The Impact of Mental Health Predictors of Internet Addiction among Pre-Service Teachers in Ghana
Abstract
:1. Introduction
- What is the prevalence of Internet addiction among pre-service teachers in Ghana?
- Is there any nexus between self-esteem, depression, loneliness, life satisfaction, and Internet addiction of pre-service teachers in Ghana?
- What percentage of variance is explained by the mental health factors in Internet addiction?
2. Materials and Methods
2.1. Design, Participants, and Procedure
2.2. Measures
2.2.1. Internet Addiction (IA)
2.2.2. Life Satisfaction (LS)
2.2.3. Self-Esteem (SE)
2.2.4. Depression (BDI-6)
2.2.5. Loneliness Scale (UCLA LS)
2.2.6. Social Desirability (SD)
2.3. Data Analysis Plan
2.4. Ethics
3. Results
3.1. Prevalence of Internet Addiction (IA)
3.2. Comparing Differences in Sociodemographic Variables and s-IAT, LS, BDI-6, UCLA LS, and SES
3.3. Correlation Analysis
3.4. Simultaneous Multiple Regression Analysis
4. Discussion
5. Conclusions and Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Turan, N.; Durgun, H.; Kaya, H.; Aştı, T.; Yilmaz, Y.; Gündüz, G.; Kuvan, D.; Ertaş, G. Relationship between nursing students’ levels of internet addiction, loneliness, and life satisfaction. Perspect. Psychiatr. Care 2020, 56, 598–604. [Google Scholar] [CrossRef] [PubMed]
- Zeng, G.; Zhang, L.; Fung, S.F.; Li, J.; Liu, Y.M.; Xiong, Z.K.; Jiang, Z.Q.; Zhu, F.F.; Chen, Z.T.; Lu, S.D.; et al. Problematic internet usage and self-esteem in Chinese undergraduate students: The mediation effects of individual affect and relationship satisfaction. Int. J. Environ. Res. Public Health 2021, 18, 6949. [Google Scholar] [CrossRef] [PubMed]
- Pereira, H.; Fehér, G.; Tibold, A.; Esgalhado, G.; Costa, V.; Monteiro, S. The Impact of Internet Addiction and Job Satisfaction on Mental Health Symptoms among a Sample of Portuguese Workers. Int. J. Environ. Res. Public Health 2021, 18, 6943. [Google Scholar] [CrossRef] [PubMed]
- Zhang, S.; Su, W.; Han, X.; Potenza, M.N. Rich Get Richer: Extraversion Statistically Predicts Reduced Internet Addiction through Less Online Anonymity Preference and Extraversion Compensation. Behav. Sci. 2022, 12, 193. [Google Scholar] [CrossRef] [PubMed]
- Miskulin, I.; Simic, I.; Pavlovic, N.; Kovacevic, J.; Fotez, I.; Kondza, G.; Palenkic, H.; Bilic-Kirin, V.; Kristic, M.; Miskulin, M. Personality Traits of Croatian University Students with Internet Addiction. Behav. Sci. 2022, 12, 173. [Google Scholar] [CrossRef]
- Alheneidi, H.; AlSumait, L.; AlSumait, D.; Smith, A.P. Loneliness and Problematic Internet Use during COVID-19 Lock-Down. Behav. Sci. 2021, 11, 5. [Google Scholar] [CrossRef]
- Mamun, M.A.; Rafi, M.A.; Al Mamun, A.H.M.S.; Hasan, M.Z.; Akter, K.; Hsan, K.; Griffiths, M. Prevalence and Psychiatric Risk Factors of Excessive Internet Use among Northern Bangladeshi Job-Seeking Graduate Students: A Pilot Study. Int. J. Ment. Health Addict. 2021, 19, 908–918. [Google Scholar] [CrossRef] [Green Version]
- Simcharoen, S.; Pinyopornpanish, M.; Haoprom, P.; Kuntawong, P.; Wongpakaran, N.; Wongpakaran, T. Prevalence, associated factors and impact of loneliness and interpersonal problems on internet addiction: A study in Chiang Mai medical students. Asian J. Psychiatr. 2018, 31, 2–7. [Google Scholar] [CrossRef]
- Lin, S.; Liu, D.; Niu, G.; Longobardi, C. Active Social Network Sites Use and Loneliness: The Mediating Role of Social Support and Self-Esteem. Curr. Psychol. 2022, 41, 1279–1286. [Google Scholar] [CrossRef]
- Cheung, J.C.S.; Chan, K.H.W.; Lui, Y.W.; Tsui, M.S.; Chan, C. Psychological well-being and adolescents’ internet addiction: A school-based cross-sectional study in Hong Kong. Child Adolesc. Soc. Work. J. 2018, 35, 477–487. [Google Scholar] [CrossRef]
- Cerniglia, L.; Guicciardi, M.; Sinatra, M.; Monacis, L.; Simonelli, A.; Cimino, S. The Use of Digital Technologies, Impulsivity and Psychopathological Symptoms in Adolescence. Behav. Sci. 2019, 9, 82. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Northrup, J.C.; Lapierre, C.; Kirk, J.; Rae, C. The Internet Process Addiction Test: Screening for Addictions to Processes Facilitated by the Internet. Behav. Sci. 2015, 5, 341–352. [Google Scholar] [CrossRef] [PubMed]
