Predictive factors for loneliness in female high school students; an unvariate and multivariate logistic regression analysis

Document Type: Original Article

Authors

1 MSc General Psychology, Ilam Department of Education, Ministry of Education, Islamic Republic of Iran, Iran

2 Psychosocial Injuries Research Center, Ilam University of Medical Sciences, Ilam, Iran

3 Statistics, Student Research Committee, Ilam University of Medical Sciences, Ilam, Iran

4 student, Psychosocial Injuries Research Center, Ilam University of Medical Sciences, Ilam, Iran

Abstract

Background and aims: Loneliness typically includes anxious feelings. It is particularly relevant to adolescence period. It has effect on physical and mental health. The present study aimed to identify the predictive factors of loneliness among high schools female students.
Methods: A cross– sectional survey was carried out among high schools female students in Ilam during the academic year 2014-15. Sampling was done by multistage method. The student's consent to participation in the study obtained by full filled the questionnaires. Data were collected by demographic and University of California, Los Angeles questionnaires. Questionnaires with incomplete information were excluded. The Cronbach’s alpha coefficient was measured as an index of internal identicalness of the questionnaire to verify its reliability.
Results: A total of 400 female high school students were studied. Overall, 62.8% of students put into non- loneliness group and 37.3% of all have loneliness. The univariate logistic regression analysis demonstrates that education field, father’s education and father’s occupation were different between the groups (P<0.05). The risk of loneliness was higher in students with a mathematical sciences education field in comparison to general education field (OR=1.75). In multivariate logistic regression analysis the education field, father’s education and father’s occupation were considered as independent predictive variables for female students’ loneliness. The AUROC criterion was applied to compute both the sensibility and the specificity of the manikin. The overall percent of correct classification of the model is 64%.
Conclusion: Identify the causes of students loneliness can prevent complications and provide appropriate solutions. 

Keywords

Main Subjects


                         
INTRODUCTION

Loneliness is defined as an unpleasant emotional response to isolation or lack of companionship that effects on various aspects of psychological well-being.1 The real prevalence of loneliness is unclear. However, it has been estimated that 60 million of American populations have been experiencing a loneliness feel in their life.2 Loneliness typically includes anxious feelings and researcher believes that loneliness is particularly relevant to adolescence period. During the adolescence period the person's social network, connect and commonality with other will be changed. Hence, the adolescence experiences the loneliness more than other people.3,4 However, there are several effective factors on loneliness, including social, mental and emotional factors,5 but previous studies have also been demonstrated that adolescence loneliness is associated with a range of psychological problems, including lowered self-esteem and increased feelings of depression and anxiety.1 Previous researches reported the relationship between loneliness and depression. 6,7 Hence, it can be said loneliness is a risk factor for suicide8,9 and alcoholism10,11 Loneliness can induce some physical health problems, such as cardiovascular diseases, sleep disturbances, increased incidence of high blood pressure, high cholesterol, obesity and other complications.12-14 Hence, several studies investigated the prevalence and risk factors on loneliness in various age groups.15-17 Therefore, the main aim of the present study was to identify the predictive factors of loneliness among high schools female students in Ilam, western area of Iran.  

METHODS

A cross– sectional study was carried out among high schools female students in Ilam, western area of Iran during the academic year 2014-15. Sampling was done by multistage method. Hence, the first five females’ high schools were randomly selected and 450 female high school students were selected as the sample group. The student's consent to participation in the study obtained by full
filled questionnaires. Questionnaires with incomplete information were eliminated from the research process. Therefore, 400 full filled questionnaires were considered. Demographic information questionnaire: This questionnaire was designed by the authors and assessed variables such as age, education field, education level, parents’ education and parents’ occupation. University of California, Los Angeles (UCLA) questionnaire (1978): This questionnaire was first published in 1978 by Russell and revised in 1980 and 1993. This questionnaire is the first and the most famous loneliness scale. This 20-item questionnaire has been designed based on a four-point Likert scale to measure feelings of social isolation. Participants rate each item as either 1=“I never feel this way”, 2=“I rarely feel this way”, 3=“I sometimes feel this way” and 4=“I often feel this way”. Questionnaire consists of 11 positive items and 9 negative items. All negative items including; 1-5-6-9-10-15-16-19 and 20 are inversely scoring.18 The lowest score is 20, which means there is no loneliness. However, the score 80 is the highest score and is a sign of severe loneliness. Previous studies have confirmed the questionnaire validity19,20 The Cronbach’s alpha coefficient was measured as an index of internal identicalness of the questionnaire to verify its reliability. The obtained values were 0.82 for the questionnaire.  

