The coronavirus, COVID-19, is an airborne virus that has caused government officials to demand that people stay inside. Being locked inside may lead to increased feelings of loneliness, but the use of technology may lessen isolation’s effects. As a result, many high schoolers have been forced to go from in-person schooling to virtual schooling. This transition causes high school students to lose upwards of 7 hours (the duration of a high school day) of face to face social interaction daily. The use of collaborative technology may be able to lessen the amount of loneliness that students are feeling. Collaborative technology in this case is students using technology to interact with other people online. In this study, student participants were sent a 55 question survey and the University of California, Los Angeles (UCLA) loneliness scale to complete anonymously. Results demonstrated that technology use did not significantly decrease how lonely someone is. This research is important because although technology may help to distract high schoolers, it does not lessen their loneliness. Even though this was the result, there were many issues with the process and survey. The findings suggest that other studies need to be conducted on specific types of technology use and on other age ranges. Moreover, conducting studies over a period of time and testing people’s different levels of loneliness during the pandemic and how they feel after a return to normal would also be beneficial.
Recently, the coronavirus (COVID-19) pandemic has caused lockdowns across the globe. These lockdowns socially isolate people and have changed the way people live. This forced change in behavior takes a toll on people mentally because of their lack of in-person interaction. However, unlike other epidemics or outbreaks, during COVID-19, people have had access to technology that may help reduce the psychological effects of a pandemic such as this one. Most teenagers have been forced to make the change from in-person to online school. While this may seem insignificant, students end up losing the opportunity to socialise in person for the entire school day. This lack of in-person socialisation can affect students’ development and lead to feelings of isolation. It is important to understand how a lack of in-person socialisation can impact a student\’s mental health because if the impact is known it is easier to help students.
There are multiple forms of remote communication: calling, texting, and social media, to name a few. With current technology, one can be connected with others 24 hours a day, 7 days a week. It was found from a survey in 2018 that 95% of teenagers in the US report using smartphones, and 45% of teenagers say that they are online “constantly”. Extended technology use may have negative physical and mental side effects, but Moore and March (2020) found that during COVID-19, staying connected with others is healthy. Being connected is beneficial because it is human nature to interact with one another, whether through technology or not. Due to people’s need for contact with one another, it is important to determine the effectiveness of technology use versus the effectiveness of in-person interactions when it comes to reducing loneliness. Although virtual interaction is important, Burholt(2020) found that nothing truly compares to face to face contact. The only forms of electronic communication that are close to in-person interaction in terms of reducing loneliness are texting and calling, but only when it is within a familial relationship. Thus, the utilisation of technology and communication online can help alleviate loneliness but cannot fully prevent it.
Loneliness and anxiety are often grouped together as emotions that affect how people act. The UCLA loneliness scale has been used since 1980 to determine a base reading of someone’s loneliness (see figure A2 in the appendix). Rosen found that 44% of people in their survey experienced anxiety about not checking their phone enough for texts. Social media, just like texts, play an important role in online communication. There was a 35% correlation between social network use (e.g. Facebook, Instagram, and Snapchat) and anxiety and dependence. Social media plays an important role in people’s lives for the worse or for the better. Wilson (2017) found that the more one uses an electronic device, the more one is attached to it. Although technology can help lessen loneliness, there are multiple links between anxiety and technology. Some other studies have investigated whether collaborative electronic use is more harmful psychologically than it is beneficial.
Materials and Methods
Google sheets Google docs
Participants: ages 14-18, high school students
Questions from The Media and Technology Usage and Attitudes Scale: An empirical investigation Survey sent to the students asking about loneliness and technology use
Human informed consent form for the survey
University of California, Los Angeles (UCLA) Loneliness scale
The purpose of this project was to determine whether collaborative electronic device use decreases loneliness. It was hypothesised that if students, ages 14-18, spent more time with each other online, then they would feel less lonely because collaborative time online may replace some of that interaction that was once in-person. A human consent form was sent to all students at Spring Valley High School. After a two week period, students who agreed to participate were sent a 55 question survey and the UCLA loneliness scale to complete(see figure A2 in the appendix). All of the questions on the survey were from Questions from The Media and Technology Usage and Attitudes Scale: An empirical investigation. The questions were shortened and the wording was changed for the actual form to make them easier to understand and read. Responses were anonymous due to the fact that the email addresses of the participants were not recorded, and the human informed consent form was separate ensuring no way to track the replies. This was crucial to achieve full anonymity of the students who answered the form. The reason full anonymity was needed was due to the fact that anonymity helps remove bias and protect the people who took the survey.