- Kuss, D.J.; Griffiths, M.D.; Binder, J.F. Internet addiction in students: Prevalence and risk factors. Comput. Hum. Behav. 2013, 29, 959–966. [Google Scholar] [CrossRef] [Green Version]
- Younes, F.; Halawi, G.; Jabbour, H.; El Osta, N.; Karam, L.; Hajj, A.; Khabbaz, L.R. Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: A cross-sectional designed study. PLoS ONE 2016, 11, e0161126. [Google Scholar] [CrossRef] [Green Version]
- Islam, M.A.; Hossin, M.Z. Prevalence and risk factors of problematic internet use and the associated psychological distress among graduate students of Bangladesh. Asian J. Gambl. Issues Public Health 2016, 6, 11. [Google Scholar] [CrossRef] [Green Version]
- Uddin, M.; Mamun, A.; Iqbal, M.; Nasrullah, M.; Asaduzzaman, M.; Sarwar, M.; Amran, M. Internet Addiction Disorder and Its Pathogenicity to Psychological Distress and Depression among University Students: A Cross-Sectional Pilot Study in Bangladesh. Psychology 2016, 7, 1126–1137. [Google Scholar] [CrossRef] [Green Version]
- Bozoglan, B.; Demirer, V.; Sahin, I. Loneliness, self-esteem, and life satisfaction as predictors of internet addiction: A cross-sectional study among Turkish university students. Scand. J. Psychol. 2013, 54, 313–319. [Google Scholar] [CrossRef]
- Marzilli, E.; Cerniglia, L.; Cimino, S.; Tambelli, R. Internet Addiction among Young Adult University Students during the COVID-19 Pandemic: The Role of Peritraumatic Distress, Attachment, and Alexithymia. Int. J. Environ. Res. Public Health 2022, 19, 15582. [Google Scholar] [CrossRef]
- Pan, L.; Li, J.; Hu, Z.; Wu, H. The Effect of COVID-19 Perceived Risk on Internet Addiction among College Students in China: An Empirical Study Based on the Structural Equation Model. Int. J. Environ. Res. Public Health 2022, 19, 13377. [Google Scholar] [CrossRef]
- Liu, W.; Chen, J.-S.; Gan, W.Y.; Poon, W.C.; Tung, S.E.H.; Lee, L.J.; Xu, P.; Chen, I.-H.; Griffiths, M.D.; Lin, C.-Y. Associations of Problematic Internet Use, Weight-Related Self-Stigma, and Nomophobia with Physical Activity: Findings from Mainland China, Taiwan, and Malaysia. Int. J. Environ. Res. Public Health 2022, 19, 12135. [Google Scholar] [CrossRef]
- Khazaal, Y.; El Abiddine, F.Z.; Penzenstadler, L.; Berbiche, D.; Bteich, G.; Valizadeh-Haghi, S.; Rochat, L.; Achab, S.; Khan, R.; Chatton, A. Evaluation of the Psychometric Properties of the Arab Compulsive Internet Use Scale (CIUS) by Item Response Theory Modeling (IRT). Int. J. Environ. Res. Public Health 2022, 19, 12099. [Google Scholar] [CrossRef] [PubMed]
- Khatcherian, E.; Zullino, D.; De Leo, D.; Achab, S. Feelings of Loneliness: Understanding the Risk of Suicidal Ideation in Adolescents with Internet Addiction. A Theoretical Model to Answer to a Systematic Literature Review, without Results. Int. J. Environ. Res. Public Health 2022, 19, 2012. [Google Scholar] [CrossRef] [PubMed]
- Pohl, M.; Feher, G.; Kapus, K.; Feher, A.; Nagy, G.D.; Kiss, J.; Fejes, É.; Horvath, L.; Tibold, A. The Association of Internet Addiction with Burnout, Depression, Insomnia, and Quality of Life among Hungarian High School Teachers. Int. J. Environ. Res. Public Health 2022, 19, 438. [Google Scholar] [CrossRef]
- Zalewska, A.; Gałczyk, M.; Sobolewski, M.; Białokoz-Kalinowska, I. Depression as Compared to Level of Physical Activity and Internet Addiction among Polish Physiotherapy Students during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2021, 18, 10072. [Google Scholar] [CrossRef] [PubMed]
- Laconi, S.; Kaliszewska-Czeremska, K.; Gnisci, A.; Sergi, I.; Barke, A.; Jeromin, F.; Growth, J.; Gamez-Guadix, M.; Ozcan, N.K.; Demetrovics, Z. Cross-cultural study of problematic internet use in nine European countries. Comput. Hum. Behav. 2018, 84, 430–440. [Google Scholar] [CrossRef] [Green Version]
- Canan, F.; Ataoglu, A.; Ozcetin, A.; Icmeli, C. The association between Internet addiction and dissociation among Turkish college students. Compr. Psychiatry 2018, 53, 422–426. [Google Scholar] [CrossRef]
- Young, K. Internet addiction: Diagnosis and treatment considerations. J. Contemp. Psychother. 2009, 39, 241–246. [Google Scholar] [CrossRef]
- Kandell, J.J. Internet addiction on campus: The vulnerability of college students. CyberPsychol. Behav. 1998, 1, 11–17. [Google Scholar] [CrossRef]
