "iphonexs" ...
## $ phoneAge : chr "2-4 years" "0-2 years" "0-2 years" "0-2 years" ...
## $ screenTime : chr "<3hrs" "3-7hrs" "3-7hrs" "3-7hrs" ...
## $ reach : chr "Every 15mins" "Every 15mins" "Every 15mins" "Every 15mins" ...
## $ study : chr "Yes" "Yes" "Yes" "Yes" ...
## $ distraction : num 5 7 5 8 9 9 5 0 8 8 ...
## $ usageLocation: chr "On the commute" "On the toilet" "On the commute" "At home" ...
## $ use : chr "Books/podcasts" "Social Media" "Browsing" "Social Media" ...R’s classification of the variables is incorrect, as they are not all “characters” (chr). Therefore, the reclassification of the variables is important for the later analysis of data.
### Reclassify all variables to be factor (categorical) variables, except for "distraction" as a numerical variable
TechUse$age = as.factor(TechUse$age)
TechUse$gender = as.factor(TechUse$gender)
TechUse$brand = as.factor(TechUse$brand)
TechUse$model = as.factor(TechUse$model)
TechUse$phoneAge = as.factor(TechUse$phoneAge)
TechUse$screenTime = as.factor(TechUse$screenTime)
TechUse$reach = as.factor(TechUse$reach)
TechUse$study = as.factor(TechUse$study)
TechUse$distraction = as.numeric(TechUse$distraction)
TechUse$usageLocation = as.factor(TechUse$usageLocation)
TechUse$use = as.factor(TechUse$use)New classifications:
str(TechUse)## Classes 'tbl_df', 'tbl' and 'data.frame': 25 obs. of 11 variables:
## $ age : Factor w/ 3 levels "20-24","25-29",..: 1 1 3 1 3 2 3 1 3 3 ...
## $ gender : Factor w/ 2 levels "Female","Male": 2 1 1 1 1 1 1 2 1 1 ...
## $ brand : Factor w/ 6 levels "Apple","asus",..: 1 2 1 1 1 1 6 1 1 6 ...
## $ model : Factor w/ 21 levels "5SE","8","A70",..: 10 5 12 15 7 14 20 2 11 17 ...
## $ phoneAge : Factor w/ 3 levels "0-2 years","2-4 years",..: 2 1 1 1 1 1 1 1 1 1 ...
## $ screenTime : Factor w/ 3 levels "<3hrs","3-7hrs",..: 1 2 2 2 3 2 1 1 1 1 ...
## $ reach : Factor w/ 4 levels "Every 1 min",..: 2 2 2 2 4 3 2 3 2 2 ...
## $ study : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ distraction : num 5 7 5 8 9 9 5 0 8 8 ...
## $ usageLocation: Factor w/ 5 levels "At home","On the commute",..: 2 3 2 1 4 5 4 2 2 1 ...
## $ use : Factor w/ 6 levels "Books/podcasts",..: 1 6 2 6 6 6 1 5 6 1 ...2.1.3 Stakeholders
Some stakeholders that may be interested in the data include:
Technology companies A study by the Pew Research Centre found “more than nine-in-ten millennials own smartphones” with the statistics for phone use decreasing as they age brackets went up. Therefore it is conclusive that any forward-thinking tech company should be seeing the patterns of usage in the younger generations as their most primary customer.
Mental health institutions Mental health institutions often look at the link mental the use of technology and stress and anxiety, especially in university students.
2.2 Exploring Data
2.2.1 Research Question 1
According to the data collected by the survey, most students deemed mobile phones to be more of a distraction than as a useful tool, with only two students ranking below 5 on the scale provided (one a scale of one to ten, with one being useful and ten being a distraction).
### Median of scale of one to ten, from useful to distracting
median(TechUse$distraction)## [1] 7The median for the level of distraction for phones equated as a 7. This shows that, although most students believe that phones are a significant distraction in their lives, there is an undeniable level of usefulness that makes it difficult to cut out of their lives completely.
The study “The invisible addiction: Cell-phone activities and addiction among male and female college students” similarly underwent a questionnaire that surveyed college students on their phone usage. It attempts to draw the link between the usefulness of phones and the countenance of the negative distractions they put into our lives. It argues that that phone use has devolved from habit to addiction, wherein the urge to use phones becomes a habitual and compulsive urge that is hard to fight. This is further back by a study from the Taiwan university (David et al., 2014) *1 that stated that “[the use of] mobile phones when coupled with deficient self-regulation can evolve into an addiction, resulting in poor academic performance.”