There were 4 sections of the survey. The first was general information, which had 3 questions. The second section was the amount of time spent on electronics that had 38 questions with 10 answer options (never, once a month, several times a month, once a week, several times a week, once a day, several times a day, once an hour, several times an hour, and all the time). The third section was 12 Likert statements on a 5 point scale: strongly disagree, disagree, neither agree nor disagree, agree, and strongly agree (see figure A1 in the appendix). The final section was the UCLA loneliness test[1 ](see figure A2 in the appendix). The UCLA loneliness test has 20 questions that relate to and determine the average loneliness of the test taker. After providing another two weeks for responses, the survey was closed and the loneliness and electronic use scores were calculated by assigning number values to the questions’ answers. The Likert scale questions were scored 1-5, the questions on time spent were scored 1-10, and the UCLA loneliness scale questions were scored 1-4 with items 1, 5, 6, 9, 10, 15, 16, 19, and 20 having reverse scoring. The reversed scoring questions were written, so a higher number is better instead of a lower number. It is a specific way of reducing bias. A z-test, which tests the mean of a distribution, and a linear regression test, which demonstrates the relationship between variables with a straight line, were done to analyse the significance of the data.
Figure 1: Experimental Design Diagram
If students who use electronics to interact with others online, then their UCLA loneliness scores would be lower than those who use electronics by themselves because they will be getting some form of human interaction.
Amount of time participants used electronics in general and to communicate with others.
The loneliness of the participants, (based on the UCLA Loneliness Scale)(see figure A2 in the appendix)
Age of participants: 14-18 High school students
Figure 1 (above) is a diagram that aims to simplify and convey what the experiment is.
All of the following were calculated from the raw data table, which can be found in Appendix 3. The possible loneliness scores could range anywhere from 20 to 80, and the technology uses scores anywhere from 38 to 380. Although these were the highest and lowest possible scores, in our research the maximum and minimum for loneliness were 70.0 and 25.0 respectively (Figure 2). The maximum and minimum for technology use were 335 and 108 respectively (Figure 3). This means that the range for the loneliness scale in the research was 45, and the range for technology use was 227. The standard deviation of loneliness scale was 10.5 and its average was 41.4. The median for technology use and the loneliness scale were both closer to the minimums than the maximums. Knowing these two values, standard deviation and mean, leads to better understanding of the data as a whole. Mean is calculated by adding up all the scores and dividing them by how many scores there are. Standard deviation is calculated by squaring the difference between each value and the mean, then adding all those numbers up, dividing by the number of responses, and taking the square root of all of that. An r value is a number that shows the strength of a correlation between two events. In this experiment, the r value of r = 0.0942 was found using a linear regression test. After that, the r value was then used to give the t value of t = 0.824885. The t-value was used to find the critical value. With the critical value being 2.048, an alpha value of α = 0.05, and the t value of t = 0.824885, the p-value of p = 0.412016 was found. The null hypothesis, H0: ꞵ1 = 0 means that there was no significant correlation between technology use and loneliness. There was insufficient evidence to reject the null hypothesis.
Figure 2: Loneliness Boxplot
|Only electronics during school||1-7 hours every week||7-21 hours every week||21-42 hours every week|
Figure 2 (above) is a visual representation to aid in the understanding of the loneliness scores and their ranges.
Figure 3: Technology Use Boxplot
Figure 3 (above) is a visual representation of the technology use scores and their ranges.
Figure 4: The Effect of Technology Use on Loneliness
Figure 4 (above) displays all of the data points as well as a line of best fit for the scatterplot.
The purpose of this project was to determine whether collaborative electronic device use decreases loneliness. There was a gap between the possible electronic use minimum of 38 and the
actual electronic use minimum of 108 (Figures 2&3). The lowest technology score showed that even the student who used technology the least still spent a considerable amount of time using it. The null hypothesis being rejected means that technology use and loneliness are not correlated to one another. Figures 2 and 3 help to visualise the ranges and medians of the data. Figure 4 shows that the data showed little correlation between the tested variables.