- Davis, R.I. A cognitive-behavioral model of pathological Internet use. Comput. Hum. Bahavior 2001, 2, 187–195. [Google Scholar] [CrossRef]
- Pratarelli, M.; Browne, B.; Johnson, K. The bits and bytes of computer/Internet addictions: A factor analytic approach. Behav. Res. Methods 1999, 31, 305–314. [Google Scholar] [CrossRef]
- Lee, J.; Hwang, J.Y.; Park, S.M.; Jung, H.Y.; Choi, S.W.; Lee, J.Y.; Choi, J.S. Differential resting-state EEG patterns associated with comorbid depression in Internet addiction. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2014, 50, 21–26. [Google Scholar] [CrossRef] [PubMed]
- Orsal, O.; Orsal, O.; Unsal, A.; Ozalp, S.S. Evaluation of internet addiction and depression among university students. Procedia-Soc. Behav. Sci. 2013, 82, 445–454. [Google Scholar] [CrossRef] [Green Version]
- Seki, T.; Hamazaki, K.; Natori, T.; Inadera, H. Relationship between internet addiction and depression among Japanese university students. J. Affect. Disord. 2019, 256, 668–672. [Google Scholar] [CrossRef] [PubMed]
- Hardie, E.; Tee, M.Y. Excessive Internet use: The role of personality, loneliness and social support networks in Internet addiction. Aust. J. Emerg. Technol. Soc. 2007, 5, 34–47. [Google Scholar]
- Sechi, C.; Loi, G.; Cabras, C. Addictive internet behaviors: The role of trait emotional intelligence, self-esteem, age, and gender. Scand. J. Psychol. 2021, 62, 409–417. [Google Scholar] [CrossRef]
- Gentile, B.; Grabe, S.; Dolan-Pascoe, B.; Twenge, J.M.; Wells, B.E.; Maitino, A. Gender differences in domain-specific self-esteem: A meta-analysis. Rev. Gen. Psychol. 2018, 13, 34–45. [Google Scholar] [CrossRef] [Green Version]
- Yang, S.C.; Tung, C.J. Comparison of Internet addicts and non-addicts in Taiwanese high school. Comput. Hum. Behav. 2007, 23, 79–96. [Google Scholar] [CrossRef]
- Kim, H.K.; Davis, K.E. Toward a comprehensive theory of problematic Internet use: Evaluating the role of self-esteem, anxiety, flow, and the self-rated importance of Internet activities. Comput. Hum. Behav. 2009, 25, 490–500. [Google Scholar] [CrossRef]
- Seabra, L.; Loureiro, M.; Pereira, H.; Monteiro, S.; Marina Afonso, R.; Esgalhado, G. Relationship between Internet addiction and self-esteem: Cross-cultural study in Portugal and Brazil. Interact. Comput. 2017, 29, 767–778. [Google Scholar] [CrossRef] [Green Version]
- Gao, F.; Guo, Z.; Tian, Y.; Si, Y.; Wang, P. Relationship between shyness and generalized pathological internet use among Chinese school students: The serial mediating roles of loneliness, depression, and self-esteem. Front. Psychol. 2018, 9, 1822. [Google Scholar] [CrossRef]
- Senol-Durak, E.; Durak, M. The mediator roles of life satisfaction and self-esteem between the affective components of psychological well-being and the cognitive symptoms of problematic Internet use. Soc. Indic. Res. 2011, 103, 23–32. [Google Scholar] [CrossRef]
- Essel, H.B.; Vlachopoulos, D.; Adom, D.; Tachie-Menson, A. Transforming higher education in Ghana in times of disruption: Flexible learning in rural communities with high latency internet connectivity. J. Enterprising Communities People Places Glob. Econ. 2021, 15, 296–312. [Google Scholar] [CrossRef]
- Essel, H.B.; Vlachopoulos, D.; Tachie-Menson, A.; Nunoo, F.K.N.; Johnson, E.E. Nomophobia among Preservice Teachers: A descriptive correlational study at Ghanaian Colleges of Education. Educ. Inf. Technol. 2022, 27, 9541–9561. [Google Scholar] [CrossRef] [PubMed]
- Essel, H.B.; Vlachopoulos, D.; Tachie-Menson, A.; Johnson, E.E.; Ebeheakey, A.K. Technology-Induced Stress, Sociodemographic Factors, and Association with Academic Achievement and Productivity in Ghanaian Higher Education during the COVID-19 Pandemic. Information 2021, 12, 497. [Google Scholar] [CrossRef]
- Buabeng-Andoh, C.; Issifu, Y. Implementation of ICT in learning: A study of students in Ghanaian secondary schools. Procedia-Soc. Behav. Sci. 2015, 191, 1282–1287. [Google Scholar] [CrossRef] [Green Version]
- Total Number of Internet Users in Ghana from 2017 to 2022. Available online: https://www.statista.com/statistics/1171416/number-of-internet-users-ghana/#:~:text=As%20of%20January%202022%2C%20Ghana,in%20the%20West%20African%20country (accessed on 28 November 2022).
