In this paper, I will be reviewing the topic of the relationship between loneliness and social networking addiction in students. According to past researchers (Karakose et al., 2016), the study of loneliness and social networking addiction is relevant because despite the prevalence of technologies that allow people to communicate online, the number of people who constantly experience feelings of loneliness continues to grow. Likewise, Shettar et al. (2017) stated that the growing popularity of Facebook was a relevant reason to study the relationship between these variables. Finally, Yu et al. (2017) explained that the topic of loneliness and social networking addiction is worthy of further investigation because there is an extended amount of evidence that younger people, especially students, are more vulnerable to social media addiction than other groups.
Shettar et al. (2017) defined loneliness as a factor that impacts Facebook and social networking addiction. Specifically, for Shettar et al., loneliness entails constraints that circumstances impose on an individual and force him to stay alone, despite his desire for shared experiences. That is, lonely people have no way to solve their problem, despite the desire to solve it. Regarding social networking addiction, Shettar al. (2017) conceptualized it in the following manner: social networking addiction is a factor predetermining loneliness. In other words, according to these authors, social networking addiction is both a reason for loneliness and its consequence. In their study examining loneliness and social networking addiction, Shettar et al. hypothesized that Facebook users’ patterns are associated with loneliness in a particular way. They conducted a study with 100 post-graduate students of Yenepoya University in India using the Bergen Facebook Addiction Scale (BFAS) and the University of California and Los Angeles (UCLA) loneliness scale to determine the relationships between the severity of Facebook addiction and loneliness. Shettar et al. (2017) found that their hypothesis was supported. They concluded that loneliness is a determining factor for the severity of Facebook addiction and that the two concepts have a bidirectional relationship.
Yu et al. (2017) stated that loneliness is a psychosocial health factor determining social networking addiction since social media enhances social interactions. Specifically, for Yu et al. (2017), loneliness entails the risk of addiction, including social media overuse. That is, loneliness is one of the main factors that lead to addictive behavior. With regards to social networking addiction, Yu et al. (2017) determined it as an unruly behavior implying dependence on the source of the desired activity. According to these authors, people develop an addiction to social networking the same way they develop an addiction to Internet use or other activities. In their study that explores the relationship between loneliness and social networking addiction, Yu et al. (2017) hypothesized that lower levels of optimism regarding the reduction of Internet use correlate with higher addictive tendencies. These researchers conducted a study with 395 Chinese students who completed an online questionnaire. Yu et al. (2017) found that their hypothesis was supported. They concluded that negative outcome expectancies, low self-efficacy, and loneliness directly and positively correlate with addiction, while low optimism had an indirect effect.
Karakose et al. (2016) determined loneliness as a widespread phenomenon experienced by many people. In their own words, loneliness is “an important psychological problem that more people suffer in this modern era although there are a lot of tools which enable them to become more sociable” (Karakose et al., 2016, p. 2420). Specifically, for Karakose et al. (2016), loneliness entails physical and mental exhaustion when it becomes chronic. With regards to social networking addiction, Karakose et al. (2016) have conceptualized it as most crucial for teenagers who are only learning to develop healthy face-to-face relationships. In other words, the authors state that social networking addiction pushes people into loneliness and is especially dangerous for teens since they are experiencing their personality development. In their study examining relationships between loneliness and Facebook addiction, Karakose et al. (2016) hypothesized that high school students have high Facebook addiction levels. These researchers conducted a study with 712 randomly selected high school students in Turkey using the Bergen Facebook Addiction Scale (BFAS) and the UCLA Loneliness Scale. Karakose et al. (2016) found that their hypothesis was not supported. They concluded that the study participants were not in the risk group, but there is still a need to develop centers where teenagers could spend their time and have face-to-face interactions.
The goal of the present study was to study the relationship between loneliness and social networking addiction in young people. That is, the type of study conducted was a quantitative study. The hypothesis of the study was as follows: the higher the level of loneliness, the higher the likelihood of social networking addiction; conversely, the lower the level of loneliness, the lower the likelihood of social networking addiction. One of the variables in the study was the level of loneliness. The other variable in the study was the level of social networking addiction.
Method
Participants
98 college students from Douglas College were recruited from the introductory psychology courses to participate in this study. Of the 98 (100%) participants, 69 (70.40%) were females, 29 (29.60%) were males, and 0 (0%) participants self-identified as Gender X. The mean age for participants was 21.16, and the standard deviation was 3.75, indicating that the sample was comprised primarily of young adults. The results of the study were used for a class assignment.
Measures
Three questionnaires were used to conduct this study: UCLA Loneliness Scale – Version 3 (UCLA-LS3; Russell, 1996); Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2016); and, Demographics Questionnaire (Gender [Male, Female, and Gender X] and Age). The UCLA Loneliness Scale – Version 3 (UCLA-LS3; Russell, 1996) was used to measure an individual’s level of loneliness. This tool consists of 20 statements, where 9 statements are positively worded (e.g., “How often do you feel that there are people you can talk to?”), And 11 statements are negatively worded (e.g., “How often do you feel that you are no longer close to anyone?”). Each statement was rated using a 4-point Likert-scale with anchors of 1 (Never) to 4 (Always). Scoring and interpretation were conducted as follows: the 9 positively worded items were reverse scored and then summed together with the 11 negatively worded items to yield a single UCLA-LS3 score. This score can range from 20 to 80, with higher scores suggesting more significant levels of loneliness and lower scores suggesting lower levels of loneliness. Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2016) was applied to measure the degree to which an individual might be addicted to social media. This tool consists of 6 statements (e.g., “How often during the last year have you used social media to forget about personal problems?”). Each statement was rated using a 5-point Likert-scale with anchors of 1 (Very Rarely) to 5 (Very Often). Scoring and interpretation were conducted as follows: all 6 statements were summed together to yield a single BSMAS score; this score can range from 6 to 30, with higher scores suggesting a greater likelihood of being addicted to social media (e.g., Facebook, Twitter, Instagram, and the like) and lower scores suggesting a lower likelihood of being addicted to social media (e.g., Facebook, Twitter, Instagram, and the like). Demographics Questionnaire (Gender and Age) was used to measure Gender (Male, Female, Gender X) and Age. This tool consists of 2 questions that assess a respondent’s gender and age.