The lack of a significant relationship between technology use and loneliness showed that a student’s loneliness may not be affected by online interaction. Burholt found that the only electronic use that serves as a real substitute for person to person interaction was texting and calling, but only in familial relationships. In reality, a majority of the people that students would have familial relationships with would only be the people they are currently living with. If they are already living with those people, then there is no lack of face to face interaction with them. Since no other type of electronic use decreased loneliness in Burholt’s study, students would be left feeling lonely and missing out on meaningful interaction with others. Khosravi found that while technology use reduces loneliness, it does not compare to face to face interaction. These studies were different from the findings of this research project because in this research project there was little variation in loneliness, which could have easily been due to differences in the social and home lives of the students. In general, Kotwal found that people ages 65 and older have a hard time adjusting to life during a pandemic. Elderly people are not as familiar with technology and may feel outcast in their community due to this lack of knowledge. Kotwal suggests that forcing senior citizens to use communication methods that are unfamiliar may result in them feeling isolated and alone. Moreover, Noone found that older people’s loneliness is not affected by video calls, supporting the lack of correlation between technology use and loneliness found in high school participants. Sarwar Shah found that while people during the pandemic experience
increased loneliness, not everyone realizes it yet because of the unique situation in which everyone is currently living in. The reasoning behind loneliness not affecting everyone yet is that people have distracted themselves with technology and other pursuits to prevent themselves from feeling lonely.
There were multiple sources of error. 36 of the 78 students who completed the survey stated that they were in hybrid schooling. Hybrid schooling is when students are in person for two days and then are online for the other three school days in the week. While the majority of students were all online, 46% of them were hybrid. This may have been a source of error and skewed the results because students who were in hybrid had time in-person with others. The students who were all online may have lacked some of the face-to-face interactions that students who did hybrid schooling may have gotten.
The research could have been improved with larger sample sizes and consistent learning environments. Including adults in this study would also have been beneficial. Knowing participants’ loneliness scores before the pandemic would have been helpful and allowed more accuracy in the quantification of the change in loneliness during COVID-19. An assumption was made that all of the students answered honestly and took their time, which may not have been correct. A procedural improvement would be connecting the consent form concurrently with the actual survey as a significant number of responses were lost transitioning between the two forms.
For future studies on the relationship between technology use and loneliness more factors should be considered, such as a student’s background, grades, exact course rigor, physical activities, and in-person social interactions outside of school. This survey could also be sent out to different schools or different ages to garner more data and allow more comparisons.
The study attempted to determine if there is a relationship between collaborative technology use and loneliness of high school students. It was found that there is no significant relationship between the two. Although this may have happened, the technology use questions did not properly address collaborative technology use. They did not properly address collaborative technology use because the questions were built for general technology use and were not modified correctly to address specifically collaborative technology use. This means that the research did not fulfill the objective and there should be more research on the topic in order to truly determine whether there is a correlation or not. It is important for people to understand whether an increase in loneliness occurred during the COVID-19 pandemic for high school students because then it is easier for people to help them or search for solutions.
The author would like to thank his parents for giving up their time with their child in order for the child to complete research work, their siblings for reviewing their paper, and their research teachers, Dr. Michelle Wyatt and Lindsey Rega, for spending their own time helping them to understand, complete, and analyse their research.
Russell, D., Peplau, L.A., & Cutrona, C.E. (1980). The revised UCLA loneliness scale: Concurrent and discriminant validity evidence. Journal of Personality and Social Psychology, 39, 472-480.
Saltzman, Leia Y., Tonya Cross Hansel, and Patrick S. Bordnick. \”Loneliness, Isolation, and Social Support Factors in Post-COVID-19 Mental Health.\” Psychological Trauma: Theory, Research, Practice, and Policy 12, no. S1 (2020): S55-S57. http://doi.org/10.1037/tra0000703.
Leiberman, Alicea, and Juliana Schroeder. \”Two Social Lives: How Differences between Online and Offline Interaction Influence Social Outcomes.\” Berkeley University of California. Last modified February 1, 2020. https://escholarship.org/uc/item/94n9w8b9.
Moore, Kathleen Anne, and Evita March. \”Socially Connected during COVID-19: Online Social Connections Mediate the Relationship between Loneliness and Positive Coping Strategies.\” Research Square. Last modified June 22, 2020. https://www.researchsquare.com/article/rs-35835/v1.
Burholt, Vanessa, Gill Windle, Merryn Gott, and Deborah Jane Morgan. \”Technology-Mediated Communication in Familial Relationships: Moderated-Mediation Models of Isolation and Loneliness.\” The Gerontologist 60, no. 7 (2020): 1202-12. https://doi.org/10.1093/geront/gnaa040.
Rosen, L. D., K. Whaling, L. M. Carrier, N. A. Cheever, and J. Rokkum. \”The Media and Technology Usage and Attitudes Scale: An Empirical Investigation.\” Comput Human Behavior 29, no. 6 (2013): 2501-11. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4338964/.