- Muche, H.; Asrese, K. Prevalence of Internet Addiction and Associated Factors Among Students in an Ethiopian University: A Cross-Sectional Study. J. Soc. Work Pract. Addict. 2022, 22, 143–159. [Google Scholar] [CrossRef]
- Zenebe, Y.; Kunno, K.; Mekonnen, M.; Bewuket, A.; Birkie, M.; Necho, M.; Seid, M.; Tsegaw, M.; Akele, B. Prevalence and associated factors of internet addiction among undergraduate university students in Ethiopia: A community university-based cross-sectional study. BMC Psychol. 2021, 9, 1–10. [Google Scholar] [CrossRef]
- Shehata, W.M.; Abdeldaim, D.E. Internet addiction among medical and non-medical students during COVID-19 pandemic, Tanta University, Egypt. Environ. Sci. Pollut. Res. 2021, 28, 59945–59952. [Google Scholar] [CrossRef]
- Endomba, F.T.; Demina, A.; Meille, V.; Ndoadoumgue, A.L.; Danwang, C.; Petit, B.; Trojak, B. Prevalence of internet addiction in Africa: A systematic review and meta-analysis. J. Behav. Addict. 2022, 11, 739–753. [Google Scholar] [CrossRef]
- Pawlikowski, M.; Altstötter-Gleich, C.; Brand, M. Validation and psychometric properties of a short version of Young’s Internet Addiction Test. Comput. Hum. Behav. 2013, 29, 1212–1223. [Google Scholar] [CrossRef]
- Adarkwah, M.A. “I’m not against online teaching, but what about us?”: ICT in Ghana post COVID-19. Educ. Inf. Technol. 2021, 26, 1665–1685. [Google Scholar] [CrossRef] [PubMed]
- Agyei, D.D.; Voogt, J.M. Exploring the potential of the will, skill, tool model in Ghana: Predicting prospective and practicing teachers’ use of technology. Comput. Educ. 2011, 56, 91–100. [Google Scholar] [CrossRef]
- Kwaah, C.Y.; Adu-Yeboah, C.; Amuah, E.; Essilfie, G.; Somuah, B.A. Exploring preservice teachers’ digital skills, stress, and coping strategies during online lessons amid COVID-19 pandemic in Ghana. Cogent Educ. 2022, 9, 2107292. [Google Scholar] [CrossRef]
- Debrah, A.; Yeyie, P.; Gyimah, E.; Halm, G.G.; Sarfo, F.O.; Mensah, Y.; Kwame, S.A.; Vlachopoulos, D. Online instructional experiences in an unchartered field-The challenges of student-teachers of a Ghanaian College of Education. J. Digit. Learn. Teach. Educ. 2021, 37, 99–110. [Google Scholar] [CrossRef]
- Lappe, J.M. Taking the mystery out of research. Descriptive correlational design. Orthop. Nurs. 2000, 19, 81. [Google Scholar]
- Salkind, N.J. Encyclopedia of Research Design; SAGE Publications: Thousand Oaks, CA, USA, 2010. [Google Scholar]
- Baraldi, A.N.; Enders, C.K. An introduction to modern missing data analyses. J. Sch. Psychol. 2010, 48, 5–37. [Google Scholar] [CrossRef]
- Tran, B.X.; Mai, H.T.; Nguyen, L.H.; Nguyen, C.T.; Latkin, C.A.; Zhang, M.W.; Ho, R.C. Vietnamese validation of the short version of internet addiction test. Addict. Behav. Rep. 2017, 6, 45–50. [Google Scholar] [CrossRef]
- Wang, Y.Z.; Shi, S.H. Preparation for life satisfaction scales applicable to college students (CSLSS). Chin. J. Behav. Med. Sci. 2003, 2, 199–201. [Google Scholar]
- Rosenberg, M. Society and the Adolescent Self-Image; Princeton University Press: Princeton, NJ, USA, 1965. [Google Scholar]
- Roelen, K.; Taylor, E. Assessing mental health in a context of extreme poverty: Validation of the Rosenberg self-esteem scale in rural Haiti. PLoS ONE 2020, 15, e0243457. [Google Scholar] [CrossRef]