RESULTS

A total of 400 female high school students were studied. Overall, 251 (62.8%) students were put into non- loneliness group. But 149 (37.3%) of all participants have loneliness. The Mean ± SD age was  15.80 ± 1.9 and 15.51 ±1.05 years in  non-loneliness and loneliness students, respectively (P=0.533). The univariate logistic regression analysis shows that the variables such as education field, father’s education and father’s occupation were different between the groups (P<0.05). The risk of loneliness was higher in students with a mathematical sciences education field in comparison to general education field (OR=1.75). The association between loneliness status and other variables using univariate logistic regression analysis is presented in Table 1.  
Table 1: The association between students’ loneliness status and other variables using univariate logistic regression analysis
Characteristics B SE OR (95% CI)* P  Mathematical Sciences 0.557 0. 286 1.75(0.1-3.06) 0.008 Education field Experimental Sciences -0.080 0.269 0.923(0.45-1.56)  Humanities Sciences -0.934 0.328 0.39(0.21-0.75)  Education grade -0.422 0.267 0.98(0.74-1.22) 0.114 Father´s occupation Non-governmental 0.587 0.209 1.95(1.2-2.71) 0.005 Governmental   1.0(Ref) Mather´s occupation Non-governmental -0.096 0.307 1.08(0.5-1.66) 0.756  Governmental   1.0(Ref)  Illiterate -0.272 0.633 1.42(0.22-2.63) 0.045 Father´s education Primary 0.015 0.360 1.55(0.5-2.6)  Secondary 0.038 0.231 1.14(0.66-1.63)  Diploma -1.659 0.554 0.33(0.06-0.56)  Academic   1.0(Ref)  Illiterate -0.396 0.403 0.915(0.31-1.52) 0.112 Mather´s education Primary -0.396 0.403 0.915(0.31-1.52)  Secondary 0.400 0.274 1.71(0.87-2.55)  Diploma 0.046 0.333 1.27(0.54-2.01)  Academic   1.0(Ref)  
In the multivariate logistic regression analysis, the education field, father’s education and father’s occupation were considered as independent predictive variables for female students’ loneliness. The association between female students’ loneliness and other variables using multivariate logistic regression analysis is presented in table 2.  
Table 2: The association between students’ loneliness status and other variables using multivariate logistic regression analysis
Characteristics B S.E. Wald OR df P Education field Mathematical Sciences 0.795 0.305 1.129 2.21 1 0.001  Experimental sciences 0.099 0.294 0.114 1.1 1  Humanities Sciences -0.766 0.341 5.062 0.456 1 Father´s occupation Non-governmental 1. 015 0.256 14.618 2.76 1 0.000  Governmental    1.0(Ref)   Illiterate -1.115 0.667 2.798 0.328 1 0.002 Father´s education Primary -0.546 0.396 1.898 0.579 1 0.094  Secondary -0.386 0.262 2.165 0.680 1 0.168  Diploma -2.335 0.586 15.893 0.1 1 0.141  Academic    1.0(Ref)  0.000  
The AUROC criterion was used to compute both the sensitivity and the specificity of the model (Figure 1). The overall percentage of correct classification of the model is 64%. It means that, with knowing the education field, father’s education and father’s occupation, the ability of the model to predict the actual category of the cases  is 64%.   
Figure 1: The AUROC criterion to compute both the sensitivity and the specificity of the model  