Wilson, Carolyn. \”Is It Love or Loneliness? Exploring the Impact of Everyday Digital Technology Use on the Wellbeing of Older Adults.\” Ageing and Society 38, no. 7 (2017): 1307-31. https://doi.org/10.1017/S0144686X16001537.
Khosravi, Pouria, Azadeh Rezvani, and Anna Wiewiora. \”The Impact of Technology on Older Adults\’ Social Isolation.\” Computers in Human Behavior 63 (2016): 594-603. https://doi.org/10.1016/j.chb.2016.05.092.
Kotwal, Ashwin A., Julianne Holt-Lunstad, Rebecca L. Newmark, Irena Cenzer, Alexander K. Smith, and Kenneth E. Covinsky. \”Social Isolation and Loneliness among San Francisco Bay Area Older Adults during the COVID‐19 Shelter‐in‐place Orders.\” Journal of the American Geriatrics Society, 2020. https://doi.org/10.1111/jgs.16865.
Noone, Chris, Jenny McSharry, Mike Smalle, Annette Burns, Kerry Dwan, Declan Devane, and Eimear C. Morrissey. \”Video Calls for Reducing Social Isolation and Loneliness in Older People: A Rapid Review.\” Cochrane Database of Systematic Reviews, 2020. https://doi.org/10.1002/14651858.CD013632.
Sarwar Shah, Syed Ghulam, David Nogueras, Hugo Cornelis Van Woerden, and Vasiliki Kiparoglou. \”The COVID-19 Pandemic: A Pandemic of Lockdown Loneliness and the Role of Digital Technology.\” Journal of Medical Internet Research. https://www.jmir.org/2020/11/e22287.
Figure A1: SurveyQuestions
What grade are you in? (freshman, sophomore, junior, senior) What level of classes do you normally take? (AP, Honors, CP) Are you currently doing hybrid? (yes/no)
Indicate below how often you do these activities. (never, once a month, several times a month, once a week, several times a week, once a day, several times a day, once an hour, several times an hour, and all the time)
Send, receive, and read e-mails (not including spam or junk e-mails). Check your personal e-mail.
Check your school e-mail.
Send or receive files using e-mail. Watch TV shows, movies, etc. on a TV. Watch video clips on a TV.
Watch TV shows, movies, etc. on a computer. Watch video clips on a computer.
Download files from other people using computer. Share your own files using computer.
Search the Internet for news. Search the Internet for information. Search the Internet for videos.
Search the Internet for images or photos.
Play games on a computer, video game console or smartphone BY YOURSELF.
Play games on a computer, video game console or smartphone WITH OTHER PEOPLE IN THE SAME ROOM.
Play games on a computer, video game console or smartphone WITH OTHER PEOPLE ONLINE.
Check social media.
Check social media from your phone. Check social media during school or work. Post photos or anything else.
Browse profiles and photots. Read posts.
Comment on photos or posts. \”Like\” photos or posts.
Indicate below how often you do these activities on your phone (if you don\’t have a phone, answer never for all of these). (never, once a month, several times a month,
once a week, several times a week, once a day, several times a day, once an hour, several times an hour, and all the time)
Send and receive text messages on your phone.
Make and receive phone calls on your phone(not including scam calls). Check for voice mails on your phone.
Read e-mails on your phone.
Get directions or use GPS on your phone.
Browse the internet on your phone. Listen to music on your phone.
Take pictures with your phone Check the news on your phone. Record videos on your phone. Use apps on your phone.
Search for information on your phone. Use your phone during class or work time.
Likert Scale Questions (strongly disagree, disagree, neither agree nor disagree, agree, strongly agree)
I feel it is important to be able to find any information whenever I want online. I feel it is important to be able to access the Internet any time I want.
I think it is important to keep up with the latest trends in technology. I get anxious when I don’t have my cell phone.
I get anxious when I don’t have the Internet available to me. I am dependent on my technology.
Technology will provide solutions to many of our problems. With technology anything is possible.
I feel that I get more accomplished because of technology. New technology makes people waste too much time.
New technology makes life more complicated. New technology makes people more isolated.
Technology helps people to interact.
It is beneficial to interact with others online.
The questions sent to students (above) were geared towards technology usage and provided all of the technology use data (A3).
Figure A2: UCLA Loneliness Scale
The UCLA Loneliness Scale (above) is a common and widely used scale to judge a person’s loneliness. Its questions were on the end of the survey sent out to students.
Figure A3: Raw Data Table
The full raw data table will be linked below. It was too lengthy to place it in the appendix in a way that it could be understood and read properly. Raw Data Table