- Fromont, A.; Haddad, S.; Heinmüller, R.; Dujardin, B.T.; Casini, A. Exploring the validity of scores from the Rosenberg Self-Esteem Scale (RSES) in Burundi: A multi-strategy approach. J. Psychol. Afr. 2017, 27, 316–324. [Google Scholar] [CrossRef]
- Oladipo, S.E.; Kalule-Sabiti, I. Exploring the suitability of Rosenberg self-esteem scale for adult use in south-western Nigeria. Gend. Behav. 2014, 12, 6027–6034. [Google Scholar]
- Aalto, A.M.; Elovainio, M.; Kivimäki, M.; Uutela, A.; Pirkola, S. The Beck Depression Inventory and General Health Questionnaire as measures of depression in the general population: A validation study using the Composite International Diagnostic Interview as the gold standard. Psychiatry Res. 2012, 15, 163–171. [Google Scholar] [CrossRef] [PubMed]
- Beck, A.T.; Ward, C.H.; Mendelson, M.; Mock, J.; Erbaugh, J. An inventory for measuring depression. Arch. Gen. Psychiatry 1961, 4, 561–571. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Russell, D.; Peplau, L.A.; Cutrona, C.E. The revised UCLA loneliness scale: Concurrent and discriminant validity evidence. J. Personal. Soc. Psychol. 1980, 39, 472–480. [Google Scholar] [CrossRef] [PubMed]
- Strahan, R.; Gerbasi, K.C. Short, homogeneous versions of the Marlowe-Crowne social desirability scale. J. Clin. Psychol. 1972, 28, 191–193. [Google Scholar] [CrossRef]
- Crowne, D.P.; Marlowe, D. A new scale of social desirability independent of psychopathology. J. Consult. Psychol. 1960, 24, 349–354. [Google Scholar] [CrossRef] [Green Version]
- Herrero, J.; Urueña, A.; Torres, A.; Hidalgo, A. Smartphone addiction: Psychosocial correlates, risky attitudes, and smartphone harm. J. Risk Res. 2019, 22, 81–92. [Google Scholar] [CrossRef]
- Pallant, J. SPSS Survival Manual; McGraw-Hill Education: London, UK, 2014. [Google Scholar]
- Millum, J.; Bromwich, D. Informed consent: What must be disclosed and what must be understood? Am. J. Bioeth. 2021, 21, 46–58. [Google Scholar] [CrossRef]
- Stepanikova, I.; Nie, N.H.; He, X. Time on the internet at home, loneliness, and life satisfaction: Evidence from panel time-diary data. Comput. Hum. Behav. 2010, 26, 329–338. [Google Scholar] [CrossRef]
- Meerkerk, G.J. Pwned by the Internet: Explorative Research into the Causes and Consequences of Compulsive Internet Use; Erasmus University Rotterdam: Rotterdam, The Netherlands, 2007. [Google Scholar]
- Bulut Serin, N. An Examination of Predictor Variables for Problematic Internet Use. Turk. Online J. Educ. Technol.-TOJET 2011, 10, 54–62. [Google Scholar]
- Longstreet, P.; Brooks, S. Life satisfaction: A key to managing internet & social media addiction. Technol. Soc. 2017, 50, 73–77. [Google Scholar]
- Strittmatter, E.; Parzer, P.; Brunner, R.; Fischer, G.; Durkee, T.; Carli, V.; Hoven, C.W.; Wasserman, C.; Sarchiapone, M.; Wasserman, D.; et al. A 2-year longitudinal study of prospective predictors of pathological Internet use in adolescents. Eur. Child Adolesc. Psychiatry 2016, 25, 725–734. [Google Scholar] [CrossRef] [PubMed]