DISCUSSION

In the present study, it was investigated the predictive factors for loneliness among female high school students in Ilam, western area of Iran. About one third of all participants (37.3%) have loneliness. Considering to the physical and psychological effect of loneliness in human life, there are several studies in this matter.21-23 The univariate logistic regression analysis presented the education field as a predictive factor for loneliness in our population, so that the risk of loneliness was higher in students with a mathematical sciences education field in comparison to the general education field. The univariate logistic regression analysis showed that father’s education and father’s occupation are other predictive factors for loneliness in our population. A study reported the role of family as important effective factor of loneliness among 287 children.22 Other studies confirmed the role of father in their children hope and effort.24,25 In Iranian population, most people with higher education levels have better positions and more income. At the same time, there is a significant relationship between child health and their parent’s income.26-28 The US National Health Interview Survey (NHIS) reported a positive relationship between family income and child health status. On the other hand, children in poorer families had significantly worse health than children from richer families, and some chronic health conditions such as mental and nervous system problems had higher incidence in poorer families.29,30 Based on multivariate logistic regression analysis, the education field, father’s education and father’s occupation were main independent predictive factors for loneliness among our study population. So, the students with non-governmental father’s occupation have about three times the risk of loneliness. Fathers are the main source of financing and family welfare in Iran. There are a relationship between socioeconomic situation and increase the risk of depression due to factors such as perceived low social status, cultural factors, financial problems, stressful environments, social isolation, and greater daily stress.31,32 Because of depression is a risk factor for loneliness, increase loneliness in depressed individuals with the unsuitable socioeconomic situation can be expected.  

CONCLUSSION

According to the findings, it is recommended to provide appropriate and effective counseling facilities for parents. In addition, identify the causes of students loneliness can prevent complications and provide appropriate solutions.  

CONFLICT OF INTEREST

The authors declare that they have no conflict of interests.  

ACKNOWLEDGEMENT

This study was approved by Islamic Azad University of Ilam, Iran. We thank the participants, coordinators, and data collectors who assisted in this study. 

1. Heinrich LM, Gullone E. The clinical significance of loneliness: a literature review. Clin Psychol Rev. 2006; 26(6): 695-718.

2. Cacioppo J, Patrick W. Loneliness: Human Nature and the Need for Social Connection. USA: W. W. Norton Pub; 2008.

3. Asendorpf JB, van Aken MA. Personality-relationship transaction in adolescence: Core versus surface personality characteristics. J Pers. 2003; 71(4): 629-66.

4. Asher SR, Paquette JA. Loneliness and peer relations in childhood. Curr Dir Psychol Sci. 2003; 12(3): 75-8.

5. Boomsma DI, Willemsen G, Dolan CV, Hawkley LC, Cacioppo JT. Genetic and environmental contributions to loneliness in adults: the Netherlands twin register study. Behav Genet. 2005; 35(6): 745-52.

6. Asti T, Kara M, Ipek G, Erci B. The experiences of loneliness, depression, and social support of Turkish patients with continuous ambulatory peritoneal dialysis and their caregivers. J Clin Nurs. 2006; 15(4): 490-7.

7. Ceyhan AA, Ceyhan E. Loneliness, depression, and computer self-efficacy as predictors of problematic internet use. Cyberpsychol Behav. 2008; 11(6): 699-701.

8. Stravynski A, Boyer R. Loneliness in relation to suicide ideation and parasuicide: A population-wide study. Suicide Life Threat Behav. 2001; 31(1): 32-40.

9. Durak Batigun A. [Suicide probability: An assessment terms of reasons for living, hopelessness and loneliness]. Turk Psikiyatri Derg. 2005; 16(1): 29-39.