### Barplot showing the age of the university students that participated in the survey
table(TechUse$age)##
## 20-24 25-29 Less than 20
## 9 1 15age = table(TechUse$age)
barplot(age, names.arg=c("Less than 20","20-24","25-29"),col="lightpink")
The survey conducted by us was sent to university students, with all participants coming under the age of 30, with 60% being under 20. The younger age of the participants (and of many university students) means that it is a generation that has grown up with phones, and with less developed impulse control, that makes their distracting nature more difficult to overcome.
### Double barplot to compare the participants' use of phones whilst studying/working against the number of times that they reach for their phones
table(TechUse$reach,TechUse$study)##
## No Yes
## Every 1 min 0 1
## Every 15mins 0 11
## Every 30 mins 1 5
## Every 5 mins 1 6barplot(table(TechUse$reach,TechUse$study),names.arg=c("Yes","No"),col=c("lightblue","lightgreen","lightyellow","lightpink"),legend =c("Every 1 min","Every 5 mins","Every 15 mins","Every 30 mins"))
The data collected reveals that 76% of the participants picked their phones at least every 15 minutes or less. When combined with the fact that the investigation discovered that 92% of these students then said that they kept their phone nearby when studying or working, it is easy to see the overall impact that phones have to a university students’ life. The dependence on phones and the addictive nature of them have led to the increased disturbance in their routines and greater distractions in their everyday lives.
*1: https://www.researchgate.net/publication/275487066_Mobile_phone_distraction_while_studying
Summary: The addictive nature of smartphones has led to the increased presence of smartphones in everyday life, causing them to become a distraction to university students.
2.2.2 Research Question 2
### Barplot of participants' phone use
table(TechUse$use)##
## Books/podcasts Browsing Calls/messages Music Navigation
## 3 4 2 3 1
## Social Media
## 12barplot(table(TechUse$use),col="lightyellow")
The data collected from the questionnaire found that the majority of university students use their phone most often for social media (12/48), followed by browsing the internet (4/25), for listening to music (3/25) for Reading/Listening to audiobooks/podcasts (3/25), for phone calls and messages (2/25) and for navigation (1/25). This is suggestive of the use of phones as a means through which students stay connected, allowing them to collaborate within the community, an important part of university life.
The data collected reveals that phones have become part of everyday life for the university student. It found that 76% of the participants picked their phones at least every 15 minutes or under. This is supported by a study that found it common for people to pick their phones up for 30 seconds or less throughout the day (Oulasvirta et al., 2012) *1
The present study finds that the majority of the students questioned (64%) use their phones for 3-7 hours every day, with a further 8% stating they used their phones for over 8 hours every day. This is backed by other studies that have found that college students spend as much as 9 hours on their phone (Roberts et al., 2014) *2
This study also states that “sixty-seven percent of young adults 18 to 24 years of age own a Smartphone compared to fifty-three percent of all adults” which is also suggested in the primary data collected. In the primary survey, all of the students surveyed had iPhone. This allowed them to access a greater use to their phones due to the improved capabilities smartphones provide, further entrenching them into the routine of everyday life. This is supported by the data collected which found that the three most common places to use phones, respectively, were on the commute, at home and while in bed.
Though it was not an option explored in the primary data, a study into the phone use of Taiwanese university students found that 24.2% also used their phone as an alarm clock. This continues to show the trend on how phones have become integrated into the everyday life of university students, such that they become a part of routine.
*1: https://www.researchgate.net/publication/275487066_Mobile_phone_distraction_while_studying
*2: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4291831/
Summary: Most students use their phones to access social media, and use them most frequently on the commute, suggesting an incorporation of phones into the everyday lifestyle.
3 References
Roberts, J., Yaya, L. and Manolis, C., 2014. The invisible addiction: Cell-phone activities and addiction among male and female college students. Journal of Behavioral Addictions, [online] 3(4), pp.254-265. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4291831/ [Accessed 15 March 2020].
David, P., Kim, J., Brickman, J., Ran, W. and Curtis, C., 2014. Mobile phone distraction while studying. New Media & Society, [online] 17(10), pp.1661-1679. Available at: https://journals.sagepub.com/doi/full/10.1177/1461444814531692?casa_token=RWgp3Qiv7N8AAAAA%3ATaeOvR0v8n-RRQLyL79juZ56ZlCawBu368OKe2MG9Kh1jyCb2FnNM52MJpL9unGwMngb1JOIgkFuniY& [Accessed 15 March 2020].