- Lau, J.T.; Gross, D.L.; Wu, A.; Cheng, K.M.; Lau, M. Incidence and predictive factors of Internet addiction among Chinese secondary school students in Hong Kong: A longitudinal study. Soc. Psychiatry Psychiatr. Epidemiol. 2017, 52, 657–667. [Google Scholar] [CrossRef] [PubMed]
- Hartanto, A.; Quek, F.Y.X.; Tng, G.Y.Q.; Yong, J.C. Does Social Media Use Increase Depressive Symptoms? A Reverse Causation Perspective. Front Psychiatry 2021, 23, 641934. [Google Scholar] [CrossRef]
- Baraldi, A.N. Mediational Analysis in a Planned Missingness Data Design: Alternative Model Specifications and Power of the Mediated Effect. Multivar. Behav. Res. 2015, 50, 732–733. [Google Scholar] [CrossRef]
Variables | M | SD | f (%) | |
---|---|---|---|---|
Gender | Male | 159 (39.3) | ||
Female | 246 (60.7) | |||
Age | 19.1 | 2.09 | ||
Residential status | Off-campus | 293 (72.3) | ||
On-campus | 112 (27.7) | |||
Academic level | Year 1 | 147 (36.3) | ||
Year 2 | 100 (24.7) | |||
Year 3 | 101 (24.9) | |||
Year 4 | 57 (14.1) | |||
The students owned a personal digital device (e.g., smartphone, personal computer) | Yes | 375 (92.6) | ||
No | 30 (7.4) | |||
Member of a social networking platform | Yes | 297 (73.3) | ||
No | 108 (26.7) | |||
Number of friend connections on social network platform | 1000 friend connections or less | 210 (51.9) | ||
Above 1000 friend connections | 195 (48.1) | |||
Creating new friends on social network platform | Yes | 200 (49.4) | ||
No | 205 (50.6) | |||
Experience with Internet | 0–10 years | 190 (46.9) | ||
Above 10 years | 215 (53.1) | |||
Daily Internet usage time | Less than 1 h | 27 (6.7) | ||
1–3 h | 147 (36.2) | |||
More than 3 h | 231 (57.1) | |||
Active Internet service | Yes | 335 (82.7) | ||
No | 70 (17.3) | |||
Primary purpose of Internet use | Educational purpose | 196 (48.4) | ||
Communicate with friends | 124 (30.6) | |||
Share resources | 60 (14.8) | |||
Other | 25 (6.2) | |||
Ownership of a data package | Yes | 257 (63.4) | ||
No | 148 (36.6) | |||
Does Internet use affect rest time | Yes | 224 (55.3) | ||
No | 181 (44.7) |
Skewness | Kurtosis | ||||||||
---|---|---|---|---|---|---|---|---|---|
Mean | Median | SD | Min | Max | Skewness | SE | Kurtosis | SE | |
Internet addiction | 33.76 | 34 | 7.30 | 14 | 55 | 0.183 | 0.121 | 0.466 | 0.242 |
Life satisfaction | 25.89 | 25 | 6.08 | 5 | 39 | −0.031 | 0.121 | 0.418 | 0.242 |
Depression | 8.29 | 8 | 2.44 | 1 | 12 | −0.594 | 0.121 | −0.113 | 0.242 |
Loneliness | 42.36 | 42 | 6.71 | 20 | 64 | 0.323 | 0.121 | 0.823 | 0.242 |
Self-esteem | 17.37 | 18 | 3.15 | 10 | 22 | −0.714 | 0.121 | −0.683 | 0.242 |
Internet Addiction Scale | Depression Scale | UCLA Loneliness Scale | Life Satisfaction Scale | Self-Esteem Scale | ||
---|---|---|---|---|---|---|
Variables | Levels | m (±SD) | m (±SD) | m (±SD) | m (±SD) | m (±SD) |
Gender | Male | 34.0 (7.54) | 8.36 (2.52) | 42.51 (7.23) | 26.40 (6.27) | 17.47 (3.13) |
Female | 33.62 (7.18) | 8.25 (2.40) | 42.27 (6.42) | 24.97 (5.64) | 17.31 (3.17) | |
t/F; p | −0.51; 0.614 | −0.40; 0.689 | −0.34; 0.738 | 2.28; 0.023 * | −0.481; 0.631 | |
Age | 21 years and less | 33.35 (7.21) | 8.39 (2.52) | 42.50 (6.71) | 25.99 (6.05) | 17.42 (3.12) |
Above 21 years | 34.16 (7.39) | 8.19 (2.36) | 42.21 (6.73) | 25.79 (6.13) | 17.31 (3.19) | |