10. Allen HA, Peterson JS, Whipple S. Loneliness and alcoholism: a study of three groups of male alcoholics. Int J Addict. 1981; 16(7): 1255-8.

11. Page RM, Cole GE. Loneliness and alcoholism risk in late adolescence: a comparative study of adults and adolescents. Adolescence. 1991; 26(104): 925-30.

12. Hawkley LC, Cacioppo JT. Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Ann Behav Med. 2010; 40(2): 218-27.

13. Jaremka LM, Andridge RR, Fagundes CP, Alfano CM, Povoski SP, Lipari AM, et al. Pain, depression, and fatigue: Loneliness as a longitudinal risk factor. Health Psychol. 2014; 33(9): 948-57.

14. Jaremka LM, Fagundes CP, Peng J, Bennett JM, Glaser R, Malarkey WB, et al. Loneliness promotes inflammation during acute stress. Psychol Sci. 2013; 24(7):1089-97.

15. Ivbijaro G. Loneliness and the elderly: opportunities for health promotion. Ment Health Fam Med. 2013; 10(1): 1-2.

16. Alspach JG. Loneliness and social isolation: risk factors long overdue for surveillance. Crit Care Nurse. 2013; 33(6): 8-13.

17. Teppers E, Luyckx K, Klimstra TA, Goossens L. Loneliness and Facebook motives in adolescence: a longitudinal inquiry into directionality of effect. J Adolesc. 2014; 37(5): 691-9.

18. Survey on loneliness uses scale developed by ISU professors. Available from: http://phys.org/news/2010-12-survey- loneliness-scale-isu-professors.html.

19. Russell DW. UCLA Loneliness Scale (Version 3): Reliability, validity, and factor structure. J Pers Ass. 1996; 66(1): 20-40.

20. Russell D, Peplau LA, Cutrona CE. The revised UCLA Loneliness Scale: concurrent and discriminant validity evidence. J Pers Soc Psychol. 1980; 39(3): 472-80.

21. Holt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci. 2015; 10(2): 227-37.

22. Sharabi A, Levi U, Margalit M. Children's loneliness, sense of coherence, family climate, and hope: developmental risk and protective factors. J Psychol. 2012; 146(1-2): 61-83.

23. Dussault M, Deaudelin C. Loneliness and self-efficacy in education majors. Psychol Rep. 2001; 89(2): 285-9.
24. Al-Yagon M. Child-mother and child- father attachment security: links to internalizing adjustment among children with learning disabilities. Child Psychiatry Hum Dev. 2014; 45(1): 119-31.

25. Al-Yagon M. Fathers' coping resources and children's socioemotional adjustment among children with learning disabilities. J Learn Disabil. 2011; 44(6): 491-507.

26. Donnelly R, Springer A. Parental social support, ethnicity, and energy balance- related behaviors in ethnically diverse, low- income, urban elementary schoolchildren. J Nutr Educ Behav. 2015; 47(1): 10-8.

27. Parent J, Clifton J, Forehand R, Golub A, Reid M, Pichler ER. Parental Mindfulness and Dyadic Relationship Quality in Low-income Cohabiting Black Stepfamilies: Associations with Parenting Experienced by Adolescents. Couple Family Psychol. 2014; 3(2): 67-82.

28. Goode A, Mavromaras K. Family income and child health in China. China Econ R. 2014; 29: 152-65.

29. Case A, Lubotsky D, Paxson C. Economic status and health in childhood: The origins of the gradient. Am Econ Rev. 2002; 92:1308-34.

30. Currie J. Viewpoint: Child research comes of age. Can J Econ. 2004; 37(3): 509-27.

31. Janlert U. Economic crisis, unemployment and public health. Scand J Public Health. 2009; 37(8): 783-4.

32. Falagas ME, Vouloumanou EK, Mavros MN, Karageorgopoulos DE. Economic crises and mortality: A review of the literature. Int J Clin Pract. 2009; 63(8): 1128-35.