Procedures
Several procedures were performed to conduct this study. Firstly, the instructions were presented to the students via Blackboard regarding how the study would be run. Specifically, the instructions stated that the study involved two distinct phases: data collection and debriefing. Secondly, students were asked to read the instructions; subsequently, they were asked to select the option “Yes” to indicate they understood the instructions or “No” to indicate they did not. Those students who selected “No” were allowed to re-read the instructions for clarification. The testing materials – the BSMAS (Andreassen et al., 2016), the UCLA-LS3 (Russell, 1996), and the Demographics Questionnaire – were then made available to the participants. After that, participants were asked to read the instructions for each questionnaire before responding. Then participants completed the three questionnaires. Upon completing the three questionnaires, participants were asked to read the Debriefing Workbook to understand the purpose of the study, the variables, and how they were measured.
Results
In this study, it was hypothesized that the higher the level of loneliness, the higher the likelihood of social networking addiction; conversely, the lower the level of loneliness, the lower the likelihood of social networking addiction. The mean for the UCLA-LS3 scale was 46.06, indicating a “moderate” level of loneliness among participants. The standard deviation for this scale was 11.57. The range of scores was 52. The lowest score was 24, and the highest score was 76. The mean for the BSMAS scale was 18.46, suggesting that participants had “moderate” to “moderate-to-high” levels of social networking addiction. The standard deviation for this scale was 4.55. The range of scores was 19. The lowest score was 9, and the highest score was 28. The correlation between the UCLA-LS3 scale and the BSMAS scale was r =.268. It means that there was a positive correlation between loneliness and social networking addiction. Specifically, results indicated that there is a small positive correlation between loneliness and social networking addiction.
Discussion
The results show there is a small positive correlation between loneliness and social networking addiction. Therefore, the hypothesis is supported by the results; the correlation of r =.268 is positive which means that there is a positive correlation between the level of loneliness and social networking addiction. The correlation of r =.268 is considered a small correlation. The sample mean of 46.06 for the UCLA-LS3 indicates that the participants have “moderate” levels of loneliness. The standard deviation of 11.57 presents a relatively high variability in the spread of scores. With the range of 52, the lowest score of 24, and the highest score of 76, the participants scored at both ends of the UCLA-LS3.
The sample mean of 18.46 for BSMAS indicates that participants have “moderate” to “moderate-to-high” levels of social networking addiction. There is a small variability in answers according to the standard deviation of 4.55, but it is lower than in UCLA-LS3. With the range of scores of 19, the lowest score of 9, and the highest score of 28, participants scored at both ends of the BSMAS.
These results are theoretically important since they prove the direct relationship between loneliness and social networking addiction. Other studies (Karakose et al., 2016; Shettar et al., 2017; Yu et al., 2017) studying the relationship between loneliness and social networking addiction in young people show similar results. Therefore, other studies support the initial hypothesis of the research and add to evidence showing a positive correlation between these two variables.
The study faced some limitations, such as the limited number of participants. If the number of participants were higher, there would be more space for generalization. Studying only the younger adults is another limitation since social networking addiction and loneliness are acute problems for all human beings. The strength of the study is that it shows that there is no necessarily should be a high positive correlation between loneliness and social networking addiction. Therefore, these two variables can be studied independently, and the solutions can be found independently as well.
The implications of this study are that it gives more information on the psychological problems of younger people and shows that the issue of social networking addiction is more acute among teenagers and young adults than the problem of loneliness. However, since Karakose et al. (2016) mention that addiction may lead to loneliness, the addiction issue needs to be addressed urgently. After completing this study, it was concluded that loneliness and social networking addiction are two separate problems that teens face daily. It was also concluded that even though these problems reinforce each other, both independent and common solutions could be found to reduce the adverse impact of loneliness and social networking addiction.
References
Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). Bergen Social Media Addiction Scale (BSMAS) [Database record]. PsycTESTS. Web.
Karakose, T., Yirci, R., Uygun, H., & Ozdemir, T. Y. (2016). Relationship between high school students’ Facebook addiction and loneliness status. Eurasia Journal of Mathematics, Science & Technology Education, 12(9), 2419-2429. Web.
Russell, D. W. (1996). UCLA Loneliness scale (Version 3): Reliability, validity, and factor structure. Journal of Personality Assessment, 66(1), 20–40. Web.
Shettar, M., Karkal, R., Kakunge, A., Mendonsa, R. D., & Chandran, V. V. M. (2017). Facebook addiction and loneliness in the post-graduate students of a university in southern India. International Journal of Social Psychiatry, 63(4), 325-329. Web.
Yu, S., Wu, A. M. S., & Pesigan, I. J. A. (2016). Cognitive and psychosocial health risk factors of social networking addiction. International Journal of Mental Health and Addiction, 14(4), 550-564. Web.