t/F; p | −1.11; 0.269 | 0.81; 0.419 | 0.43; 0.665 | 0.33; 0.744 | 0.34; 0.733 | |
Residential Status | Off campus | 32.58 (7.71) | 7.89 (2.23) | 43.77 (7.17) | 25.52 (5.66) | 17.14 (2.88) |
On campus | 34.21 (7.10) | 8.45 (2.51) | 41.81 (6.46) | 30.20 (4.25) | 17.45 (3.25) | |
t/F; p | −1.94; 0.053 | −2.16; 0.032 * | 2.53; 0.012 | −7.96; <0 .001 | −0.94; 0.35 | |
Academic level | Year 1 | 33.6 (7.34) | 7.68 (2.31) | 42.9 (7.03) | 24.8 (5.37) | 16.9 (2.98) |
Year 2 | 32.0 (7.19) | 9.71 (1.47) | 42.8 (6.22) | 30.2 (2.42) | 18.4 (3.19) | |
Year 3 | 34.3 (7.68) | 6.91 (2.55) | 42.4 (7.37) | 31.5 (3.28) | 16.4 (3.05) | |
Year 4 | 36.2 (5.96) | 9.82 (1.81) | 40.3 (5.05) | 32.5 (3.68) | 18.5 (2.90) | |
t/F; p | 4.28; 0.005 *** | 42.7; <0.001 *** | 2.23; 0.084 | 80.7; <0.001 | 10.3; <0.001 | |
Marital status | Married | 34.1 (7.34) | 8.33 (2.46) | 42.2 (6.90) | 28.1 (5.53) | 17.3 (3.14) |
Single | 34.3 (7.45) | 7.90 (2.68) | 41.7 (6.57) | 31.1 (3.06) | 16.8 (3.33) | |
Other | 29.7 (5.36) | 8.84 (1.54) | 45.2 (4.62) | 30.8 (2.90) | 18.8 (2.49) | |
t/F; p | 6.45; 0.002 *** | 1.98; 0.140 | 3.99; 0.019 | 14.8; < 0.001 | 4.99; 0.007 *** | |
The students owned a personal digital device (e.g., smartphone, personal computer) | Yes | 33.75 (7.36) | 8.30 (2.43) | 42.40 (6.81) | 28.95 (5.18) | 17.38 (3.19) |
No | 33.83 (6.73) | 8.14 (2.57) | 41.83 (5.35) | 28.14 (4.49) | 17.17 (2.66) | |
t/F; p | −0.06; 0.953 | 0.335; 0.740 | 0.543; 0.591 | 0.931; 0.359 | 0.399; 0.692 | |
Member of a social networking platform | Yes | 33.99 (7.59) | 8.37 (2.44) | 42.39 (6.98) | 29.01 (5.17) | 17.47 (3.15) |
No | 33.11 (6.43) | 8.06 (2.43) | 42.26 (5.95) | 28.56 (5.03) | 17.06 (3.14) | |
t/F; p | 1.16; 0.249 | 1.13; 0.260 | 0.19; 0.848 | 0.79; 0.432 | 1.16; 0.247 | |
Number of friend connections on social network platform | 1000 friend connections or less | 33.77 (7.59) | 8.09 (2.51) | 42.25 (6.80) | 28.76 (5.13) | 17.49 (3.03) |
Above 1000 friend connections | 33.74 (7.00) | 8.51 (2.35) | 42.48 (6.63) | 29.04 (5.13) | 17.23 (3.28) | |
t/F; p | 0.03; 0.975 | −1.77; 0.078 | −0.34; 0.731 | −0.56; 0.578 | 0.83; 0.409 | |
Creating new friends on social network platform | Yes | 34.05 (6.97) | 8.19 (2.58) | 41.77 (6.46) | 28.27 (5.26) | 17.33 (3.13) |
No | 33.47 (7.62) | 8.39 (2.30) | 42.93 (6.91) | 29.50 (4.94) | 17.40 (3.18) | |
t/F; p | 0.79; 0.431 | −0.82; 0.410 | −1.73; 0.084 | −2.43; 0.016 | −0.22; 0.823 | |
Experience with Internet | 0–10 years | 33.17 (7.09) | 8.43 (2.35) | 42.73 (6.58) | 27.99 (5.79) | 17.67 (2.95) |
Above 10 years | 34.27 (7.47) | 8.17 (2.52) | 42.03 (6.82) | 29.69 (4.33) | 17.09 (3.30) | |
t/F; p | −1.53; 0.127 | 1.05; 0.294 | 1.06; 0.292 | −3.30; 0.001 | 1.87; 0.062 | |
Daily Internet usage time | Less than 1 h | 34.3 (5.84) | 8.04 (2.38) | 42.3 (6.30) | 28.0 (4.86) | 17.1 (2.97) |
1–3 h | 33.2 (6.59) | 8.11 (2.38) | 43.1 (6.27) | 28.7 (4.81) | 17.2 (3.17) | |
More than 3 h | 34.1 (7.87) | 8.44 (2.48) | 41.9 (7.01) | 29.1 (5.35) | 17.5 (3.16) | |
t/F; p | 0.77; 0.462 | 0.97; 0.380 | 1.35; 0.260 | 0.85; 0.427 | 0.56; 0.575 | |
Active Internet service | Yes | 33.36 (7.27) | 8.22 (2.38) | 42.74 (6.78) | 28.39 (5.32) | 17.33 (3.13) |
No | 35.63 (7.24) | 8.64 (2.68) | 40.53 (6.11) | 31.31 (3.17) | 17.53 (3.25) | |
t/F; p | 2.38; 0.019 ** | 1.23; 0.222 | −2.70; 0.008 | 6.13; <0.001 *** | 0.47; 0.643 | |
Purposes of Internet use | Educational purpose | 33.9 (7.25) | 8.34 (2.38) | 42.2 (5.89) | 28.6 (5.54) | 17.6 (3.04) |
Communicate with friends | 33.8 (6.77) | 8.61 (2.39) | 42.1 (6.60) | 29.6 (3.80) | 17.4 (3.22) | |
Share resources | 33.3 (7.36) | 7.83 (2.43) | 43.1 (7.35) | 28.2 (5.48) | 16.9 (3.07) | |
Other | 34.0 (10.13) | 7.40 (2.89) | 42.9 (10.86) | 29.4 (6.42) | 15.9 (3.53) | |
t/F; p | 0.11; 0.953 | 2.56; 0.052 | 0.40; 0.752 | 1.33; 0.264 | 2.65; 0.048 * | |
Ownership of a data package | Yes | 33.92 (7.39) | 8.57 (2.39) | 42.35 (6.59) | 29.42 (4.77) | 17.65 (3.10) |
No | 33.47 (7.16) | 7.80 (2.46) | 42.37 (6.94) | 27.98 (5.60) | 16.88 (3.19) | |
t/F; p | −0.60; 0.552 | −3.06; 0.002 *** | 0.03; 0.976 | −2.63; 0.009 *** | −2.36; 0.019 ** | |
Does Internet use affect rest time | Yes | 34.23 (7.82) | 8.48 (2.44) | 42.39 (7.16) | 29.32 (5.35) | 17.41 (3.28) |
No | 33.17 (6.59) | 8.06 (2.42) | 42.32 (6.14) | 28.36 (4.80) | 17.31 (3.00) | |
t/F; p | −1.49; 0.137 | −1.72; 0.087 | −0.10; 0.918 | −1.89; 0.059 | −0.29; 0.770 |
Constructs | ||||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | ||
1 | Internet addiction | — | ||||
2 | Life satisfaction | −0.694 *** | — | |||
3 | Depression | −0.270 *** | 0.164 ** | — | ||
4 | Loneliness | 0.740 *** | 0.533 *** | 0.361 *** | — | |
5 | Self-esteem | −0.321 *** | 0.170 ** | 0.369 *** | 0.341 *** | — |
Overall Model Test | |||||||
---|---|---|---|---|---|---|---|
Model | R | R2 | Adjusted R2 | F | df1 | df2 | p |
1 | 0.750 | 0.563 | 0.559 | 129 | 4 | 400 | <0.001 |
Sum of Squares | df | Mean Square | F | p | |
---|---|---|---|---|---|
Life satisfaction | 217.1 | 1 | 217.1 | 9.22 | 0.003 |
Depression | 29.9 | 1 | 29.9 | 1.27 | 0.260 |
Loneliness | 7870.7 | 1 | 7870.7 | 334.25 | <0.001 |
Self-esteem | 139.1 | 1 | 139.1 | 5.91 | 0.016 |
Residuals | 9419.0 | 400 | 23.5 |
Predictor | Estimate | SE | t | p | Stand. Estimate (β) |
---|---|---|---|---|---|
Intercept | 72.534 | 1.992 | 36.410 | <0.001 | |
Life satisfaction | −0.154 | 0.051 | −3.040 | 0.003 | −0.108 |
Depression | 0.127 | 0.113 | 1.130 | 0.260 | 0.042 |
Loneliness | 0.751 | 0.041 | 18.280 | <0.001 | 0.690 |
Self-esteem | −0.207 | 0.085 | −2.430 | 0.016 | −0.089 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Essel, H.B.; Vlachopoulos, D.; Nyadu-Addo, R.; Tachie-Menson, A.; Baah, P.K.; Owusu-Antwi, C. The Impact of Mental Health Predictors of Internet Addiction among Pre-Service Teachers in Ghana. Behav. Sci. 2023, 13, 20. https://doi.org/10.3390/bs13010020
Essel HB, Vlachopoulos D, Nyadu-Addo R, Tachie-Menson A, Baah PK, Owusu-Antwi C. The Impact of Mental Health Predictors of Internet Addiction among Pre-Service Teachers in Ghana. Behavioral Sciences. 2023; 13(1):20. https://doi.org/10.3390/bs13010020
Chicago/Turabian StyleEssel, Harry Barton, Dimitrios Vlachopoulos, Ralph Nyadu-Addo, Akosua Tachie-Menson, Paa Kwame Baah, and Charles Owusu-Antwi. 2023. "The Impact of Mental Health Predictors of Internet Addiction among Pre-Service Teachers in Ghana" Behavioral Sciences 13, no. 1: 20. https://doi.org/10.3390/bs13010020